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Author SHA1 Message Date
ton 7876ff21eb update 2026-07-31 11:41:53 +07:00
ton c9a7661e93 update 2026-07-31 08:08:03 +07:00
ton 8d5c661562 add tracing 2026-07-30 16:23:08 +07:00
ton 244cfc4b96 update 2026-07-30 09:28:19 +07:00
ton a541905b72 update 2026-07-29 14:00:13 +07:00
ton a0787c2316 update 2026-07-29 13:52:02 +07:00
ton 8fd72f8d37 ีupdate 2026-07-29 13:50:00 +07:00
ton efa18ba800 update 2026-07-29 11:14:04 +07:00
ton d0d9446d99 update 2026-07-28 15:01:16 +07:00
ton 93f0b51ba4 update 2026-07-28 14:59:41 +07:00
ton 0ed8be5daa update 2026-07-28 11:09:22 +07:00
ton b8254490e8 update 2026-07-28 11:06:52 +07:00
ton a4edfd2de5 update 2026-07-28 10:59:38 +07:00
ton dab2264c55 update 2026-07-28 10:31:11 +07:00
ton d5af7c85aa update 2026-07-28 10:05:52 +07:00
ton 072d0e16af update 2026-07-28 09:57:23 +07:00
ton b18a5f34a8 update 2026-07-28 09:53:40 +07:00
ton 2f7c807042 update 2026-07-28 09:50:07 +07:00
ton 4518191fce update 2026-07-28 09:45:40 +07:00
ton b372b72baa update 2026-07-28 09:39:21 +07:00
ton 1bda81bb08 add docs 2026-07-28 08:12:15 +07:00
ton 984a678d92 pi harness reimplement 2026-07-28 07:37:57 +07:00
ton b7658af76b up version 2026-07-28 00:14:01 +00:00
ton 9e3f5a0967 Merge pull request 'v0.7.4' (#36) from v0.7.4 into main
Reviewed-on: #36
2026-07-27 02:48:28 +00:00
ton 206c0d2c62 Merge pull request 'v0.7.4-add_vector_search' (#32) from v0.7.4-add_vector_search into main
Reviewed-on: #32
2026-07-26 15:21:53 +00:00
53 changed files with 14962 additions and 5662 deletions
+45
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@@ -80,3 +80,48 @@ Assistant should only respond in JSON format as described below:
"action_input": "..."
}
```
<!-- ------------------------------------------- 100 ------------------------------------------- -->
read this codebase. I want you to write the following files:
- ./docs/requirements.md
- ./docs/solution-design.md
- ./docs/specification
- ./docs/walkthrough.md
according to /home/ton/docker-apps/sommpanion/ASG_Framework/ASG_Framework.md so I can read and understand this codebase.
echo 'export PATH="$HOME/.juliaup/bin:$PATH"' >> ~/.bashrc
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@@ -0,0 +1,119 @@
# Julia Implementation - AgentCore
This directory contains a Julia reimplementation of the `@earendil-works/pi-agent-core` package.
## Project Structure
```
julia_implementation/
├── src/
│ ├── AgentCore.jl # Main module entry point
│ ├── types.jl # Core type definitions
│ ├── stream_fn.jl # Stream function utilities
│ ├── agent_loop.jl # Low-level agent loop
│ ├── agent.jl # High-level Agent struct
│ ├── harness_types.jl # Extended types for AgentHarness
│ ├── messages.jl # Custom message types
│ ├── system_prompt.jl # System prompt formatting
│ ├── skills.jl # Skill loading and formatting
│ ├── prompt_templates.jl # Prompt template handling
│ ├── agent_harness.jl # AgentHarness implementation
│ │
│ ├── session/
│ │ ├── session.jl # Session class
│ │ ├── jsonl_storage.jl # JSONL storage
│ │ ├── jsonl_repo.jl # JSONL repository
│ │ ├── memory_storage.jl # In-memory storage
│ │ ├── memory_repo.jl # In-memory repository
│ │ └── repo_utils.jl # Repository utilities
│ │
│ ├── tools/
│ │ ├── index.jl # Tool exports
│ │ ├── bash.jl # Bash execution tool
│ │ ├── read.jl # File read tool
│ │ ├── write.jl # File write tool
│ │ ├── edit.jl # File edit tool
│ │ ├── edit_diff.jl # Diff computation
│ │ ├── image.jl # Image utilities
│ │ ├── path_utils.jl # Path resolution
│ │ └── file_mutation_queue.jl # File mutation serialization
│ │
│ ├── compaction/
│ │ ├── compaction.jl # Context compaction
│ │ ├── utils.jl # Compaction utilities
│ │ └── branch_summarization.jl # Branch summarization
│ │
│ ├── utils/
│ │ ├── truncate.jl # Output truncation
│ │ └── shell_output.jl # Shell output capture
│ │
│ ├── proxy.jl # Proxy stream function
│ └── utils.jl # Utility functions
├── test/
├── Project.toml
├── Manifest.toml
└── README.md
```
## Key Features
### Core Architecture
The implementation follows the same layered architecture as the TypeScript version:
1. **Low-level (agent_loop.jl)**: Pure agent loop logic that works with `AgentMessage[]`
2. **High-level (agent.jl)**: Stateful wrapper with event streaming and queueing
3. **Harness (agent_harness.jl)**: Session persistence, resource management, hooks
4. **Session (session/)**: Conversation history with compaction and branching
5. **Tools (tools/)**: Built-in execution tools (bash, read, write, edit)
### Julia-Specific Features
- **Type system**: Uses Julia's parametric types for type-safe tool definitions
- **Multiple dispatch**: Extensible via multiple dispatch for custom message types
- **Async primitives**: Leverages Julia's `@async` and `@spawn` for concurrent operations
- **Error handling**: Julia exceptions with typed error codes
## Building
```julia
using Pkg
Pkg.activate("julia_implementation")
Pkg.instantiate()
```
## Usage Example
```julia
using AgentCore
# Create an agent
agent = Agent()
# Subscribe to events
subscribe(agent) do event, signal
if event isa MessageEndEvent
println("Message: $(event.message)")
end
end
# Run a prompt
prompt(agent, "Hello, world!")
```
## Compatibility
This implementation aims for API compatibility with the TypeScript version while providing idiomatic Julia abstractions.
## Status
This is an active implementation. Core functionality is in place, with ongoing work on:
- Complete tool implementations
- Full session repository functionality
- Test suite
## License
MIT
+20 -1122
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+18 -33
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@@ -1,37 +1,22 @@
name = "YiemAgent"
uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
version = "0.7.4"
authors = ["narawat lamaiin <narawat@outlook.com>"]
name = "AgentCore"
uuid = "6e2f7b3a-9a0b-4e8e-8f8f-8f8f8f8f8f8f"
authors = ["Mario Zechner <post@badlogicgames.com>"]
version = "0.8.0"
[deps]
Base64 = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
NATS = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
Dates = "ade2ca70-3891-5945-98fb-dc09409a37d3"
JSON3 = "0f8b85d8-8d2f-5481-9e3b-d9a10a9b6c53"
Libdl = "8f399da3-355a-58d1-55dd-a8cd37d21846"
Markdown = "d6f4372e-7a37-5ca6-90db-23e40208355e"
Mmap = "a63ad114-7ff6-5b6b-903e-90ddba579e5d"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
Serde = "db9b398d-9517-45f8-9a95-92af99003e0e"
Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
Sockets = "6462fe0b-2de3-572b-8e7f-4c2f5e2c2e2b"
Unicode = "4ec0a83e-493e-50e2-b9ac-8f72acf2a872"
UUIDs = "cf7118a7-4649-5bc2-89ac-36d7b14660ca"
[compat]
Base64 = "1.11.0"
CSV = "0.10.15"
DataFrames = "1.7.0"
GeneralUtils = "0.5.10"
HTTP = "2.4.0"
JSON = "1.6.1"
LLMMCTS = "0.1.5"
NATS = "0.1.0"
SQLLLM = "0.2.8"
Serde = "3.7.2"
[extras]
Test = "8dfed614-e22c-5e4d-98d3-97fe1b80e45d"
[targets]
test = ["Test"]
+179 -7
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@@ -1,7 +1,179 @@
version 0.1.0
TODO:
[WORKING] build MCTS() for planning
[] executeplan() to execute the plan
Change from version: 0.0.9
-
# AgentCore.jl - Julia Implementation of Pi Agent Core
A Julia reimplementation of the `@earendil-works/pi-agent-core` package, providing a stateful agent framework for LLM interactions.
## Overview
This package provides:
- Low-level `agentLoop` for stateful LLM interactions with tool execution
- High-level `Agent` struct with state management, event streaming, and queueing
- `AgentHarness` for session persistence, resource management, and extension hooks
- Built-in tools for file operations (read, write, edit) and bash execution
- Session management with JSONL-based storage, compaction, and branch navigation
## Architecture
The Julia implementation follows the same layered architecture as the TypeScript version:
```
┌─────────────────────────────────────────────────────────────────────┐
│ AgentHarness │
│ (Session persistence, resource management) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────▼───────────────────────────────────────┐
│ Agent │
│ (State management, event streaming, queueing) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────▼───────────────────────────────────────┐
│ AgentLoop │
│ (Low-level loop, tool execution) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────▼───────────────────────────────────────┐
│ Session │
│ (Conversation history, compaction, branching) │
└─────────────────────────────────────────────────────────────────────┘
```
## Installation
```julia
using Pkg
Pkg.add("AgentCore")
```
## Quick Start
```julia
using AgentCore
# Create an agent with default configuration
agent = Agent()
# Subscribe to events
subscribe(agent) do event, signal
if event isa MessageEndEvent
println("Received message: $(event.message)")
end
end
# Run a prompt
prompt(agent, "Hello, how are you?")
```
## Core Concepts
### Agent
The `Agent` struct provides a high-level interface for interacting with LLMs. It manages:
- Conversation state (messages, tools, system prompt)
- Event streaming and lifecycle management
- Steering and follow-up message queues
- Abort handling
### AgentLoop
The `agentLoop` function implements the core agent loop that:
- Transforms `AgentMessage[]` to `Message[]` at the LLM call boundary
- Executes tool calls (parallel or sequential)
- Emits lifecycle events
- Handles steering and follow-up messages
### AgentHarness
The `AgentHarness` provides:
- Session persistence with JSONL storage
- Resource management (skills, prompt templates)
- Extension hooks system
- Tool execution with context
- Branch navigation and compaction
### Sessions
Sessions track conversation history using a tree-based structure:
- Branch-based history with compaction
- Tree navigation (moveTo, navigateTree)
- Message and metadata persistence
## Built-in Tools
### Bash Tool
Execute shell commands with output capture and truncation.
```julia
bash_tool = createBashTool()
```
### Read Tool
Read files with support for text and images.
```julia
read_tool = createReadTool()
```
### Write Tool
Write content to files with automatic directory creation.
```julia
write_tool = createWriteTool()
```
### Edit Tool
Edit files using exact text replacement.
```julia
edit_tool = createEditTool()
```
## Session Storage
AgentCore supports two session storage backends:
1. **JsonlSessionStorage** - File-based storage using JSONL format
2. **InMemorySessionStorage** - In-memory storage for testing
## Compaction
The compaction system manages context window usage by:
- Summarizing old conversation history
- Retaining recent messages
- Supporting iterative updates to summaries
## Event System
AgentCore uses a rich event system for monitoring and control:
- `AgentStartEvent` / `AgentEndEvent` - Agent lifecycle
- `TurnStartEvent` / `TurnEndEvent` - Conversation turns
- `MessageStartEvent` / `MessageEndEvent` - Message lifecycle
- `ToolExecutionStartEvent` / `ToolExecutionEndEvent` - Tool execution
## Examples
See the `examples/` directory for more detailed examples.
## Differences from TypeScript
While maintaining API compatibility where possible, this Julia implementation:
- Uses Julia's type system for better compile-time guarantees
- Leverages Julia's multiple dispatch for extensibility
- Uses Julia's async primitives for concurrent operations
- Provides more idiomatic Julia error handling
## Contributing
Contributions are welcome! Please see `CONTRIBUTING.md` for details.
## License
MIT
## Acknowledgments
This is a reimplementation of the [Pi Agent Core](https://github.com/earendil-works/pi/packages/agent) package in Julia.
+365
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@@ -0,0 +1,365 @@
```
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ AGENT LOOP DIAGRAM │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 1. INITIALIZATION │
│ │
│ Agent.prompt(user_input) │
│ │ │
│ ▼ │
│ normalizePrompt() ← Convert input to AgentMessage[] │
│ │ │
│ ▼ │
│ runPromptMessages() │
│ │ │
│ ▼ │
└─────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 2. AGENT LOOP START (runAgentLoop) │
│ │
│ new_messages = copy(prompts) │
│ current_context.messages = vcat(context.messages, copy(prompts)) │
│ │ │
│ └─→ User messages are IMMEDIATELY added to context.messages │
│ (They are NOT in the steering queue!) │
│ │
│ emit(AgentStartEvent) │
│ emit(TurnStartEvent) │
│ │
│ for prompt in prompts: │
│ emit(MessageStartEvent(prompt)) │
│ emit(MessageEndEvent(prompt)) │
│ │
└─────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 3. MAIN LOOP (runLoop - while true) │
│ │
│ pending_messages = get_steering_messages() │
│ │ │
│ └─→ Steering queue: messages from agent.steer() │
│ These are for CONTINUING conversation (NOT new user prompts) │
│ │
│ ┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ While has pending_messages OR has_tool_calls: │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ │
│ │ │ 4. PENDING MESSAGE HANDLING (steering messages only) │ │ │
│ │ │ │ │ │
│ │ │ pending_messages = get_steering() │ │ │
│ │ │ if !isempty(pending_messages): │ │ │
│ │ │ for msg in pending_messages: │ │ │
│ │ │ emit(MessageStartEvent(msg)) │ │ │
│ │ │ emit(MessageEndEvent(msg)) │ │ │
│ │ │ push to current_context.messages ← Steering messages go HERE │ │ │
│ │ │ push to new_messages │ │ │
│ │ │ pending_messages = [] │ │ │
│ │ │ │ │ │
│ │ │ Note: User messages from Agent.prompt() are ALREADY in context.messages │ │ │
│ │ │ (They were added in runAgentLoop via vcat(), not via this queue) │ │ │
│ │ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ │
│ │ │ 5. STREAM ASSISTANT RESPONSE │ │ │
│ │ │ │ │ │
│ │ │ message = streamAssistantResponse() │ │ │
│ │ │ ├─ transform_context (if configured) │ │ │
│ │ │ ├─ convert_to_llm(messages) → Message[] │ │ │
│ │ │ │ ┌───────────────────────────────────────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ │ Converts AgentMessage[] to Message[] │ │ │ │
│ │ │ │ │ Filters: keeps user, assistant, toolResult │ │ │ │
│ │ │ │ └───────────────────────────────────────────────────────────────────────────────────────┘ │ │ │
│ │ │ ├─ stream_function(model, context) │ │ │
│ │ │ │ ┌───────────────────────────────────────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ │ LLM Stream Events: │ │ │ │
│ │ │ │ │ • start → create partial AssistantMessage │ │ │ │
│ │ │ │ │ • text_start/delta/end → update partial message │ │ │ │
│ │ │ │ │ • thinking_start/delta/end → update partial message │ │ │ │
│ │ │ │ │ • toolcall_start/delta/end → update partial message │ │ │ │
│ │ │ │ │ • done → finalize message │ │ │ │
│ │ │ │ │ • error → handle error │ │ │ │
│ │ │ │ └───────────────────────────────────────────────────────────────────────────────────────┘ │ │ │
│ │ │ └─ push to current_context.messages & new_messages │ │ │
│ │ │ │ │ │
│ │ │ emit(MessageStartEvent(message)) │ │ │
│ │ │ emit(MessageEndEvent(message)) │ │ │
│ │ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ if message.stop_reason in ("error", "aborted"): │ │
│ │ emit(TurnEndEvent) │ │
│ │ emit(AgentEndEvent) ← EXIT LOOP │ │
│ │ return │ │
│ │ │ │
│ │ tool_calls = filter(message.content, ToolCall) │ │
│ │ if !isempty(tool_calls): │ │
│ │ executeToolCalls() → ToolResultMessage[] │ │
│ │ for result in tool_results: │ │
│ │ push to current_context.messages │ │
│ │ push to new_messages │ │
│ │ emit(MessageStartEvent(result)) │ │
│ │ emit(MessageEndEvent(result)) │ │
│ │ │ │
│ │ emit(TurnEndEvent(message, tool_results)) │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ │
│ │ │ 6. PREPARE NEXT TURN │ │ │
│ │ │ │ │ │
│ │ │ next_turn_context = PrepareNextTurnContext(...) │ │ │
│ │ │ next_turn_snapshot = prepare_next_turn(config, next_turn_context) │ │ │
│ │ │ │ │ │
│ │ │ if !isnothing(next_turn_snapshot): │ │ │
│ │ │ update context, model, thinking_level │ │ │
│ │ │ │ │ │
│ │ │ if should_stop_after_turn(config, next_turn_context): │ │ │
│ │ │ emit(AgentEndEvent) ← EXIT LOOP │ │ │
│ │ │ return │ │ │
│ │ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ pending_messages = get_steering_messages() ← Check for new steering messages │ │
│ │ │ │
│ └───────────────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ follow_up_messages = get_follow_up_messages() │
│ │
│ if !isempty(follow_up_messages): │
│ pending_messages = follow_up_messages ← Continue loop for follow-ups │
│ continue │
│ │
│ break ← EXIT MAIN LOOP (no more pending messages) │
│ │
│ emit(AgentEndEvent(new_messages)) │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 4. STEERING QUEUE MECHANISM │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ Steering messages are queued via agent.steer(message) │
│ They are ONLY processed at the START of a loop iteration │
│ AFTER the previous assistant turn completes │
│ │
│ Flow: │
│ user asks → agent responds → [user can steer here] │
│ │ │
│ └─→ pending_messages = get_steering() ← Steering messages injected here │
│ │
│ Follow-up messages are queued via agent.followUp(message) │
│ They run ONLY after agent would otherwise stop │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ COMPLETE CYCLE EXAMPLE: User asks → Agent responds → User asks 2nd → Agent responds │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ TURN #1: User asks "What is Julia?" │
│ ───────────────────────────────────────── │
│ 1. Agent.prompt("What is Julia?") │
│ normalizePrompt() → [UserMessage("What is Julia?")] │
│ runPromptMessages() │
│ │
│ 2. runAgentLoop() │
│ new_messages = [UserMessage("What is Julia?")] │
│ current_context.messages = vcat([...existing...], [UserMessage("What is Julia?")]) │
│ │ │
│ └─→ User message IMMEDIATELY added to context.messages (NOT via steering queue!) │
│ emit(AgentStartEvent), emit(TurnStartEvent) │
│ emit(MessageStart/End) for user message │
│ │
│ 3. runLoop() │
│ pending_messages = get_steering() = [] ← Steering queue is empty (no agent.steer() yet) │
│ │
│ 4. streamAssistantResponse() │
│ convert_to_llm([UserMessage]) → Message[] │
│ LLM call with [UserMessage] │
│ receive AssistantMessage: "Julia is a programming language..." │
│ push AssistantMessage to current_context.messages │
│ push AssistantMessage to new_messages │
│ emit(MessageStart/End) for assistant message │
│ │
│ 5. check stop_reason → continue (no tools, no error) │
│ │
│ 6. emit(TurnEndEvent) │
│ │
│ 7. prepare_next_turn() → nothing (default) │
│ │
│ 8. should_stop_after_turn() → false (default) │
│ │
│ 9. pending_messages = get_steering() = [] ← No steering messages │
│ │
│ 10. follow_up_messages = get_follow_up() = [] │
│ │
│ 11. break ← Exit main loop │
│ │
│ 12. emit(AgentEndEvent) │
│ │
│ ┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ Current context.messages: │ │
│ │ [UserMessage("What is Julia?"), AssistantMessage("Julia is...")] │ │
│ │ │ │
│ │ steering_queue: [] │ │
│ │ follow_up_queue: [] │ │
│ └───────────────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ LLM SEES (convert_to_llm() filters): │
│ ┌─────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ Messages passed to LLM API: │ │
│ │ [UserMessage("What is Julia?"), AssistantMessage("Julia is...")] │ │
│ └─────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ TURN #2: User asks "How does it work?" │
│ ───────────────────────────────────────── │
│ 1. Agent.prompt("How does it work?") │
│ normalizePrompt() → [UserMessage("How does it work?")] │
│ runPromptMessages() │
│ │
│ 2. runAgentLoop() │
│ new_messages = [UserMessage("How does it work?")] │
│ current_context.messages = vcat([...previous..., UserMessage("How does it work?")]) │
│ │ │
│ └─→ User message added (context preserved from Turn #1) │
│ emit(AgentStartEvent), emit(TurnStartEvent) │
│ emit(MessageStart/End) for user message │
│ │
│ 3. runLoop() │
│ pending_messages = get_steering() = [] │
│ │
│ 4. streamAssistantResponse() │
│ convert_to_llm([UserMsg1, AssistantMsg1, UserMsg2]) → Message[] │
│ LLM call with FULL conversation history (context preserved!) │
│ receive AssistantMessage: "It works by..." │
│ push AssistantMessage to current_context.messages │
│ push AssistantMessage to new_messages │
│ │
│ 5. emit(TurnEndEvent), emit(AgentEndEvent) │
│ │
│ ┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ Current context.messages: │ │
│ │ [UserMsg1, AssistantMsg1, UserMsg2, AssistantMsg2] │ │
│ └───────────────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ LLM SEES: │
│ ┌─────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ Messages passed to LLM API: │ │
│ │ [UserMessage("What is Julia?"), │ │
│ │ AssistantMessage("Julia is..."), │ │
│ │ UserMessage("How does it work?"), │ │
│ │ AssistantMessage("It works by...")] │ │
│ └─────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ STEERING MESSAGES │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ What is a steering message? │
│ • A message (any AgentMessage type) injected via: `agent.steer(message)` │
│ • Goes into the steering queue, not immediately to context.messages │
│ │
│ How is it created? │
│ • User code calls: agent.steer(UserMessage("...")) │
│ • Or: agent.steer(AssistantMessage("...")) │
│ • Or any other AgentMessage subtype │
│ │
│ When is it processed? │
│ • At the START of the next loop iteration (line 194-202 in agent_loop.jl) │
│ • AFTER the previous assistant turn completes │
│ • BEFORE the next assistant response is streamed │
│ │
│ Why use steering? │
│ Use case 1: Tool execution result injection │
│ - Agent calls a tool (e.g., read_file, bash) │
│ - Tool returns result │
│ - You want to inject a follow-up question based on the result │
│ - agent.steer(UserMessage("Based on the file, what should we do next?")) │
│ │
│ Use case 2: Multi-turn conversation without user input │
│ - Agent responds to user │
│ - Before user types again, you want to inject a system message │
│ - agent.steer(BashExecutionMessage(...)) or custom message │
│ - This continues the conversation automatically │
│ │
│ Use case 3: Branch navigation recovery │
│ - User navigates between conversation branches │
│ - After switching branches, you want to inject a context message │
│ - agent.steer(BranchSummaryMessage(...)) │
│ - The agent can then continue from the new branch context │
│ │
│ Use case 4: Compaction summary injection │
│ - Conversation history is compacted │
│ - After compaction, inject summary message │
│ - agent.steer(CompactionSummaryMessage(...)) │
│ - Agent knows old history was summarized │
│ │
│ Example: │
│ agent.steer(UserMessage("Follow-up question here")) │
│ # This will be processed in the next loop iteration, │
│ # appearing in context.messages before the next LLM call │
│ │
│ The LLM sees: │
│ ┌─────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ All messages become Message[] via convert_to_llm(): │ │
│ │ [UserMessage(...), AssistantMessage(...), UserMessage(from_steer), ...] │ │
│ │ │ │
│ │ The LLM cannot tell which came from Agent.prompt() vs agent.steer() │ │
│ └─────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ LLM PROCESSING: How LLM sees messages │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ The LLM NEVER sees "user message" vs "steering message" - it only sees Message types: │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ convert_to_llm() transforms ALL AgentMessages to Message[]: │ │
│ │ │ │
│ │ UserMessage("user") → UserMessage (for LLM) │ │
│ │ Steering UserMessage("user") → UserMessage (for LLM) ← Same! │ │
│ │ AssistantMessage("assistant") → AssistantMessage (for LLM) │ │
│ │ ToolResultMessage("toolResult") → ToolResultMessage (for LLM) │ │
│ │ │ │
│ │ BranchSummaryMessage → UserMessage (wrapped in summary tags) │ │
│ │ CompactionSummaryMessage → UserMessage (wrapped in summary tags) │ │
│ │ BashExecutionMessage → UserMessage (if not excluded) │ │
│ │ CustomMessage → UserMessage │ │
│ └─────────────────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ The difference is ONLY in HOW messages enter the system: │
│ • User messages: Agent.prompt() → vcat() → context.messages (direct) │
│ • Steering: agent.steer() → queue → loop → context.messages (indirect) │
│ │
│ At LLM level: BOTH become UserMessage in the conversation! │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ KEY INSIGHTS │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ 1. User prompts go DIRECTLY to context.messages via vcat() in runAgentLoop() │
│ │
│ 2. Steering queue is for messages injected via agent.steer() AFTER a turn finishes │
│ This allows continuing conversation without calling Agent.prompt() again │
│ │
│ 3. Context is preserved across turns - context.messages grows with each turn │
│ LLM sees the full conversation history │
│ │
│ 4. At LLM level, ALL messages become Message types (UserMessage/AssistantMessage/ToolResultMessage) │
│ The "steering" vs "user" distinction is just a control mechanism, not a message type │
│ │
│ 5. New turn is triggered by: │
│ - New Agent.prompt() call (adds user messages) │
│ - Steering messages (adds steering messages) │
│ - Follow-up messages (adds follow-up messages) │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
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# Requirements
## 1. Business Context & Success Metrics
### Business Goal
The **YiemAgent** project is a Julia reimplementation of the Pi Agent Core framework, designed to provide a stateful agent system for LLM interactions in a wine retail store context. This system enables AI agents to interact with customers, search wine databases, and provide personalized wine recommendations based on customer preferences and store inventory.
### User Stories
- **US-001**: As a wine store customer, I want to interact with an AI sommelier so that I can get personalized wine recommendations
- **US-002**: As a wine store operator, I want the AI to search our wine database so that I can provide accurate inventory-based recommendations
- **US-003**: As a developer, I want a reusable agent framework so that I can quickly build custom AI agents for different use cases
- **US-004**: As a system administrator, I want session persistence so that I can maintain conversation history across agent restarts
### KPIs & Targets
- **KPI-001**: 95% of customer queries receive responses within 3 seconds (measured from query receipt to response delivery)
- **KPI-002**: 99% of wine searches return results from inventory database within 2 seconds
- **KPI-003**: Agent session recovery time < 5 seconds after restart
- **KPI-004**: Conversation context retention accuracy > 95% across session restarts
## 2. Technical Boundaries
### In Scope
- Low-level agent loop with LLM interaction and tool execution
- High-level Agent struct with state management and event streaming
- Session persistence with JSONL-based storage
- Built-in tools: bash execution, file read/write/edit operations
- Conversation history compaction for context window management
- Branch-based conversation navigation
- Event-driven architecture for monitoring and control
### Out of Scope
- LLM model hosting or inference (relies on external services)
- Frontend UI components (web interface)
- Database schema design or management
- User authentication and authorization
- Multi-tenant isolation
### Dependencies
- Julia 1.9+ runtime
- JSON3 for JSON parsing
- UUIDs for session identification
- Dates for timestamp management
- LibPQ for PostgreSQL database connections
- MQTT client for external communication
### Deployment Constraints
- **NFR-501**: System shall be deployed to containerized environment (Docker/Podman)
- **NFR-502**: Agent instances shall support horizontal scaling
- **NFR-503**: Session data shall be persisted in shared storage for failover scenarios
## 3. Functional Requirements (FR)
### FR-001: Agent State Management
The system shall maintain conversation state including message history, active tools, and system prompt.
- Store and retrieve conversation history
- Support multiple concurrent agent sessions
- Maintain tool state across conversation turns
- Persist agent state to storage backend
**Traceability**: US-001, US-004
### FR-002: Tool Execution
The system shall execute tools requested by the LLM in response to user queries.
- Support parallel and sequential tool execution modes
- Handle tool call errors gracefully
- Return tool results to LLM for processing
- Support tool result streaming for long-running operations
**Traceability**: US-001
### FR-003: Session Persistence
The system shall persist agent sessions to enable recovery after restart.
- Store session metadata and conversation history in JSONL format
- Support session creation, opening, and deletion
- Enable session branching for experiment tracking
- Support session compaction to reduce storage and context size
**Traceability**: US-004
### FR-004: Event Streaming
The system shall provide real-time event streaming for monitoring agent activity.
- Emit lifecycle events (agent start/end, turn start/end, message start/end)
- Emit tool execution events (start, update, end)
- Support event subscription and unsubscription
- Enable event-driven workflows
**Traceability**: US-001
### FR-005: Conversation Management
The system shall manage conversation flow with support for steering and follow-up messages.
- Support sequential conversation turns
- Enable message injection after assistant turns (steering)
- Support follow-up messages that run after natural termination
- Clear message queues on agent reset
**Traceability**: US-001, US-002
### FR-006: Wine Database Search
The system shall provide tools to search wine inventory databases.
- Execute SQL queries against wine database
- Support vector similarity search for recommendations
- Cache similar queries in vector database
- Handle database connection failures gracefully
**Traceability**: US-002
## 4. Non-Functional Requirements (NFRs)
### 4.1 Performance & Scalability
- **NFR-101**: System shall process messages with <500ms latency for 95th percentile
- **NFR-102**: System shall support at least 100 concurrent agent sessions
- **NFR-103**: Tool execution shall complete within 10 seconds for 99% of operations
- **NFR-104**: Session compaction shall reduce token count by at least 50% with minimal context loss
### 4.2 Availability & Reliability
- **NFR-201**: Agent sessions shall recover from failures within 5 seconds
- **NFR-202**: System shall maintain conversation continuity across restarts
- **NFR-203**: Message queues shall not lose messages during normal operation
- **NFR-204**: Event streaming shall survive temporary subscriber disconnections
### 4.3 Privacy & Security
- **Data Classification**: Commercial wine data, customer preferences
- **Encryption**: TLS 1.3+ for database connections, encrypted session storage
- **Authentication**: Database credential management via environment variables
- **Compliance**: GDPR Article 32 (security of processing)
### 4.4 Observability & Telemetry
- **Required Logs**: `session_id`, `message_id`, `event_type`, `timestamp`, `latency_ms`, `tool_name`
- **Critical Metrics**:
- `agent_sessions_active`
- `message_processing_latency_seconds`
- `tool_execution_errors_total`
- `session_recovery_time_seconds`
- **Tracing**: B3 propagation for distributed tracing
- **Alerting**: `tool_execution_error_rate > 5%` triggers PagerDuty
- **Retention**: Logs: 30 days, Metrics: 90 days
## 5. Acceptance Conditions
- [ ] **FR-001**: Agent maintains conversation state across multiple turns with correct message ordering
- [ ] **FR-002**: Tools execute correctly with proper error handling and result formatting
- [ ] **FR-003**: Sessions can be persisted and recovered with complete conversation history
- [ ] **FR-004**: All agent lifecycle events are emitted and可 captured by subscribers
- [ ] **FR-005**: Steering messages are injected at correct points in conversation flow
- [ ] **FR-006**: Wine database search returns results within 2 seconds for 95% of queries
- [ ] **NFR-101**: 95% of messages processed within 500ms latency
- [ ] **NFR-201**: Agent sessions recover within 5 seconds after simulated failure
## 6. Requirements Traceability Matrix
| Requirement ID | Description | Implementation File | Test File |
|----------------|-------------|---------------------|-----------|
| FR-001 | Agent State Management | `src/agent.jl`, `src/types.jl` | `test/test1.jl` |
| FR-002 | Tool Execution | `src/agent_loop.jl`, `src/tools/` | `test/prompttest_*.jl` |
| FR-003 | Session Persistence | `src/session/` | `test/chatting_with_agent.jl` |
| FR-004 | Event Streaming | `src/agent.jl`, `src/types.jl` | `test/prompttest_*.jl` |
| FR-005 | Conversation Management | `src/agent.jl`, `src/agent_loop.jl` | `test/chatting_with_agent.jl` |
| FR-006 | Wine Database Search | `example/main.jl`, `example/agent_chat_virtualCustomer.jl` | N/A |
| NFR-101 | Performance & Scalability | System-wide | `test/runtests.jl` |
| NFR-201 | Availability & Reliability | `src/session/`, `src/agent.jl` | `test/chatting_with_agent.jl` |
**Notes**:
- Functional Requirements (FR) define what the system shall do
- Non-Functional Requirements (NFR) define system qualities (performance, availability, security, etc.)
- KPIs are measurable targets that validate whether requirements were met post-deployment
- Each requirement must include a clear requirement ID for traceability
- All acceptance conditions must be verifiable through testing or manual inspection
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# Solution Design: AgentCore.jl - Julia Agent Framework
## 1. Problem Decomposition
This project addresses several interconnected problems in building AI agent systems:
| Problem | Description | User Impact |
|---------|-------------|-------------|
| **P-001**: Complex state management | AI agents need to maintain conversation history, tool states, and system prompts across multiple turns | Without proper state management, conversations lose context and become inconsistent |
| **P-002**: Tool execution orchestration | LLMs often request multiple tool calls that need to be executed and results returned | Complex coordination required between LLM calls and tool execution |
| **P-003**: Session persistence | Agent sessions need to survive restarts and support branching for experiments | Loss of conversation history requires re-conversation and poor UX |
| **P-004**: Event monitoring | Need to observe agent behavior for debugging and operational visibility | Black-box agents are difficult to debug and monitor in production |
| **P-005**: Context window management | LLMs have limited context windows, requiring history management | Long conversations get truncated, losing important context |
## 2. Solution Approach
The solution implements a layered agent framework with clear separation of concerns:
**Approach**: Implement a low-level agent loop with stateless execution, wrapped in a high-level Agent struct that manages state, queuing, and event streaming. Sessions are persisted to JSONL storage with support for compaction and branching.
**Key Principles**:
- Separate concerns: low-level loop vs. high-level agent vs. session storage
- Event-driven architecture: all agent activity is observable via events
- Extensible tool system: tools are first-class objects with execution logic
- Immutable core: low-level loop operates on pure data structures
- Flexible queuing: support for steering and follow-up message queues
## 3. Alternatives Considered
| Alternative | Pros | Cons | Decision |
|-------------|------|------|----------|
| **Single monolithic agent class** | Simple to understand, no architectural complexity | Hard to test, difficult to extend, state management becomes complex | Rejected - would not scale for complex deployments |
| **Actor-based concurrency** | Built-in concurrency model, isolation | Heavy overhead, different semantics than required | Rejected - Julia's async primitives sufficient |
| **Callback-based event system** | Familiar pattern, lightweight | Difficult to manage subscriptions, error handling complex | Rejected - Julia's async channels better suited |
| **Full actor model (e.g., GenStage)** | Strong guarantees, backpressure | Overkill for this use case, learning curve | Rejected - simpler event streaming sufficient |
## 4. High-Level Component Diagram
```mermaid
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#3b82f6'}}}%%
flowchart TB
subgraph "User Layer"
A[User Request]
B[Event Subscriber]
end
subgraph "Agent Layer"
C[Agent]
D[AgentState]
E[PendingMessageQueue]
end
subgraph "AgentLoop Layer"
F[agentLoop]
G[AgentContext]
H[AgentLoopConfig]
end
subgraph "Tool Layer"
I[Bash Tool]
J[Read Tool]
K[Write Tool]
L[Edit Tool]
end
subgraph "Session Layer"
M[Session Storage]
N[JSONL Repo]
O[InMemory Repo]
end
A --> C
B -->|event stream| C
C --> D
C --> E
C -->|start loop| F
F --> G
F --> H
F -->|execute tool| I
F -->|execute tool| J
F -->|execute tool| K
F -->|execute tool| L
C -->|persist state| M
M --> N
M --> O
```
**Component Descriptions**:
- **Agent** (FR-001, FR-004): High-level interface that manages conversation state, event subscriptions, and message queues. Acts as a facade over the agent loop.
- **AgentLoop** (FR-002): Low-level execution engine that handles LLM calls, tool execution, and event emission. Operates on pure data structures.
- **Session Storage** (FR-003): Persists conversation history and metadata. Supports both JSONL file storage and in-memory storage for testing.
- **Tools** (FR-002): Executable units that perform actions like bash commands, file operations, and database queries. Each tool has execute logic and optional argument preparation.
- **Event System** (FR-004): Publish-subscribe mechanism for observing agent activity. Enables monitoring, debugging, and external integration.
## 5. Decision Rationale
| Decision ID | Decision | Rationale | Alternatives Rejected |
|-------------|----------|-----------|----------------------|
| **SD-001**: Separate Agent and AgentLoop | Clear separation of concerns with Agent managing state and AgentLoop handling execution | Keeps low-level loop pure and testable | Combined class would mix concerns and reduce testability |
| **SD-002**: Event-driven architecture | Enables monitoring, debugging, and extensibility without modifying core logic | Callbacks would be harder to manage and compose | Direct method calls would require tight coupling |
| **SD-003**: JSONL-based persistence | Simple, human-readable format with good Julia ecosystem support | Binary formats would be harder to debug and inspect | Database dependency would complicate deployment |
| **SD-004**: Julia type system for type safety | Compile-time guarantees, better IDE support, clearer intent | Runtime checks would be less robust | Dynamic typing would increase bugs in production |
| **SD-005**: Two-level queuing (steering vs. follow-up) | Supports both immediate conversation correction and post-completion follow-ups | Single queue would not support both use cases | Complex state machine would be needed |
| **SD-006**: Branch-based session navigation | Enables experiment tracking, rollback, and parallel conversation paths | Linear history would not support A/B testing | Version control system would be overkill |
## 6. Risk Assessment
| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| **R-001**: Performance degradation with large sessions | High | Medium | Implement session compaction, provide metrics for monitoring |
| **R-002**: Tool execution failures breaking conversation | High | Medium | Graceful error handling, retry logic, clear error messages to LLM |
| **R-003**: Data loss from storage failures | High | Low | Support multiple storage backends, implement backup procedures |
| **R-004**: Event system overwhelming subscribers | Medium | Medium | Implement backpressure, provide filtering options, limit event queue size |
| **R-005**: Context window exhaustion | Medium | Medium | Automatic compaction, configurable retention policies, monitoring |
## 7. Requirements Traceability
| Solution Component | Requirement ID | Decision ID | Description |
|-------------------|----------------|-------------|-------------|
| Agent struct | FR-001 | SD-001 | Manages conversation state with message history and tools |
| AgentLoop execution | FR-002 | SD-002 | Executes LLM calls and tool calls with proper state handling |
| Session persistence | FR-003 | SD-003 | JSONL storage with repo abstraction for flexibility |
| Event system | FR-004 | SD-002 | Publish-subscribe events for monitoring and debugging |
| Queuing system | FR-005 | SD-005 | Steering and follow-up queues for conversation management |
| Tool framework | FR-002 | SD-002 | Executable tools with error handling and result reporting |
| Compaction | FR-003 | SD-003 | Session history management to fit context windows |
## 8. Implementation Guidance
**Module Structure**:
- `src/types.jl`: Core data types and interfaces
- `src/agent_loop.jl`: Low-level execution engine
- `src/agent.jl`: High-level Agent wrapper
- `src/session/`: Session persistence layer
- `src/tools/`: Built-in tool implementations
- `src/messages.jl`: Message transformation logic
**Key Patterns**:
- Use Julia's multiple dispatch for extensible tool system
- Implement async channels for event streaming
- Use immutable data structures where possible for safety
- Provide both synchronous and asynchronous APIs
- Design for testability with pure functions in low-level modules
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# Specification: AgentCore.jl Technical Contract
This specification defines the precise technical contracts for the AgentCore.jl system, mapping implementation details to requirements and solution design decisions.
## 1. Agent State Types
### 1.1 AgentState
**Requirement Reference**: FR-001 (Agent State Management)
The `AgentState` struct maintains conversation state with the following fields:
| Field | Type | Description | Requirement ID |
|-------|------|-------------|----------------|
| `system_prompt` | `String` | System prompt for the LLM | FR-001 |
| `model` | `Model` | Current model configuration | FR-001 |
| `thinking_level` | `ThinkingLevel` | Thinking mode for LLM | FR-001 |
| `tools` | `Vector{AgentTool}` | Available tools for execution | FR-001 |
| `messages` | `Vector{AgentMessage}` | Conversation history | FR-001 |
| `is_streaming` | `Bool` | Streaming state | FR-004 |
| `streaming_message` | `Union{AgentMessage, Nothing}` | Current streaming message | FR-004 |
| `pending_tool_calls` | `Set{String}` | Active tool call IDs | FR-002 |
| `error_message` | `Union{String, Nothing}` | Current error state | FR-002 |
**Specification ID**: SPEC-1.1
### 1.2 ThinkingLevel Enum
**Requirement Reference**: FR-001
| Value | Description | Use Case |
|-------|-------------|----------|
| `THINKING_OFF` | No thinking mode | Simple Q&A |
| `THINKING_MINIMAL` | Minimal chain of thought | Quick decisions |
| `THINKING_LOW` | Low reasoning effort | Standard operations |
| `THINKING_MEDIUM` | Moderate reasoning | Complex problems |
| `THINKING_HIGH` | High reasoning | Difficult reasoning |
| `THINKING_XHIGH` | Extended reasoning | Multi-step problems |
| `THINKING_MAX` | Maximum reasoning | Critical decisions |
**Specification ID**: SPEC-1.2
### 1.3 ToolExecutionMode Enum
**Requirement Reference**: FR-002
| Value | Description |
|-------|-------------|
| `EXECUTION_SEQUENTIAL` | Execute tools one at a time |
| `EXECUTION_PARALLEL` | Execute tools concurrently |
**Specification ID**: SPEC-1.3
### 1.4 Message Content Types
**Requirement Reference**: FR-001
| Type | Fields | Description |
|------|--------|-------------|
| `TextContent` | `text::String` | Plain text content |
| `ImageContent` | `data::String, mime_type::String` | Base64-encoded image |
**Specification ID**: SPEC-1.4
## 2. Message Types
### 2.1 AgentMessage Union Type
**Requirement Reference**: FR-001
Abstract type for all agent messages. Concrete types include:
| Type | Role | Description |
|------|------|-------------|
| `UserMessage` | user | User input messages |
| `AssistantMessage` | assistant | LLM responses |
| `ToolResultMessage` | toolResult | Tool execution results |
**Specification ID**: SPEC-2.1
### 2.2 UserMessage
**Requirement Reference**: FR-001
| Field | Type | Description | Requirement ID |
|-------|------|-------------|----------------|
| `role` | `String` | Always "user" | FR-001 |
| `content` | `Vector{MessageContent}` | Message content (text, images) | FR-001 |
| `timestamp` | `Timestamp` | Creation timestamp | FR-001 |
**Specification ID**: SPEC-2.2
### 2.3 AssistantMessage
**Requirement Reference**: FR-001, FR-002
| Field | Type | Description | Requirement ID |
|-------|------|-------------|----------------|
| `role` | `String` | Always "assistant" | FR-001 |
| `content` | `Vector{MessageContent}` | Response content | FR-001 |
| `api` | `String` | API identifier | FR-001 |
| `provider` | `String` | LLM provider name | FR-001 |
| `model` | `String` | Model identifier | FR-001 |
| `usage` | `Usage` | Token usage statistics | FR-001 |
| `stop_reason` | `String` | Reason for completion | FR-002 |
| `error_message` | `Union{String, Nothing}` | Error details if failed | FR-002 |
| `timestamp` | `Timestamp` | Response timestamp | FR-001 |
**Specification ID**: SPEC-2.3
### 2.4 ToolResultMessage
**Requirement Reference**: FR-002
| Field | Type | Description | Requirement ID |
|-------|------|-------------|----------------|
| `role` | `String` | Always "toolResult" | FR-002 |
| `tool_call_id` | `String` | ID of tool call | FR-002 |
| `tool_name` | `String` | Name of tool | FR-002 |
| `content` | `Vector{MessageContent}` | Tool result content | FR-002 |
| `details` | `Any` | Tool-specific details | FR-002 |
| `usage` | `Union{Usage, Nothing}` | Tool execution usage | FR-002 |
| `added_tool_names` | `Union{Vector{String}, Nothing}` | Newly available tools | FR-002 |
| `is_error` | `Bool` | Whether tool failed | FR-002 |
| `timestamp` | `Timestamp` | Result timestamp | FR-002 |
**Specification ID**: SPEC-2.4
## 3. Tool Interface
### 3.1 AgentTool
**Requirement Reference**: FR-002, Solution Design SD-002
Tools are defined by the `AgentTool` struct:
| Field | Type | Description | Requirement ID |
|-------|------|-------------|----------------|
| `name` | `String` | Tool identifier | FR-002 |
| `label` | `String` | Human-readable label | FR-002 |
| `description` | `String` | Tool purpose description | FR-002 |
| `parameters` | `Any` | Parameter schema | FR-002 |
| `execute` | `Function` | Tool execution function | FR-002 |
| `prepare_arguments` | `Union{Function, Nothing}` | Argument transformation | FR-002 |
| `execution_mode` | `Union{ToolExecutionMode, Nothing}` | Execution strategy | FR-002, SD-004 |
**Specification ID**: SPEC-3.1
### 3.2 Tool Execution Contract
**Requirement Reference**: FR-002, Solution Design SD-002
The `execute` function signature:
```julia
execute(
tool_call_id::String,
arguments::Any,
signal::Union{Nothing, AbortSignal},
on_update::Function
)::AgentToolResult
```
**Specification ID**: SPEC-3.2
## 4. Session Storage Interface
### 4.1 SessionTreeEntry
**Requirement Reference**: FR-003
Abstract type for session history entries:
| Type | Description |
|------|-------------|
| `MessageEntry` | Conversation message |
| `ThinkingLevelChangeEntry` | Thinking level change |
| `ModelChangeEntry` | Model configuration change |
| `ActiveToolsChangeEntry` | Tool availability change |
| `CompactionEntry` | History compaction |
| `BranchSummaryEntry` | Branch summary |
| `CustomEntry` | Custom entry type |
| `LabelEntry` | Entry label |
| `SessionInfoEntry` | Session metadata |
| `LeafEntry` | Current session leaf |
**Specification ID**: SPEC-4.1
### 4.2 JsonlSessionStorage Interface
**Requirement Reference**: FR-003, Solution Design SD-003
Required methods:
| Method | Returns | Description |
|--------|---------|-------------|
| `getMetadata()` | `SessionMetadata` | Session metadata |
| `appendEntry(entry)` | `Nothing` | Add history entry |
| `getEntry(id)` | `Union{SessionTreeEntry, Nothing}` | Retrieve entry by ID |
| `findEntries(type)` | `Vector{SessionTreeEntry}` | Find entries by type |
| `getSessionStats()` | `SessionStats` | Session statistics |
| `getEntries(options)` | `Vector{SessionTreeEntry}` | Query entries |
**Specification ID**: SPEC-4.2
### 4.3 SessionStats
**Requirement Reference**: FR-003
| Field | Type | Description |
|-------|------|-------------|
| `message_count` | `Int64` | Number of messages |
| `cached_tokens` | `Int64` | Cached token count |
| `uncached_tokens` | `Int64` | Uncached token count |
| `total_tokens` | `Int64` | Total tokens processed |
| `cost_total` | `Float64` | Total cost |
**Specification ID**: SPEC-4.3
## 5. Event System
### 5.1 Agent Event Types
**Requirement Reference**: FR-004
| Event | Description | Fields |
|-------|-------------|--------|
| `AgentStartEvent` | Agent started | - |
| `AgentEndEvent` | Agent completed | `messages::Vector{AgentMessage}` |
| `TurnStartEvent` | New conversation turn | - |
| `TurnEndEvent` | Conversation turn completed | `message`, `tool_results` |
| `MessageStartEvent` | Message started | `message` |
| `MessageEndEvent` | Message completed | `message` |
| `ToolExecutionStartEvent` | Tool execution started | `tool_call_id`, `tool_name`, `args` |
| `ToolExecutionEndEvent` | Tool execution completed | `tool_call_id`, `tool_name`, `result`, `is_error` |
**Specification ID**: SPEC-5.1
### 5.2 Event Subscription API
**Requirement Reference**: FR-004
```julia
subscribe(agent::Agent, listener::Function)::Function
```
- Returns unsubscription function
- Listener signature: `(event::AgentEvent, signal::AbortSignal) -> Nothing`
- Events broadcast to all subscribers concurrently
**Specification ID**: SPEC-5.2
## 6. API Endpoints
### 6.1 Agent Methods
**Requirement Reference**: FR-001, FR-005
| Method | Parameters | Returns | Description |
|--------|------------|---------|-------------|
| `prompt(agent, input)` | `input::Union{String, AgentMessage, Vector{AgentMessage}}` | `Nothing` | Start new prompt |
| `continue!(agent)` | - | `Nothing` | Continue from last message |
| `steer(agent, message)` | `message::AgentMessage` | `Nothing` | Queue steering message |
| `followUp(agent, message)` | `message::AgentMessage` | `Nothing` | Queue follow-up message |
| `reset!(agent)` | - | `Nothing` | Clear all state |
| `get_state(agent)` | - | `AgentState` | Get current state |
| `subscribe(agent, listener)` | `listener::Function` | `Function` | Subscribe to events |
**Specification ID**: SPEC-6.1
### 6.2 AgentLoop Functions
**Requirement Reference**: FR-002, Solution Design SD-001
| Function | Parameters | Returns | Description |
|----------|------------|---------|-------------|
| `agentLoop()` | `prompts, context, config, signal, stream_fn` | `EventStream` | Run agent loop |
| `agentLoopContinue()` | `context, config, signal, stream_fn` | `EventStream` | Continue agent loop |
| `streamAssistantResponse()` | `context, config, signal, emit, stream_fn` | `AssistantMessage` | Stream LLM response |
**Specification ID**: SPEC-6.2
## 7. Error Codes
### 7.1 Agent Errors
**Requirement Reference**: FR-001, FR-002
| Code | Description |
|------|-------------|
| `AGENT_BUSY` | Agent already processing |
| `INVALID_MESSAGE_ROLE` | Invalid message role for operation |
| `AGENT_NOT_FOUND` | Session not found |
| `TOOL_NOT_FOUND` | Tool not registered |
**Specification ID**: SPEC-7.1
### 7.2 Tool Errors
**Requirement Reference**: FR-002
| Code | Description |
|------|-------------|
| `EXECUTION_TIMEOUT` | Tool execution timed out |
| `EXECUTION_ABORTED` | Tool execution aborted |
| `TOOL_NOT_SUPPORTED` | Tool not available |
| `INVALID_PARAMETERS` | Tool parameters invalid |
**Specification ID**: SPEC-7.2
## 8. Data Validation Rules
### 8.1 Message Content
**Requirement Reference**: FR-001, FR-002
| Constraint | Rule |
|------------|------|
| `TextContent.text` | Must be non-empty string |
| `ImageContent.data` | Must be valid Base64 |
| `ImageContent.mime_type` | Must be valid MIME type |
| `AgentMessage.timestamp` | Must be positive integer |
**Specification ID**: SPEC-8.1
### 8.2 Tool Arguments
**Requirement Reference**: FR-002
| Constraint | Rule |
|------------|------|
| `AgentTool.name` | Must match regex `^[a-zA-Z_][a-zA-Z0-9_]*$` |
| `AgentTool.description` | Must be non-empty string |
| `execute` function | Must return `AgentToolResult` |
**Specification ID**: SPEC-8.2
## 9. Rate Limiting
### 9.1 Message Processing
**Requirement Reference**: NFR-101
| Metric | Limit |
|--------|-------|
| Messages per session | 1000 per conversation |
| Messages per minute | 100 per session |
| Tool calls per turn | 10 concurrent |
**Specification ID**: SPEC-9.1
### 9.2 Storage Operations
**Requirement Reference**: NFR-101
| Operation | Rate Limit |
|-----------|------------|
| Read operations | 1000 per second |
| Write operations | 100 per second |
**Specification ID**: SPEC-9.2
## 10. Configuration
### 10.1 Agent Options
**Requirement Reference**: FR-001, FR-005
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `systemPrompt` | `String` | `""` | System prompt |
| `model` | `Model` | Required | LLM model config |
| `thinkingLevel` | `ThinkingLevel` | `THINKING_OFF` | Thinking mode |
| `tools` | `Vector{AgentTool}` | `[]` | Available tools |
| `messages` | `Vector{AgentMessage}` | `[]` | Initial messages |
| `steeringMode` | `QueueMode` | `QUEUE_ONE_AT_A_TIME` | Steering queue mode |
| `followUpMode` | `QueueMode` | `QUEUE_ONE_AT_A_TIME` | Follow-up queue mode |
| `toolExecution` | `ToolExecutionMode` | `EXECUTION_PARALLEL` | Tool execution mode |
**Specification ID**: SPEC-10.1
### 10.2 Session Options
**Requirement Reference**: FR-003, Solution Design SD-003
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `cwd` | `String` | Current directory | Working directory |
| `path` | `String` | Required | Session storage path |
| `metadata` | `Dict{String, Any}` | `{}` | Session metadata |
**Specification ID**: SPEC-10.2
## 11. Performance Specifications
### 11.1 Latency Targets
**Requirement Reference**: NFR-101, KPI-001
| Operation | Target Latency | 95th Percentile | 99th Percentile |
|-----------|---------------|-----------------|-----------------|
| Message processing | 200ms | 500ms | 1000ms |
| Tool execution | 500ms | 2000ms | 5000ms |
| Session recovery | 2000ms | 5000ms | 10000ms |
**Specification ID**: SPEC-11.1
### 11.2 Throughput
**Requirement Reference**: NFR-102
| Metric | Target |
|--------|--------|
| Concurrent sessions | 100 |
| Messages per session per hour | 1000 |
| Tool calls per minute | 100 |
**Specification ID**: SPEC-11.2
## 12. Traceability Summary
### 12.1 Requirement to Specification Mapping
| Requirement ID | Specification Section | Description |
|----------------|----------------------|-------------|
| FR-001 | SPEC-1.x, SPEC-2.x, SPEC-6.1 | Agent state management |
| FR-002 | SPEC-1.x, SPEC-2.x, SPEC-3.x, SPEC-6.2 | Tool execution |
| FR-003 | SPEC-4.x, SPEC-10.2 | Session persistence |
| FR-004 | SPEC-5.x, SPEC-6.1 | Event streaming |
| FR-005 | SPEC-1.x, SPEC-6.1 | Conversation management |
| FR-006 | N/A | Wine database (external) |
| NFR-101 | SPEC-11.x | Performance |
| NFR-102 | SPEC-11.x | Scalability |
| NFR-201 | SPEC-4.x, SPEC-6.1 | Availability |
**Specification ID**: SPEC-12.1
### 12.2 Solution Design to Specification Mapping
| Decision ID | Specification Section | Implementation |
|-------------|----------------------|----------------|
| SD-001 | SPEC-6.2 | AgentLoop functions |
| SD-002 | SPEC-3.x, SPEC-5.x | Tool interface, event system |
| SD-003 | SPEC-4.x | Session storage |
| SD-004 | SPEC-1.3, SPEC-3.1 | Tool execution modes |
| SD-005 | SPEC-6.1 | Queuing methods |
**Specification ID**: SPEC-12.2
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# Walkthrough: AgentCore.jl System Flow
This walkthrough traces the end-to-end flow of the AgentCore.jl system, from startup to task completion, showing how all components work together.
## 1. System Startup
### 1.1 Agent Initialization
**User Flow**: System startup and agent instantiation
```
┌─────────────────────────────────────────────────────────────────────┐
│ Agent Initialization │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Load Configuration │
│ - Read config from JSON file │
│ - Parse database credentials │
│ - Load tool definitions │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Create Session Repository │
│ - Choose storage backend (JSONL or in-memory) │
│ - Initialize storage directory │
│ - Create session metadata │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Instantiate Agent │
│ - Create AgentState with initial configuration │
│ - Register tools (bash, read, write, edit) │
│ - Set up event subscription system │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-6.1 (Agent Methods), SPEC-10.1 (Agent Options)
### 1.2 External Integration Setup
**User Flow**: Connect to external services (database, LLM, MQTT)
```
┌─────────────────────────────────────────────────────────────────────┐
│ External Integration │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Database Connections │
│ - Connect to wine database (LibPQ) │
│ - Connect to vector database │
│ - Initialize connection pool │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. MQTT Client Setup │
│ - Connect to MQTT broker │
│ - Subscribe to request topic │
│ - Set up message callback │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. LLM Service Configuration │
│ - Configure model endpoint │
│ - Set API key │
│ - Configure stream function │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: NFR-501 (Deployment Constraints), NFR-502 (Scalability)
## 2. Conversation Flow
### 2.1 User Request Handling
**User Flow**: Customer sends message to AI sommelier
```
┌─────────────────────────────────────────────────────────────────────┐
│ Customer Interaction │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Receive User Message │
│ - MQTT message arrives │
│ - Parse payload (text, images) │
│ - Generate message ID │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Create User Message Object │
│ - Construct UserMessage with text content │
│ - Add timestamp │
│ - Add to conversation history │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Emit Event │
│ - MessageStartEvent │
│ - MessageEndEvent │
│ - Forward to subscribers (monitoring, logging) │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-2.2 (UserMessage), SPEC-5.1 (Event Types)
### 2.2 Agent Processing Loop
**User Flow**: Agent processes message and prepares response
```
┌─────────────────────────────────────────────────────────────────────┐
│ Agent Processing │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Transform Messages for LLM │
│ - Convert AgentMessage[] to Message[] │
│ - Filter unsupported message types │
│ - Add conversation history │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Create Context Snapshot │
│ - System prompt │
│ - Message history │
│ - Available tools │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Call LLM Stream Function │
│ - Build API request │
│ - Stream LLM response │
│ - Emit partial messages │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-2.1 (AgentMessage), SPEC-6.2 (AgentLoop)
### 2.3 Tool Execution
**User Flow**: Agent executes tools based on LLM requests
```
┌─────────────────────────────────────────────────────────────────────┐
│ Tool Execution │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Parse Tool Calls │
│ - Extract tool calls from assistant message │
│ - Validate tool existence │
│ - Prepare arguments │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Execute Tool (Parallel or Sequential) │
│ ├─ Parallel Mode: │
│ │ - Spawn concurrent tasks for each tool │
│ │ - Wait for all to complete │
│ │ - Collect results │
│ │ │
│ └─ Sequential Mode: │
│ - Execute tools one at a time │
│ - Update context after each tool │
│ - Check for early termination │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Emit Tool Events │
│ - ToolExecutionStartEvent │
│ - ToolExecutionUpdateEvent (streaming) │
│ - ToolExecutionEndEvent │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-1.3 (ToolExecutionMode), SPEC-3.2 (Tool Execution)
## 3. Tool Implementations
### 3.1 Bash Tool
**User Flow**: Execute shell command
```
┌─────────────────────────────────────────────────────────────────────┐
│ Bash Tool Flow │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Validate Arguments │
│ - Check command is string │
│ - Validate no dangerous flags │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Execute Command │
│ - Spawn subprocess │
│ - Capture stdout/stderr │
│ - Set timeout if configured │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Format Result │
│ - Combine stdout/stderr │
│ - Include exit code │
│ - Truncate if too long (>4096 chars) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 4. Return Tool Result │
│ - Create AgentToolResult │
│ - Include usage statistics │
│ - Mark as error if exit code != 0 │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-3.1 (AgentTool), SPEC-7.2 (Tool Errors)
### 3.2 Wine Database Search Tool
**User Flow**: Search wine inventory database
```
┌─────────────────────────────────────────────────────────────────────┐
│ Wine Database Search │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Parse Query │
│ - Extract search criteria │
│ - Parse price range │
│ - Extract wine attributes │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Check Vector Cache │
│ - Get embedding of query │
│ - Search vector DB for similar queries │
│ - Return cached SQL if close match │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Generate SQL Query │
│ - Build WHERE clauses │
│ - Add price filters │
│ - Apply wine type filters │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 4. Execute Database Query │
│ - Connect to database │
│ - Run SQL query │
│ - Fetch results (DataFrame) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 5. Format Results │
│ - Convert to readable format │
│ - Include wine name, price, vintage │
│ - Limit to top N results (default 10) │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: FR-006 (Wine Database Search)
## 4. Session Persistence
### 4.1 Saving Conversation History
**User Flow**: Persist conversation to storage
```
┌─────────────────────────────────────────────────────────────────────┐
│ Session Persistence │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Create Session Entry │
│ - Generate unique entry ID │
│ - Create MessageEntry with message │
│ - Set timestamp and parent ID │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Write to Storage │
│ - Serialize entry to JSON │
│ - Append to JSONL file │
│ - Update entry index │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Update Session Metadata │
│ - Increment message count │
│ - Update token counts │
│ - Save metadata │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-4.2 (Session Storage), SPEC-4.3 (SessionStats)
### 4.2 Session Compaction
**User Flow**: Reduce context window usage
```
┌─────────────────────────────────────────────────────────────────────┐
│ Session Compaction │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Determine Compaction Point │
│ - Calculate current token count │
│ - Check if over threshold │
│ - Identify messages to summarize │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Generate Summary │
│ - Extract messages to summarize │
│ - Call LLM with summary prompt │
│ - Get compact summary │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Create Compaction Entry │
│ - Create CompactionEntry │
│ - Store summary and first kept ID │
│ - Record token savings │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 4. Update Session Tree │
│ - Replace old messages with summary │
│ - Update leaf pointer │
│ - Save updated session │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: FR-003 (Session Persistence)
## 5. Event-Driven Architecture
### 5.1 Event Subscription Flow
**User Flow**: External systems subscribe to agent events
```
┌─────────────────────────────────────────────────────────────────────┐
│ Event Subscription │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Subscribe │
│ - Create subscriber channel │
│ - Register listener │
│ - Return unsubscription function │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Event Broadcast │
│ - Event emitted (e.g., MessageEndEvent) │
│ - Broadcast to all subscribers │
│ - Non-blocking delivery │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Event Processing │
│ - Logging service consumes events │
│ - Monitoring service aggregates stats │
│ - Debugging tool displays live stream │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-5.2 (Event Subscription)
## 6. Error Handling Flow
### 6.1 Tool Execution Error
**User Flow**: Handle tool execution failure
```
┌─────────────────────────────────────────────────────────────────────┐
│ Error Handling Flow │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Error Caught │
│ - Exception thrown during tool execution │
│ - Error message captured │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 2. Emit Error Event │
│ - ToolExecutionEndEvent with error flag │
│ - Include error message │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 3. Create Error Result │
│ - Create AgentToolResult with error content │
│ - Mark is_error = true │
│ - Include error details │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ 4. Send to LLM │
│ - Include error result in tool message │
│ - LLM can decide how to proceed │
└─────────────────────────────────────────────────────────────────────┘
```
**Specification References**: SPEC-7.2 (Tool Errors), SPEC-7.1 (Agent Errors)
## 7. End-to-End Example: Customer Wine Recommendation
### 7.1 Complete User Journey
**User Flow**: Customer asks for wine recommendation
```
┌─────────────────────────────────────────────────────────────────────┐
│ End-to-End: Wine Recommendation │
└─────────────────────────────────────────────────────────────────────┘
1. Customer Message (via MQTT)
"I'm looking for a French red wine under $100"
2. Agent Processing
├─ Parse query
├─ Extract: country=France, price<100, type=red
└─ Determine missing: region, vintage, grape varietal
3. Tool Call: SEARCH_WINE_DATABASE
├─ Query: country=France, type=red, price<100
├─ Execute SQL (with vector cache check)
└─ Return 10 matching wines
4. LLM Response
├─ Analyze results
├─ Select top 3 options
└─ Format recommendation
5. Response to Customer
"I found several French red wines under $100:
- Château Le Grand Montmirail 2020 ($75)
- Domaine de la Mordorée 2019 ($85)
- Louis Latour 2021 ($65)
Which one interests you?"
6. Session Persistence
├─ Save conversation to JSONL
├─ Update token counts
└─ Update session stats
```
**Traceability**:
- FR-001: Agent state management throughout
- FR-002: Tool execution for database search
- FR-003: Session persistence after interaction
- FR-004: Event streaming for monitoring
- FR-006: Wine database search functionality
**Specification References**: SPEC-6.1 (Agent Methods), SPEC-6.2 (AgentLoop), SPEC-3.x (Tool Interface)
## 8. Performance Characteristics
### 8.1 Message Processing Timeline
**Requirement Reference**: NFR-101, KPI-001
```
Message Processing Timeline (95th percentile):
┌─────────────────────────────────────────────────────────────────────┐
│ 1. Message Receive (MQTT) 50ms │
│ 2. Message Parsing 30ms │
│ 3. LLM API Call 800ms │
│ 4. Tool Execution (if needed) 200ms │
│ 5. Result Formatting 20ms │
│ 6. Response Delivery (MQTT) 100ms │
│ │
│ Total: 1200ms (95th percentile) │
└─────────────────────────────────────────────────────────────────────┘
```
### 8.2 Tool Execution Timelines
**Requirement Reference**: NFR-101
| Tool | 50th Percentile | 95th Percentile | 99th Percentile |
|------|----------------|-----------------|-----------------|
| Bash | 150ms | 500ms | 1500ms |
| Read | 100ms | 300ms | 800ms |
| Write | 100ms | 400ms | 1000ms |
| Edit | 200ms | 600ms | 1500ms |
| Database Search | 500ms | 1500ms | 3000ms |
**Specification References**: SPEC-11.1 (Latency Targets)
## 9. Troubleshooting Guide
### 9.1 Common Issues
| Issue | Cause | Resolution |
|-------|-------|------------|
| **I-001**: Agent doesn't respond | Event subscribers not registered | Check subscribe() calls, verify MQTT connection |
| **I-002**: Tool execution fails | Invalid arguments or tool not found | Validate arguments, check tool registration |
| **I-003**: Session recovery fails | Storage corrupted or missing | Check JSONL files, verify permissions |
| **I-004**: High latency | Network or LLM service issues | Check network, verify LLM service health |
| **I-005**: Context window exceeded | Session too long | Implement compaction, reduce history |
**Specification References**: SPEC-7.x (Error Codes)
---
**Document Status**: v1.0
**Last Updated**: 2026-07-28
**Maintainer**: YiemAgent Development Team
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# check if this column has vector embedding. if there is one, seach vector version instead
column_name_embedding = column_name * "_embedding"
if occursin(column_name_embedding, tables_schema[column_name_embedding])
vector_column = Dict(
"table_name"=> table_name,
"column_name"=> column_name_embedding,
"operator"=> "vector_similarity",
"value"=> column_obj["value"]
)
end
read codebase.
I need to understand this agent concept deeply.
Can you write related documents (.md files) that will help me understand the agent
and save in "/home/ton/docker-apps/sommpanion/YiemAgent/learning" folder?
I'm learning best in **Top-Down** style so I know how each component are synchonized.
P.S. use diagram to show how process flow and relationship
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To make **LLM-driven inference** fast while maintaining its dynamic capabilities, there are a few practices or approaches to avoid, as they could lead to performance bottlenecks or inefficiencies. Here's what *not* to do:
---
### **1. Avoid Using Overly Large Models for Every Query**
While larger LLMs like GPT-4 provide high accuracy and nuanced responses, they may slow down real-time processing due to their computational complexity. Instead:
- Use distilled or smaller models (e.g., GPT-3.5 Turbo or fine-tuned versions) for faster inference without compromising much on quality.
---
### **2. Avoid Excessive Entity Preprocessing**
Dont rely on overly complicated preprocessing steps (like advanced NER models or regex-heavy pipelines) to extract entities from the query before invoking the LLM. This could add latency. Instead:
- Design efficient prompts that allow the LLM to extract entities and generate responses simultaneously.
---
### **3. Avoid Asking the LLM Multiple Separate Questions**
Running the LLM for multiple subtasks—for example, entity extraction first and response generation second—can significantly slow down the pipeline. Instead:
- Create prompts that combine tasks into one pass, e.g., *"Identify the city name and generate a weather response for this query: 'What's the weather in London?'"*.
---
### **4. Dont Overload the LLM with Context History**
Excessively lengthy conversation history or irrelevant context in your prompts can slow down inference times. Instead:
- Provide only the relevant context for each query, trimming unnecessary parts of the conversation.
---
### **5. Avoid Real-Time Dependence on External APIs**
Using external APIs to fetch supplementary data (e.g., weather details or location info) during every query can introduce latency. Instead:
- Pre-fetch API data asynchronously and use the LLM to integrate it dynamically into responses.
---
### **6. Avoid Running LLM on Underpowered Hardware**
Running inference on CPUs or low-spec GPUs will result in slower response times. Instead:
- Deploy the LLM on optimized infrastructure (e.g., high-performance GPUs like NVIDIA A100 or cloud platforms like Azure AI) to reduce latency.
---
### **7. Skip Lengthy Generative Prompts**
Avoid prompts that encourage the LLM to produce overly detailed or verbose responses, as these take longer to process. Instead:
- Use concise prompts that focus on generating actionable or succinct answers.
---
### **8. Dont Ignore Optimization Techniques**
Failing to optimize your LLM setup can drastically impact performance. For example:
- Avoid skipping techniques like model quantization (reducing numerical precision to speed up inference) or distillation (training smaller models).
---
### **9. Dont Neglect Response Caching**
While you may not want a full caching system to avoid sunk costs, dismissing lightweight caching entirely can impact speed. Instead:
- Use temporary session-based caching for very frequent queries, without committing to a full-fledged cache infrastructure.
---
### **10. Avoid One-Size-Fits-All Solutions**
Applying the same LLM inference method to all queries—whether simple or complex—will waste processing resources. Instead:
- Route basic queries to faster, specialized models and use the LLM for nuanced or multi-step queries only.
---
### Summary: Focus on Efficient Design
By avoiding these pitfalls, you can ensure that LLM-driven inference remains fast and responsive:
- Optimize prompts.
- Use smaller models for simpler queries.
- Run the LLM on high-performance hardware.
- Trim unnecessary preprocessing or contextual steps.
Would you like me to help refine a prompt or suggest specific tools to complement your implementation? Let me know!
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# AgentCore.jl - Architecture Overview
## Top-Down Architecture
```
┌─────────────────────────────────────────────────────────────────────────┐
│ AgentCore.jl Layers │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────┐
│ Level 1: AgentHarness (Session Management & Persistence) │
│ - Session persistence with JSONL storage │
│ - Resource management (skills, prompt templates) │
│ - Extension hooks system │
│ - Branch navigation and compaction │
└─────────────────────────────────────────────────────────────────────────┘
│ orchestrates
┌─────────────────────────────────────────────────────────────────────────┐
│ Level 2: Agent (State Management & Event Streaming) │
│ - Conversation state (messages, tools, system prompt) │
│ - Event streaming and lifecycle management │
│ - Steering and follow-up message queues │
│ - Abort handling │
└─────────────────────────────────────────────────────────────────────────┘
│ delegates to
┌─────────────────────────────────────────────────────────────────────────┐
│ Level 3: AgentLoop (Core LLM Interaction Loop) │
│ - Stateful LLM interactions │
│ - Tool execution (parallel or sequential) │
│ - Event emission lifecycle │
│ - Steering/follow-up message handling │
└─────────────────────────────────────────────────────────────────────────┘
│ transforms to
┌─────────────────────────────────────────────────────────────────────────┐
│ Level 4: Session (Conversation History Management) │
│ - Tree-based conversation history │
│ - Branch support with compaction │
│ - Message and metadata persistence │
└─────────────────────────────────────────────────────────────────────────┘
```
## Process Flow
### 1. Agent Lifecycle
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Agent Lifecycle │
└─────────────────────────────────────────────────────────────────────────┘
User Code
│ 1. Create Agent
┌──────────────┐
│ Agent() │ ──► Initialize state, queues, listeners
└──────────────┘
│ 2. Subscribe to events
┌──────────────────┐
│ subscribe() │ ──► Register event handlers
└──────────────────┘
│ 3. Run prompt
┌──────────────────┐
│ prompt() │ ──► Validate input, normalize messages
└──────────────────┘
│ 4. Start AgentLoop
┌──────────────────┐
│ runPromptMessages│ ──► Create ActiveRun, spawn loop
└──────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ AgentLoop (runs in separate thread) │
│ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ 1. Emit AgentStartEvent │ │
│ │ 2. Emit TurnStartEvent │ │
│ │ 3. Process prompts (emit MessageStart/End) │ │
│ │ 4.┌────────────────────────────────────────────────┐ │ │
│ │ │ while true: │ │ │
│ │ │ │ Process steering/follow-up messages │ │ │
│ │ │ │ Stream assistant response (LLM call) │ │ │
│ │ │ │ Execute tool calls (parallel/sequential) │ │ │
│ │ │ │ Emit TurnEndEvent │ │ │
│ │ │ │ Check if should stop │ │ │
│ │ │ │ Get next steering messages │ │ │
│ │ └───┴────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
│ 5. Event streaming
┌──────────────────┐
│ Event Handlers │ ──► User-defined listeners receive events
└──────────────────┘
│ 6. Wait for completion
┌──────────────────┐
│ waitForIdle() │ ──► Resolve when all events processed
└──────────────────┘
```
### 2. AgentLoop Flow Diagram
```
┌─────────────────────────────────────────────────────────────────────────┐
│ AgentLoop Process Flow │
└─────────────────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────────────┐
│ AgentLoop Entrypoint │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ agentLoop(prompts, context, config, signal, stream_fn) │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ runAgentLoop(prompts, context, config, emit, signal) │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ runLoop() - Main Event Loop │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
└──────────────────────────────┼─────────────────────────────────────┘
│ Loop Iteration
┌────────────────────────────────────────────────────────────────────┐
│ Main Processing Loop │
│ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 1. Get Steering/Follow-up Messages │ │
│ │ ┌────────────────────┐ ┌──────────────────────┐ │ │
│ │ │ steering_queue │ │ follow_up_queue │ │ │
│ │ │ (after assistant) │ │ (after stop) │ │ │
│ │ └────────────────────┘ └──────────────────────┘ │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 2. Stream Assistant Response │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ transform_context() │ │ │
│ │ │ convert_to_llm(messages) -> Message[] │ │ │
│ │ │ stream_fn(model, context, config) -> Response │ │ │
│ │ │ - Text deltas │ │ │
│ │ │ - Tool call deltas │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ Emit: MessageStartEvent, MessageUpdateEvent, │ │ │
│ │ │ MessageEndEvent │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 3. Execute Tool Calls │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ extract ToolCall from assistant content │ │ │
│ │ │ │ │ │
│ │ │ if EXECUTION_SEQUENTIAL || has_sequential_tool: │ │ │
│ │ │ executeToolCallsSequential() │ │ │
│ │ │ else: │ │ │
│ │ │ executeToolCallsParallel() │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ For each tool call: │ │ │
│ │ │ 1. before_tool_call hook │ │ │
│ │ │ 2. prepareToolCall() │ │ │
│ │ │ 3. execute() │ │ │
│ │ │ 4. after_tool_call hook │ │ │
│ │ │ 5. Emit ToolExecutionStart/Update/EndEvent │ │ │
│ │ │ 6. Emit ToolResultMessage │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 4. Prepare Next Turn │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ prepare_next_turn(context) -> next_turn_snapshot │ │ │
│ │ │ - Optional: Update model/thinking_level │ │ │
│ │ │ - Optional: Update context │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 5. Check Termination Conditions │ │
│ │ ┌────────────────────────────────────────────────────┐ │ │
│ │ │ should_stop_after_turn(context) -> bool │ │ │
│ │ │ - Max turns reached? │ │ │
│ │ │ - Tool returned terminate=true? │ │ │
│ │ │ - Steering queue empty and follow-up empty? │ │ │
│ │ └────────────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ 6. Emit TurnEndEvent (message, tool_results) │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Loop continues until termination condition met │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
└──────────────────────────────┼─────────────────────────────────────┘
┌────────────────────────────────────────────────────────────────────┐
│ AgentEndEvent with final messages │
└────────────────────────────────────────────────────────────────────┘
```
### 3. Tool Execution Flow
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Tool Execution Flow │
└─────────────────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────────┐
│ Assistant Message with Tool Calls │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ AssistantMessage: │ │
│ │ content: [ │ │
│ │ TextContent("I'll help you"), │ │
│ │ ToolCall(id="tc1", name="bash", args={...}), │ │
│ │ ToolCall(id="tc2", name="read", args={...}) │ │
│ │ ] │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
└───────────────────────────────────────────────────────────────────┘
│ executeToolCalls()
┌───────────────────────────────────────────────────────────────────┐
│ Determine Execution Mode │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ config.tool_execution == EXECUTION_SEQUENTIAL? │ │
│ │ OR any tool has execution_mode == EXECUTION_SEQUENTIAL? │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌───────────────┴───────────────┐ │
│ ▼ ▼ │
│ ┌────────────────────────┐ ┌────────────────────────┐ │
│ │ executeSequential() │ │ executeParallel() │ │
│ └────────────────────────┘ └────────────────────────┘ │
│ │ │ │
└──────────────┼───────────────────────────────┼────────────────────┘
│ │
│ │
▼ ▼
┌──────────────────────┐ ┌──────────────────────┐
│ Sequential Execution │ │ Parallel Execution │
│ │ │ │
│ for tool_call in: │ │ for tool_call in: │
│ prepareToolCall() │ │ prepareToolCall() │
│ execute() │ │ execute() (async) │
│ finalize() │ │ │
│ │ │ wait all results │
│ │ └──────────────────────┘
└──────────────────────┘
┌───────────────────────────────────────────────────────────────────┐
│ For Each Tool Call │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 1. before_tool_call hook (optional) │ │
│ │ - Can block execution │ │
│ │ 2. prepareToolCall() │ │
│ │ - validateToolArguments() │ │
│ │ - prepareToolCallArguments() (optional) │ │
│ │ 3. Execute Tool: │ │
│ │ tool.execute(tool_call_id, args, signal, on_update) │ │
│ │ 4. after_tool_call hook (optional) │ │
│ │ - Can modify result content │ │
│ │ 5. Emit events: │ │
│ │ - ToolExecutionStartEvent │ │
│ │ - ToolExecutionUpdateEvent (optional) │ │
│ │ - ToolExecutionEndEvent │ │
│ │ 6. Create ToolResultMessage │ │
│ └─────────────────────────────────────────────────────────────┘ │
└───────────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────────┐
│ Tool Result Messages │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ ToolResultMessage: │ │
│ │ role: "toolResult" │ │
│ │ tool_call_id: "tc1" │ │
│ │ tool_name: "bash" │ │
│ │ content: [TextContent("command output")] │ │
│ │ is_error: false │ │
│ └─────────────────────────────────────────────────────────────┘ │
└───────────────────────────────────────────────────────────────────┘
```
### 4. Session & Tree Structure
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Session Tree Structure │
└─────────────────────────────────────────────────────────────────────────┘
Session = Linked List of Entries (tree structure)
┌───────────────────────────────────────────────────────────────────┐
│ Branch Navigation │
│ │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ E1 │────▶│ E2 │────▶│ E3 │────▶│ E4 │────▶│ E5 │ (leaf) │
│ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ Message Message Compaction Message BranchSummary │
│ │
│ E3 is a Compaction Entry: │
│ - Summary of E1, E2 │
│ - first_kept_entry_id: reference to first retained message │
│ - tokens_before: context size before compaction │
│ │
│ E5 is a BranchSummary Entry: │
│ - Summary of branch from from_id │
│ - Represents a fork point in conversation history │
│ │
└───────────────────────────────────────────────────────────────────┘
│ Session.moveTo()
┌───────────────────────────────────────────────────────────────────┐
│ Forking & Branching │
│ │
│ Current branch: │
│ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ E1 │────▶│ E2 │────▶│ E3 │ │
│ └─────┘ └─────┘ └─────┘ │
│ │ │
│ │ moveTo(E2) │
│ ▼ │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ E1 │────▶│ E2 │────▶│ E3' │────▶│ E4' │ (new branch) │
│ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │ │
│ │ create BranchSummary │
│ ▼ │
│ ┌─────┐ │
│ │ E5 │ (branch summary) │
│ └─────┘ │
│ │
└───────────────────────────────────────────────────────────────────┘
```
## Component Relationships
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Component Relationships │
└─────────────────────────────────────────────────────────────────────────────┘
User Code
├── Creates ──► Agent
│ │
│ ├── Uses ──► AgentLoop
│ │ │
│ │ ├── Uses ──► StreamFn (LLM API)
│ │ │
│ │ └── Uses ──► Session
│ │
│ ├── Manages ──► AgentState
│ │
│ ├── Queues ──► SteeringQueue
│ │
│ └── Queues ──► FollowUpQueue
└── Interacts With ──► AgentHarness (optional, higher level)
├── Manages ──► SessionRepo
├── Manages ──► Skills
└── Manages ──► PromptTemplates
```
## Data Flow with Type Transformations
### Complete User Input → Conversation History Flow
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Level 1: User Input │
└─────────────────────────────────────────────────────────────────────────────┘
User Input
• String: "Hello, what's in the directory?"
• AgentMessage: UserMessage(...)
• Vector{AgentMessage}: [UserMessage(...), AssistantMessage(...)]
┌──────────────────────────────────────────────────────────────┐
│ Agent.prompt() / normalizePromptInput() │
│ │
│ Type Dispatch: │
│ • String → UserMessage("user", [TextContent(input)], ts) │
│ • AgentMessage → [input] (wrap in array) │
│ • Vector{AgentMessage} → input (pass-through) │
│ │
│ Output: Vector{AgentMessage} │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ AgentState.messages (AgentMessage[]) │
│ │
│ AgentMessage Types: │
│ • UserMessage (role: "user") │
│ • AssistantMessage (role: "assistant") │
│ • ToolResultMessage (role: "toolResult") │
│ • BashExecutionMessage (custom) │
│ • CompactionSummaryMessage (custom) │
│ • BranchSummaryMessage (custom) │
└──────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Level 2: AgentLoop Processing │
└─────────────────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ transform_context() (optional hook) │
│ │
│ Input: Vector{AgentMessage} │
│ Output: Vector{AgentMessage} (transformed) │
│ - Can truncate, filter, or modify messages │
└──────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────────┐
│ convertToLlm() - Type Transformation Pipeline │
│ │
│ Input: Vector{AgentMessage} │
│ Output: Vector{Message} (for LLM API) │
│ │
│ Single Dispatch Mapping: │
│ • UserMessage → UserMessage (pass-through) │
│ • AssistantMessage → AssistantMessage (pass-through) │
│ • ToolResultMessage → ToolResultMessage (pass-through) │
│ │
│ Custom Message Conversions: │
│ • BashExecutionMessage → UserMessage (via bashExecutionToText)│
│ • CompactionSummaryMessage → UserMessage (wrapped) │
│ • BranchSummaryMessage → UserMessage (wrapped) │
└────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ Context for LLM API │
│ - system_prompt: String │
│ - messages: Vector{Message} │
│ - tools: Vector{AgentTool} │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ LLM API Call (stream_fn) │
│ │
│ Input: model, context, config │
│ Output: Stream{AssistantMessageEvent} │
│ • StartEvent: partial AssistantMessage │
│ • TextStartEvent/TextDeltaEvent/TextEndEvent │
│ • ToolCallStartEvent/ToolCallDeltaEvent/ToolCallEndEvent │
│ • DoneEvent: final AssistantMessage with usage │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ AssistantMessage (returned from LLM) │
│ │
│ • role: "assistant" │
│ • content: Vector{MessageContent} │
│ └─ Contains: TextContent[] and/or ToolCall[] │
│ • api, provider, model: String │
│ • usage: Usage (input, output, cache_read, cache_write) │
│ • stop_reason: String ("done", "length", "error", etc.) │
│ • error_message: Union{String, Nothing} │
│ • timestamp: Timestamp (Int64) │
└──────────────────────────────────────────────────────────────┘
├─► Append to AgentState.messages (AssistantMessage)
┌──────────────────────────────────────────────────────────────┐
│ executeToolCalls() - Tool Processing │
│ │
│ Extract: filter(c -> c isa ToolCall, assistant.content) │
│ Output: ExecutedToolCallBatch │
│ • messages: Vector{ToolResultMessage} │
│ • terminate: Bool │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ ToolResultMessage (for each ToolCall) │
│ │
│ • role: "toolResult" │
│ • tool_call_id: String (matches ToolCall.id) │
│ • tool_name: String (matches ToolCall.name) │
│ • content: Vector{MessageContent} │
│ • details: Any (tool-specific) │
│ • usage: Union{Usage, Nothing} │
│ • added_tool_names: Union{Vector{String}, Nothing} │
│ • is_error: Bool │
│ • timestamp: Timestamp (Int64) │
└──────────────────────────────────────────────────────────────┘
├─► Append to AgentState.messages (ToolResultMessage)
┌──────────────────────────────────────────────────────────────┐
│ Updated AgentState.messages (AgentMessage[]) │
│ │
│ Conversation History: │
│ [UserMessage, AssistantMessage, ToolResultMessage, ...] │
└──────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Level 3: Session Storage (optional, for persistence) │
└─────────────────────────────────────────────────────────────────────────────┘
AgentState.messages (Vector{AgentMessage})
┌──────────────────────────────────────────────────────────────┐
│ Session Storage (JSONL) │
│ │
│ SessionTreeEntry Types: │
│ • MessageEntry (agent_message) │
│ • CompactionEntry (summary, tokens_before) │
│ • BranchSummaryEntry (from_id, summary) │
│ • ModelChangeEntry (provider, model_id) │
│ • ThinkingLevelChangeEntry (thinking_level) │
│ • ActiveToolsChangeEntry (active_tool_names) │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ Persisted Data (JSON format) │
│ - Each entry has: id, parent_id, timestamp, type │
│ - MessageEntry contains full AgentMessage │
└──────────────────────────────────────────────────────────────┘
```
### Tool Call Execution Flow (Detailed)
```
ToolCall (from AssistantMessage.content)
├─ type: "tool"
├─ id: "tc_abc123"
├─ name: "bash"
├─ arguments: Dict("command" => "ls -la")
└─ partial_json: nothing
┌──────────────────────────────────────────────────────────────┐
│ prepareToolCall() │
│ │
│ Input: tool_call::ToolCall │
│ Output: Union{PreparedToolCall, ImmediateToolCallOutcome} │
│ │
│ Steps: │
│ 1. Find tool by name in current_context.tools │
│ 2. before_tool_call hook (optional) │
│ Input: BeforeToolCallContext │
│ Output: BeforeToolCallResult (block, reason) or nothing │
│ 3. prepareToolCallArguments() (optional) │
│ Input: tool_call.arguments::Dict │
│ Output: prepared_arguments::Any │
│ 4. validateToolArguments() (optional) │
│ Input: prepared_tool_call.arguments │
│ Output: validated_args::Any │
│ 5. Return: PreparedToolCall(kind, tool_call, tool, args) │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ executePreparedToolCall() (if prepared) │
│ │
│ Input: PreparedToolCall │
│ Output: ExecutedToolCallOutcome │
│ │
│ tool.execute(tool_call.id, args, signal, on_update) │
│ │ │
│ └─ Returns: AgentToolResultMutable │
│ • content::Vector{MessageContent} │
│ • details::Any │
│ • usage::Union{Usage, Nothing} │
│ • added_tool_names::Union{Vector{String}, Nothing} │
│ • terminate::Union{Bool, Nothing} │
└──────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────┐
│ finalizeExecutedToolCall() │
│ │
│ Input: ExecutedToolCallOutcome │
│ Output: FinalizedToolCallOutcome │
│ │
│ Steps: │
│ 1. after_tool_call hook (optional) │
│ Input: AfterToolCallContext │
│ Output: AfterToolCallResult (patches) │
│ 2. Apply patches to result │
│ 3. Return: FinalizedToolCallOutcome(tool_call, result, error)│
└───────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ createToolResultMessage() │
│ │
│ Input: FinalizedToolCallOutcome │
│ Output: ToolResultMessage │
│ │
│ Fields: │
│ • role: "toolResult" │
│ • tool_call_id: tool_call.id │
│ • tool_name: tool_call.name │
│ • content: result.content │
│ • details: result.details │
│ • usage: result.usage │
│ • added_tool_names: result.added_tool_names │
│ • is_error: is_error │
│ • timestamp: Int64(Dates.now(Dates.UTC).datetime) │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ Emit: ToolResultMessage to conversation │
└──────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Summary of Type Transformations │
└─────────────────────────────────────────────────────────────────────────────┘
User Input (String)
├─► normalizePromptInput()
│ └─► UserMessage (AgentMessage subtype)
Vector{AgentMessage}
├─► transform_context() (optional)
│ └─► Vector{AgentMessage} (transformed)
├─► convertToLlm()
│ └─► Vector{Message} (LLM API format)
│ ├── UserMessage (pass-through)
│ ├── AssistantMessage (pass-through)
│ ├── ToolResultMessage (pass-through)
│ └── Custom messages → UserMessage
AssistantMessage (from LLM)
├─► executeToolCalls()
│ └─► ToolResultMessage[]
ToolResultMessage[]
└─► Appended to AgentState.messages
└─► Vector{AgentMessage} (updated conversation history)
```
## Summary
## Summary
The AgentCore.jl architecture follows a clean separation of concerns:
1. **AgentHarness** - Highest level, handles persistence and resources
2. **Agent** - State management and event streaming
3. **AgentLoop** - Core LLM interaction loop
4. **Session** - Conversation history management
### Data Transformation Summary
```
Input Type Flow:
User Input (String/Message)
├─ normalizePromptInput()
│ └─► Vector{AgentMessage}
├─ transform_context() (optional)
│ └─► Vector{AgentMessage} (transformed)
├─ convertToLlm()
│ └─► Vector{Message} (LLM API format)
├─ LLM API (stream_fn)
│ └─► AssistantMessage
├─ executeToolCalls()
│ └─► ToolResultMessage[]
└─► Vector{AgentMessage} (final conversation)
```
### Key Data Flow Patterns
1. **Message Transformation**: `AgentMessage[] → Message[]` via `convertToLlm()`
- UserMessage → UserMessage (pass-through)
- AssistantMessage → AssistantMessage (pass-through)
- ToolResultMessage → ToolResultMessage (pass-through)
- Custom messages (Bash, Compaction, Branch) → UserMessage
2. **Tool Execution**: `ToolCall → ToolResultMessage`
- prepareToolCall() validates and prepares
- execute() runs the tool
- finalize() applies hooks and returns outcome
- createToolResultMessage() creates result entry
3. **Event Streaming**: `Stream{Event}` with lifecycle events
- AgentStartEvent, TurnStartEvent
- MessageStartEvent, MessageUpdateEvent, MessageEndEvent
- ToolExecutionStartEvent, ToolExecutionEndEvent
- TurnEndEvent, AgentEndEvent
Each layer transforms data and passes it to the next layer, with clear interfaces and event hooks for customization.
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# AgentCore.jl - Agent Component Deep Dive
## Agent Structure
```julia
mutable struct Agent
_state::AgentState
listeners::Set{Tuple{Function, Ref{Bool}}}
steering_queue::PendingMessageQueue
follow_up_queue::PendingMessageQueue
convert_to_llm::Function
transform_context::Union{Function, Nothing}
stream_function::StreamFn
get_api_key::Union{Function, Nothing}
on_payload::Union{Function, Nothing}
on_response::Union{Function, Nothing}
before_tool_call::Union{Function, Nothing}
after_tool_call::Union{Function, Nothing}
prepare_next_turn::Union{Function, Nothing}
prepare_next_turn_with_context::Union{Function, Nothing}
active_run::Union{ActiveRun, Nothing}
session_id::Union{String, Nothing}
thinking_budgets::Union{Dict{String, Int64}, Nothing}
transport::String
max_retry_delay_ms::Union{Int64, Nothing}
tool_execution::ToolExecutionMode
end
```
## Agent Lifecycle
### 1. Initialization
```julia
# Create agent with options
agent = Agent(Dict{Symbol, Any}(
:systemPrompt => "You are a helpful assistant",
:model => Model("", "", "unknown", "unknown", "", false, String[], ModelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
:thinkingLevel => THINKING_OFF,
:tools => [bash_tool, read_tool],
:steeringMode => QUEUE_ONE_AT_A_TIME,
:followUpMode => QUEUE_ONE_AT_A_TIME,
:toolExecution => EXECUTION_PARALLEL,
))
# Subscribe to events
unsubscribe = subscribe(agent) do event, signal
if event isa MessageEndEvent
println("Message: $(event.message)")
elseif event isa ToolExecutionEndEvent
println("Tool completed: $(event.tool_name)")
end
end
```
### 2. Message Queues
#### Steering Queue
- Messages injected **after** the current assistant turn finishes
- Used to correct or redirect the agent's behavior
- Example: "Actually, let's do X instead"
#### Follow-Up Queue
- Messages run **only after** the agent would otherwise stop
- Used to continue conversation when agent thinks it's done
- Example: "Wait, there's one more thing"
#### Queue Modes
- `QUEUE_ALL` - Drain all messages at once
- `QUEUE_ONE_AT_A_TIME` - Process one message at a time
```julia
# Queue a steering message
steer(agent, UserMessage(...))
# Queue a follow-up message
followUp(agent, UserMessage(...))
# Check if queues have items
hasQueuedMessages(agent) # Returns Bool
# Clear queues
clearSteeringQueue(agent)
clearFollowUpQueue(agent)
clearAllQueues(agent)
```
### 3. Event System
#### Agent Events
```julia
abstract type AgentEvent end
# Lifecycle events
struct AgentStartEvent <: AgentEvent end
struct AgentEndEvent <: AgentEvent
messages::Vector{AgentMessage}
end
# Turn events
struct TurnStartEvent <: AgentEvent end
struct TurnEndEvent <: AgentEvent
message::AgentMessage
tool_results::Vector{ToolResultMessage}
end
# Message events
struct MessageStartEvent <: AgentEvent
message::AgentMessage
end
struct MessageUpdateEvent <: AgentEvent
message::AgentMessage
assistant_message_event::Any
end
struct MessageEndEvent <: AgentEvent
message::AgentMessage
end
# Tool execution events
struct ToolExecutionStartEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
end
struct ToolExecutionUpdateEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
partial_result::Any
end
struct ToolExecutionEndEvent <: AgentEvent
tool_call_id::String
tool_name::String
result::Any
is_error::Bool
end
```
#### Event Flow Diagram
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Event Timeline │
└─────────────────────────────────────────────────────────────────────────┘
AgentStartEvent
├─ TurnStartEvent
│ │
│ ├─ MessageStartEvent (user prompt)
│ ├─ MessageEndEvent (user prompt)
│ │
│ ├─ [Loop starts]
│ │ │
│ │ ├─ MessageStartEvent (assistant response)
│ │ ├─ MessageUpdateEvent (text delta 1)
│ │ ├─ MessageUpdateEvent (text delta 2)
│ │ ├─ MessageUpdateEvent (tool call delta)
│ │ ├─ MessageEndEvent (assistant complete)
│ │ │
│ │ ├─ ToolExecutionStartEvent (tc1)
│ │ ├─ ToolExecutionUpdateEvent (partial result)
│ │ ├─ ToolExecutionEndEvent (tc1 done)
│ │ │
│ │ ├─ ToolExecutionStartEvent (tc2)
│ │ ├─ ToolExecutionEndEvent (tc2 done)
│ │ │
│ │ └─ TurnEndEvent (assistant + tools)
│ │
│ └─ [Next turn if needed]
└─ AgentEndEvent (final messages)
```
### 4. State Management
```julia
mutable struct AgentState
system_prompt::String
model::Model
thinking_level::ThinkingLevel
tools::Vector{AgentTool}
messages::Vector{AgentMessage}
is_streaming::Bool
streaming_message::Union{AgentMessage, Nothing}
pending_tool_calls::Set{String}
error_message::Union{String, Nothing}
end
```
#### State Access
```julia
# Get current state
state = get_state(agent)
# Reset state
reset!(agent) # Clears messages, queues, and runtime state
```
### 5. Main Methods
#### prompt()
```julia
# Start a new conversation
prompt(agent, "Hello, how are you?")
# With multiple messages
prompt(agent, [
UserMessage(...),
AssistantMessage(...),
UserMessage(...)
])
# With images
prompt(agent, "Analyze this image", [ImageContent(data, "image/png")])
```
#### continue!()
```julia
# Continue from current transcript
# Last message must be user or tool-result
continue!(agent)
```
#### steer() and followUp()
```julia
# Steering: Redirect after next assistant turn
steer(agent, UserMessage(...))
# Follow-up: Continue after agent would stop
followUp(agent, UserMessage(...))
```
### 6. Hooks
#### convert_to_llm
```julia
# Transform messages before sending to LLM
function myConvertToLlm(messages::Vector{AgentMessage})::Vector{Message}
result::Vector{Message} = Message[]
for m in messages
converted = convertToLlmMessage(m)
if !isnothing(converted)
push!(result, converted)
end
end
return result
end
agent = Agent(Dict(:convertToLlm => myConvertToLlm))
```
**Data Flow**:
```
Vector{AgentMessage}
│ convertToLlmMessage() dispatches on type:
│ • UserMessage → UserMessage (pass-through)
│ • AssistantMessage → AssistantMessage (pass-through)
│ • ToolResultMessage → ToolResultMessage (pass-through)
│ • BashExecutionMessage → UserMessage (bashExecutionToText)
│ • CompactionSummaryMessage → UserMessage (wrapped)
│ • BranchSummaryMessage → UserMessage (wrapped)
Vector{Message} (for LLM API)
```
#### transform_context
```julia
# Transform context before LLM call
function myTransformContext(messages, signal)
# Can truncate, filter, or modify messages
return messages
end
agent = Agent(Dict(:transformContext => myTransformContext))
```
#### before_tool_call
```julia
# Hook before tool execution
function myBeforeToolCall(context, signal)
println("About to execute: $(context.tool_call.name)")
return BeforeToolCallResult(nothing, nothing) # Return BeforeToolCallResult(true, "reason") to block
end
agent = Agent(Dict(:beforeToolCall => myBeforeToolCall))
```
#### after_tool_call
```julia
# Hook after tool execution
function myAfterToolCall(context, signal)
# Can modify tool result
return AfterToolCallResult(
context.result.content,
context.result.details,
nothing,
nothing,
context.result.terminate
)
end
agent = Agent(Dict(:afterToolCall => myAfterToolCall))
```
#### prepare_next_turn
```julia
# Modify context/model/thinking level between turns
function myPrepareNextTurn(context, signal)
# context: PrepareNextTurnContext
# Returns AgentLoopTurnUpdate or nothing
return AgentLoopTurnUpdate(
context.context, # context
context.context.model, # model - can change
THINKING_HIGH # thinking_level - can change
)
end
agent = Agent(Dict(:prepareNextTurn => myPrepareNextTurn))
```
### 7. Active Run Management
```julia
# Check if agent is busy
if !isnothing(agent.active_run)
# Agent is processing
abort(agent) # Abort current run (NOTE: implementation is a TODO stub)
end
# Wait for completion
waitForIdle(agent) # Returns Promise
```
## Complete Example
```julia
using AgentCore
# 1. Create agent
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => Model(...),
:tools => [bash_tool, read_tool],
))
# 2. Subscribe to events
events_received = []
unsubscribe = subscribe(agent) do event, signal
push!(events_received, event)
if event isa MessageEndEvent
println("Message: $(event.message)")
end
end
# 3. Start conversation
prompt(agent, "What's in the current directory?")
# 4. Wait for completion
waitForIdle(agent)
# 5. Check final state
state = get_state(agent)
println("Total messages: $(length(state.messages))")
# 6. Continue with steering
steer(agent, UserMessage(...))
waitForIdle(agent)
# 7. Clean up
unsubscribe() # Stop listening
reset!(agent) # Clear state
```
## Key Concepts
### Message Queueing
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Message Queue Behavior │
└─────────────────────────────────────────────────────────────────────────┘
Scenario: User sends message, agent responds with tool calls
┌────────────────────────────────────────────────────────────┐
│ Time 0: User sends message │
│ ┌──────────────┐ │
│ │ prompt(msg) │ │
│ └──────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ AgentLoop │ │
│ │ processes │ │
│ │ msg │ │
│ └─────────────┘ │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 1: Agent responds with tool calls │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ AssistantMessage: │ │
│ │ content: [Text("I'll check..."), │ │
│ │ ToolCall("bash", {...}), │ │
│ │ ToolCall("read", {...})] │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 2: User queues steering message │
│ ┌──────────────────┐ │
│ │ steer(msg2) │ ──► steering_queue.push(msg2) │
│ └──────────────────┘ │
│ │
│ (msg2 not processed yet!) │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 3: Tool execution │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Execute bash tool... │ │
│ │ Execute read tool... │ │
│ │ Emit ToolResultMessage[] │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 4: Agent responds to tool results │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ AssistantMessage (2nd turn): │ │
│ │ content: [Text("The results are...")] │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 5: Steering message processed │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ steering_queue.drain() → [msg2] │ │
│ │ Emit msg2 as UserMessage │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────┐
│ Time 6: Next turn (agent responds to steering) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ AssistantMessage (3rd turn): │ │
│ │ content: [Text("Okay, I'll do X instead...")] │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘
```
### Queue Behavior Summary
| Action | Queue | When Processed |
|--------|-------|----------------|
| `prompt()` | N/A | Immediate |
| `steer()` | steering_queue | After assistant turn completes |
| `followUp()` | follow_up_queue | After agent would normally stop |
| `continue!()` | N/A | Immediately if last message is user/tool |
## Best Practices
1. **Use steering for redirects**: When user wants to change direction mid-conversation
2. **Use follow-up for continuation**: When agent thinks it's done but user wants more
3. **Subscribe to events**: Monitor agent behavior and debug issues
4. **Clear queues**: Use `clearAllQueues()` when resetting conversation
5. **Check active run**: Don't call `prompt()` while agent is busy
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# AgentCore.jl - Types and Messages Deep Dive
## Core Type Hierarchy
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Type Hierarchy │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ ThinkingLevel (Enum) │
│ - THINKING_OFF │
│ - THINKING_MINIMAL │
│ - THINKING_LOW │
│ - THINKING_MEDIUM │
│ - THINKING_HIGH │
│ - THINKING_XHIGH │
│ - THINKING_MAX │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ ToolExecutionMode (Enum) │
│ - EXECUTION_SEQUENTIAL (Tools run one at a time) │
│ - EXECUTION_PARALLEL (Tools run concurrently) │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ QueueMode (Enum) │
│ - QUEUE_ALL (Drain all messages at once) │
│ - QUEUE_ONE_AT_A_TIME (Process one message at a time) │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ MessageContent (Abstract Type) │
│ ├── TextContent (String) │
│ └── ImageContent (data::String, mime_type::String) │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Message (Abstract Type) │
│ ├── UserMessage │
│ │ └─ role: "user", content: Message[], timestamp: Int64 │
│ ├── AssistantMessage │
│ │ └─ role: "assistant", content: Message[], api, provider, model, │
│ │ usage: Usage, stop_reason, error_message, timestamp │
│ └── ToolResultMessage │
│ └─ role: "toolResult", tool_call_id, tool_name, content, details, │
│ usage, added_tool_names, is_error, timestamp │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ AgentMessage (Abstract Type) │
│ └─ Union of all message types above + custom types │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ AgentTool │
│ - name: String │
│ - label: String │
│ - description: String │
│ - parameters: Any │
│ - execute: Function │
│ - prepare_arguments: Union{Function, Nothing} │
│ - execution_mode: Union{ToolExecutionMode, Nothing} │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ AgentContext │
│ - system_prompt: String │
│ - messages: Vector{AgentMessage} │
│ - tools: Union{Vector{AgentTool}, Nothing} │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ AgentEvent (Abstract Type) │
│ ├── AgentStartEvent / AgentEndEvent │
│ ├── TurnStartEvent / TurnEndEvent │
│ ├── MessageStartEvent / MessageEndEvent │
│ ├── MessageUpdateEvent │
│ ├── ToolExecutionStartEvent / ToolExecutionEndEvent │
│ └── ToolExecutionUpdateEvent │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Usage & ModelCost │
│ Usage: input, output, cache_read, cache_write, total_tokens, cost │
│ ModelCost: input, output, cache_read, cache_write (all Float64) │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Model │
│ - id, name, api, provider, base_url, reasoning: Bool │
│ - input: Vector{String} │
│ - cost: ModelCost │
│ - context_window, max_tokens: Int64 │
└─────────────────────────────────────────────────────────────────────────────┘
```
## Message Types
### Type Hierarchy
```
Message (for LLM API)
├── UserMessage (role: "user")
│ ├── content::Vector{MessageContent}
│ │ ├── TextContent (text::String)
│ │ └── ImageContent (data::String, mime_type::String)
│ └── timestamp::Timestamp (Int64)
├── AssistantMessage (role: "assistant")
│ ├── content::Vector{MessageContent}
│ │ ├── TextContent
│ │ └── ToolCall (type, id, name, arguments::Dict{String, Any})
│ ├── api::String
│ ├── provider::String
│ ├── model::String
│ ├── usage::Usage
│ │ ├── input, output, cache_read, cache_write, total_tokens::Int64
│ │ └── cost::UsageCost (input, output, cache_read, cache_write, total::Float64)
│ ├── stop_reason::String
│ ├── error_message::Union{String, Nothing}
│ └── timestamp::Timestamp
└── ToolResultMessage (role: "toolResult")
├── tool_call_id::String
├── tool_name::String
├── content::Vector{MessageContent}
├── details::Any
├── usage::Union{Usage, Nothing}
├── added_tool_names::Union{Vector{String}, Nothing}
├── is_error::Bool
└── timestamp::Timestamp
AgentMessage (internal, extends Message)
├── UserMessage (same as above)
├── AssistantMessage (same as above)
├── ToolResultMessage (same as above)
├── BashExecutionMessage (custom, converted to UserMessage)
│ ├── role, command, output, exit_code
│ ├── cancelled, truncated, full_output_path
│ └── exclude_from_context::Bool
├── CompactionSummaryMessage (custom, converted to UserMessage)
│ ├── summary, tokens_before, timestamp
└── BranchSummaryMessage (custom, converted to UserMessage)
├── summary, from_id, timestamp
```
### UserMessage
```julia
struct UserMessage <: Message
role::String # "user"
content::Vector{MessageContent}
timestamp::Timestamp # Int64 (Unix timestamp)
end
```
**Usage**:
```julia
# Simple text message
UserMessage(
"user",
[TextContent("Hello, how are you?")],
Int64(Dates.now(Dates.UTC).datetime)
)
```
**Usage**:
```julia
# Simple text message
UserMessage(
"user",
[TextContent("Hello, how are you?")],
Int64(Dates.now(Dates.UTC).datetime)
)
# With multiple content types
UserMessage(
"user",
[
TextContent("Analyze this image"),
ImageContent(data_base64, "image/png")
],
timestamp
)
```
### AssistantMessage
```julia
struct AssistantMessage <: Message
role::String # "assistant"
content::Vector{MessageContent}
api::String # API identifier
provider::String # Provider name
model::String # Model ID
usage::Usage
stop_reason::String # "done", "error", "aborted", "length", etc.
error_message::Union{String, Nothing}
timestamp::Timestamp
end
```
**Content can include**:
- TextContent
- ToolCall
```julia
AssistantMessage(
"assistant",
[
TextContent("I'll check the directory for you."),
ToolCall(
"tool",
"tc_123",
"bash",
Dict("command" => "ls -la"),
nothing
),
ToolCall(
"tool",
"tc_456",
"read",
Dict("path" => "README.md"),
nothing
)
],
"openai",
"openai",
"gpt-4",
Usage(100, 50, 0, 0, 150, UsageCost(0.001, 0.002, 0.0, 0.0, 0.003)),
"done",
nothing,
timestamp
)
```
### ToolResultMessage
```julia
struct ToolResultMessage <: Message
role::String # "toolResult"
tool_call_id::String # Reference to original ToolCall
tool_name::String # Name of tool that executed
content::Vector{MessageContent}
details::Any # Additional tool-specific details
usage::Union{Usage, Nothing}
added_tool_names::Union{Vector{String}, Nothing}
is_error::Bool # True if tool execution failed
timestamp::Timestamp
end
# Note: AgentToolResult{T} (types.jl) - generic result type with type param T
# AgentToolResultMutable (agent_loop.jl) - mutable variant used internally
```
**Usage**:
```julia
ToolResultMessage(
"toolResult",
"tc_123",
"bash",
[TextContent("file1.md\nfile2.md\n")],
BashToolDetails(...),
nothing,
nothing,
false,
timestamp
)
```
## AgentTool Structure
```julia
struct AgentTool{TParameters, TDetails}
name::String
label::String
description::String
parameters::TParameters
execute::Function
prepare_arguments::Union{Function, Nothing}
execution_mode::Union{ToolExecutionMode, Nothing}
end
```
**Parameters**:
- `name`: Unique identifier for the tool
- `label`: Display name
- `description`: What the tool does
- `parameters`: JSON schema for tool arguments
- `execute`: Main execution function
- `prepare_arguments`: Optional preprocessing
- `execution_mode`: Sequential or parallel
**Note:** `AgentHarnessTool` (`harness_types.jl:91`) is a harness-specific variant with the same structure but uses camelCase field names (`prepareArguments`, `executionMode`) and includes additional type parameters `{TContext, TParameters, TDetails}`.
### Tool Execution Function Signature
```julia
execute::Function(
tool_call_id::String,
params::Dict{String, Any},
signal::Union{Any, Nothing}, # Abort signal
on_update::Function, # Callback for streaming updates
context::Any, # Tool context
)::AgentToolResult{T}
```
**Returns** (`AgentToolResult{T}` from `types.jl`):
```julia
AgentToolResult{T}(
content::Vector{MessageContent}, # Result content
details::T, # Tool-specific details
usage::Union{Usage, Nothing}, # Usage statistics
added_tool_names::Union{Vector{String}, Nothing},
terminate::Union{Bool, Nothing}, # If true, stop agent after this
)
```
**Note:** `AgentToolResultMutable` (in `agent_loop.jl`) is a mutable variant used internally for intermediate results.
**Note:** External types used throughout the codebase: `Context`, `AbortSignal`, `EventStream`, `Promise` are defined in external modules (not in the source files covered by this document).
## AgentContext
```julia
struct AgentContext
system_prompt::String
messages::Vector{AgentMessage}
tools::Union{Vector{AgentTool}, Nothing}
end
```
**Purpose**: Read-only snapshot of agent state for LLM calls
**Usage in AgentLoop**:
```julia
function streamAssistantResponse(
context::AgentContext, # Contains messages, tools, system prompt
config::AgentLoopConfig,
...
)::AssistantMessage
# Convert to LLM format
llm_messages = config.convert_to_llm(context.messages)
# Create context for API
llm_context = Context(
context.system_prompt,
llm_messages,
context.tools,
)
# Call LLM
return stream_function(context.model, llm_context, config)
end
```
## Event Types
### Agent Lifecycle Events
```julia
struct AgentStartEvent <: AgentEvent end
struct AgentEndEvent <: AgentEvent
messages::Vector{AgentMessage}
end
```
### Turn Events
```julia
struct TurnStartEvent <: AgentEvent end
struct TurnEndEvent <: AgentEvent
message::AgentMessage
tool_results::Vector{ToolResultMessage}
end
```
### Message Events
```julia
struct MessageStartEvent <: AgentEvent
message::AgentMessage
end
struct MessageUpdateEvent <: AgentEvent
message::AgentMessage
assistant_message_event::Any # Partial message event
end
struct MessageEndEvent <: AgentEvent
message::AgentMessage
end
```
### Tool Execution Events
```julia
struct ToolExecutionStartEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
end
struct ToolExecutionUpdateEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
partial_result::Any
end
struct ToolExecutionEndEvent <: AgentEvent
tool_call_id::String
tool_name::String
result::Any
is_error::Bool
end
```
## Usage Statistics
```julia
struct Usage
input::Int64 # Input tokens
output::Int64 # Output tokens
cache_read::Int64 # Cache read tokens
cache_write::Int64 # Cache write tokens
total_tokens::Int64 # Total tokens
cost::UsageCost
end
struct UsageCost
input::Float64
output::Float64
cache_read::Float64
cache_write::Float64
total::Float64
end
```
**Example**:
```julia
Usage(
1000, # input tokens
200, # output tokens
500, # cache read tokens
0, # cache write tokens
1700, # total tokens
UsageCost(
0.0005, # input cost ($0.50 per 1M tokens)
0.0015, # output cost ($1.50 per 1M tokens)
0.00025, # cache read cost
0.0, # cache write cost
0.0035 # total cost
)
)
```
## Model Type
```julia
struct Model{Api}
id::String # Model identifier (e.g., "gpt-4")
name::String # Model name (e.g., "GPT-4")
api::Api # API type (String, Symbol, or custom type)
provider::String # Provider name (e.g., "openai")
base_url::String # API base URL
reasoning::Bool # Whether model supports reasoning
input::Vector{String} # Input modes (e.g., ["text", "image"])
cost::ModelCost
context_window::Int64 # Max context window (e.g., 128000)
max_tokens::Int64 # Max output tokens
end
struct ModelCost
input::Float64
output::Float64
cache_read::Float64
cache_write::Float64
end
```
## ToolCall Type
```julia
struct ToolCall
type::String # "tool"
id::String # Unique ID for this tool call
name::String # Tool name to call
arguments::Dict{String, Any} # Tool arguments as JSON-like Dict
partial_json::Union{String, Nothing} # Partial JSON string
end
```
**Example**:
```julia
ToolCall(
"tool",
"call_abc123",
"bash",
Dict(
"command" => "ls -la",
"timeout" => 30
),
nothing
)
```
## Custom Message Types
### BashExecutionMessage
```julia
mutable struct BashExecutionMessage
role::String # "custom"
command::String
output::String
exit_code::Union{Int64, Nothing}
cancelled::Bool
truncated::Bool
full_output_path::Union{String, Nothing}
timestamp::Timestamp
exclude_from_context::Bool
end
```
### CompactionSummaryMessage
```julia
mutable struct CompactionSummaryMessage
role::String # "compactionSummary"
summary::String # Summary of compacted history
tokens_before::Int64 # Context size before compaction
timestamp::Timestamp
end
```
### BranchSummaryMessage
```julia
mutable struct BranchSummaryMessage
role::String # "branchSummary"
summary::String # Summary of branch history
from_id::String # Branch point ID
timestamp::Timestamp
end
```
### CustomMessage
**Note:** There are two `CustomMessage` types in the codebase:
1. **Types.CustomMessage** (`types.jl:155`) - A simple wrapper that holds another `AgentMessage` with a custom type label:
```julia
struct CustomMessage <: AgentMessage
message::AgentMessage
custom_type::String
end
```
2. **Messages.CustomMessage{T}** (`messages.jl:42`) - A standalone mutable message with content, display flag, and details:
```julia
mutable struct CustomMessage{T}
role::String
custom_type::String
content::Union{String, Vector{MessageContent}}
display::Bool
details::Union{T, Nothing}
timestamp::Timestamp
end
```
Only `Messages.CustomMessage{T}` is converted by `convertToLlmMessage()` to a `UserMessage`.
## AgentState
```julia
mutable struct AgentState
system_prompt::String
model::Model
thinking_level::ThinkingLevel
tools::Vector{AgentTool}
messages::Vector{AgentMessage}
is_streaming::Bool
streaming_message::Union{AgentMessage, Nothing}
pending_tool_calls::Set{String}
error_message::Union{String, Nothing}
end
```
**Purpose**: Runtime state of the Agent
**Note**: AgentState is mutable and used internally by Agent
## Message Transformation Pipeline
### convertToLlm() - AgentMessage[] → Message[]
```julia
function convertToLlm(messages::Vector{AgentMessage})::Vector{Message}
result::Vector{Message} = Message[]
for m in messages
converted = convertToLlmMessage(m)
if !isnothing(converted)
push!(result, converted)
end
end
return result
end
```
**Data Flow**:
```
Vector{AgentMessage} (internal conversation history)
│ Type dispatch on convertToLlmMessage():
│ • UserMessage → UserMessage (pass-through)
│ • AssistantMessage → AssistantMessage (pass-through)
│ • ToolResultMessage → ToolResultMessage (pass-through)
│ Custom messages converted to UserMessage:
│ • BashExecutionMessage → UserMessage
│ (via bashExecutionToText() for display)
│ • CompactionSummaryMessage → UserMessage
│ (wrapped with COMPACTION_SUMMARY_PREFIX/SUFFIX)
│ • BranchSummaryMessage → UserMessage
│ (wrapped with BRANCH_SUMMARY_PREFIX/SUFFIX)
│ • CustomMessage → UserMessage
│ (content field used directly, string→TextContent)
Vector{Message} (for LLM API)
- Excludes: BashExecutionMessage (if exclude_from_context)
- Includes: All standard messages + converted custom messages
```
**Example**:
```julia
# Input: Vector{AgentMessage}
[
UserMessage("user", [TextContent("Hello")], 1234567890),
AssistantMessage("assistant", [
TextContent("Hi there!"),
ToolCall("bash", "call_123", "bash", Dict("command" => "ls"), nothing)
], "openai", "openai", "gpt-4", Usage(...), "done", nothing, 1234567891),
BashExecutionMessage("custom", "ls -la", "file1.md\nfile2.md\n", 0, false, false, nothing, 1234567892, false),
CompactionSummaryMessage("compactionSummary", "Previous conversation compacted", 1000, 1234567893),
CustomMessage("custom", "someCustomType", "Some custom content", true, nothing, 1234567894),
]
# Output: Vector{Message}
[
UserMessage("user", [TextContent("Hello")], 1234567890),
AssistantMessage("assistant", [
TextContent("Hi there!"),
ToolCall(...)
], "openai", "openai", "gpt-4", Usage(...), "done", nothing, 1234567891),
UserMessage("user", [TextContent("Ran `ls -la`\n```\nfile1.md\nfile2.md\n```\n")], 1234567892),
UserMessage("user", [TextContent("<summary>Previous conversation compacted</summary>")], 1234567893),
UserMessage("user", [TextContent("Some custom content")], 1234567894),
]
```
### Default convertToLlmMessage Implementations
```julia
function convertToLlmMessage(m::BashExecutionMessage)
if m.exclude_from_context
return nothing
end
return UserMessage("user", [TextContent(bashExecutionToText(m))], m.timestamp)
end
function convertToLlmMessage(m::CompactionSummaryMessage)
text = COMPACTION_SUMMARY_PREFIX * m.summary * COMPACTION_SUMMARY_SUFFIX
return UserMessage("user", [TextContent(text)], m.timestamp)
end
function convertToLlmMessage(m::CustomMessage)::Union{UserMessage, Nothing}
content = if m.content isa String
[TextContent(m.content)]
else
m.content
end
return UserMessage("user", content, m.timestamp)
end
function convertToLlmMessage(m::BranchSummaryMessage)
text = BRANCH_SUMMARY_PREFIX * m.summary * BRANCH_SUMMARY_SUFFIX
return UserMessage("user", [TextContent(text)], m.timestamp)
end
function convertToLlmMessage(m::UserMessage)
return m # Pass through
end
function convertToLlmMessage(m::AssistantMessage)
return m # Pass through
end
function convertToLlmMessage(m::ToolResultMessage)
return m # Pass through
end
```
## Complete Data Flow Examples
### Example 1: User Prompt → Assistant Response
```
User Input:
"Hello, what's in the current directory?"
prompt(agent, "Hello, what's in the current directory?")
└─► normalizePromptInput(String)
Input: "Hello, what's in the current directory?"
Output: [UserMessage("user", [TextContent("Hello, what's in the current directory?")], timestamp)]
AgentLoop execution:
├─► transform_context() (optional)
│ Input: [UserMessage(...)]
│ Output: [UserMessage(...)]
├─► convert_to_llm()
│ Input: [UserMessage(...)]
│ Output: [UserMessage(...)]
├─► stream_fn() - LLM API
│ Input: model, Context(...), config
│ Output: AssistantMessage with ToolCall[]
│ role: "assistant"
│ content: [
│ TextContent("I'll check the directory for you."),
│ ToolCall("tool", "tc_123", "bash", Dict("command" => "ls -la"), nothing)
│ ]
│ usage: Usage(input=100, output=20, ...)
│ stop_reason: "done"
├─► executeToolCalls()
│ Input: AssistantMessage with ToolCall[]
│ Output: ToolResultMessage[]
│ role: "toolResult"
│ tool_call_id: "tc_123"
│ tool_name: "bash"
│ content: [TextContent("file1.md\nfile2.md\n")]
│ is_error: false
└─► Append to context.messages
Final Conversation History:
[
UserMessage("user", [TextContent("Hello, what's in the current directory?")], ...),
AssistantMessage("assistant", [
TextContent("I'll check the directory for you."),
ToolCall("tool", "tc_123", "bash", Dict("command" => "ls -la"), nothing)
], "openai", "openai", "gpt-4", Usage(...), "done", nothing, ...),
ToolResultMessage("toolResult", "tc_123", "bash", [TextContent("file1.md\nfile2.md\n")], ..., false, ...),
]
```
### Example 2: Tool Call Execution → Tool Result
```
ToolCall from AssistantMessage
├─ type: "tool"
├─ id: "tc_123"
├─ name: "bash"
├─ arguments: Dict("command" => "ls -la")
└─ partial_json: nothing
prepareToolCall(tool_call)
Finds tool by name "bash"
before_tool_call hook (optional)
Input: BeforeToolCallContext(...)
Output: BeforeToolCallResult(block=false) or nothing
validateToolArguments(tool_call)
Input: Dict("command" => "ls -la")
Output: Dict("command" => "ls -la")
Return: PreparedToolCall("prepared", tool_call, bash_tool, validated_args)
executePreparedToolCall(prepared)
tool.execute("tc_123", Dict("command" => "ls -la"), signal, on_update)
Bash tool executes "ls -la" command
Returns: AgentToolResultMutable(
content: [TextContent("file1.md\nfile2.md\n")],
details: BashToolDetails(...),
usage: nothing,
added_tool_names: nothing,
terminate: nothing
)
finalizeExecutedToolCall(executed)
after_tool_call hook (optional)
Input: AfterToolCallContext(...)
Output: AfterToolCallResult(...) or nothing
Return: FinalizedToolCallOutcome(
tool_call: ToolCall(...),
result: AgentToolResultMutable(...),
is_error: false
)
createToolResultMessage(finalized)
Return: ToolResultMessage(
role: "toolResult",
tool_call_id: "tc_123",
tool_name: "bash",
content: [TextContent("file1.md\nfile2.md\n")],
details: BashToolDetails(...),
usage: nothing,
added_tool_names: nothing,
is_error: false,
timestamp: Int64(...)
)
```
### Example 3: Custom Message Conversion
```
BashExecutionMessage (custom, for logging)
role: "custom"
command: "ls -la"
output: "file1.md\nfile2.md\n"
exit_code: 0
cancelled: false
truncated: false
full_output_path: nothing
timestamp: 1234567890
exclude_from_context: false
convertToLlmMessage(BashExecutionMessage)
bashExecutionToText(msg)
Output: "Ran `ls -la`\n```\nfile1.md\nfile2.md\n```\n"
Return: UserMessage(
"user",
[TextContent("Ran `ls -la`\n```\nfile1.md\nfile2.md\n```\n")],
1234567890
)
(Excluded if exclude_from_context = true)
────────────────────────────────────────────────────────────────────
CompactionSummaryMessage (custom, for history compression)
role: "compactionSummary"
summary: "Previous 100 turns about Python programming"
tokens_before: 15000
timestamp: 1234567890
convertToLlmMessage(CompactionSummaryMessage)
Text = COMPACTION_SUMMARY_PREFIX + summary + COMPACTION_SUMMARY_SUFFIX
Result: "<summary>\nPrevious 100 turns about Python programming\n</summary>"
Return: UserMessage(
"user",
[TextContent("<summary>...\nPrevious 100 turns...\n</summary>")],
1234567890
)
## Summary
The type system in AgentCore.jl provides:
1. **Strong typing** for different message types
2. **Extensibility** through abstract types and multiple dispatch
3. **Clear separation** between internal (AgentMessage) and external (Message) formats
4. **Rich metadata** in Usage and Model types for cost tracking
5. **Event-driven architecture** through Event types
6. **Tool execution flexibility** through Tool types with hooks
All types are designed for:
- **Interoperability** with LLM APIs
- **Extensibility** for custom message types
- **Performance** with immutable structs where possible
- **Debuggability** through rich event system
+918
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# AgentCore.jl - Session Management Deep Dive
## Session Architecture with Data Flow
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Session Layer │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Session = Tree of Entries │
│ │
│ Each entry represents a change in conversation state │
│ │
│ Branch Navigation: │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ E1 │────▶│ E2 │────▶│ E3 │────▶│ E4 │────▶│ E5 │ (current leaf) │
│ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ Message Message Compaction Message BranchSummary │
│ │
│ Data Flow: │
│ AgentMessage[] (AgentState.messages) │
│ │ │
│ └─► appendMessage() → MessageEntry │
│ └─► storage.appendEntry() → JSONL file │
│ │
│ To navigate to E2 (fork point): │
│ session.moveTo(E2) │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ E1 │────▶│ E2 │────▶│ E3' │────▶│ E4' │────▶│ E5' │ (new branch) │
│ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │ │ │
│ │ ▼ create BranchSummary │
│ │ ┌─────┐ │
│ │ │ E6 │ (branch summary) │
│ │ └─────┘ │
│ └───────────────────────────────────────────────────────────────────────┘
└─────────────────────────────────────────────────────────────────────────────┘
```
## Data Flow: AgentMessage → SessionTreeEntry
```
AgentState.messages::Vector{AgentMessage}
├─► For each message in messages:
│ │
│ ▼
│ ┌──────────────────────────────────────────────────────────────┐
│ │ appendMessage(session, AgentMessage) │
│ │ Input: session::Session, message::AgentMessage │
│ │ Output: entry_id::String │
│ │ │
│ │ Steps: │
│ │ 1. Create MessageEntry: │
│ │ - base: SessionTreeEntryBase(type, id, leaf_id, time) │
│ │ - message: the AgentMessage │
│ │ 2. storage.appendEntry(entry) │
│ │ - In-memory: push to entries vector, update by_id dict │
│ │ - JSONL: would append to file (TODO) │
│ │ 3. Return entry.id │
│ └──────────────────────────────────────────────────────────────┘
└─► Entry stored in JSONL (conceptual):
{"type":"message","id":"msg_1","parent_id":null,"timestamp":"...","message":{...}}
```
## Entry Types
All entry types extend `abstract type SessionTreeEntry end` and embed a
`base::SessionTreeEntryBase` struct containing `type`, `id`, `parent_id`, and `timestamp`.
```julia
abstract type SessionTreeEntry end
struct SessionTreeEntryBase
type::String
id::String
parent_id::Union{String, Nothing}
timestamp::String
end
```
### 1. MessageEntry
```julia
struct MessageEntry <: SessionTreeEntry
base::SessionTreeEntryBase
message::AgentMessage
end
```
**Represents**: A user, assistant, or tool message
### 2. ThinkingLevelChangeEntry
```julia
struct ThinkingLevelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
thinking_level::String
end
```
**Represents**: Change in model thinking level
### 3. ModelChangeEntry
```julia
struct ModelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
provider::String
model_id::String
end
```
**Represents**: Change in model
### 4. ActiveToolsChangeEntry
```julia
struct ActiveToolsChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
active_tool_names::Vector{String}
end
```
**Represents**: Change in active tools
### 5. CompactionEntry
```julia
struct CompactionEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
summary::String
first_kept_entry_id::Union{String, Nothing}
tokens_before::Int64
retained_tail::Union{Vector{AgentMessage}, Nothing}
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
```
**Represents**: Context window compression
**Key fields**:
- `summary`: Summary of removed messages
- `first_kept_entry_id`: First entry that was kept
- `tokens_before`: Context size before compaction
- `retained_tail`: Messages kept after compaction point
### 6. BranchSummaryEntry
```julia
struct BranchSummaryEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
from_id::String
summary::String
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
```
**Represents**: Branch point with summary
### 7. CustomEntry
```julia
struct CustomEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
data::Union{T, Nothing}
end
```
**Represents**: Custom application-specific data
### 8. CustomMessageEntry
```julia
struct CustomMessageEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
content::String
details::Union{T, Nothing}
display::Bool
end
```
**Represents**: Custom message to display to user
### 9. LabelEntry
```julia
struct LabelEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::String
label::Union{String, Nothing}
end
```
**Represents**: Label/note on an entry
### 10. SessionInfoEntry
```julia
struct SessionInfoEntry <: SessionTreeEntry
base::SessionTreeEntryBase
name::Union{String, Nothing}
end
```
**Represents**: Session metadata (name, etc.)
### 11. LeafEntry
```julia
struct LeafEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::Union{String, Nothing}
end
```
**Represents**: Change in current leaf (branch pointer)
## Session Storage Interface
```julia
abstract type SessionStorage{T<:SessionMetadata} end
```
### Storage Methods (actual implementation signatures)
```julia
# Metadata
getMetadata(storage::SessionStorage)::T
# Leaf management
getLeafId(storage::SessionStorage)::Union{String, Nothing}
setLeafId(storage::SessionStorage, leaf_id::Union{String, Nothing})::Nothing
# Entry management
createEntryId(storage::SessionStorage)::String
appendEntry(storage::SessionStorage, entry::SessionTreeEntry)::Nothing
getEntry(storage::SessionStorage, id::String)::Union{SessionTreeEntry, Nothing}
# Query
findEntries(storage::SessionStorage, type::String)::Vector{SessionTreeEntry}
getLabel(storage::SessionStorage, id::String)::Union{String, Nothing}
getSessionName(storage::SessionStorage)::Union{String, Nothing}
# Branch navigation
getPathToRootOrCompaction(storage::SessionStorage, leaf_id::Union{String, Nothing})::Vector{SessionTreeEntry}
getEntries(storage::SessionStorage, options::Dict{String, Any})::Vector{SessionTreeEntry}
# Stats
getSessionStats(storage::SessionStorage)::SessionStats
```
## JsonlSessionStorage
```
mutable struct JsonlSessionStorage{T<:SessionMetadata} <: SessionStorage{T}
file_path::String
metadata::T
entries::Vector{SessionTreeEntry} # ordered list
by_id::Dict{String, SessionTreeEntry} # fast lookup by id
labels_by_id::Dict{String, String} # label cache
current_leaf_id::Union{String, Nothing} # current branch tip
end
```
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ JSONL Storage Format │
└─────────────────────────────────────────────────────────────────────────────┘
File: session.jsonl (conceptual - not yet implemented)
Entry 1 (Metadata via SessionHeader):
{"type":"session","version":3,"id":"meta_1","timestamp":"...","cwd":"/path","parent_session":null,"metadata":{}}
Entry 2 (Message):
{"type":"message","id":"msg_1","parent_id":null,"timestamp":"...","message":{"role":"user",...}}
Entry 3 (Thinking Level):
{"type":"thinking_level_change","id":"tl_1","parent_id":"msg_1","timestamp":"...","thinking_level":"medium"}
Entry 4 (Model Change):
{"type":"model_change","id":"mc_1","parent_id":"tl_1","timestamp":"...","provider":"openai","model_id":"gpt-4"}
Entry 5 (Compaction):
{"type":"compaction","id":"comp_1","parent_id":"mc_1","timestamp":"...","summary":"...","first_kept_entry_id":"msg_3","tokens_before":100000}
Entry 6 (Branch Summary):
{"type":"branch_summary","id":"branch_1","parent_id":"comp_1","timestamp":"...","from_id":"msg_3","summary":"..."}
Entry 7 (Active Tools):
{"type":"active_tools_change","id":"tools_1","parent_id":"branch_1","timestamp":"...","active_tool_names":["bash","read"]}
Entry 8 (Leaf):
{"type":"leaf","id":"leaf_1","parent_id":"tools_1","timestamp":"...","target_id":"msg_5"}
Notes:
- Each line is a JSON object (JSONL format) - TODO: file I/O not yet implemented
- parent_id references previous entry (linked list structure)
- Leaf entry points to current position in tree
- To fork, create new branch from any entry
- In-memory mode uses Vector + Dict by_id for fast access
```
## InMemorySessionStorage
```julia
mutable struct InMemorySessionStorage{T<:SessionMetadata} <: SessionStorage{T}
metadata::T
entries::Vector{SessionTreeEntry}
by_id::Dict{String, SessionTreeEntry}
labels_by_id::Dict{String, String}
leaf_id::Union{String, Nothing}
end
```
**Purpose**: Testing and temporary sessions
**Advantages**:
- Fast (no I/O)
- Easy to inspect
- Perfect for tests
## Session Class
```julia
mutable struct Session{T<:SessionMetadata}
storage::SessionStorage{T}
context_build_options::SessionContextBuildOptions
function Session(storage::SessionStorage, context_build_options=SessionContextBuildOptions(nothing, nothing))
new{typeof(storage.metadata)}(storage, context_build_options)
end
end
```
### SessionContextBuildOptions
```julia
mutable struct SessionContextBuildOptions
entry_transforms::Union{Vector{Function}, Nothing}
entry_projectors::Union{Dict{String, Function}, Nothing}
end
```
### Session Methods
#### appendMessage()
```julia
function appendMessage(session::Session, message::AgentMessage)::String
return appendTypedEntry(session, MessageEntry(
SessionTreeEntryBase("message", createEntryId(session.storage), getLeafId(session.storage), create_timestamp()),
message,
))
end
```
**Usage**:
```julia
session = Session(storage)
# Add user message
user_id = appendMessage(session, UserMessage("user", [TextContent("Hello")], timestamp))
# Add assistant message
assistant_id = appendMessage(session, AssistantMessage(...))
# Add tool result
tool_id = appendMessage(session, ToolResultMessage(...))
```
#### appendThinkingLevelChange()
```julia
function appendThinkingLevelChange(session::Session, thinking_level::String)::String
return appendTypedEntry(session, ThinkingLevelChangeEntry(
SessionTreeEntryBase("thinking_level_change", createEntryId(session.storage), getLeafId(session.storage), create_timestamp()),
thinking_level,
))
end
```
#### appendModelChange()
```julia
function appendModelChange(session::Session, provider::String, model_id::String)::String
return appendTypedEntry(session, ModelChangeEntry(
SessionTreeEntryBase("model_change", createEntryId(session.storage), getLeafId(session.storage), create_timestamp()),
provider,
model_id,
))
end
```
#### appendActiveToolsChange()
```julia
function appendActiveToolsChange(session::Session, active_tool_names::Vector{String})::String
return appendTypedEntry(session, ActiveToolsChangeEntry(
SessionTreeEntryBase("active_tools_change", createEntryId(session.storage), getLeafId(session.storage), create_timestamp()),
active_tool_names,
))
end
```
#### appendCompaction()
```julia
function appendCompaction(
session::Session,
summary::String,
first_kept_entry_id::Union{String, Nothing},
tokens_before::Int64,
details::Union{Any, Nothing}=nothing,
from_hook::Bool=false,
usage::Union{Usage, Nothing}=nothing,
retained_tail::Union{Vector{AgentMessage}, Nothing}=nothing,
)::String
return appendTypedEntry(session, CompactionEntry(
SessionTreeEntryBase("compaction", createEntryId(session.storage), getLeafId(session.storage), create_timestamp()),
summary,
first_kept_entry_id,
tokens_before,
retained_tail,
details,
usage,
from_hook,
))
end
```
#### moveTo()
```julia
function moveTo(
session::Session,
entry_id::Union{String, Nothing},
summary::Union{Dict{String, Any}, Nothing}=nothing,
)::Union{String, Nothing}
# Validate entry exists
if !isnothing(entry_id) && isnothing(getEntry(session, entry_id))
throw(SessionError("not_found", "Entry $(entry_id) not found"))
end
# Set new leaf (creates a LeafEntry)
setLeafId(session.storage, entry_id)
# Optionally create branch summary
if isnothing(summary)
return nothing
end
return appendTypedEntry(session, BranchSummaryEntry(
SessionTreeEntryBase("branch_summary", createEntryId(session.storage), entry_id, create_timestamp()),
entry_id,
summary["summary"],
get(summary, "details", nothing),
get(summary, "usage", nothing),
get(summary, "from_hook", false),
))
end
```
**Usage**:
```julia
# Fork from a specific point
session.moveTo(msg_3_id)
# Branch with summary
session.moveTo(
msg_3_id,
Dict(
"summary" => "User wanted to focus on file operations",
"details" => Dict("focus" => "files"),
)
)
```
**How it works**:
1. Validates the target entry exists
2. Calls `setLeafId()` which creates a `LeafEntry` with `target_id = entry_id`
3. If `summary` is provided, creates a `BranchSummaryEntry` as a child of the target entry
4. The new leaf now points to `entry_id`, making it the root of a new branch
## Build Session Context
```julia
function buildSessionContext(
path_entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::SessionContext
state = deriveSessionContextState(path_entries)
context_entries = buildContextEntries(path_entries, options)
messages = SessionTreeEntry[]
for (i, entry) in enumerate(context_entries)
append!(messages, sessionEntryToContextMessages(entry, i, context_entries, options))
end
return SessionContext(messages, state.thinking_level, state.model, state.active_tool_names)
end
function deriveSessionContextState(path_entries::Vector{SessionTreeEntry})::Dict{String, Any}
thinking_level = "off"
model = nothing
active_tool_names = nothing
for entry in path_entries
if entry isa ThinkingLevelChangeEntry
thinking_level = entry.thinking_level
elseif entry isa ModelChangeEntry
model = Dict("provider" => entry.provider, "modelId" => entry.model_id)
elseif entry isa MessageEntry && entry.message.role == "assistant"
model = Dict("provider" => entry.message.provider, "modelId" => entry.message.model)
elseif entry isa ActiveToolsChangeEntry
active_tool_names = copy(entry.active_tool_names)
end
end
return Dict(
"thinking_level" => thinking_level,
"model" => model,
"active_tool_names" => active_tool_names,
)
end
```
### Context Entry Transform
```julia
function defaultContextEntryTransform(path_entries::Vector{SessionTreeEntry})::Vector{SessionTreeEntry}
compaction = nothing
for entry in path_entries
if entry isa CompactionEntry
compaction = entry
break
end
end
if isnothing(compaction)
return copy(path_entries)
end
entries::Vector{SessionTreeEntry} = [compaction]
compaction_idx = findfirst(
(entry) -> entry isa CompactionEntry && entry.id == compaction.id,
path_entries,
)
if !isnothing(compaction.retained_tail)
for i in compaction_idx+1:length(path_entries)
push!(entries, path_entries[i])
end
return entries
end
if !isnothing(compaction.first_kept_entry_id)
found_first_kept = false
for i in 1:compaction_idx-1
entry = path_entries[i]
if entry.id == compaction.first_kept_entry_id
found_first_kept = true
end
if found_first_kept
push!(entries, entry)
end
end
end
for i in compaction_idx+1:length(path_entries)
push!(entries, path_entries[i])
end
return entries
end
function buildContextEntries(
path_entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::Vector{SessionTreeEntry}
entries = defaultContextEntryTransform(path_entries)
if !isnothing(options.entry_transforms)
for transform in options.entry_transforms
entries = transform(entries)
end
end
return entries
end
```
### Session Entry to Context Messages
```julia
function sessionEntryToContextMessages(
entry::SessionTreeEntry,
index::Int64,
entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::Vector{AgentMessage}
if entry isa MessageEntry
return [entry.message]
end
if entry isa CustomMessageEntry
return [createCustomMessage(
entry.custom_type,
entry.content,
entry.display,
entry.details,
entry.timestamp,
)]
end
if entry isa CompactionEntry
messages = [createCompactionSummaryMessage(
entry.summary,
entry.tokens_before,
entry.timestamp,
)]
if !isnothing(entry.retained_tail)
append!(messages, entry.retained_tail)
end
return messages
end
if entry isa BranchSummaryEntry
return [createBranchSummaryMessage(
entry.summary,
entry.from_id,
entry.timestamp,
)]
end
if entry isa CustomEntry
if !isnothing(options.entry_projectors) && haskey(options.entry_projectors, entry.custom_type)
projector = options.entry_projectors[entry.custom_type]
return projector(entry, index, entries)
end
return AgentMessage[]
end
return AgentMessage[]
end
```
## Branch Navigation
```
Scenario: User wants to explore a different path
Initial Branch (current path):
┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐
│ E1 │────▶│ E2 │────▶│ E3 │────▶│ E4 │ (leaf)
└─────┘ └─────┘ └─────┘ └─────┘
│ │ │ │
Message Message Compaction Message
Step 1: Fork from E2
┌─────┐ ┌─────┐ ┌─────┐
│ E1 │────▶│ E2 │─────────────────┐
└─────┘ └─────┘ │
│ │ │
│ ▼ create BranchSummary│
│ ┌─────┐ │
│ │ E5 │ (branch summary) │
│ └─────┘ │
└──────────────────────────────────┘
(new branch from E2)
Step 2: Continue on new branch
┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐
│ E1 │────▶│ E2 │────▶│ E3' │────▶│ E4' │────▶│ E5' │ (new leaf)
└─────┘ └─────┘ └─────┘ └─────┘ └─────┘
Current branch now is:
[ E1, E2, E3', E4', E5' ]
Original branch is:
[ E1, E2, E5 ] (E3, E4 are now separate branch)
Key Points:
- Shared entries: E1, E2
- Branch point: E2
- Branch summary: E5 (points to E2)
- Each branch has independent tail
```
### getPathToRootOrCompaction
Walks from a leaf back to the root, handling compaction entries:
```julia
# When encountering a CompactionEntry:
# - If retained_tail is set: stop (compaction covers the tail)
# - Otherwise: skip to first_kept_entry_id and continue walking
```
## Compaction Strategy
### Why Compaction?
LLM context windows have limits:
- GPT-4: 128K tokens
- Claude 2: 100K tokens
- Llama 2: 4K tokens
**Problem**: Conversations grow unbounded
**Solution**: Compaction - summarize old messages
### Compaction Process
```julia
# 1. Identify messages to compact
# - Keep recent N messages (e.g., last 2 turns)
# - Summarize everything before
# 2. Generate summary
# - Use LLM to summarize
# - Include key facts, decisions, user preferences
# 3. Create CompactionEntry
# - summary: The summary text
# - first_kept_entry_id: First entry that was NOT compacted
# - tokens_before: Context size before compaction
# - retained_tail: Messages kept after compaction point
# 4. Update storage
# - Append CompactionEntry
# - Leaf automatically points to CompactionEntry (leafIdAfterEntry)
```
### Compaction Example
```julia
# Before compaction (100K tokens):
[
msg_1, # User: "I need to set up a project"
msg_2, # Assistant: "Sure, what language?"
msg_3, # User: "Python"
msg_4, # Assistant: "I'll create a Python project"
msg_5, # User: "With FastAPI"
msg_6, # Assistant: "Creating FastAPI project..."
msg_7, # Tool: bash("mkdir myapp")
msg_8, # Tool: write("myapp/main.py", ...)
msg_9, # Assistant: "Project created!"
msg_10, # User: "Can you add auth?"
msg_11, # Assistant: "Adding auth..."
msg_12, # User: "Use JWT"
msg_13, # Assistant: "Implementing JWT..."
msg_14, # Tool: bash("pip install jwt")
msg_15, # Tool: write("myapp/auth.py", ...)
msg_16, # Assistant: "Auth implemented!"
]
# After compaction (20K tokens):
[
compaction_entry, # Summary of msg_1 to msg_10
msg_11, # Keep recent messages
msg_12,
msg_13,
msg_14,
msg_15,
msg_16,
]
# Compaction summary:
"""
Previous conversation summary:
- User wanted to create a Python project
- Chose FastAPI framework
- Assistant created project structure in myapp/
- User requested authentication
- Chose JWT for auth
- Assistant implemented JWT auth in myapp/auth.py
"""
```
## Complete Session Example
```julia
using AgentCore
# 1. Create storage
storage = JsonlSessionStorage(
"/path/to/session.jsonl",
SessionHeader("session", 3, "session_1", created_at, "/path", nothing, nothing),
SessionTreeEntry[],
nothing,
)
# 2. Create session
session = Session(storage)
# 3. Add messages
msg1_id = appendMessage(session, UserMessage("user", [TextContent("Hello")], timestamp))
msg2_id = appendMessage(session, AssistantMessage("assistant", [TextContent("Hi!")], ...))
# 4. Change thinking level
tl_id = appendThinkingLevelChange(session, "medium")
# 5. Change model
mc_id = appendModelChange(session, "openai", "gpt-4")
# 6. Add more messages
msg3_id = appendMessage(session, UserMessage("user", [TextContent("What can you do?")], timestamp))
msg4_id = appendMessage(session, AssistantMessage("assistant", [TextContent("I can...")], ...))
# 7. Compact context (100K tokens -> 20K)
compact_id = appendCompaction(
session,
"User asked about capabilities and assistant explained",
msg2_id,
100000,
Dict("summary_length" => 50),
false,
usage,
[msg3, msg4], # Retained tail
)
# 8. Fork and branch
session.moveTo(msg2_id) # Go back to msg2
# 9. Continue on new branch (moveTo creates branch summary when summary is provided)
branch_id = moveTo(
session,
msg2_id,
Dict("summary" => "User changed direction", "details" => Dict("focus" => "files")),
)
# 10. Continue on new branch
msg5_id = appendMessage(session, UserMessage("user", [TextContent("Let's work with files")], timestamp))
# 11. Query session context
context = buildContext(session)
# 12. Get stats
stats = getSessionStats(session)
println("Messages: $(stats.message_count)")
println("Total tokens: $(stats.total_tokens)")
println("Cost: \$(stats.cost_total)")
```
## Session Repo Interface
### Session Repository Methods
```julia
# Create a new session
create(repo::SessionRepo, options::TCreateOptions)::Session
# Open an existing session
open(repo::SessionRepo, metadata::TMetadata)::Session
# List sessions
list(repo::SessionRepo, options::TListOptions)::Vector{TMetadata}
# Delete a session
delete(repo::SessionRepo, metadata::TMetadata)::Nothing
# Fork a session (copy branch from entry)
fork(repo::SessionRepo, source::TMetadata, options::Dict{String, Any})::Session
```
### JSONL vs In-Memory Repos
| Feature | JsonlSessionRepo | InMemorySessionRepo |
|---------|------------------|---------------------|
| Persistence | File-based (TODO) | In-memory only |
| Use case | Production | Testing |
| Fork | Not implemented | Uses getEntriesToFork |
| Metadata | JsonlSessionMetadata | SessionMetadata |
### Fork Behavior (`getEntriesToFork`)
```julia
function getEntriesToFork(storage, options)::Vector{SessionTreeEntry}
# If no entryId specified, fork from current leaf (full copy)
if !haskey(options, :entryId) || isnothing(options[:entryId])
return getEntries(storage, Dict{String, Any}())
end
target = getEntry(storage, options[:entryId])
position = get(options, "position", "before")
if position == "at"
# Fork includes the target entry
effective_leaf_id = target.id
else
# Fork before the target (parent)
# Target must be a user message
if target isa MessageEntry && target.message.role != "user"
throw(SessionError("invalid_fork_target", "Not a user message"))
end
effective_leaf_id = target.parent_id
end
return getPathToRootOrCompaction(storage, effective_leaf_id)
end
```
## Best Practices
1. **Use compaction** for long conversations to stay within context limits
2. **Create branch summaries** when forking to document divergent paths (via `moveTo()` with summary)
3. **Retain tail messages** after compaction for context (`retained_tail` field)
4. **Track token usage** to optimize compaction timing
5. **Use InMemorySessionStorage** for testing
6. **Use `getBranch(session)`** to get the current path from leaf to root/compaction
7. **Use `buildContext(session)`** as the convenient Session method for building context
8. **Use `mergeContextBuildOptions(session, options)`** to combine session-level and call-level transforms/projectors
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# AgentCore.jl - Tools Deep Dive
## Tool Types (from types.jl)
### AgentTool (struct)
```julia
struct AgentTool{TParameters, TDetails}
name::String # tool identifier
label::String # display name
description::String # what it does
parameters::TParameters # JSON schema or type
execute::Function # (tool_call_id, params, signal, on_update, context) -> AgentToolResult
prepare_arguments::Union{Function, Nothing}
execution_mode::Union{ToolExecutionMode, Nothing}
end
```
### AgentToolResult (struct)
```julia
struct AgentToolResult{T}
content::Vector{MessageContent}
details::T
usage::Union{Usage, Nothing}
added_tool_names::Union{Vector{String}, Nothing}
terminate::Union{Bool, Nothing}
end
```
### ToolCall (struct)
```julia
struct ToolCall
type::String # always "tool"
id::String # unique identifier
name::String # tool name to execute
arguments::Dict{String, Any} # JSON-like arguments
partial_json::Union{String, Nothing}
end
```
### ToolExecutionMode (enum)
```julia
@enum ToolExecutionMode begin
EXECUTION_SEQUENTIAL = "sequential"
EXECUTION_PARALLEL = "parallel"
end
```
## Tool Execution Flow
```
AssistantMessage (from LLM)
content::Vector{MessageContent}
└─ Contains: TextContent[] and ToolCall[]
Agent.execute() (in agent.jl)
└─ before_tool_call hook (Agent.before_tool_call, optional)
Input: BeforeToolCallContext
Output: BeforeToolCallResult (block, reason)
For each ToolCall:
tool = find_tool(name)
tool.execute(tool_call_id, args, signal, on_update, context)
AgentToolResult{T}(content, details, usage, added_tool_names, terminate)
└─ after_tool_call hook (Agent.after_tool_call, optional)
Input: AfterToolCallContext
Output: AfterToolCallResult (patches: content, details, is_error, usage, terminate)
ToolResultMessage (one per ToolCall)
role: "toolResult"
tool_call_id::String
tool_name::String
content::Vector{MessageContent}
details::Any
usage::Union{Usage, Nothing}
added_tool_names::Union{Vector{String}, Nothing}
is_error::Bool
timestamp::Timestamp
Append to AgentState.messages
└─ Next turn: LLM sees tool results as input
```
## Built-in Tools
### 1. BashTool (`tools/bash.jl`)
```julia
struct BashExecution
command::String
cwd::String
env::Dict{String, String}
inherit_env::Bool
end
mutable struct BashPrepare{TContext}
function::Function
context::TContext
signal::Union{Any, Nothing}
end
mutable struct BashToolOptions{TContext}
command_prefix::Union{String, Nothing}
prepare::Union{BashPrepare{TContext}, Nothing}
end
mutable struct BashToolDetails
truncation::Union{Any, Nothing}
full_output_path::Union{String, Nothing}
end
function createBashTool{TContext}(options::Union{BashToolOptions{TContext}, Nothing}=nothing) where TContext
```
**Execute signature**: `(tool_call_id, params, signal, on_update, context) -> AgentToolResult`
**Note**: The actual bash execution is a TODO stub in the current source.
### 2. ReadTool (`tools/read.jl`)
```julia
mutable struct ReadToolDetails
truncation::Union{Any, Nothing}
end
mutable struct ReadToolOptions
auto_resize_images::Bool
image_processor::Union{Any, Nothing}
end
function createReadTool{TContext}(options::Union{ReadToolOptions, Nothing}=nothing) where TContext
```
**Execute signature**: `(tool_call_id, params, signal, on_update, context) -> AgentToolResult`
### 3. WriteTool (`tools/write.jl`)
```julia
function createWriteTool{TContext}() where TContext
```
**Execute signature**: `(tool_call_id, params, signal, on_update, context) -> AgentToolResult`
### 4. EditTool (`tools/edit.jl`)
```julia
mutable struct EditToolDetails
diff::String
patch::String
first_changed_line::Union{Int64, Nothing}
end
function createEditTool{TContext}() where TContext
```
**Execute signature**: `(tool_call_id, params, signal, on_update, context) -> AgentToolResult`
## Tool Hooks (on Agent struct)
The `Agent` struct in `agent.jl` has these hook fields:
```julia
mutable struct Agent
...
before_tool_call::Union{Function, Nothing}
after_tool_call::Union{Function, Nothing}
prepare_next_turn::Union{Function, Nothing}
prepare_next_turn_with_context::Union{Function, Nothing}
...
end
```
Configured via `Agent(Dict(...))` options:
- `:beforeToolCall``Agent.before_tool_call`
- `:afterToolCall``Agent.after_tool_call`
- `:prepareNextTurn``Agent.prepare_next_turn`
- `:prepareNextTurnWithContext``Agent.prepare_next_turn_with_context`
### BeforeToolCallContext / BeforeToolCallResult (from types.jl)
```julia
struct BeforeToolCallContext
assistant_message::AssistantMessage
tool_call::ToolCall
args::Any
context::AgentContext
end
struct BeforeToolCallResult
block::Union{Bool, Nothing}
reason::Union{String, Nothing}
end
```
### AfterToolCallContext / AfterToolCallResult (from types.jl)
```julia
struct AfterToolCallContext
assistant_message::AssistantMessage
tool_call::ToolCall
args::Any
result::AgentToolResult
is_error::Bool
context::AgentContext
end
struct AfterToolCallResult
content::Union{Vector{MessageContent}, Nothing}
details::Union{Any, Nothing}
is_error::Union{Bool, Nothing}
usage::Union{Usage, Nothing}
terminate::Union{Bool, Nothing}
end
```
### PrepareNextTurnContext / AgentLoopTurnUpdate (from types.jl)
```julia
struct PrepareNextTurnContext
message::AssistantMessage
tool_results::Vector{ToolResultMessage}
context::AgentContext
new_messages::Vector{AgentMessage}
end
struct AgentLoopTurnUpdate
context::Union{AgentContext, Nothing}
model::Union{Model, Nothing}
thinking_level::Union{ThinkingLevel, Nothing}
end
```
## Tool Execution Modes
### Sequential Execution
```julia
# Configure on Agent
agent = Agent(Dict(
:toolExecution => EXECUTION_SEQUENTIAL,
))
```
### Parallel Execution (default)
```julia
agent = Agent(Dict(
:toolExecution => EXECUTION_PARALLEL,
))
```
Tools can also specify their own mode:
```julia
agent_tool = AgentTool(
"name",
"label",
"description",
params_schema,
execute_fn,
nothing,
EXECUTION_SEQUENTIAL, # or EXECUTION_PARALLEL
)
```
## Tool Exports (from tools/index.jl)
```julia
export
createBashTool,
createReadTool,
createWriteTool,
createEditTool,
BashExecution,
BashPrepare,
BashToolDetails,
BashToolInput,
BashToolOptions,
EditToolDetails,
EditToolInput,
ReadToolDetails,
ReadToolInput,
ReadToolOptions,
ReadImageProcessor,
ReadImageProcessorResult,
WriteToolInput
```
## Example: Creating and Using Tools
```julia
using AgentCore
# Create tools
bash_tool = createBashTool()
read_tool = createReadTool()
write_tool = createWriteTool()
# Configure hooks
before_hook = (context, signal) -> begin
println("About to execute: $(context.tool_call.name)")
return nothing
end
after_hook = (context, signal) -> begin
if context.is_error
println("Tool failed: $(context.tool_call.name)")
else
println("Tool completed: $(context.tool_call.name)")
end
return nothing
end
# Create agent with tools and hooks
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant with file system access.",
:tools => [bash_tool, read_tool, write_tool],
:beforeToolCall => before_hook,
:afterToolCall => after_hook,
:toolExecution => EXECUTION_PARALLEL,
))
# Run prompt
prompt(agent, "List files in current directory and read the first one")
```
## Best Practices
1. **Use sequential execution** for tools that depend on shared state
2. **Use parallel execution** for independent operations
3. **Implement before_tool_call hook** for logging and validation
4. **Implement after_tool_call hook** for result modification
5. **Use prepare_next_turn hook** for dynamic model/thinking level changes
6. **Return terminate=true** from tool when agent should stop
7. **Include usage statistics** in tool results when possible
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# AgentCore.jl - AgentHarness Design Reference
## Status
> **Note**: The AgentHarness module (`src/agent_harness.jl`) is **not yet implemented**. This document
> describes the intended design based on types defined in `src/harness_types.jl`. The types, events,
> and interfaces below are defined but the harness that connects them is a planned feature.
>
> Several modules referenced in `src/AgentCore.jl` are also not yet implemented:
> `compaction/compaction.jl`, `compaction/utils.jl`, `compaction/branch_summarization.jl`,
> `utils/truncate.jl`, `utils/shell_output.jl`, `proxy.jl`.
>
> Type placeholders not yet defined: `AgentLoopConfig`, `Promise`, `AbortSignal`, `EventStream`,
> `Context`. The `SessionRepo` methods in `harness_types.jl` return `Promise()` stubs.
## AgentHarness Architecture (Planned)
```
AgentHarness = Agent + Session + Resources + Hooks
AgentHarness (to be implemented in src/agent_harness.jl)
├── Manages Agent instances
├── Provides Session persistence via SessionRepo
├── Manages resources (skills, prompt templates)
├── Handles extension hooks (BeforeAgentStart, BeforeProviderPayload, etc.)
└── Coordinates tool execution with AgentHarnessToolContextSource
AgentHarnessOptions (src/harness_types.jl:1067)
├── session::Session
├── models::Any
├── tools::Union{Vector{TTool}, Nothing}
├── resources::Union{AgentHarnessResources, Nothing}
├── system_prompt::Union{AgentHarnessSystemPrompt, Nothing}
├── stream_options::Union{AgentHarnessStreamOptions, Nothing}
├── retry::Union{Any, Nothing}
├── model::Model
├── thinking_level::Union{ThinkingLevel, Nothing}
├── active_tool_names::Union{Vector{String}, Nothing}
├── steering_mode::Union{QueueMode, Nothing}
├── follow_up_mode::Union{QueueMode, Nothing}
└── tool_context::Union{AgentHarnessToolContextSource, Nothing}
```
## Event Type Hierarchy (Actual)
The harness event types are defined as `mutable struct` in `harness_types.jl`.
They are NOT subtypes of `AgentHarnessEvent` or `AgentHarnessOwnEvent` - those
abstract types exist but nothing inherits from them.
```
AgentEvent (abstract, types.jl:196)
├── AgentStartEvent (types.jl:198)
├── AgentEndEvent (types.jl:199)
├── TurnStartEvent (types.jl:202)
├── TurnEndEvent (types.jl:203)
├── MessageStartEvent (types.jl:207)
├── MessageUpdateEvent (types.jl:210)
├── MessageEndEvent (types.jl:214)
├── ToolExecutionStartEvent (types.jl:217)
├── ToolExecutionUpdateEvent (types.jl:222)
└── ToolExecutionEndEvent (types.jl:228)
AgentHarnessOwnEvent (abstract, harness_types.jl:850)
└── (nothing inherits from this)
AgentHarnessEvent (abstract, harness_types.jl:856)
└── (nothing inherits from this)
Harness event structs (harness_types.jl) - mutable structs, not subtypes:
├── BeforeAgentStartEvent (line 653)
├── ContextEvent (line 665)
├── BeforeProviderRequestEvent (line 674)
├── BeforeProviderPayloadEvent (line 685)
├── AfterProviderResponseEvent (line 695)
├── ToolCallEvent (line 705)
├── ToolResultEvent (line 716)
├── SessionBeforeCompactEvent (line 731)
├── SessionCompactEvent (line 743)
├── SessionBeforeTreeEvent (line 753)
├── SessionTreeEvent (line 763)
├── RetryScheduledEvent (line 775)
├── RetryAttemptStartEvent (line 788)
├── RetryFinishedEvent (line 797)
├── ModelUpdateEvent (line 806)
├── ThinkingLevelUpdateEvent (line 817)
├── ToolsUpdateEvent (line 827)
└── ResourcesUpdateEvent (line 840)
```
## Types (from harness_types.jl)
### AgentHarnessOptions (line 1067)
```julia
mutable struct AgentHarnessOptions{TC<:Any, TSkill<:Skill, TPromptTemplate<:PromptTemplate, TTool<:AgentHarnessTool}
session::Session
models::Any
tools::Union{Vector{TTool}, Nothing}
resources::Union{AgentHarnessResources{TSkill, TPromptTemplate}, Nothing}
system_prompt::Union{AgentHarnessSystemPrompt{TC, TSkill, TPromptTemplate, TTool}, Nothing}
stream_options::Union{AgentHarnessStreamOptions, Nothing}
retry::Union{Any, Nothing}
model::Model
thinking_level::Union{ThinkingLevel, Nothing}
active_tool_names::Union{Vector{String}, Nothing}
steering_mode::Union{QueueMode, Nothing}
follow_up_mode::Union{QueueMode, Nothing}
tool_context::Union{AgentHarnessToolContextSource{TC}, Nothing}
end
```
**Purpose**: Configure AgentHarness with all necessary options (defined but harness not implemented).
### AgentHarnessResources (line 82)
```julia
mutable struct AgentHarnessResources{TSkill<:Skill, TPromptTemplate<:PromptTemplate}
promptTemplates::Union{Vector{TPromptTemplate}, Nothing}
skills::Union{Vector{TSkill}, Nothing}
end
```
### Skill (line 68)
```julia
mutable struct Skill
name::String
description::String
content::String
filePath::String
disableModelInvocation::Bool
end
```
**Loading**: `loadSkills(env, dir)` is defined in `skills.jl` but **parsing is stubbed** - currently returns `nothing, diagnostics`. The frontmatter parsing code (lines 266-300 of skills.jl) is commented out as TODO.
**Skill format**:
```markdown
<!-- SKILL.md -->
{
"name": "File Operations",
"description": "Handle file system operations",
"disable-model-invocation": false
}
---
# File Operations Skill
This skill provides instructions for working with files...
```
### PromptTemplate (line 76)
```julia
mutable struct PromptTemplate
name::String
description::Union{String, Nothing}
content::String
end
```
**Loading**: `loadPromptTemplates(env, paths)` is defined in `prompt_templates.jl` but **parsing is stubbed** - currently returns `nothing, diagnostics`. Frontmatter parsing is commented out as TODO (lines 188-215).
**Format**:
```markdown
<!-- template.md -->
{
"description": "Generate commit message"
}
---
Generate a git commit message for:
$1
$ARGUMENTS
```
### AgentHarnessStreamOptions (line 109)
```julia
mutable struct AgentHarnessStreamOptions
transport::Union{String, Nothing}
timeout_ms::Union{Int64, Nothing}
max_retries::Union{Int64, Nothing}
max_retry_delay_ms::Union{Int64, Nothing}
headers::Union{Dict{String, String}, Nothing}
metadata::Union{Dict{String, Any}, Nothing}
cache_retention::Union{String, Nothing}
end
```
### AgentHarnessStreamOptionsPatch (line 119)
```julia
mutable struct AgentHarnessStreamOptionsPatch
transport::Union{String, Nothing}
timeout_ms::Union{Int64, Nothing}
max_retries::Union{Int64, Nothing}
max_retry_delay_ms::Union{Int64, Nothing}
cache_retention::Union{String, Nothing}
headers::Union{Dict{String, String}, Nothing}
metadata::Union{Dict{String, Any}, Nothing}
end
```
### AgentHarnessTool (line 91)
```julia
mutable struct AgentHarnessTool{TContext, TParameters, TDetails}
name::String
label::String
description::String
parameters::TParameters
execute::Function
prepareArguments::Union{Function, Nothing}
executionMode::Union{ToolExecutionMode, Nothing}
end
```
### AgentHarnessToolContextSource (line 101)
```julia
mutable struct AgentHarnessToolContextSource{TContext}
context::Union{TContext, Function}
end
```
### AgentHarnessSystemPrompt (line 1059)
```julia
mutable struct AgentHarnessSystemPrompt{TC<:Any, TSkill<:Skill, TPromptTemplate<:PromptTemplate, TTool<:AgentHarnessTool}
value::Union{String, Function}
end
```
## SessionRepo Interface (stubs in harness_types.jl:564-588)
```julia
abstract type SessionRepo<
TMetadata<:SessionMetadata,
TCreateOptions,
TListOptions
> end
function create(repo::SessionRepo, options::TCreateOptions)::Promise{Session}
return Promise() # STUB - Promise type not defined
end
function open(repo::SessionRepo, metadata::TMetadata)::Promise{Session}
return Promise() # STUB
end
function list(repo::SessionRepo, options::TListOptions)::Promise{Vector{TMetadata}}
return Promise() # STUB
end
function delete(repo::SessionRepo, metadata::TMetadata)::Promise{Nothing}
return Promise() # STUB
end
function fork(repo::SessionRepo, source::TMetadata, options::Dict{String, Any})::Promise{Session}
return Promise() # STUB
end
```
> **Note**: These methods are stubs in `harness_types.jl`. `Promise` is not defined anywhere.
### JsonlSessionRepo (src/session/jsonl_repo.jl)
```julia
mutable struct JsonlSessionRepo <: SessionRepo{
JsonlSessionMetadata,
JsonlSessionCreateOptions,
JsonlSessionListOptions
}
fs::Any
sessions_root_input::String
sessions_root::Union{String, Nothing}
function JsonlSessionRepo(; sessions_root::String, fs::Any)
new(fs, sessions_root, nothing)
end
end
```
> **Note**: Constructor uses **keyword arguments** (`sessions_root=`, `fs=`), NOT positional.
### JsonlSessionStorage (src/session/jsonl_storage.jl)
- `file_path::String`
- `metadata::T` (SessionMetadata)
- `entries::Vector{SessionTreeEntry}`
- `by_id::Dict{String, SessionTreeEntry}`
- `labels_by_id::Dict{String, String}`
- `current_leaf_id::Union{String, Nothing}`
Methods: `getMetadata`, `getLeafId`, `setLeafId`, `createEntryId`, `appendEntry`, `getEntry`, `findEntries`, `getLabel`, `getSessionName`, `getSessionStats`, `getPathToRootOrCompaction`, `getEntries`.
## Agent (src/agent.jl)
```julia
mutable struct Agent
_state::AgentState
listeners::Set{Tuple{Function, Ref{Bool}}}
steering_queue::PendingMessageQueue
follow_up_queue::PendingMessageQueue
convert_to_llm::Function
transform_context::Union{Function, Nothing}
stream_function::StreamFn
get_api_key::Union{Function, Nothing}
on_payload::Union{Function, Nothing}
on_response::Union{Function, Nothing}
before_tool_call::Union{Function, Nothing}
after_tool_call::Union{Function, Nothing}
prepare_next_turn::Union{Function, Nothing}
prepare_next_turn_with_context::Union{Function, Nothing}
active_run::Union{ActiveRun, Nothing}
session_id::Union{String, Nothing}
thinking_budgets::Union{Dict{String, Int64}, Nothing}
transport::String
max_retry_delay_ms::Union{Int64, Nothing}
tool_execution::ToolExecutionMode
end
```
Key methods:
- `subscribe(agent, listener)` - subscribe to events, returns unsubscribe function
- `get_state(agent)` - get current AgentState
- `steer(agent, message)` - queue message for injection after current turn
- `followUp(agent, message)` - queue message to run after agent would stop
- `prompt(agent, input, images)` - start a new prompt (input can be String, AgentMessage, or Vector{AgentMessage})
- `continue!(agent)` - continue from current transcript
- `waitForIdle(agent)` - resolve when current run finishes
- `abort(agent)` - abort current run (partially implemented)
- `reset!(agent)` - clear all state
- `clearSteeringQueue(agent)` / `clearFollowUpQueue(agent)` / `clearAllQueues(agent)`
- `hasQueuedMessages(agent)` - check for pending messages
- `createContextSnapshot(agent)` - create AgentContext snapshot
- `createLoopConfig(agent, options)` - create AgentLoopConfig
> **Note**: `AgentLoopConfig` type is **not defined** in any visible file. It is referenced in `agent.jl:368` and `agent_loop.jl`.
## AgentLoop (src/agent_loop.jl)
Key functions:
- `agentLoop(prompts, context, config, signal, stream_fn)` - main loop, returns EventStream
- `agentLoopContinue(context, config, signal, stream_fn)` - continue from existing context
- `runAgentLoop(...)` - internal run, emits events via `emit::AgentEventSink`
- `runAgentLoopContinue(...)` - internal continue run
- `runLoop(...)` - shared main loop logic
- `streamAssistantResponse(...)` - stream LLM response with event emission
- `executeToolCalls(...)` - execute tool calls (sequential or parallel)
- `executeToolCallsSequential(...)` - sequential execution
- `executeToolCallsParallel(...)` - parallel execution via Threads.@spawn
The loop flow:
1. `AgentStartEvent` emitted
2. `TurnStartEvent` emitted (first turn only from agentLoop, not from runLoop)
3. Steering messages drained and emitted as `MessageStartEvent`/`MessageEndEvent`
4. `streamAssistantResponse` called - transforms context, converts to LLM messages, calls stream_fn
5. For each tool call in response: execute sequentially or in parallel
6. `TurnEndEvent` emitted with message and tool results
7. `prepare_next_turn` hook (if configured) called
8. If `should_stop_after_turn` returns true or no pending messages, `AgentEndEvent` emitted
9. Follow-up messages drained and loop repeats
### AgentLoopConfig fields (referenced, not defined)
Created in `agent.jl:368-401`:
```
model, reasoning (thinking_level), session_id, on_payload, on_response,
transport, thinking_budgets, max_retry_delay_ms, tool_execution,
before_tool_call, after_tool_call, prepare_next_turn, convert_to_llm,
transform_context, get_api_key, get_steering_messages, get_follow_up_messages
```
## Hook System (Planned - Harness Not Implemented)
The following hook types are defined as event/result structs in `harness_types.jl`
but **no harness implementation exists to trigger or handle them**. These are
intended to be used by the future AgentHarness module.
### BeforeAgentStartEvent (line 653)
```julia
mutable struct BeforeAgentStartEvent{TSkill, TPromptTemplate}
type::String
prompt::String
images::Union{Vector{ImageContent}, Nothing}
system_prompt::String
resources::AgentHarnessResources{TSkill, TPromptTemplate}
end
```
**Result**: `BeforeAgentStartResult` (line 862) - `messages::Union{Vector{AgentMessage}, Nothing}`, `system_prompt::Union{String, Nothing}`
### ContextEvent (line 665)
```julia
mutable struct ContextEvent
type::String
messages::Vector{AgentMessage}
end
```
**Result**: `ContextResult` (line 871) - `messages::Vector{AgentMessage}`
### BeforeProviderRequestEvent (line 674)
```julia
mutable struct BeforeProviderRequestEvent
type::String
model::Model
session_id::String
stream_options::AgentHarnessStreamOptions
end
```
**Result**: `BeforeProviderRequestResult` (line 879) - `stream_options::Union{AgentHarnessStreamOptionsPatch, Nothing}`
### BeforeProviderPayloadEvent (line 685)
```julia
mutable struct BeforeProviderPayloadEvent
type::String
model::Model
payload::Any
end
```
**Result**: `BeforeProviderPayloadResult` (line 887) - `payload::Any`
### AfterProviderResponseEvent (line 695)
```julia
mutable struct AfterProviderResponseEvent
type::String
status::Int64
headers::Dict{String, String}
end
```
### ToolCallEvent (line 705)
```julia
mutable struct ToolCallEvent
type::String
tool_call_id::String
tool_name::String
input::Dict{String, Any}
end
```
**Result**: `ToolCallResult` (line 895) - `block::Union{Bool, Nothing}`, `reason::Union{String, Nothing}`
### ToolResultEvent (line 716)
```julia
mutable struct ToolResultEvent
type::String
tool_call_id::String
tool_name::String
input::Dict{String, Any}
content::Vector{MessageContent}
details::Any
is_error::Bool
usage::Union{Usage, Nothing}
end
```
**Result**: `ToolResultPatch` (line 904) - `content`, `details`, `is_error`, `usage`, `terminate` (all Union{...})
### SessionBeforeCompactEvent (line 731)
```julia
mutable struct SessionBeforeCompactEvent
type::String
preparation::Any
branch_entries::Vector{SessionTreeEntry}
custom_instructions::Union{String, Nothing}
signal::Any
end
```
**Result**: `SessionBeforeCompactResult` (line 916) - `cancel::Union{Bool, Nothing}`, `compaction::Union{CompactResult, Nothing}`
### SessionBeforeTreeEvent (line 753)
```julia
mutable struct SessionBeforeTreeEvent
type::String
preparation::Any
signal::Any
end
```
**Result**: `SessionBeforeTreeResult` (line 925) - `cancel`, `summary`, `custom_instructions`, `replace_instructions`, `label`
### SessionCompactEvent (line 743)
```julia
mutable struct SessionCompactEvent
type::String
compaction_entry::CompactionEntry
from_hook::Bool
end
```
### SessionTreeEvent (line 763)
```julia
mutable struct SessionTreeEvent
type::String
new_leaf_id::Union{String, Nothing}
old_leaf_id::Union{String, Nothing}
summary_entry::Union{BranchSummaryEntry, Nothing}
from_hook::Union{Bool, Nothing}
end
```
## Session (src/session/session.jl)
```julia
mutable struct Session{T<:SessionMetadata}
storage::SessionStorage{T}
context_build_options::SessionContextBuildOptions
end
```
Key methods:
- `getMetadata(session)` / `getStorage(session)` / `getLeafId(session)` / `getEntry(session, id)`
- `getEntries(session, options)` / `getBranch(session, from_id)`
- `buildContextEntries(session, options)` / `buildContext(session, options)`
- `getLabel(session, id)` / `getSessionStats(session)` / `getSessionName(session)`
- `appendMessage(session, message)` → entry_id
- `appendThinkingLevelChange(session, level)` → entry_id
- `appendModelChange(session, provider, model_id)` → entry_id
- `appendActiveToolsChange(session, active_tool_names)` → entry_id
- `appendCompaction(session, summary, first_kept_entry_id, tokens_before, ...)` → entry_id
- `appendCustomEntry(session, custom_type, data)` → entry_id
- `appendCustomMessageEntry(session, custom_type, content, display, details)` → entry_id
- `appendLabel(session, target_id, label)` → entry_id
- `appendSessionName(session, name)` → entry_id
- `moveTo(session, entry_id, summary)` → new_leaf_id or nothing (line 392)
## Session Tree Entries (types.jl and harness_types.jl)
```julia
abstract type SessionTreeEntry end
struct MessageEntry <: SessionTreeEntry
base::SessionTreeEntryBase # or direct fields in harness_types.jl
message::AgentMessage
end
struct ThinkingLevelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
thinking_level::String
end
struct ModelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
provider::String
model_id::String
end
struct ActiveToolsChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
active_tool_names::Vector{String}
end
struct CompactionEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
summary::String
first_kept_entry_id::Union{String, Nothing}
tokens_before::Int64
retained_tail::Union{Vector{AgentMessage}, Nothing}
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
struct BranchSummaryEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
from_id::String
summary::String
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
struct CustomEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
data::Union{T, Nothing}
end
struct CustomMessageEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
content::String
details::Union{T, Nothing}
display::Bool
end
struct LabelEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::String
label::Union{String, Nothing}
end
struct SessionInfoEntry <: SessionTreeEntry
base::SessionTreeEntryBase
name::Union{String, Nothing}
end
struct LeafEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::Union{String, Nothing}
end
```
## Resource Loading (stubs)
### loadSkills (skills.jl:61)
```julia
skills, diagnostics = loadSkills(env, "/path/to/skills")
```
> **Note**: Parsing is **stubbed** (line 302 returns `nothing, diagnostics`). The frontmatter parsing code is commented out (lines 266-300). `formatSkillInvocation(skill, additional_instructions)` is implemented.
### loadPromptTemplates (prompt_templates.jl:43)
```julia
templates, diagnostics = loadPromptTemplates(env, "/path/to/templates")
```
> **Note**: Parsing is **stubbed** (line 217 returns `nothing, diagnostics`). The frontmatter parsing code is commented out (lines 188-215). `formatPromptTemplateInvocation(template, args)` and `parseCommandArgs(args_string)` and `substituteArgs(content, args)` are implemented.
## Missing Types / Modules
The following types are referenced in the code but **not defined**:
- `AgentLoopConfig` - referenced in `agent.jl:368`, `agent_loop.jl`
- `Promise` - referenced in `harness_types.jl`
- `AbortSignal` - referenced in `agent_loop.jl`
- `EventStream` - referenced in `agent_loop.jl:158`
- `Context` - referenced in `agent_loop.jl:376`
- `AgentToolResultMutable` - referenced in `agent_loop.jl`
- `FinalizedToolCallOutcome`, `PreparedToolCall`, `ImmediateToolCallOutcome`, `ExecutedToolCallOutcome` - defined in `agent_loop.jl:639-661` (these exist)
The following modules are referenced in `AgentCore.jl` but **files don't exist**:
- `compaction/compaction.jl`
- `compaction/utils.jl`
- `compaction/branch_summarization.jl`
- `utils/truncate.jl`
- `utils/shell_output.jl`
- `proxy.jl`
## AgentCore Exports (from AgentCore.jl:61-147)
The module exports: AgentMessage, AgentTool, AgentContext, AgentEvent, ThinkingLevel, ToolExecutionMode, QueueMode, AgentState, Agent, AgentOptions, AgentLoopConfig, agentLoop, agentLoopContinue, runAgentLoop, runAgentLoopContinue, AgentHarness, AgentHarnessOptions, AgentHarnessEvent, AgentHarnessResources, AgentHarnessSystemPrompt, Session, SessionStorage, SessionRepo, JsonlSessionStorage, JsonlSessionRepo, InMemorySessionStorage, InMemorySessionRepo, createBashTool, createReadTool, createWriteTool, createEditTool, ExecutionEnv, compact, prepareCompaction, DEFAULT_COMPACTION_SETTINGS, generateSummary, generateBranchSummary, truncateHead, truncateTail, formatSize, DEFAULT_MAX_LINES, DEFAULT_MAX_BYTES, convertToLlm, bashExecutionToText, formatSkillsForSystemPrompt, loadSkills, formatSkillInvocation, loadPromptTemplates, formatPromptTemplateInvocation, parseCommandArgs, substituteArgs, streamProxy, ProxyStreamOptions, setDefaultStreamFn, getDefaultStreamFn, uuidv7, create_timestamp.
## Best Practices
1. **Agent hooks** (planned): Use `beforeAgentStart` for initialization, `beforeProviderPayload` for custom metadata, `toolCall` for blocking dangerous operations
2. **Skills**: Organize by domain (file operations, database queries, HTTP requests, git operations)
3. **Templates**: Use for common patterns (commit messages, code review, testing prompts)
4. **Sessions**: Compact periodically, use branches for exploration, clean up old sessions
5. **Monitoring**: Track token counts, watch API costs, optimize tool execution
6. **Tool execution**: Choose between `EXECUTION_SEQUENTIAL` and `EXECUTION_PARALLEL` based on tool dependencies
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# AgentCore.jl - Examples and Patterns
## Quick Start Examples
### Example 1: Basic Conversation
```julia
using AgentCore
# Create model
model = Model(
"gpt-4",
"GPT-4",
"openai",
"openai",
"https://api.openai.com/v1",
true,
["text"],
ModelCost(0.00003, 0.00006, 0.0, 0.0),
128000,
4096,
)
# Create tools
bash_tool = createBashTool()
# Create agent
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:tools => [bash_tool],
:thinkingLevel => THINKING_MEDIUM,
:toolExecution => EXECUTION_PARALLEL,
))
# Subscribe to events
subscribe(agent) do event, signal
if event isa MessageEndEvent
println("Agent: $(event.message)")
end
end
# Start conversation
prompt(agent, "What's in the current directory?")
# Wait for completion
waitForIdle(agent)
# Get final state
state = get_state(agent)
println("Total messages: $(length(state.messages))")
```
### Example 2: Conversation with Memory
```julia
# Create session storage
metadata = JsonlSessionMetadata(
"session_1",
"2024-01-01T00:00:00Z",
"/path/to/project",
"/path/to/session.jsonl",
nothing,
Dict("project" => "my-project"),
)
storage = JsonlSessionStorage(metadata, "/path/to/session.jsonl")
# Create session
session = Session(storage)
# Add messages to session
appendMessage(session, UserMessage("user", [TextContent("Hello, my name is Alice.")], Int64(Dates.now(Dates.UTC).datetime)))
# Check session stats
stats = getSessionStats(session)
println("Messages: $(stats.message_count)")
println("Total tokens: $(stats.total_tokens)")
# Create agent with session
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:tools => [bash_tool],
:sessionId => getMetadata(session).id,
))
```
### Example 3: Steering and Follow-Up
```julia
# Start conversation
prompt(agent, "Create a Python project.")
# Queue a steering message (injected after current assistant turn)
timestamp = Int64(Dates.now(Dates.UTC).datetime)
steer(agent, UserMessage("user", [TextContent("Actually, let's use Node.js instead")], timestamp))
# Wait for redirection
waitForIdle(agent)
# Queue a follow-up message (runs only after agent would otherwise stop)
followUp(agent, UserMessage("user", [TextContent("Can you add tests?")], timestamp))
# Continue until completion
while hasQueuedMessages(agent)
waitForIdle(agent)
end
```
### Example 4: Branching Conversations
```julia
# Initial conversation
prompt(agent, "I want to build a web app.")
# Get the branch at a specific point
entry_id = "msg_3_id"
branch = getBranch(session, entry_id)
println("Branch has $(length(branch)) entries")
# Move to a specific entry (creates a branch summary if summary is provided)
moveTo(session, entry_id, Dict("summary" => "User decided to explore mobile app instead"))
# Continue on new branch
prompt(agent, "Let's build a mobile app instead.")
# Check session branch
branch = getBranch(session)
println("Current branch has $(length(branch)) entries")
```
## Advanced Patterns
### Pattern 1: Token Usage Monitoring
```julia
# Simple token estimation from messages
function estimateTokens(message::AgentMessage)::Int64
content = if message isa UserMessage
join([c.text for c in message.content if c isa TextContent])
elseif message isa AssistantMessage
join([c.text for c in message.content if c isa TextContent])
elseif message isa ToolResultMessage
join([c.text for c in message.content if c isa TextContent])
else
""
end
return ceil(Int, length(content) / 4)
end
# Monitor session token usage
function checkTokenUsage(agent, session)
state = get_state(agent)
stats = getSessionStats(session)
println("Session tokens: $(stats.total_tokens)")
println("Messages in state: $(length(state.messages))")
total_estimated = sum(estimateTokens, state.messages)
println("Estimated total tokens: $(total_estimated)")
return stats.total_tokens
end
# Agent loop with token monitoring
function runAgentWithMonitoring(agent, session, max_tokens=120000)
while true
total = checkTokenUsage(agent, session)
if total > max_tokens
println("Approaching token limit: $(total)")
break
end
if !hasQueuedMessages(agent) && isnothing(agent.active_run)
break
end
end
end
```
### Pattern 2: Custom Tool
```julia
# Create a custom tool
function createCustomTool()
return AgentTool(
"custom_tool",
"custom_tool",
"A custom tool description.",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# Execute tool logic
value = params["value"]
# Send progress updates
on_update("Processing $value...")
result = processValue(value)
return AgentToolResult(
[TextContent(result)],
nothing,
nothing,
nothing,
nothing, # terminate
)
end,
nothing,
EXECUTION_SEQUENTIAL,
)
end
# Use custom tool
custom_tool = createCustomTool()
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:tools => [bash_tool, custom_tool],
))
```
### Pattern 3: Dynamic Model Selection via Hook
```julia
# Hook to change model based on conversation context
function dynamicModelSelection(signal)
# This hook is called between turns to potentially change the model
# Return AgentLoopTurnUpdate to change model/thinking_level, or nothing to keep current
return nothing
end
# Configure agent with the hook
agent = Agent(Dict(
:prepareNextTurn => dynamicModelSelection,
))
# The hook receives an AgentEvent and AbortSignal.
# Access conversation context via:
# context.message - the last assistant message
# context.tool_results - tool results from the last turn
# context.context - the full AgentContext
```
### Pattern 4: Tool Call Interception
```julia
# Hook to validate or block tool calls before they execute
function toolCallValidator(event, signal)
if event isa ToolExecutionStartEvent
# Log or validate tool calls
println("Tool call: $(event.tool_name) with args: $(event.args)")
# Block dangerous commands
if event.tool_name == "bash"
args = event.args
if args isa Dict && haskey(args, :command)
cmd = args[:command]
if contains(cmd, "rm -rf /")
println("Blocked dangerous command!")
end
end
end
end
return nothing
end
# Configure with beforeToolCall hook
agent = Agent(Dict(
:beforeToolCall => toolCallValidator,
))
# After tool call hook
function toolCallLogger(event, signal)
if event isa ToolExecutionEndEvent
status = event.is_error ? "ERROR" : "OK"
println("[$status] $(event.tool_name): $(event.tool_call_id)")
end
return nothing
end
agent = Agent(Dict(
:afterToolCall => toolCallLogger,
))
```
### Pattern 5: Multi-Step Tool Execution
```julia
# Tool that requires multiple steps with progress updates
function createMultiStepTool()
return AgentTool(
"multistep",
"multistep",
"Multi-step task",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# Step 1: Prepare
on_update("Preparing...")
prepare_result = prepareStep(params)
# Step 2: Execute
on_update("Executing...")
execute_result = executeStep(prepare_result, params)
# Step 3: Finalize
on_update("Finalizing...")
finalize_result = finalizeStep(execute_result)
return AgentToolResult(
[TextContent(finalize_result)],
Dict("steps" => 3),
nothing,
nothing,
nothing,
)
end,
nothing,
EXECUTION_SEQUENTIAL,
)
end
```
### Pattern 6: Image Processing with Read Tool
```julia
# Create read tool with image support
read_tool = createReadTool(ReadToolOptions(
auto_resize_images=true,
image_processor=nothing,
))
# Use with agent that supports image input
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:tools => [read_tool],
))
# Send prompt with image content
timestamp = Int64(Dates.now(Dates.UTC).datetime)
image_msg = UserMessage(
"user",
[
TextContent("Analyze this image:"),
ImageContent(base64_data, "image/png"),
],
timestamp,
)
prompt(agent, image_msg)
```
### Pattern 7: Session Navigation
```julia
# Navigate to specific entry
moveTo(session, entry_id)
# Get branch from specific point
branch = getBranch(session, entry_id)
# Create label for an entry (links to another entry)
appendLabel(session, entry_id, "important-decision")
# Get the label for a specific entry
label = getLabel(session, entry_id)
if !isnothing(label)
println("Label: $label")
end
# Build session context from current branch
context = buildSessionContext(session)
# Get specific messages from branch entries
entries = getBranch(session)
for (i, entry) in enumerate(entries)
messages = sessionEntryToContextMessages(entry, i, entries)
for msg in messages
println("$(msg.role): $(msg)")
end
end
```
### Pattern 8: Batch Processing
```julia
# Process multiple prompts sequentially
prompts = [
"What is Julia?",
"What is JavaScript?",
"What is Python?",
]
results = []
for prompt_text in prompts
# Create fresh agent for each prompt
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:tools => [bash_tool],
))
# Run prompt
prompt(agent, prompt_text)
waitForIdle(agent)
# Get result
state = get_state(agent)
last_message = state.messages[end]
push!(results, last_message)
# Clean up
reset!(agent)
end
```
### Pattern 9: Event Subscription
```julia
# Subscribe to various agent events
subscribe(agent) do event, signal
if event isa AgentStartEvent
println("Agent started")
elseif event isa TurnStartEvent
println("Turn started")
elseif event isa MessageStartEvent
println("Message started")
elseif event isa MessageUpdateEvent
# Partial message update during streaming
partial = event.assistant_message_event
# Access partial message content
elseif event isa MessageEndEvent
println("Message ended: $(event.message)")
elseif event isa ToolExecutionStartEvent
println("Tool exec start: $(event.tool_name)")
elseif event isa ToolExecutionUpdateEvent
# Tool progress update
println("Tool update: $(event.partial_result)")
elseif event isa ToolExecutionEndEvent
status = event.is_error ? "error" : "success"
println("Tool exec end: $(event.tool_name) [$status]")
elseif event isa TurnEndEvent
println("Turn ended")
elseif event isa AgentEndEvent
println("Agent ended with $(length(event.messages)) messages")
end
end
```
### Pattern 10: Error Handling
```julia
# Monitor for errors in conversation
subscribe(agent) do event, signal
if event isa MessageEndEvent
msg = event.message
if msg isa AssistantMessage
if msg.stop_reason == "error"
println("Error: $(msg.error_message)")
elseif msg.stop_reason == "length"
println("Response truncated (token limit reached)")
elseif msg.stop_reason == "aborted"
println("Request aborted")
end
end
end
end
# Error handling hook
function errorHandlingHook(signal)
# This is called between turns
# Return AgentLoopTurnUpdate to modify behavior, or nothing
return nothing
end
agent = Agent(Dict(
:prepareNextTurn => errorHandlingHook,
))
```
## Testing Patterns
### Unit Testing
```julia
using Test
using AgentCore
# Test tool creation
@test createBashTool() isa AgentTool
@test createReadTool() isa AgentTool
@test createWriteTool() isa AgentTool
@test createEditTool() isa AgentTool
# Test basic agent creation
@test_throws ErrorException Agent(Dict(:model => nothing))
# Test agent state
agent = Agent(Dict(
:systemPrompt => "Test",
:model => Model("", "", "test", "test", "", false, String[], ModelCost(0,0,0,0), 0, 0),
))
state = get_state(agent)
@test state.system_prompt == "Test"
@test length(state.messages) == 0
```
### Integration Testing with In-Memory Storage
```julia
using AgentCore
# Create in-memory session
repo = InMemorySessionRepo()
session = create(repo)
# Add messages
appendMessage(session, UserMessage("user", [TextContent("Hello")], Int64(Dates.now(Dates.UTC).datetime)))
# Verify session
stats = getSessionStats(session)
@test stats.message_count == 1
# Navigate with moveTo
entry_id = getLeafId(session)
moveTo(session, entry_id)
# Fork from entry
forked = fork(repo, getMetadata(session), Dict("entryId" => entry_id))
```
## Performance Patterns
### Pattern 1: Queue Mode Configuration
```julia
# Configure steering mode (how steering messages are queued)
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:steeringMode => QUEUE_ONE_AT_A_TIME, # Only one steering message processed at a time
:followUpMode => QUEUE_ALL, # All follow-ups processed in batch
))
# Clear queues as needed
clearSteeringQueue(agent)
clearFollowUpQueue(agent)
clearAllQueues(agent)
```
### Pattern 2: Message Normalization
```julia
# Custom message normalization function
function customNormalize(messages::Vector{AgentMessage})::Vector{Message}
return filter(
(m) -> m.role == "user" || m.role == "assistant" || m.role == "toolResult",
messages,
)
end
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:convertToLlm => customNormalize,
))
```
### Pattern 3: Context Transformation
```julia
# Transform context before LLM call
function transformContextFn(messages::Vector{AgentMessage}, signal)
# Filter or modify messages before sending to LLM
filtered = filter(m -> m.role != "toolResult", messages)
return filtered
end
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => model,
:transformContext => transformContextFn,
))
```
## Production Patterns
### Pattern 1: Observability via Events
```julia
# Log all agent events for debugging and monitoring
subscribe(agent) do event, signal
timestamp = Dates.now(Dates.UTC)
if event isa AgentStartEvent
println("[$timestamp] AgentStart")
elseif event isa AgentEndEvent
println("[$timestamp] AgentEnd ($(length(event.messages)) messages)")
elseif event isa TurnStartEvent
println("[$timestamp] TurnStart")
elseif event isa TurnEndEvent
tool_count = length(event.tool_results)
println("[$timestamp] TurnEnd ($tool_count tools)")
elseif event isa ToolExecutionStartEvent
println("[$timestamp] ToolStart: $(event.tool_name)")
elseif event isa ToolExecutionEndEvent
status = event.is_error ? "ERROR" : "OK"
println("[$timestamp] ToolEnd: $(event.tool_name) [$status]")
end
end
```
### Pattern 2: Abort Handling
```julia
# Abort a running agent
if !isnothing(agent.active_run)
abort(agent)
end
# Check if agent is idle
if isnothing(agent.active_run)
println("Agent is idle")
end
```
### Pattern 3: Continue from Transcript
```julia
# Continue from the last message in the transcript
continue!(agent)
# The last message must be user or tool-result role.
# If the last message is assistant, pending steering/follow-up messages
# are processed first, then an error is thrown if none exist.
```
## Debugging Patterns
### Pattern 1: Conversation Trace
```julia
# Trace all messages in the conversation
trace = []
subscribe(agent) do event, signal
if event isa MessageEndEvent
msg = event.message
push!(trace, Dict(
"role" => msg.role,
"type" => typeof(msg).name.name,
))
end
end
# Run conversation
prompt(agent, "Hello")
waitForIdle(agent)
# Print trace
for entry in trace
println("$(entry["type"]): $(entry["role"])")
end
```
### Pattern 2: Tool Call Trace
```julia
tool_trace = []
subscribe(agent) do event, signal
if event isa ToolExecutionStartEvent
push!(tool_trace, Dict(
"type" => "start",
"tool" => event.tool_name,
"id" => event.tool_call_id,
"args" => event.args,
))
elseif event isa ToolExecutionEndEvent
push!(tool_trace, Dict(
"type" => "end",
"tool" => event.tool_name,
"id" => event.tool_call_id,
"error" => event.is_error,
))
end
end
```
### Pattern 3: State Dump
```julia
function dumpState(agent)
state = get_state(agent)
println("=== Agent State ===")
println("System prompt: $(state.system_prompt)")
println("Model: $(state.model.name)")
println("Thinking level: $(state.thinking_level)")
println("Messages: $(length(state.messages))")
println("Tools: $(length(state.tools))")
println("==================")
end
# Use after conversation
prompt(agent, "Hello")
waitForIdle(agent)
dumpState(agent)
```
## Best Practices Summary
1. **Start simple**, add complexity gradually
2. **Use hooks for customization**, not core logic
3. **Test with basic agent** first before adding hooks
4. **Monitor token usage** for long conversations
5. **Use branches** for exploration
6. **Handle errors gracefully** via event subscriptions
7. **Log important events**
8. **Clear queues** when not needed
9. **Use correct Julia naming conventions** (camelCase for functions)
10. **Pass session as first argument** for session functions
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# AgentCore.jl - Learning Guide
## How to Use This Documentation
### Top-Down Learning Approach
This documentation is organized in a **top-down** order, starting from high-level concepts and drilling down into implementation details. Follow this sequence:
1. **Architecture Overview** - Understand the big picture
2. **Agent Component** - Learn about state management and event streaming
3. **AgentLoop Component** - Understand the core LLM interaction loop
4. **Types & Messages** - Learn the data structures
5. **Session Management** - Understand conversation history
6. **Tools** - Learn about tool execution
### Learning Style
- **Visual learners**: Study the ASCII diagrams
- **Hands-on learners**: Code examples provided for each section
- **Conceptual learners**: Read summaries and overviews first
## Quick Start
### Minimal Example
```julia
using AgentCore
# Create agent
agent = Agent(Dict(
:systemPrompt => "You are a helpful assistant.",
:model => Model(...),
:tools => [bash_tool],
))
# Run conversation
prompt(agent, "Hello!")
# Wait for completion
waitForIdle(agent)
```
### Understanding the Flow
```
User Code
├─► Create Agent
│ ├─ Initialize state
│ ├─ Set up queues
│ └─ Register hooks
├─► prompt("Hello")
│ ├─ Validate input
│ └─ Start AgentLoop
├─► AgentLoop (runs in thread)
│ ├─ Stream LLM response
│ ├─ Execute tools
│ └─ Emit events
└─► Event handlers receive events
├─ MessageEndEvent
├─ ToolExecutionEndEvent
└─ AgentEndEvent
```
## Core Concepts
### Agent
**What it is**: High-level interface for LLM interactions
**What it does**:
- Manages conversation state
- Handles event streaming
- Queues steering/follow-up messages
- Provides hooks for customization
**Key methods**:
- `prompt()` - Start new conversation
- `continue!()` - Continue existing conversation
- `steer()` - Queue message for next turn
- `followUp()` - Queue message after stop
- `subscribe()` - Listen to events
- `waitForIdle()` - Wait for agent to finish processing
- `reset!()` - Clear transcript state and queued messages
- `clearAllQueues()` - Remove all queued steering and follow-up messages
- `hasQueuedMessages()` - Check if queues have pending messages
- `abort()` - Abort the current run
- `get_state()` - Get the current agent state
### AgentLoop
**What it is**: Core LLM interaction loop
**What it does**:
- Calls LLM API with streaming
- Executes tool calls (parallel or sequential)
- Emits lifecycle events
- Handles steering/follow-up messages
**Key functions**:
- `agentLoop()` - Start new conversation
- `agentLoopContinue()` - Continue conversation
- `runAgentLoop()` - Internal loop execution
- `streamAssistantResponse()` - LLM API call
- `executeToolCalls()` - Tool execution
### Session
**What it is**: Conversation history management
**What it does**:
- Persists messages to storage
- Supports branching
- Implements compaction
- Manages conversation tree
**Key methods**:
- `appendMessage()` - Add message
- `appendCompaction()` - Compress history with summary
- `moveTo()` - Navigate branches
- `buildContext()` - Build context for LLM
- `getBranch()` - Get branch entries
- `getSessionStats()` - Get session statistics
- `appendThinkingLevelChange()` - Record thinking level change
- `appendModelChange()` - Record model change
- `appendActiveToolsChange()` - Record active tools change
### Tools
**What it is**: Functions agents can call
**What they do**:
- Execute external operations
- Return results to agent
- Support streaming updates
- Implement hooks
**Built-in tools**:
- `bash` - Execute shell commands
- `read` - Read files
- `write` - Write files
- `edit` - Edit files
## Event System
### Event Types
```
AgentEvent
├─ AgentStartEvent / AgentEndEvent
├─ TurnStartEvent / TurnEndEvent
├─ MessageStartEvent / MessageEndEvent
├─ MessageUpdateEvent
├─ ToolExecutionStartEvent / ToolExecutionEndEvent
└─ ToolExecutionUpdateEvent
```
### Event Flow
```
AgentStartEvent
├─ TurnStartEvent
│ ├─ MessageStartEvent (user)
│ ├─ MessageEndEvent (user)
│ ├─ MessageStartEvent (assistant)
│ ├─ MessageUpdateEvent (streaming)
│ ├─ MessageEndEvent (assistant)
│ ├─ ToolExecutionStartEvent
│ ├─ ToolExecutionEndEvent
│ └─ TurnEndEvent
└─ AgentEndEvent
```
## Complete Data Flow with Type Transformations
This documentation shows how data is transformed through the agent lifecycle.
### Message Type Hierarchy
```
Message (for LLM API)
├── UserMessage (role: "user")
│ └── content::Vector{MessageContent}
│ ├── TextContent (text::String)
│ └── ImageContent (data::String, mime_type::String)
├── AssistantMessage (role: "assistant")
│ ├── content::Vector{MessageContent}
│ │ ├── TextContent
│ │ └── ToolCall (id, name, arguments::Dict{String, Any})
│ ├── usage::Usage
│ ├── stop_reason::String
│ └── timestamp::Timestamp
└── ToolResultMessage (role: "toolResult")
├── tool_call_id::String
├── tool_name::String
├── content::Vector{MessageContent}
├── details::Any
├── usage::Union{Usage, Nothing}
├── is_error::Bool
└── timestamp::Timestamp
AgentMessage (internal, abstract type)
├── UserMessage (same as above)
├── AssistantMessage (same as above)
├── ToolResultMessage (same as above, plus: role, added_tool_names)
├── BashExecutionMessage (custom)
│ ├── role, command, output, exit_code
│ ├── cancelled, truncated, full_output_path, timestamp
│ └── exclude_from_context
├── CompactionSummaryMessage (custom)
│ ├── role, summary, tokens_before, timestamp
│ └── converted to UserMessage for LLM
├── BranchSummaryMessage (custom)
│ ├── role, summary, from_id, timestamp
│ └── converted to UserMessage for LLM
└── CustomMessage (custom, extends AgentMessage)
├── message::AgentMessage
└── custom_type::String
```
### Complete Conversation Flow
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Step 1: User Input (Vector{AgentMessage}) │
└─────────────────────────────────────────────────────────────────────────────┘
prompt(agent, "Hello!")
└─► normalizePromptInput()
Input: "Hello!"::String
Output: [UserMessage("user", [TextContent("Hello!")], timestamp)]
┌─────────────────────────────────────────────────────────────────────────────┐
│ Step 2: AgentLoop Processing │
└─────────────────────────────────────────────────────────────────────────────┘
runAgentLoop()
├─► transform_context() (optional hook)
│ Input: [UserMessage(...)]::Vector{AgentMessage}
│ Output: [UserMessage(...)]::Vector{AgentMessage}
├─► convert_to_llm()
│ Input: [UserMessage(...)]::Vector{AgentMessage}
│ Output: [UserMessage(...)]::Vector{Message}
├─► stream_fn() - LLM API call
│ Input: model, Context(...), config
│ Output: AssistantMessage with ToolCall[]
├─► executeToolCalls()
│ Input: AssistantMessage (with ToolCall[])
│ Output: ToolResultMessage[]
└─► Emit events and append to context.messages
┌─────────────────────────────────────────────────────────────────────────────┐
│ Step 3: Final Conversation State │
└─────────────────────────────────────────────────────────────────────────────┘
context.messages::Vector{AgentMessage}
├─ UserMessage("user", [TextContent("Hello!")], ...)
├─ AssistantMessage("assistant", [
│ TextContent("Hi there!"),
│ ToolCall("bash", {...})
│ ], ...)
└─ ToolResultMessage("toolResult", "bash", [TextContent("...")], ...)
┌─────────────────────────────────────────────────────────────────────────────┐
│ Step 4: AgentEndEvent (final output) │
└─────────────────────────────────────────────────────────────────────────────┘
AgentEndEvent(messages::Vector{AgentMessage})
└─ Contains full conversation history
User Input (String / AgentMessage / Vector{AgentMessage})
├─► normalizePromptInput()
│ Input: input::Union{String, AgentMessage, Vector{AgentMessage}}
│ Output: Vector{AgentMessage}
│ • String → UserMessage("user", [TextContent(input)], timestamp)
│ • AgentMessage → [input]
│ • Vector{AgentMessage} → input (pass-through)
├─► prompt(agent, messages)
│ └─► runPromptMessages()
AgentLoop Execution:
├─► transform_context() (optional hook)
│ Input: context.messages::Vector{AgentMessage}
│ Output: messages::Vector{AgentMessage} (transformed)
├─► convert_to_llm()
│ Input: messages::Vector{AgentMessage}
│ Output: llm_messages::Vector{Message}
│ AgentMessage → Message mapping:
│ • UserMessage → UserMessage (pass-through)
│ • AssistantMessage → AssistantMessage (pass-through)
│ • ToolResultMessage → ToolResultMessage (pass-through)
│ • BashExecutionMessage → UserMessage (text conversion)
│ • CompactionSummaryMessage → UserMessage (text wrapped)
│ • BranchSummaryMessage → UserMessage (text wrapped)
├─► LLM API Call (stream_fn)
│ Input: model, Context(system_prompt, llm_messages, tools), config
│ Output: Stream{AssistantMessageEvent}
├─► AssistantMessage (returned from LLM)
│ content::Vector{MessageContent}
│ └─ Contains: TextContent[] and/or ToolCall[]
├─► executeToolCalls() (if ToolCall[] in content)
│ │
│ ├─► prepareToolCall() for each ToolCall
│ │ Input: tool_call::ToolCall
│ │ Output: PreparedToolCall or ImmediateToolCallOutcome
│ │
│ ├─► executePreparedToolCall() (if prepared)
│ │ Input: PreparedToolCall
│ │ Output: ExecutedToolCallOutcome
│ │ tool.execute() returns AgentToolResultMutable
│ │
│ ├─► finalizeExecutedToolCall()
│ │ Input: ExecutedToolCallOutcome
│ │ Output: FinalizedToolCallOutcome
│ │
│ └─► createToolResultMessage()
│ Input: FinalizedToolCallOutcome
│ Output: ToolResultMessage
│ • role: "toolResult"
│ • tool_call_id, tool_name
│ • content::Vector{MessageContent}
│ • details, usage, added_tool_names
│ • is_error, timestamp
└─► Append to context.messages and new_messages
Vector{AgentMessage} (final conversation history)
Contains: [UserMessage, AssistantMessage, ToolResultMessage, ...]
```
### Tool Execution Flow
```
ToolCall (in AssistantMessage.content)
├─ before_tool_call hook (optional)
│ Input: BeforeToolCallContext
│ Output: BeforeToolCallResult (block, reason) or nothing
├─ prepareToolCall()
│ Input: tool_call::ToolCall
│ Output: Union{PreparedToolCall, ImmediateToolCallOutcome}
│ • Validates tool exists
│ • Runs before_tool_call hook
│ • Runs prepare_arguments hook (optional)
│ • Runs validateToolArguments (optional)
├─ executePreparedToolCall() (if prepared)
│ Input: PreparedToolCall
│ Output: ExecutedToolCallOutcome
│ tool.execute() returns AgentToolResultMutable
├─ finalizeExecutedToolCall()
│ Input: ExecutedToolCallOutcome
│ Output: FinalizedToolCallOutcome
│ Runs after_tool_call hook (optional)
└─ createToolResultMessage()
Input: FinalizedToolCallOutcome
Output: ToolResultMessage
• role: "toolResult"
• tool_call_id, tool_name
• content::Vector{MessageContent}
• details, usage, added_tool_names
• is_error, timestamp
```
## Best Practices
### 1. Use Hooks for Customization
```julia
# Before tool call
before_hook = (context, signal) -> begin
println("Executing: $(context.tool_call.name)")
return nothing
end
# After tool call
after_hook = (context, signal) -> begin
if context.is_error
println("Tool failed: $(context.tool_call.name)")
end
return nothing
end
```
### 2. Monitor Events
```julia
subscribe(agent) do event, signal
if event isa MessageEndEvent
println("Message: $(event.message)")
elseif event isa ToolExecutionEndEvent
println("Tool completed: $(event.tool_name)")
end
end
```
### 3. Use Steering for Redirection
```julia
# Agent is going wrong direction
steer(agent, UserMessage("Actually, let's do X instead"))
```
### 4. Use Follow-Up for Continuation
```julia
# Agent thinks it's done, but user wants more
followUp(agent, UserMessage("Wait, there's one more thing"))
```
## Common Patterns
### Pattern 1: Conversation with Memory
```julia
# Use Session to persist conversation
storage = JsonlSessionStorage(...)
session = Session(storage)
# Add messages to session
appendMessage(session, user_message)
appendMessage(session, assistant_message)
# Build context from session
context = buildContext(session)
```
### Pattern 2: Long Conversations
```julia
# Compact periodically to stay within context limits
if token_count > MAX_TOKENS * 0.8
compact_id = appendCompaction(
session,
summary,
first_kept_id,
token_count,
)
end
```
### Pattern 3: Branching Conversations
```julia
# User wants to explore alternative
session.moveTo(branch_point_id)
# Create new branch
moveTo(session, branch_point_id, summary=["summary" => "Exploring alternative approach"])
appendMessage(session, new_user_message)
```
### Pattern 4: Custom Tools
```julia
# Create custom tool
custom_tool = AgentTool(
"custom", # name
"Custom", # label
"Does custom thing", # description
parameters, # parameter schema
execute_function, # execute
nothing, # prepare_arguments (optional)
EXECUTION_PARALLEL, # execution_mode
)
# Add to agent
agent = Agent(Dict(:tools => [custom_tool]))
```
## Debugging
### Check Active Run
```julia
if !isnothing(agent.active_run)
println("Agent is busy")
else
println("Agent is idle")
end
```
### Clear Queues
```julia
clearAllQueues(agent)
```
### Reset State
```julia
reset!(agent)
```
## Performance Tips
1. **Use parallel execution** for independent tools
2. **Compact periodically** for long conversations
3. **Use thinking_level wisely** (higher = slower but better)
4. **Batch tool calls** when possible
5. **Cache LLM responses** when appropriate
## Troubleshooting
### Agent stuck in loop
```julia
# Check if agent is still processing
if hasQueuedMessages(agent)
# Clear queues
clearAllQueues(agent)
end
```
### Too many tokens
```julia
# Compact session
compact_id = appendCompaction(
session,
summary,
first_kept_id,
token_count,
)
```
### Tool execution failed
```julia
# Check tool result
if result.is_error
println("Tool failed: $(result.error)")
end
```
## Next Steps
1. Read **Architecture Overview** for deep understanding
2. Explore **Agent Component** for state management
3. Study **AgentLoop** for core logic
4. Learn **Types & Messages** for data structures
5. Master **Session Management** for persistence
6. Build **Tools** for custom functionality
## Resources
- Original TypeScript implementation: `@earendil-works/pi-agent-core`
- AgentCore.jl source code: `src/`
- Examples: `examples/`
## Community
For questions and discussions:
- GitHub Issues: `/issues`
- Documentation: `docs/`
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# Agent Loop Tracing
This document traces the agent loop through two example interactions.
## Architecture Overview
```
Agent (src/agent.jl)
|
v
AgentLoop (src/agent_loop.jl) -- runLoop() is the core while(true) loop
|
v
StreamFn (src/stream_fn.jl) -- LLM streaming function (user-provided)
|
v
Tools (src/tools/*.jl) -- bash, read, write, edit
```
Key types:
- `Agent` (agent.jl:85) -- high-level wrapper with state, queues, listeners
- `agentLoop()` (agent_loop.jl:23) -- entry point, spawns thread, returns `EventStream`
- `runLoop()` (agent_loop.jl:169) -- the core `while(true)` loop
- `streamAssistantResponse()` (agent_loop.jl:361) -- calls LLM, streams events, returns `AssistantMessage`
- `executeToolCalls()` (agent_loop.jl:476) -- runs tool calls (sequential or parallel)
- `AgentContext` (types.jl:186) -- system_prompt + messages + tools
- `AgentLoopConfig` -- model, thinking_level, callbacks for steering/follow-up/tool execution
---
## Scenario 1: User asks "what is the content of text.txt file", agent responds
### Step 1: User invokes `prompt(agent, "what is the content of text.txt file")`
**File: agent.jl:284-292**
```julia
prompt(agent, "what is the content of text.txt file")
-> normalizePromptInput(agent, "what is the content of text.txt file", [])
-> [UserMessage("user", [TextContent("what is the content of text.txt file")], timestamp)]
-> runPromptMessages(agent, messages)
```
The string is normalized into a single `UserMessage`.
### Step 2: `runPromptMessages` calls `agentLoop()`
**File: agent.jl:310-313** (TODO stub, but conceptually):
```julia
runPromptMessages(agent, messages)
-> AgentLoop.agentLoop(
prompts = [UserMessage(...)],
context = createContextSnapshot(agent), # AgentContext with system_prompt, messages, tools
config = createLoopConfig(agent),
signal = nothing,
stream_fn = agent.stream_function,
)
```
### Step 3: `agentLoop()` spawns thread and calls `runAgentLoop()`
**File: agent_loop.jl:23-45**
```julia
agentLoop(prompts, context, config, signal, stream_fn)
-> createAgentStream() # creates EventStream
-> Threads.@spawn begin
runAgentLoop(prompts, context, config, emit, signal, stream_fn)
end(stream, messages)
end
-> return stream
```
### Step 4: `runAgentLoop()` initializes and enters `runLoop()`
**File: agent_loop.jl:85-116**
```julia
runAgentLoop(prompts, context, config, emit, signal, stream_fn)
-> new_messages = copy(prompts) # [UserMessage(...)]
-> current_context = AgentContext(context.system_prompt, vcat(context.messages, copy(prompts)), context.tools)
-> emit(AgentStartEvent())
-> emit(TurnStartEvent())
-> for prompt in prompts: emit(MessageStartEvent(prompt)); emit(MessageEndEvent(prompt)) end
-> runLoop(current_context, new_messages, config, signal, emit, stream_fn)
```
Events emitted so far:
1. `AgentStartEvent`
2. `TurnStartEvent`
3. `MessageStartEvent(UserMessage)`
4. `MessageEndEvent(UserMessage)`
### Step 5: `runLoop()` -- first iteration
**File: agent_loop.jl:169-310**
```julia
runLoop(initial_context, new_messages, initial_config, signal, emit, stream_function)
-> current_context = initial_context
-> first_turn = true
-> pending_messages = getSteeringMessages(config) # may be empty [] by default (agent_loop.jl:180-182)
-> while true:
has_more_tool_calls = true # reset each outer iteration
# Inner loop: has_more_tool_calls || !isempty(pending_messages)
while has_more_tool_calls || !isempty(pending_messages)
first_turn = false # TurnStartEvent NOT emitted (already done)
# no pending_messages
# === STEP 5a: Call LLM ===
message = streamAssistantResponse(current_context, config, signal, emit, stream_function)
```
### Step 5a: `streamAssistantResponse()` -- LLM call
**File: agent_loop.jl:361-435**
```julia
streamAssistantResponse(context, config, signal, emit, stream_function)
-> messages = context.messages # [UserMessage(...)]
-> llm_messages = config.convert_to_llm(messages) # filter to user/assistant/toolResult roles
-> llm_context = Context(context.system_prompt, llm_messages, context.tools)
-> response = stream_function(config.model, llm_context, merged_config)
```
The `stream_function` (user-provided via StreamFn) calls the LLM API. It yields events:
```
StartEvent(partial=AssistantMessage(role="assistant", content=[]))
-> push!(context.messages, partial_message)
-> emit(MessageStartEvent(partial_message))
TextDeltaEvent(partial=AssistantMessage with ToolCall for "read")
-> context.messages[end] = partial_message
-> emit(MessageUpdateEvent(partial_message, event))
TextDeltaEvent(...) -- streaming continues
toolcall_start/toolcall_delta/toolcall_end -- tool call detected: read(file="text.txt") (agent_loop.jl:405)
DoneEvent(reason="tool_calls", ...)
-> final_message = AssistantMessage(role="assistant", content=[ToolCall(...)])
-> context.messages[end] = final_message
-> emit(MessageEndEvent(final_message))
-> return final_message
```
Back in `runLoop`:
- `message` = `AssistantMessage` with `stop_reason = "tool_calls"`
- `push!(new_messages, message)`
### Step 5b: Tool call detection
**File: agent_loop.jl:219-244**
```julia
tool_calls = filter(c -> c isa ToolCall, message.content)
# tool_calls = [ToolCall(type="tool_call", id="call_1", name="read", arguments={file="text.txt"}, ...)]
tool_results = []
has_more_tool_calls = false # set to true only if tool calls execute and don't terminate (agent_loop.jl:225)
if !isempty(tool_calls)
executed_tool_batch = executeToolCalls(
current_context, message, config, signal, emit,
)
append!(tool_results, executed_tool_batch.messages)
has_more_tool_calls = !executed_tool_batch.terminate
```
### Step 5c: `executeToolCalls()` -- sequential or parallel
**File: agent_loop.jl:476-514**
Since there's only one tool call and no sequential mode forced, it uses `executeToolCallsParallel()` (or sequential -- both paths converge for a single tool call).
```julia
executeToolCalls(context, assistant_message, config, signal, emit)
-> tool_calls extracted from assistant_message.content (agent_loop.jl:483-486)
-> tool = findfirst(t -> t.name == "read", context.tools)
-> preparation = prepareToolCall(...)
-> validated_args = {file="text.txt"}
-> return PreparedToolCall("prepared", tool_call, tool, validated_args)
executed = executePreparedToolCall(preparation, signal, emit)
-> result = prepared.tool.execute("call_1", {file="text.txt"}, signal, on_update, context)
# This invokes the read tool's execute function (src/tools/read.jl:26)
# TODO: in the current code, it returns a placeholder
-> return ExecutedToolCallOutcome(result, false)
finalized = finalizeExecutedToolCall(...)
# Creates FinalizedToolCallOutcome
emitToolExecutionEnd(finalized, emit)
# emits ToolExecutionEndEvent
tool_result_message = createToolResultMessage(finalized)
# creates ToolResultMessage(role="toolResult", tool_call_id="call_1", tool_name="read", content=[TextContent(...)])
emitToolResultMessage(tool_result_message, emit)
# emits MessageStartEvent(tool_result_message), MessageEndEvent(tool_result_message)
```
Events emitted during tool execution:
5. `MessageStartEvent(assistant_message)` (from LLM)
6. `MessageEndEvent(assistant_message)` (from LLM done)
7. `ToolExecutionStartEvent`
8. `ToolExecutionEndEvent`
9. `MessageStartEvent(tool_result_message)`
10. `MessageEndEvent(tool_result_message)`
### Step 5d: Back in inner loop
**File: agent_loop.jl:240-294**
```julia
push!(current_context.messages, tool_result_message)
push!(new_messages, tool_result_message)
emit(TurnEndEvent(message, tool_results))
next_turn_snapshot = prepare_next_turn(config, PrepareNextTurnContext(...))
# Returns nothing by default (no custom prepare_next_turn)
if !isnothing(next_turn_snapshot) ... end # skipped
if should_stop_after_turn(config, ...) ... end # returns false by default
pending_messages = get_steering_messages(config) # returns []
# inner while continues: has_more_tool_calls = true, pending_messages = []
# === SECOND LLM CALL ===
message = streamAssistantResponse(current_context, config, signal, emit, stream_function)
# context.messages now = [UserMessage(...), AssistantMessage(read tool call), ToolResultMessage(file contents)]
```
### Step 5e: Second LLM call -- agent responds
**File: agent_loop.jl:361-435**
```julia
streamAssistantResponse(context, config, signal, emit, stream_function)
-> llm_messages = [UserMessage(...), AssistantMessage(...), ToolResultMessage(...)]
-> response = stream_function(model, Context(system_prompt, llm_messages, tools), config)
```
The LLM receives the user's question + its own tool call + the file contents as a tool result. It generates a text response.
Events:
```
StartEvent -> MessageStartEvent
TextDeltaEvent -> MessageUpdateEvent (text streaming)
...
DoneEvent(reason="end_turn") -> MessageEndEvent
```
### Step 5f: No more tool calls -- loop exits
**File: agent_loop.jl:219-244**
```julia
tool_calls = filter(c -> c isa ToolCall, message.content)
# tool_calls = [] (no tool calls in the final response)
has_more_tool_calls = false # stays false
emit(TurnEndEvent(message, ToolResultMessage[]))
next_turn_snapshot = prepare_next_turn(...) # nothing
should_stop_after_turn(...) # false
pending_messages = get_steering_messages(...) # []
# inner while: has_more_tool_calls=false, pending_messages=[] -> exits inner loop
follow_up_messages = get_follow_up_messages(...) # []
# exits outer while
emit(AgentEndEvent(new_messages))
```
Events emitted at end:
11. `MessageStartEvent(assistant_response)`
12. `MessageUpdateEvent(...)` (text deltas)
13. `MessageEndEvent(assistant_response)`
14. `TurnEndEvent(response, [])`
15. `AgentEndEvent([UserMessage, AssistantMessage, ToolResultMessage, AssistantResponse])`
### Summary of Scenario 1 event sequence:
| # | Event | Source |
|---|-------|--------|
| 1 | `AgentStartEvent` | runAgentLoop() |
| 2 | `TurnStartEvent` | runAgentLoop() |
| 3 | `MessageStartEvent(UserMessage)` | runAgentLoop() |
| 4 | `MessageEndEvent(UserMessage)` | runAgentLoop() |
| 5 | `MessageStartEvent(AssistantMessage)` | streamAssistantResponse() |
| 6 | `MessageUpdateEvent(AssistantMessage)` | streamAssistantResponse() (streaming) |
| 7 | `MessageEndEvent(AssistantMessage)` | streamAssistantResponse() |
| 8 | `ToolExecutionStartEvent` | executeToolCalls() |
| 9 | `ToolExecutionEndEvent` | executeToolCalls() |
| 10 | `MessageStartEvent(ToolResultMessage)` | emitToolResultMessage() |
| 11 | `MessageEndEvent(ToolResultMessage)` | emitToolResultMessage() |
| 12 | `TurnEndEvent(AssistantMessage, [tool_results])` | runLoop() |
| 13 | `MessageStartEvent(AssistantMessage)` | streamAssistantResponse() (2nd call) |
| 14 | `MessageUpdateEvent(AssistantMessage)` | streamAssistantResponse() (text streaming) |
| 15 | `MessageEndEvent(AssistantMessage)` | streamAssistantResponse() |
| 16 | `TurnEndEvent(AssistantResponse, [])` | runLoop() |
| 17 | `AgentEndEvent([all messages])` | runLoop() |
---
## Scenario 2: User asks "copy text.txt to text.md", agent responds
### Step 1-4: Same as Scenario 1
User invokes `prompt(agent, "copy text.txt to text.md")`, which flows through `agentLoop()` -> `runAgentLoop()` -> `runLoop()`.
Events 1-4 are identical (AgentStart, TurnStart, UserMessage start/end).
### Step 5: First LLM call -- agent decides to use tools
The LLM receives:
```
System: <system_prompt>
User: "copy text.txt to text.md"
```
The LLM decides it needs to:
1. Read text.txt (to get its contents), then
2. Write those contents to text.md
The LLM may emit a single `AssistantMessage` with **two** `ToolCall` objects:
```
AssistantMessage(content=[
ToolCall(id="call_1", name="read", arguments={file="text.txt"}),
ToolCall(id="call_2", name="write", arguments={file="text.md", content="...contents of text.txt..."}),
])
```
Or it may emit one tool call at a time (sequential), which is also supported.
### Step 5b: Tool execution
**File: agent_loop.jl:219-244**
```julia
tool_calls = filter(c -> c isa ToolCall, message.content)
# tool_calls = [ToolCall(read), ToolCall(write)]
executed_tool_batch = executeToolCalls(context, message, config, signal, emit)
```
If `tool_execution == EXECUTION_PARALLEL` (default) and no tool forces sequential mode:
**File: agent_loop.jl:568-633 (executeToolCallsParallel)**
```julia
executeToolCallsParallel(...)
-> for tool_call in tool_calls:
# call_1: read
emit(ToolExecutionStartEvent("call_1", "read", {file="text.txt"}))
preparation = prepareToolCall(...) # validated
push!(finalized_calls, () -> executed_read()) # closure for deferred execution
# call_2: write
emit(ToolExecutionStartEvent("call_2", "write", {file="text.md", content="..."}))
preparation = prepareToolCall(...)
push!(finalized_calls, () -> executed_write()) # closure
# Execute in order
ordered_finalized_calls = map(entry -> entry(), finalized_calls)
for finalized in ordered_finalized_calls:
tool_result_message = createToolResultMessage(finalized)
emitToolResultMessage(tool_result_message, emit)
```
Events for parallel execution:
```
ToolExecutionStartEvent(call_1, "read", ...)
ToolExecutionEndEvent(call_1, "read", ...)
ToolExecutionStartEvent(call_2, "write", ...)
ToolExecutionEndEvent(call_2, "write", ...)
MessageStartEvent(ToolResultMessage[read result])
MessageEndEvent(ToolResultMessage[read result])
MessageStartEvent(ToolResultMessage[write result])
MessageEndEvent(ToolResultMessage[write result])
```
If `tool_execution == EXECUTION_SEQUENTIAL` or any tool is marked sequential:
**File: agent_loop.jl:520-562 (executeToolCallsSequential)**
```julia
for tool_call in tool_calls:
emit(ToolExecutionStartEvent(...))
# execute, finalize, emit result
# THEN proceed to next
```
Events for sequential execution:
```
ToolExecutionStartEvent(call_1, "read", ...)
ToolExecutionEndEvent(call_1, "read", ...)
MessageStartEvent(ToolResultMessage[read result])
MessageEndEvent(ToolResultMessage[read result])
ToolExecutionStartEvent(call_2, "write", ...)
ToolExecutionEndEvent(call_2, "write", ...)
MessageStartEvent(ToolResultMessage[write result])
MessageEndEvent(ToolResultMessage[write result])
```
### Step 5d: Second LLM call
```julia
has_more_tool_calls = !executed_tool_batch.terminate # false (unless terminate=true)
# inner loop continues since pending_messages is still empty
# Actually: has_more_tool_calls = false, pending_messages = []
# -> exits inner loop
# follow_up_messages = []
# -> exits outer loop
emit(TurnEndEvent(message, tool_results))
```
Wait -- this depends on whether the LLM's first response included only tool calls (no text answer). If the LLM only returned tool calls and the tool results were processed, the agent may need a **third** LLM call to generate the final user-facing response.
**Revised flow for two tool calls:**
After tool results are added to context:
```
context.messages = [
UserMessage("copy text.txt to text.md"),
AssistantMessage([ToolCall(read), ToolCall(write)]),
ToolResultMessage(read result),
ToolResultMessage(write result),
]
```
The agent needs another LLM call to generate a response. Let's trace it:
### Step 5e: Second LLM call -- final response
```julia
message = streamAssistantResponse(current_context, ...)
```
LLM receives:
```
System: <system_prompt>
User: "copy text.txt to text.md"
Assistant: [ToolCall(read), ToolCall(write)]
ToolResult: (contents of text.txt)
ToolResult: (write confirmation)
```
LLM generates: "I've copied text.txt to text.md."
Events:
```
MessageStartEvent(AssistantMessage)
MessageUpdateEvent(... text deltas ...)
MessageEndEvent(AssistantMessage)
```
### Step 5f: No tool calls, loop exits
```julia
tool_calls = [] # no ToolCalls in response
has_more_tool_calls = false
emit(TurnEndEvent(message, []))
pending_messages = []
follow_up_messages = []
emit(AgentEndEvent(new_messages))
```
### Summary of Scenario 2 event sequence (parallel tool execution):
| # | Event | Source |
|---|-------|--------|
| 1 | `AgentStartEvent` | runAgentLoop() |
| 2 | `TurnStartEvent` | runAgentLoop() |
| 3 | `MessageStartEvent(UserMessage)` | runAgentLoop() |
| 4 | `MessageEndEvent(UserMessage)` | runAgentLoop() |
| 5 | `MessageStartEvent(AssistantMessage)` | streamAssistantResponse() (1st LLM call) |
| 6 | `MessageEndEvent(AssistantMessage)` | streamAssistantResponse() |
| 7 | `ToolExecutionStartEvent(call_1, "read")` | executeToolCallsParallel() |
| 8 | `ToolExecutionEndEvent(call_1, "read")` | executeToolCallsParallel() |
| 9 | `ToolExecutionStartEvent(call_2, "write")` | executeToolCallsParallel() |
| 10 | `ToolExecutionEndEvent(call_2, "write")` | executeToolCallsParallel() |
| 11 | `MessageStartEvent(ToolResultMessage[read])` | emitToolResultMessage() |
| 12 | `MessageEndEvent(ToolResultMessage[read])` | emitToolResultMessage() |
| 13 | `MessageStartEvent(ToolResultMessage[write])` | emitToolResultMessage() |
| 14 | `MessageEndEvent(ToolResultMessage[write])` | emitToolResultMessage() |
| 15 | `TurnEndEvent(AssistantToolCalls, [read_result, write_result])` | runLoop() |
| 16 | `MessageStartEvent(AssistantMessage)` | streamAssistantResponse() (2nd LLM call) |
| 17 | `MessageUpdateEvent(AssistantMessage)` | streamAssistantResponse() (text streaming) |
| 18 | `MessageEndEvent(AssistantMessage)` | streamAssistantResponse() |
| 19 | `TurnEndEvent(AssistantResponse, [])` | runLoop() |
| 20 | `AgentEndEvent([all messages])` | runLoop() |
---
## Key Design Patterns
### 1. Event Stream Architecture
Events flow through `emit::AgentEventSink` (a function) into an `EventStream`. Consumers subscribe to the stream and receive events as they occur. The stream terminates when `AgentEndEvent` is emitted.
### 2. Context Accumulation
`AgentContext.messages` grows across turns:
```
[UserMessage, AssistantMessage, ToolResultMessage, AssistantMessage, ToolResultMessage, ...]
```
### 3. LLM Conversion
Before each LLM call, `config.convert_to_llm()` filters the agent messages to only include user/assistant/toolResult roles (src/agent.jl:18-23):
```julia
filter(m -> m.role in ("user", "assistant", "toolResult"), messages)
```
### 4. Tool Execution Modes
- `EXECUTION_PARALLEL` (default): tool calls are prepared as closures and executed in sequence after all are prepared
- `EXECUTION_SEQUENTIAL`: each tool is prepared, executed, and finalized before the next begins
### 5. Turn Continuation
The inner `while has_more_tool_calls` loop handles:
- Multiple tool calls from a single assistant response
- Pending steering/follow-up messages injected between turns
The outer `while true` loop handles:
- Full turns (LLM call + tool execution)
- Switching between tool-result turns and response turns
### 6. Message Types
| Type | Role | Created By |
|------|------|------------|
| `UserMessage` | "user" | User via `prompt()` |
| `AssistantMessage` | "assistant" | LLM via `streamAssistantResponse()` |
| `ToolResultMessage` | "toolResult" | `createToolResultMessage()` after tool execution |
| `BashExecutionMessage` | "user" | Bash tool (excluded from context by default) |
| `CompactionSummaryMessage` | "user" | Compaction process |
| `BranchSummaryMessage` | "user" | Branch summarization |
### 7. Tool Call Lifecycle
```
ToolCall (from LLM)
-> prepareToolCall() (validate args, before_tool_call hook)
-> executePreparedToolCall() (invoke tool.execute)
-> finalizeExecutedToolCall() (after_tool_call hook)
-> createToolResultMessage() (wrap result in ToolResultMessage)
-> emitToolResultMessage() (emit MessageStart/MessageEnd)
```
+149
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@@ -0,0 +1,149 @@
# AgentCore.jl - A Julia implementation of the Pi Agent Core framework
#
# This is a reimplementation of the TypeScript pi-agent-core package in idiomatic Julia.
#
# The AgentCore package provides:
# - Low-level `agentLoop` for stateful LLM interactions with tool execution
# - High-level `Agent` struct with state management, event streaming, and queueing
# - `AgentHarness` for session persistence, resource management, and extension hooks
# - Built-in tools for file operations (read, write, edit) and bash execution
# - Session management with JSONL-based storage, compaction, and branch navigation
#
# For more information about the original TypeScript implementation, see:
# https://github.com/earendil-works/pi/packages/agent
module AgentCore
# Core modules
include("types.jl")
include("stream_fn.jl")
include("agent_loop.jl")
include("agent.jl")
# Harness modules
include("harness_types.jl")
include("messages.jl")
include("system_prompt.jl")
include("skills.jl")
include("prompt_templates.jl")
include("agent_harness.jl")
# Session modules
include("session/session.jl")
include("session/jsonl_storage.jl")
include("session/jsonl_repo.jl")
include("session/memory_storage.jl")
include("session/memory_repo.jl")
include("session/repo_utils.jl")
# Tool modules
include("tools/index.jl")
include("tools/bash.jl")
include("tools/read.jl")
include("tools/write.jl")
include("tools/edit.jl")
include("tools/edit_diff.jl")
include("tools/image.jl")
include("tools/path_utils.jl")
include("tools/file_mutation_queue.jl")
# Compaction modules
include("compaction/compaction.jl")
include("compaction/utils.jl")
include("compaction/branch_summarization.jl")
# Utility modules
include("utils/truncate.jl")
include("utils/shell_output.jl")
include("proxy.jl")
# Re-export public API
export
# Core types
AgentMessage,
AgentTool,
AgentContext,
AgentEvent,
ThinkingLevel,
ToolExecutionMode,
QueueMode,
AgentState,
# Agent
Agent,
AgentOptions,
# AgentLoop
AgentLoopConfig,
agentLoop,
agentLoopContinue,
runAgentLoop,
runAgentLoopContinue,
# AgentHarness
AgentHarness,
AgentHarnessOptions,
AgentHarnessEvent,
AgentHarnessResources,
AgentHarnessSystemPrompt,
# Session
Session,
SessionStorage,
SessionRepo,
JsonlSessionStorage,
JsonlSessionRepo,
InMemorySessionStorage,
InMemorySessionRepo,
# Tools
createBashTool,
createReadTool,
createWriteTool,
createEditTool,
ExecutionEnv,
# Compaction
compact,
prepareCompaction,
DEFAULT_COMPACTION_SETTINGS,
generateSummary,
generateBranchSummary,
# Utils
truncateHead,
truncateTail,
formatSize,
DEFAULT_MAX_LINES,
DEFAULT_MAX_BYTES,
# Messages
convertToLlm,
bashExecutionToText,
# System prompt
formatSkillsForSystemPrompt,
# Skills
loadSkills,
formatSkillInvocation,
# Prompt templates
loadPromptTemplates,
formatPromptTemplateInvocation,
parseCommandArgs,
substituteArgs,
# Proxy
streamProxy,
ProxyStreamOptions,
# Stream
setDefaultStreamFn,
getDefaultStreamFn,
# Utility functions
uuidv7,
create_timestamp
end
-537
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@@ -1,537 +0,0 @@
https://github.com/andyz245/LanguageAgentTreeSearch/blob/main/hotpot/hotpot.py
standard_prompt = '''
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
'''
reflection_prompt = '''You are an advanced reasoning agent that can improve based on self refection. You will be given a previous reasoning trial in which you were given access to an Docstore API environment and a question to answer. You were unsuccessful in answering the question either because you guessed the wrong answer with Finish[<answer>], or you used up your set number of reasoning steps. In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. Use complete sentences.
Here are some examples:
Previous Trial:
Question: The Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of what?
Thought 1: I need to search Rome Protocols, find the three Prime Ministers, then find what they were assassinated as part of.
Action 1: Search[Rome Protocols]
Observation 1: The Rome Protocols were a series of three international agreements concluded in Rome on 17 March 1934 between the governments of Austria, Hungary and Italy. They were signed by Italian Prime Minister Benito Mussolini, Austrian Chancellor Engelbert Dollfuss and Hungarian Prime Minister Gyula Gömbös. All of the protocols went into effect on 12 July 1934 and were registered in League of Nations Treaty Series on 12 December 1934.
Thought 2: The three Prime Ministers were Benito Mussolini, Engelbert Dollfuss, and Gyula Gömbös. I need to search Benito Mussolini and find what he was assassinated as part of.
Action 2: Search[Benito Mussolini]
Observation 2: Benito Amilcare Andrea Mussolini (UK: MU(U)SS-ə-LEE-nee, US: MOOSS-, Italian: [beˈniːto aˈmilkare anˈdrɛːa mussoˈliːni]; 29 July 1883 28 April 1945) was an Italian politician and journalist who founded and led the National Fascist Party (PNF). He was Prime Minister of Italy from the March on Rome in 1922 until his deposition in 1943, as well as "Duce" of Italian fascism from the establishment of the Italian Fasces of Combat in 1919 until his summary execution in 1945 by Italian partisans. As dictator of Italy and principal founder of fascism, Mussolini inspired and supported the international spread of fascist movements during the inter-war period.Mussolini was originally a socialist politician and a journalist at the Avanti! newspaper. In 1912, he became a member of the National Directorate of the Italian Socialist Party (PSI), but he was expelled from the PSI for advocating military intervention in World War I, in opposition to the party's stance on neutrality. In 1914, Mussolini founded a new journal, Il Popolo d'Italia, and served in the Royal Italian Army during the war until he was wounded and discharged in 1917. Mussolini denounced the PSI, his views now centering on Italian nationalism instead of socialism, and later founded the fascist movement which came to oppose egalitarianism and class conflict, instead advocating "revolutionary nationalism" transcending class lines. On 31 October 1922, following the March on Rome (2830 October), Mussolini was appointed prime minister by King Victor Emmanuel III, becoming the youngest individual to hold the office up to that time. After removing all political opposition through his secret police and outlawing labor strikes, Mussolini and his followers consolidated power through a series of laws that transformed the nation into a one-party dictatorship. Within five years, Mussolini had established dictatorial authority by both legal and illegal means and aspired to create a totalitarian state. In 1929, Mussolini signed the Lateran Treaty with the Holy See to establish Vatican City.
Mussolini's foreign policy aimed to restore the ancient grandeur of the Roman Empire by expanding Italian colonial possessions and the fascist sphere of influence. In the 1920s, he ordered the Pacification of Libya, instructed the bombing of Corfu over an incident with Greece, established a protectorate over Albania, and incorporated the city of Fiume into the Italian state via agreements with Yugoslavia. In 1936, Ethiopia was conquered following the Second Italo-Ethiopian War and merged into Italian East Africa (AOI) with Eritrea and Somalia. In 1939, Italian forces annexed Albania. Between 1936 and 1939, Mussolini ordered the successful Italian military intervention in Spain in favor of Francisco Franco during the Spanish Civil War. Mussolini's Italy initially tried to avoid the outbreak of a second global war, sending troops at the Brenner Pass to delay Anschluss and taking part in the Stresa Front, the Lytton Report, the Treaty of Lausanne, the Four-Power Pact and the Munich Agreement. However, Italy then alienated itself from Britain and France by aligning with Germany and Japan. Germany invaded Poland on 1 September 1939, resulting in declarations of war by France and the UK and the start of World War II.
On 10 June 1940, Mussolini decided to enter the war on the Axis side. Despite initial success, the subsequent Axis collapse on multiple fronts and eventual Allied invasion of Sicily made Mussolini lose the support of the population and members of the Fascist Party. As a consequence, early on 25 July 1943, the Grand Council of Fascism passed a motion of no confidence in Mussolini; later that day King Victor Emmanuel III dismissed him as head of government and had him placed in custody, appointing Pietro Badoglio to succeed him as Prime Minister. After the king agreed to an armistice with the Allies, on 12 September 1943 Mussolini was rescued from captivity in the Gran Sasso raid by German paratroopers and Waffen-SS commandos led by Major Otto-Harald Mors. Adolf Hitler, after meeting with the rescued former dictator, then put Mussolini in charge of a puppet regime in northern Italy, the Italian Social Republic (Italian: Repubblica Sociale Italiana, RSI), informally known as the Salò Republic, causing a civil war. In late April 1945, in the wake of near total defeat, Mussolini and his mistress Clara Petacci attempted to flee to Switzerland, but both were captured by Italian communist partisans and summarily executed by firing squad on 28 April 1945 near Lake Como. The bodies of Mussolini and his mistress were then taken to Milan, where they were hung upside down at a service station to publicly confirm their demise.
Thought 3: Benito Mussolini was assassinated as part of the collapse of the Axis powers during World War II. So the Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of World War II.
Action 3: Finish[World War II]
Reflection: I searched one of the prime ministers involved in the signing, then attemted to answer right away. I should have searched each of the prime ministers, then looked up 'death' on each of their pages in order to get more information before answering.
Previous Trial:
Question: Kam Heskin plays Paige Morgan in a 2004 film directed by who?
Thought 1: I need to search Kam Heskin and Paige Morgan, find the 2004 film they are in, then find the director of the film.
Action 1: Search[Kam Heskin]
Observation 1: Kam Erika Heskin (born May 8, 1973) is an American actress. She began her career playing Caitlin Deschanel on the NBC daytime soap opera Sunset Beach (19981999), before appearing in films Planet of the Apes (2001) and Catch Me If You Can (2002). Heskin went on in 2003 to play Elizabeth Bennet in an independent film Pride & Prejudice: A Latter-Day Comedy, Robin in Charmed (2004), and Paige Morgan in The Prince and Me film franchise (20062010).
Thought 2: Kam Heskin plays Paige Morgan in The Prince and Me film franchise. I need to search The Prince and Me film franchise and find the director of the 2004 film.
Action 2: Search[The Prince and Me film franchise]
Observation 2: Could not find [The Prince and Me film franchise]. Similar: ['Kam Heskin', 'Prince of Persia', 'Harry Potter and the Half-Blood Prince (film)', 'Prince of Persia: The Sands of Time (film)', 'The Chronicles of Narnia (film series)', 'Majestic Prince (franchise)', 'Kingsman (franchise)', 'The Little Prince (2015 film)', 'Shrek (franchise)', 'Harry Potter (film series)']
Thought 3: I need to search The Prince and Me (2004 film) instead.
Action 3: Search[The Prince and Me (2004 film)]
Observation 3: Could not find [The Prince and Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Kam Heskin', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince of Persia', 'Brooklynn Prince', 'Prince (musician)']
Thought 4: I need to search The Prince & Me (2004 film).
Action 4: Search[The Prince & Me (2004 film)]
Observation 4: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Thought 5: I need to search The Prince & Me (2004 film) instead.
Action 5: Search[The Prince & Me (2004 film)]
Observation 5: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Thought 6: I need to search The Prince & Me (2004 film).
Action 6: Search[The Prince & Me (2004 film)]
Observation 6: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Reflection: I got stuck in a loop where I kept trying to search 'The Prince & Me (2004 film)' but the page could not be found. Instead I should have tried to search the similar results that had a similar name to see and they were made in 2004.
Previous Trial:
Question: The Oberoi family is part of a hotel company that has a head office in what city?
Thought 1: I need to search the Oberoi family and find the city where the hotel company's head office is located.
Action 1: Search[Oberoi family]
Observation 1: The Oberoi Group is a luxury hotel group with its head office in New Delhi, India.[2] Founded in 1934, the company owns and operates 32 luxury hotels and two river cruise ships in 7 countries, primarily under its Oberoi Hotels & Resorts and Trident brands.[3] The group also operates The Oberoi Centre for Learning and Development, which is regarded as one of Asia's top institutions for hospitality education.[4]. The foundations of the Oberoi Group date back to 1934 when The Rai Bahadur Mohan Singh Oberoi, the founder of the group, bought two properties: the Maidens in Delhi and the Clarke's in Shimla.[5] In the following years Oberoi, assisted by his two sons, Tilak Raj Singh Oberoi and Prithvi Raj Singh Oberoi (P.R.S. Oberoi
Thought 2: The Oberoi Group has its head office in New Delhi, India. I just need to finish with New Delhi.
Action 2: Finish[New Delhi]
Observation 2: Episode finished, reward = 0
Reflection: I appeared to have retrieved the correct information about The Oberoi Family and the location of it's head office, and provided a corresponding answer. However this answer does not exactly match the ground truth answer so I should try a different wording, such as Delhi.
Previous trial:
{trajectory}Reflection:'''
cot_prompt = '''
Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Observation 1: Guitars for Wounded Warriors is the sixth solo studio album by guitarist Brian Tarquin, released in August 2014 by Cleopatra Records. In 2016, it received a Best Compilation Album nomination by the Independent Music Awards. All tracks were recorded at Tarquin's Jungle Room Studios in New Paltz (village), New York. Being moved by the lack of support for returning veterans through his life Tarquin decided to compose a heavy instrumental rock album as a way to show his appreciation to all veterans. So once again he enlisted top notch players to guest with him on the album, Billy Sheehan on Black Hawk, Ron \"Bumblefoot\" Thal and Reb Beach on Taliban Terror, Steve Morse on Freedom, Chris Poland on Charlie Surfs and Hunting, Chuck Loeb on Escape Kabul, Hal Lindes on Sand & Blood, Gary Hoey on 5 Klicks To Hell and Baghdad, Alex De Rosso Dokken on Broken Arrow, and The Flyin' Ryan Brothers on Surgical Strike. The entire album was engineered, produced and composed by Tarquin especially for each one of the guest guitarists. Partial proceeds are donated to the Fisher House Foundation from sales.
Thought 2: Guitars for Wounded Warriors was recorded in New Paltz (village), New York. I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Observation 2: New Paltz is a village in Ulster County located in the U.S. state of New York. It is approximately 80 miles (130 km) north of New York City and 70 miles (110 km) south of Albany. The population was 7,324 at the 2020 census.[3]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''
cot_prompt_short = '''
Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''
cot_prompt_feedback_short = '''You are also an advanced reasoning agent that can improve based on self refection. Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
You have attempted to answer the following question before and failed. The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.
{trajectories}
{input}
'''
cot_prompt_feedback = '''You are also an advanced reasoning agent that can improve based on self refection. Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Observation 1: Guitars for Wounded Warriors is the sixth solo studio album by guitarist Brian Tarquin, released in August 2014 by Cleopatra Records. In 2016, it received a Best Compilation Album nomination by the Independent Music Awards. All tracks were recorded at Tarquin's Jungle Room Studios in New Paltz (village), New York. Being moved by the lack of support for returning veterans through his life Tarquin decided to compose a heavy instrumental rock album as a way to show his appreciation to all veterans. So once again he enlisted top notch players to guest with him on the album, Billy Sheehan on Black Hawk, Ron \"Bumblefoot\" Thal and Reb Beach on Taliban Terror, Steve Morse on Freedom, Chris Poland on Charlie Surfs and Hunting, Chuck Loeb on Escape Kabul, Hal Lindes on Sand & Blood, Gary Hoey on 5 Klicks To Hell and Baghdad, Alex De Rosso Dokken on Broken Arrow, and The Flyin' Ryan Brothers on Surgical Strike. The entire album was engineered, produced and composed by Tarquin especially for each one of the guest guitarists. Partial proceeds are donated to the Fisher House Foundation from sales.
Thought 2: Guitars for Wounded Warriors was recorded in New Paltz (village), New York. I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Observation 2: New Paltz is a village in Ulster County located in the U.S. state of New York. It is approximately 80 miles (130 km) north of New York City and 70 miles (110 km) south of Albany. The population was 7,324 at the 2020 census.[3]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
You have attempted to answer the following question before and failed, either because your reasoning for the answer was incorrect or the phrasing of your response did not exactly match the answer. The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.
{trajectories}
When providing the thought and action for the current trial, that into account these failed trajectories and make sure not to repeat the same mistakes and incorrect answers.
{input}
'''
vote_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a list of trajectories, decide which trajectory is most promising. Analyze each trajectory in detail and consider possible errors, then conclude in the last line "The best trajectory is {s}", where s the integer id of the trajectory.
'''
compare_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Briefly analyze the correctness of the following two trajectories. Conclude in the last line "The more correct trajectory is 1", "The more correct trajectory is 2", or "The two trajectories are similarly correct".
'''
score_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, analyze the following trajectory, then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
'''
value_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
Thus the correctness score is 3
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
Thus the correctness score is 4
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
Thus the correctness score is 10
{input}
'''
value_prompt_feedback = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
Thus the correctness score is 10
{trajectories}
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
Thus the correctness score is 10
{input}
'''
value_prompt_reasoning = '''You are an advanced reasoning agent that can improve based on self refection. Analyze the trajectories of your previous solutions to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners of the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
This trajectory is correct as it is reasonable to search for the first magazine provided in the question. It is also better to have simple searches corresponding to a single entity, making this the best action.
Thus the correctness score is 10
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
This trajectory is mostly incorrect as I was not able to retrieve the relevant information to answer the question. The searches are too broad and not relevant to the specific player recruited, David Beckham. The last two actions are also too similar. It would be better to first search for David Beckham and lookup information related to his manager.
Thus the correctness score is 4
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{input}
'''
value_prompt_reasoning_feedback = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners it is reasonable to checkof the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
This trajectory is correct as it is reasonable to search for the first magazine provided in the question. It is also better to have simple searches corresponding to a single entity, making this the best action.
Thus the correctness score is 10
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 188687 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 202223 season, Manchester United have had 23 full-time managers.
This trajectory is mostly incorrect as I was not able to retrieve the relevant information to answer the question. The searches are too broad and not relevant to the specific player recruited, David Beckham. The last two actions are also too similar. It would be better to first search for David Beckham and lookup information related to his manager.
Thus the correctness score is 4
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{trajectories}
{input}
'''
value_prompt_reasoning_feedback_short = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners of the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{trajectories}
{input}
'''
rap_prompt = '''
Solve a question answering task with interleaving Thought and Action steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Thought 2: Then I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Thought 2: I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Thought 2: I can look up "named after" for finding the specific individual Milhouse is named after.
Action 2: Lookup[named after]
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''
-47
View File
@@ -1,47 +0,0 @@
module YiemAgent
# export agent
""" Order by dependencies of each file. The 1st included file must not depend on any other
files and each file can only depend on the file included before it.
"""
include("type.jl")
using .type
include("util.jl")
using .util
include("llmfunction.jl")
using .llmfunction
include("interface.jl")
using .interface
# ---------------------------------------------- 100 --------------------------------------------- #
end # module YiemAgent_v1
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"""
agent.jl - High-level Agent struct
This module implements the high-level Agent wrapper around the low-level agent loop,
providing state management, event streaming, and queueing for steering and follow-up messages.
"""
module Agent
using ..Types: *
using ..AgentLoop: *
using ..StreamFn: *
# ============================================================================
# Default convertToLlm function
# ============================================================================
function defaultConvertToLlm(messages::Vector{AgentMessage})::Vector{Message}
return filter(
(m) -> m.role == "user" || m.role == "assistant" || m.role == "toolResult",
messages,
)
end
# ============================================================================
# Empty usage constant
# ============================================================================
const EMPTY_USAGE = Usage(
0, 0, 0, 0, 0, UsageCost(0.0, 0.0, 0.0, 0.0, 0.0)
)
# ============================================================================
# Pending message queue
# ============================================================================
mutable struct PendingMessageQueue
messages::Vector{AgentMessage}
mode::QueueMode
function PendingMessageQueue(mode::QueueMode)
new(AgentMessage[], mode)
end
end
function enqueue!(queue::PendingMessageQueue, message::AgentMessage)
push!(queue.messages, message)
end
function hasItems(queue::PendingMessageQueue)::Bool
return !isempty(queue.messages)
end
function drain(queue::PendingMessageQueue)::Vector{AgentMessage}
if queue.mode == QUEUE_ALL
result = copy(queue.messages)
empty!(queue.messages)
return result
else
if isempty(queue.messages)
return AgentMessage[]
end
first = popfirst!(queue.messages)
return [first]
end
end
function clear!(queue::PendingMessageQueue)
empty!(queue.messages)
end
# ============================================================================
# Active run state
# ============================================================================
mutable struct ActiveRun
promise::Promise
abort_controller::Base.Atomic{Union{Base.AbstractLock, Nothing}}
end
# ============================================================================
# Agent struct
# ============================================================================
mutable struct Agent
_state::AgentState
listeners::Set{Tuple{Function, Ref{Bool}}}
steering_queue::PendingMessageQueue
follow_up_queue::PendingMessageQueue
convert_to_llm::Function
transform_context::Union{Function, Nothing}
stream_function::StreamFn
get_api_key::Union{Function, Nothing}
on_payload::Union{Function, Nothing}
on_response::Union{Function, Nothing}
before_tool_call::Union{Function, Nothing}
after_tool_call::Union{Function, Nothing}
prepare_next_turn::Union{Function, Nothing}
prepare_next_turn_with_context::Union{Function, Nothing}
active_run::Union{ActiveRun, Nothing}
session_id::Union{String, Nothing}
thinking_budgets::Union{Dict{String, Int64}, Nothing}
transport::String
max_retry_delay_ms::Union{Int64, Nothing}
tool_execution::ToolExecutionMode
function Agent(options::Dict{Symbol, Any}=Dict{Symbol, Any}())
runtime_options = merge(
Dict{Symbol, Any}(
:stream_fn => getDefaultStreamFn(),
:convertToLlm => defaultConvertToLlm,
:steeringMode => QUEUE_ONE_AT_A_TIME,
:followUpMode => QUEUE_ONE_AT_A_TIME,
:toolExecution => EXECUTION_PARALLEL,
:transport => "auto",
),
options,
)
state = AgentState(
get(runtime_options, :systemPrompt, ""),
get(runtime_options, :model, Model("", "", "unknown", "unknown", "", false, String[], ModelCost(0.0, 0.0, 0.0, 0.0), 0, 0)),
get(runtime_options, :thinkingLevel, THINKING_OFF),
get(runtime_options, :tools, AgentTool[]),
get(runtime_options, :messages, AgentMessage[]),
)
new(
state,
Set{Tuple{Function, Ref{Bool}}}(),
PendingMessageQueue(QUEUE_ONE_AT_A_TIME),
PendingMessageQueue(QUEUE_ONE_AT_A_TIME),
get(runtime_options, :convertToLlm, defaultConvertToLlm),
get(runtime_options, :transformContext, nothing),
get(runtime_options, :stream_fn, getDefaultStreamFn()),
get(runtime_options, :getApiKey, nothing),
get(runtime_options, :onPayload, nothing),
get(runtime_options, :onResponse, nothing),
get(runtime_options, :beforeToolCall, nothing),
get(runtime_options, :afterToolCall, nothing),
get(runtime_options, :prepareNextTurn, nothing),
get(runtime_options, :prepareNextTurnWithContext, nothing),
nothing,
get(runtime_options, :sessionId, nothing),
get(runtime_options, :thinkingBudgets, nothing),
get(runtime_options, :transport, "auto"),
get(runtime_options, :maxRetryDelayMs, nothing),
get(runtime_options, :toolExecution, EXECUTION_PARALLEL),
)
end
end
# ============================================================================
# Agent methods
# ============================================================================
"""
subscribe(agent, listener)
Subscribe to agent lifecycle events.
# Arguments
- `agent`: The agent instance
- `listener`: A function that takes (event::AgentEvent, signal::AbortSignal)
# Returns
- A function that unsubscribes the listener
"""
function subscribe(agent::Agent, listener::Function)::Function
push!(agent.listeners, (listener, Ref{Bool}(true)))
return () -> begin
filter!(x -> x[1] != listener, agent.listeners)
end
end
"""
get_state(agent)
Get the current agent state.
"""
function get_state(agent::Agent)::AgentState
return agent._state
end
"""
steer(agent, message)
Queue a message to be injected after the current assistant turn finishes.
"""
function steer(agent::Agent, message::AgentMessage)
enqueue!(agent.steering_queue, message)
end
"""
followUp(agent, message)
Queue a message to run only after the agent would otherwise stop.
"""
function followUp(agent::Agent, message::AgentMessage)
enqueue!(agent.follow_up_queue, message)
end
"""
clearSteeringQueue(agent)
Remove all queued steering messages.
"""
function clearSteeringQueue(agent::Agent)
clear!(agent.steering_queue)
end
"""
clearFollowUpQueue(agent)
Remove all queued follow-up messages.
"""
function clearFollowUpQueue(agent::Agent)
clear!(agent.follow_up_queue)
end
"""
clearAllQueues(agent)
Remove all queued steering and follow-up messages.
"""
function clearAllQueues(agent::Agent)
clearSteeringQueue(agent)
clearFollowUpQueue(agent)
end
"""
hasQueuedMessages(agent)
Returns true when either queue still contains pending messages.
"""
function hasQueuedMessages(agent::Agent)::Bool
return hasItems(agent.steering_queue) || hasItems(agent.follow_up_queue)
end
"""
abort(agent)
Abort the current run, if one is active.
"""
function abort(agent::Agent)
if !isnothing(agent.active_run)
# TODO: Implement abort signal
end
end
"""
waitForIdle(agent)
Resolve when the current run and all awaited event listeners have finished.
"""
function waitForIdle(agent::Agent)::Promise
if isnothing(agent.active_run)
return Promise()
end
return agent.active_run.promise
end
"""
reset(agent)
Clear transcript state, runtime state, and queued messages.
"""
function reset!(agent::Agent)
agent._state.messages = AgentMessage[]
agent._state.is_streaming = false
agent._state.streaming_message = nothing
agent._state.pending_tool_calls = Set{String}()
agent._state.error_message = nothing
clearFollowUpQueue(agent)
clearSteeringQueue(agent)
end
"""
prompt(agent, input[, images])
Start a new prompt from text, a single message, or a batch of messages.
"""
function prompt(agent::Agent, input::Union{String, AgentMessage, Vector{AgentMessage}}, images::Vector{ImageContent}=ImageContent[])::Nothing
if !isnothing(agent.active_run)
throw(ErrorException(
"Agent is already processing a prompt. Use steer() or followUp() to queue messages, or wait for completion."
))
end
messages = normalizePromptInput(agent, input, images)
runPromptMessages(agent, messages)
end
function normalizePromptInput(agent::Agent, input::Vector{AgentMessage}, images::Vector{ImageContent})::Vector{AgentMessage}
return input
end
function normalizePromptInput(agent::Agent, input::AgentMessage, images::Vector{ImageContent})::Vector{AgentMessage}
return [input]
end
function normalizePromptInput(agent::Agent, input::String, images::Vector{ImageContent})::Vector{AgentMessage}
content::Vector{MessageContent} = [TextContent(input)]
if !isempty(images)
append!(content, images)
end
return [UserMessage("user", content, Int64(Dates.now(Dates.UTC).datetime))]
end
function runPromptMessages(agent::Agent, messages::Vector{AgentMessage})::Nothing
# TODO: Implement run with lifecycle
return nothing
end
"""
continue(agent)
Continue from the current transcript. The last message must be a user or tool-result message.
"""
function continue!(agent::Agent)::Nothing
if !isnothing(agent.active_run)
throw(ErrorException("Agent is already processing. Wait for completion before continuing."))
end
last_message = agent._state.messages[end]
if isnothing(last_message)
throw(ErrorException("No messages to continue from"))
end
if last_message.role == "assistant"
queued_steering = drain(agent.steering_queue)
if !isempty(queued_steering)
runPromptMessages(agent, queued_steering)
return nothing
end
queued_follow_ups = drain(agent.follow_up_queue)
if !isempty(queued_follow_ups)
runPromptMessages(agent, queued_follow_ups)
return nothing
end
throw(ErrorException("Cannot continue from message role: assistant"))
end
# TODO: Implement run continuation
return nothing
end
"""
createContextSnapshot(agent)
Create a snapshot of the current context for use in the agent loop.
"""
function createContextSnapshot(agent::Agent)::AgentContext
return AgentContext(
agent._state.system_prompt,
copy(agent._state.messages),
copy(agent._state.tools),
)
end
"""
createLoopConfig(agent, options)
Create the loop configuration for the agent.
"""
function createLoopConfig(agent::Agent, options::Dict{String, Any}=Dict{String, Any}())::AgentLoopConfig
skip_initial_steering_poll = get(options, "skipInitialSteeringPoll", false)
return AgentLoopConfig(
agent._state.model,
agent._state.thinking_level == THINKING_OFF ? nothing : agent._state.thinking_level,
agent.session_id,
agent.on_payload,
agent.on_response,
agent.transport,
agent.thinking_budgets,
agent.max_retry_delay_ms,
agent.tool_execution,
agent.before_tool_call,
agent.after_tool_call,
isnothing(agent.prepare_next_turn_with_context) && isnothing(agent.prepare_next_turn) ? nothing : function(context)
if !isnothing(agent.prepare_next_turn_with_context)
return agent.prepare_next_turn_with_context(context, getSignal(agent))
end
return isnothing(agent.prepare_next_turn) ? nothing : agent.prepare_next_turn(getSignal(agent))
end,
agent.convert_to_llm,
agent.transform_context,
agent.get_api_key,
function()
if skip_initial_steering_poll
skip_initial_steering_poll = false
return AgentMessage[]
end
return drain(agent.steering_queue)
end,
function()
return drain(agent.follow_up_queue)
end,
)
end
"""
getSignal(agent)
Get the active abort signal for the current run, if any.
"""
function getSignal(agent::Agent)::Union{Nothing, Base.Atomic{Bool}}
if isnothing(agent.active_run)
return nothing
end
return agent.active_run.abort_controller
end
end
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"""
agent_loop.jl - Low-level agent loop implementation
This module implements the core agentLoop functionality that works with AgentMessage
throughout, transforming to Message[] only at the LLM call boundary.
"""
module AgentLoop
using ..Types: *
using ..StreamFn: *
# ============================================================================
# Event sink type
# ============================================================================
const AgentEventSink = Function
# ============================================================================
# Main agent loop function
# ============================================================================
function agentLoop(
prompts::Vector{AgentMessage},
context::AgentContext,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
stream_fn::StreamFn,
)::EventStream
stream = createAgentStream()
Threads.@spawn begin
messages = runAgentLoop(
prompts,
context,
config,
(event) -> push!(stream, event),
signal,
stream_fn,
)
end(stream, messages)
end
return stream
end
# ============================================================================
# Continue agent loop function
# ============================================================================
function agentLoopContinue(
context::AgentContext,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
stream_fn::StreamFn,
)::EventStream
if isempty(context.messages)
throw(ErrorException("Cannot continue: no messages in context"))
end
if context.messages[end].role == "assistant"
throw(ErrorException("Cannot continue from message role: assistant"))
end
stream = createAgentStream()
Threads.@spawn begin
messages = runAgentLoopContinue(
context,
config,
(event) -> push!(stream, event),
signal,
stream_fn,
)
end(stream, messages)
end
return stream
end
# ============================================================================
# Run agent loop function
# ============================================================================
function runAgentLoop(
prompts::Vector{AgentMessage},
context::AgentContext,
config::AgentLoopConfig,
emit::AgentEventSink,
signal::Union{Nothing, AbortSignal},
stream_fn::StreamFn,
)::Vector{AgentMessage}
new_messages::Vector{AgentMessage} = copy(prompts)
current_context::AgentContext = AgentContext(
context.system_prompt,
vcat(context.messages, copy(prompts)),
context.tools,
)
emit(AgentStartEvent())
emit(TurnStartEvent())
for prompt in prompts
emit(MessageStartEvent(prompt))
emit(MessageEndEvent(prompt))
end
runLoop(
current_context,
new_messages,
config,
signal,
emit,
stream_fn,
)
return new_messages
end
# ============================================================================
# Run agent loop continue function
# ============================================================================
function runAgentLoopContinue(
context::AgentContext,
config::AgentLoopConfig,
emit::AgentEventSink,
signal::Union{Nothing, AbortSignal},
stream_fn::StreamFn,
)::Vector{AgentMessage}
if isempty(context.messages)
throw(ErrorException("Cannot continue: no messages in context"))
end
if context.messages[end].role == "assistant"
throw(ErrorException("Cannot continue from message role: assistant"))
end
new_messages::Vector{AgentMessage} = []
current_context::AgentContext = context
emit(AgentStartEvent())
emit(TurnStartEvent())
runLoop(
current_context,
new_messages,
config,
signal,
emit,
stream_fn,
)
return new_messages
end
# ============================================================================
# Create agent stream function
# ============================================================================
function createAgentStream()::EventStream
return EventStream(
(event::AgentEvent) -> event isa AgentEndEvent,
(event::AgentEvent) -> event isa AgentEndEvent ? event.messages : AgentMessage[],
)
end
# ============================================================================
# Main loop logic shared by agentLoop and agentLoopContinue
# ============================================================================
function runLoop(
initial_context::AgentContext,
new_messages::Vector{AgentMessage},
initial_config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
stream_function::StreamFn,
)::Nothing
current_context::AgentContext = initial_context
config::AgentLoopConfig = initial_config
first_turn::Bool = true
pending_messages::Vector{AgentMessage} = getSteeringMessages(config) do
get_steering_messages(config)
end
while true
has_more_tool_calls::Bool = true
while has_more_tool_calls || !isempty(pending_messages)
if !first_turn
emit(TurnStartEvent())
else
first_turn = false
end
if !isempty(pending_messages)
for message in pending_messages
emit(MessageStartEvent(message))
emit(MessageEndEvent(message))
push!(current_context.messages, message)
push!(new_messages, message)
end
pending_messages = AgentMessage[]
end
message = streamAssistantResponse(
current_context,
config,
signal,
emit,
stream_function,
)
push!(new_messages, message)
if message.stop_reason in ("error", "aborted")
emit(TurnEndEvent(message, ToolResultMessage[]))
emit(AgentEndEvent(new_messages))
return
end
tool_calls = filter(
(c) -> c isa ToolCall,
message.content,
)
tool_results::Vector{ToolResultMessage} = []
has_more_tool_calls = false
if !isempty(tool_calls)
executed_tool_batch =
message.stop_reason == "length"
? failToolCallsFromTruncatedMessage(tool_calls, emit)
: executeToolCalls(
current_context,
message,
config,
signal,
emit,
)
append!(tool_results, executed_tool_batch.messages)
has_more_tool_calls = !executed_tool_batch.terminate
for result in tool_results
push!(current_context.messages, result)
push!(new_messages, result)
end
end
emit(TurnEndEvent(message, tool_results))
next_turn_context = PrepareNextTurnContext(
message,
tool_results,
current_context,
new_messages,
)
next_turn_snapshot = prepare_next_turn(config, next_turn_context)
if !isnothing(next_turn_snapshot)
current_context = next_turn_snapshot.context
config = AgentLoopConfig(
model = next_turn_snapshot.model,
reasoning = next_turn_snapshot.thinking_level,
convert_to_llm = config.convert_to_llm,
transform_context = config.transform_context,
get_api_key = config.get_api_key,
should_stop_after_turn = config.should_stop_after_turn,
prepare_next_turn = config.prepare_next_turn,
get_steering_messages = config.get_steering_messages,
get_follow_up_messages = config.get_follow_up_messages,
tool_execution = config.tool_execution,
before_tool_call = config.before_tool_call,
after_tool_call = config.after_tool_call,
max_tokens = config.max_tokens,
temperature = config.temperature,
reasoning = config.reasoning,
cache_retention = config.cache_retention,
session_id = config.session_id,
headers = config.headers,
metadata = config.metadata,
transport = config.transport,
signal = signal,
api_key = config.api_key,
on_payload = config.on_payload,
on_response = config.on_response,
max_retry_delay_ms = config.max_retry_delay_ms,
)
end
if should_stop_after_turn(config, next_turn_context)
emit(AgentEndEvent(new_messages))
return
end
pending_messages = getSteeringMessages(config) do
get_steering_messages(config)
end
end
follow_up_messages = getFollowUpMessages(config) do
get_follow_up_messages(config)
end
if !isempty(follow_up_messages)
pending_messages = follow_up_messages
continue
end
break
end
emit(AgentEndEvent(new_messages))
end
# ============================================================================
# Helper types
# ============================================================================
struct PrepareNextTurnContext
message::AssistantMessage
tool_results::Vector{ToolResultMessage}
context::AgentContext
new_messages::Vector{AgentMessage}
end
struct AgentLoopTurnUpdate
context::Union{AgentContext, Nothing}
model::Union{Model, Nothing}
thinking_level::Union{ThinkingLevel, Nothing}
end
# ============================================================================
# Helper functions for getting messages from queues
# ============================================================================
macro getSteeringMessages(config)
:(get_steering_messages($(esc(config))))
end
macro getFollowUpMessages(config)
:(get_follow_up_messages($(esc(config))))
end
function get_steering_messages(config::AgentLoopConfig)::Vector{AgentMessage}
return isnothing(config.get_steering_messages) ? AgentMessage[] : config.get_steering_messages()
end
function get_follow_up_messages(config::AgentLoopConfig)::Vector{AgentMessage}
return isnothing(config.get_follow_up_messages) ? AgentMessage[] : config.get_follow_up_messages()
end
function prepare_next_turn(config::AgentLoopConfig, context::PrepareNextTurnContext)::Union{AgentLoopTurnUpdate, Nothing}
return isnothing(config.prepare_next_turn) ? nothing : config.prepare_next_turn(context)
end
function should_stop_after_turn(config::AgentLoopConfig, context::PrepareNextTurnContext)::Bool
return isnothing(config.should_stop_after_turn) ? false : config.should_stop_after_turn(context)
end
# ============================================================================
# Stream assistant response function
# ============================================================================
function streamAssistantResponse(
context::AgentContext,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
stream_function::StreamFn,
)::AssistantMessage
messages::Vector{AgentMessage} = context.messages
if !isnothing(config.transform_context)
messages = config.transform_context(messages, signal)
end
llm_messages::Vector{Message} = config.convert_to_llm(messages)
llm_context::Context = Context(
context.system_prompt,
llm_messages,
context.tools,
)
resolved_api_key::Union{String, Nothing} =
!isnothing(config.get_api_key)
? config.get_api_key(config.model.provider)
: nothing
response = stream_function(
config.model,
llm_context,
merge(
config,
Dict(:apiKey => resolved_api_key, :signal => signal),
),
)
partial_message::Union{AssistantMessage, Nothing} = nothing
added_partial::Bool = false
for event in response
if event.type == "start"
partial_message = event.partial
push!(context.messages, partial_message)
added_partial = true
emit(MessageStartEvent(copy(partial_message)))
elseif event.type in ("text_start", "text_delta", "text_end", "thinking_start", "thinking_delta", "thinking_end", "toolcall_start", "toolcall_delta", "toolcall_end")
if !isnothing(partial_message)
partial_message = event.partial
context.messages[end] = partial_message
emit(MessageUpdateEvent(copy(partial_message), event))
end
elseif event.type in ("done", "error")
final_message = response.result()
if added_partial
context.messages[end] = final_message
else
push!(context.messages, final_message)
end
if !added_partial
emit(MessageStartEvent(copy(final_message)))
end
emit(MessageEndEvent(final_message))
return final_message
end
end
final_message = response.result()
if added_partial
context.messages[end] = final_message
else
push!(context.messages, final_message)
emit(MessageStartEvent(copy(final_message)))
end
emit(MessageEndEvent(final_message))
return final_message
end
# ============================================================================
# Fail tool calls from truncated message
# ============================================================================
struct ExecutedToolCallBatch
messages::Vector{ToolResultMessage}
terminate::Bool
end
function failToolCallsFromTruncatedMessage(
tool_calls::Vector{ToolCall},
emit::AgentEventSink,
)::ExecutedToolCallBatch
messages::Vector{ToolResultMessage} = []
for tool_call in tool_calls
emit(ToolExecutionStartEvent(tool_call.id, tool_call.name, tool_call.arguments))
finalized = FinalizedToolCallOutcome(
tool_call,
createErrorToolResult(
"Tool call \"$(tool_call.name)\" was not executed: the response hit the output token limit, so its arguments may be truncated. Re-issue the tool call with complete arguments.",
),
true,
)
emitToolExecutionEnd(finalized, emit)
tool_result_message = createToolResultMessage(finalized)
emitToolResultMessage(tool_result_message, emit)
push!(messages, tool_result_message)
end
return ExecutedToolCallBatch(messages, false)
end
# ============================================================================
# Execute tool calls
# ============================================================================
function executeToolCalls(
current_context::AgentContext,
assistant_message::AssistantMessage,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
)::ExecutedToolCallBatch
tool_calls = filter(
(c) -> c isa ToolCall,
assistant_message.content,
)
has_sequential_tool_call = any(
(tc) -> begin
tool = findfirst((t) -> t.name == tc.name, current_context.tools)
!isnothing(tool) && tool.execution_mode == EXECUTION_SEQUENTIAL
end,
tool_calls,
)
if config.tool_execution == EXECUTION_SEQUENTIAL || has_sequential_tool_call
return executeToolCallsSequential(
current_context,
assistant_message,
tool_calls,
config,
signal,
emit,
)
end
return executeToolCallsParallel(
current_context,
assistant_message,
tool_calls,
config,
signal,
emit,
)
end
# ============================================================================
# Execute tool calls sequentially
# ============================================================================
function executeToolCallsSequential(
current_context::AgentContext,
assistant_message::AssistantMessage,
tool_calls::Vector{ToolCall},
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
)::ExecutedToolCallBatch
finalized_calls::Vector{FinalizedToolCallOutcome} = []
messages::Vector{ToolResultMessage} = []
for tool_call in tool_calls
emit(ToolExecutionStartEvent(tool_call.id, tool_call.name, tool_call.arguments))
preparation = prepareToolCall(current_context, assistant_message, tool_call, config, signal)
finalized = if preparation.kind == "immediate"
FinalizedToolCallOutcome(tool_call, preparation.result, preparation.is_error)
else
executed = executePreparedToolCall(preparation, signal, emit)
finalizeExecutedToolCall(
current_context,
assistant_message,
preparation,
executed,
config,
signal,
)
end
emitToolExecutionEnd(finalized, emit)
tool_result_message = createToolResultMessage(finalized)
emitToolResultMessage(tool_result_message, emit)
push!(finalized_calls, finalized)
push!(messages, tool_result_message)
if !isnothing(signal) && signal.aborted
break
end
end
return ExecutedToolCallBatch(messages, shouldTerminateToolBatch(finalized_calls))
end
# ============================================================================
# Execute tool calls in parallel
# ============================================================================
function executeToolCallsParallel(
current_context::AgentContext,
assistant_message::AssistantMessage,
tool_calls::Vector{ToolCall},
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
)::ExecutedToolCallBatch
finalized_calls::Vector{Union{FinalizedToolCallOutcome, Function}} = []
for tool_call in tool_calls
emit(ToolExecutionStartEvent(tool_call.id, tool_call.name, tool_call.arguments))
preparation = prepareToolCall(current_context, assistant_message, tool_call, config, signal)
if preparation.kind == "immediate"
finalized = FinalizedToolCallOutcome(
tool_call,
preparation.result,
preparation.is_error,
)
emitToolExecutionEnd(finalized, emit)
push!(finalized_calls, finalized)
if !isnothing(signal) && signal.aborted
break
end
continue
end
push!(finalized_calls, () -> begin
executed = executePreparedToolCall(preparation, signal, emit)
finalized = finalizeExecutedToolCall(
current_context,
assistant_message,
preparation,
executed,
config,
signal,
)
emitToolExecutionEnd(finalized, emit)
return finalized
end)
if !isnothing(signal) && signal.aborted
break
end
end
ordered_finalized_calls = map(
(entry) -> if entry isa Function
entry()
else
entry
end,
finalized_calls,
)
messages::Vector{ToolResultMessage} = []
for finalized in ordered_finalized_calls
tool_result_message = createToolResultMessage(finalized)
emitToolResultMessage(tool_result_message, emit)
push!(messages, tool_result_message)
end
return ExecutedToolCallBatch(messages, shouldTerminateToolBatch(ordered_finalized_calls))
end
# ============================================================================
# Prepared tool call types
# ============================================================================
struct PreparedToolCall
kind::String
tool_call::ToolCall
tool::AgentTool
args::Any
end
struct ImmediateToolCallOutcome
kind::String
result::AgentToolResultMutable
is_error::Bool
end
struct ExecutedToolCallOutcome
result::AgentToolResultMutable
is_error::Bool
end
struct FinalizedToolCallOutcome
tool_call::ToolCall
result::AgentToolResultMutable
is_error::Bool
end
# ============================================================================
# Helper functions
# ============================================================================
function shouldTerminateToolBatch(finalized_calls::Vector{FinalizedToolCallOutcome})::Bool
return !isempty(finalized_calls) && all(
(finalized) -> finalized.result.terminate === true,
finalized_calls,
)
end
function prepareToolCallArguments(tool::AgentTool, tool_call::ToolCall)::ToolCall
if isnothing(tool.prepare_arguments)
return tool_call
end
prepared_arguments = tool.prepare_arguments(tool_call.arguments)
if prepared_arguments === tool_call.arguments
return tool_call
end
return ToolCall(
tool_call.type,
tool_call.id,
tool_call.name,
prepared_arguments,
tool_call.partial_json,
)
end
function prepareToolCall(
current_context::AgentContext,
assistant_message::AssistantMessage,
tool_call::ToolCall,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
)::Union{PreparedToolCall, ImmediateToolCallOutcome}
tool = findfirst((t) -> t.name == tool_call.name, current_context.tools)
if isnothing(tool)
return ImmediateToolCallOutcome("immediate", createErrorToolResult("Tool $(tool_call.name) not found"), true)
end
try
prepared_tool_call = prepareToolCallArguments(tool, tool_call)
validated_args = validateToolArguments(tool, prepared_tool_call)
if !isnothing(config.before_tool_call)
before_result = config.before_tool_call(
BeforeToolCallContext(assistant_message, tool_call, validated_args, current_context),
signal,
)
if !isnothing(signal) && signal.aborted
return ImmediateToolCallOutcome("immediate", createErrorToolResult("Operation aborted"), true)
end
if !isnothing(before_result) && before_result.block
reason = isnothing(before_result.reason) ? "Tool execution was blocked" : before_result.reason
return ImmediateToolCallOutcome("immediate", createErrorToolResult(reason), true)
end
end
if !isnothing(signal) && signal.aborted
return ImmediateToolCallOutcome("immediate", createErrorToolResult("Operation aborted"), true)
end
return PreparedToolCall("prepared", tool_call, tool, validated_args)
catch error
return ImmediateToolCallOutcome("immediate", createErrorToolResult(string(error)), true)
end
end
function executePreparedToolCall(
prepared::PreparedToolCall,
signal::Union{Nothing, AbortSignal},
emit::AgentEventSink,
)::ExecutedToolCallOutcome
update_events::Vector{Future} = []
accepting_updates::Bool = true
try
result = prepared.tool.execute(
prepared.tool_call.id,
prepared.args,
signal,
(partial_result) -> begin
if !accepting_updates
return
end
push!(
update_events,
Threads.@spawn begin
emit(
ToolExecutionUpdateEvent(
prepared.tool_call.id,
prepared.tool_call.name,
prepared.tool_call.arguments,
partial_result,
),
)
end,
)
end,
)
accepting_updates = false
wait.(update_events)
return ExecutedToolCallOutcome(result, false)
catch error
accepting_updates = false
wait.(update_events)
return ExecutedToolCallOutcome(createErrorToolResult(string(error)), true)
finally
accepting_updates = false
end
end
function finalizeExecutedToolCall(
current_context::AgentContext,
assistant_message::AssistantMessage,
prepared::PreparedToolCall,
executed::ExecutedToolCallOutcome,
config::AgentLoopConfig,
signal::Union{Nothing, AbortSignal},
)::FinalizedToolCallOutcome
result = executed.result
is_error = executed.is_error
if !isnothing(config.after_tool_call)
try
after_result = config.after_tool_call(
AfterToolCallContext(
assistant_message,
prepared.tool_call,
prepared.args,
result,
is_error,
current_context,
),
signal,
)
if !isnothing(after_result)
result = AgentToolResultMutable(
isnothing(after_result.content) ? result.content : after_result.content,
isnothing(after_result.details) ? result.details : after_result.details,
isnothing(after_result.usage) ? result.usage : after_result.usage,
result.added_tool_names,
isnothing(after_result.terminate) ? result.terminate : after_result.terminate,
)
is_error = isnothing(after_result.is_error) ? is_error : after_result.is_error
end
catch error
result = createErrorToolResult(string(error))
is_error = true
end
end
return FinalizedToolCallOutcome(prepared.tool_call, result, is_error)
end
function createErrorToolResult(message::String)::AgentToolResultMutable
return AgentToolResultMutable([TextContent(message)], Dict{String, Any}(), nothing, nothing, nothing)
end
function emitToolExecutionEnd(finalized::FinalizedToolCallOutcome, emit::AgentEventSink)::Nothing
emit(ToolExecutionEndEvent(
finalized.tool_call.id,
finalized.tool_call.name,
finalized.result,
finalized.is_error,
))
return nothing
end
function createToolResultMessage(finalized::FinalizedToolCallOutcome)::ToolResultMessage
return ToolResultMessage(
"toolResult",
finalized.tool_call.id,
finalized.tool_call.name,
isnothing(finalized.result.content) ? MessageContent[] : finalized.result.content,
finalized.result.details,
finalized.result.usage,
finalized.result.added_tool_names,
finalized.is_error,
Dates.now(Dates.UTC).datetime,
)
end
function emitToolResultMessage(tool_result_message::ToolResultMessage, emit::AgentEventSink)::Nothing
emit(MessageStartEvent(tool_result_message))
emit(MessageEndEvent(tool_result_message))
return nothing
end
# ============================================================================
# Validation helper
# ============================================================================
function validateToolArguments(tool::AgentTool, tool_call::ToolCall)::Any
# Simplified validation - in a full implementation, this would use TypeBox-like validation
return tool_call.arguments
end
end
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"""
messages.jl - Custom message types and LLM conversion
This module provides custom message types and the convertToLlm function.
"""
module Messages
using ..Types: *
const COMPACTION_SUMMARY_PREFIX = """The conversation history before this point was compacted into the following summary:
<summary>
"""
const COMPACTION_SUMMARY_SUFFIX = """
</summary>"""
const BRANCH_SUMMARY_PREFIX = """The following is a summary of a branch that this conversation came back from:
<summary>
"""
const BRANCH_SUMMARY_SUFFIX = """</summary>"""
# ============================================================================
# Custom message types
# ============================================================================
mutable struct BashExecutionMessage
role::String
command::String
output::String
exit_code::Union{Int64, Nothing}
cancelled::Bool
truncated::Bool
full_output_path::Union{String, Nothing}
timestamp::Timestamp
exclude_from_context::Bool
end
mutable struct CustomMessage{T}
role::String
custom_type::String
content::Union{String, Vector{MessageContent}}
display::Bool
details::Union{T, Nothing}
timestamp::Timestamp
end
mutable struct BranchSummaryMessage
role::String
summary::String
from_id::String
timestamp::Timestamp
end
mutable struct CompactionSummaryMessage
role::String
summary::String
tokens_before::Int64
timestamp::Timestamp
end
# ============================================================================
# Bash execution to text conversion
# ============================================================================
function bashExecutionToText(msg::BashExecutionMessage)::String
text = "Ran `$(msg.command)`\n"
if !isempty(msg.output)
text *= "```\n$(msg.output)\n```"
else
text *= "(no output)"
end
if msg.cancelled
text *= "\n\n(command cancelled)"
elseif !isnothing(msg.exit_code) && msg.exit_code != 0
text *= "\n\nCommand exited with code $(msg.exit_code)"
end
if msg.truncated && !isnothing(msg.full_output_path)
text *= "\n\n[Output truncated. Full output: $(msg.full_output_path)]"
end
return text
end
# ============================================================================
# Message creation functions
# ============================================================================
function createBranchSummaryMessage(summary::String, from_id::String, timestamp::String)::BranchSummaryMessage
return BranchSummaryMessage(
"branchSummary",
summary,
from_id,
Int64(Dates.now(Dates.UTC).datetime),
)
end
function createCompactionSummaryMessage(summary::String, tokens_before::Int64, timestamp::String)::CompactionSummaryMessage
return CompactionSummaryMessage(
"compactionSummary",
summary,
tokens_before,
Int64(Dates.now(Dates.UTC).datetime),
)
end
function createCustomMessage(custom_type::String, content::Union{String, Vector{MessageContent}}, display::Bool, details::Union{Any, Nothing}, timestamp::String)::CustomMessage
return CustomMessage(
"custom",
custom_type,
content,
display,
details,
Int64(Dates.now(Dates.UTC).datetime),
)
end
# ============================================================================
# Convert to LLM messages
# ============================================================================
function convertToLlm(messages::Vector{AgentMessage})::Vector{Message}
result::Vector{Message} = Message[]
for m in messages
converted = convertToLlmMessage(m)
if !isnothing(converted)
push!(result, converted)
end
end
return result
end
function convertToLlmMessage(m::BashExecutionMessage)::Union{UserMessage, Nothing}
if m.exclude_from_context
return nothing
end
return UserMessage(
"user",
[TextContent(bashExecutionToText(m))],
m.timestamp,
)
end
function convertToLlmMessage(m::CustomMessage)::Union{UserMessage, Nothing}
content = if m.content isa String
[TextContent(m.content)]
else
m.content
end
return UserMessage("user", content, m.timestamp)
end
function convertToLlmMessage(m::BranchSummaryMessage)::UserMessage
text = BRANCH_SUMMARY_PREFIX * m.summary * BRANCH_SUMMARY_SUFFIX
return UserMessage("user", [TextContent(text)], m.timestamp)
end
function convertToLlmMessage(m::CompactionSummaryMessage)::UserMessage
text = COMPACTION_SUMMARY_PREFIX * m.summary * COMPACTION_SUMMARY_SUFFIX
return UserMessage("user", [TextContent(text)], m.timestamp)
end
function convertToLlmMessage(m::UserMessage)::UserMessage
return m
end
function convertToLlmMessage(m::AssistantMessage)::AssistantMessage
return m
end
function convertToLlmMessage(m::ToolResultMessage)::ToolResultMessage
return m
end
function convertToLlmMessage(m::AgentMessage)::Union{Message, Nothing}
return nothing
end
end
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"""
prompt_templates.jl - Prompt template loading and formatting
This module provides utilities for loading prompt templates and formatting invocations.
"""
module PromptTemplates
using ..Types: *
using ..HarnessTypes: ExecutionEnv, toError, Result, ok, err
# ============================================================================
# Prompt template diagnostic types
# ============================================================================
const PromptTemplateDiagnosticCode = String
const PROMPT_TEMPLATE_DIAGNOSTIC_FILE_INFO_FAILED = "file_info_failed"
const PROMPT_TEMPLATE_DIAGNOSTIC_LIST_FAILED = "list_failed"
const PROMPT_TEMPLATE_DIAGNOSTIC_READ_FAILED = "read_failed"
const PROMPT_TEMPLATE_DIAGNOSTIC_PARSE_FAILED = "parse_failed"
mutable struct PromptTemplateDiagnostic
type::String
code::PromptTemplateDiagnosticCode
message::String
path::String
end
# ============================================================================
# Prompt template frontmatter
# ============================================================================
mutable struct PromptTemplateFrontmatter
description::Union{String, Nothing}
argument_hint::Union{String, Nothing}
extra::Dict{String, Any}
end
# ============================================================================
# Load prompt templates from paths
# ============================================================================
function loadPromptTemplates(
env::ExecutionEnv,
paths::Union{String, Vector{String}},
)::Tuple{Vector{PromptTemplate}, Vector{PromptTemplateDiagnostic}}
prompt_templates::Vector{PromptTemplate} = PromptTemplate[]
diagnostics::Vector{PromptTemplateDiagnostic} = PromptTemplateDiagnostic[]
path_list = if paths isa String
[paths]
else
paths
end
for path in path_list
info_result = fileInfo(env, path, nothing)
if !info_result.ok
if info_result.error.code != "not_found"
push!(diagnostics, PromptTemplateDiagnostic(
"warning",
"file_info_failed",
info_result.error.message,
path,
))
end
continue
end
info = info_result.value
kind = getFileKind(env, info, diagnostics)
if kind == "directory"
result = loadTemplatesFromDir(env, info.path)
append!(prompt_templates, result.prompt_templates)
append!(diagnostics, result.diagnostics)
elseif kind == "file" && endswith(info.name, ".md")
result = loadTemplateFromFile(env, info.path)
if !isnothing(result.prompt_template)
push!(prompt_templates, result.prompt_template)
end
append!(diagnostics, result.diagnostics)
end
end
return prompt_templates, diagnostics
end
function getFileKind(env::ExecutionEnv, info::FileInfo, diagnostics::Vector{PromptTemplateDiagnostic})::Union{String, Nothing}
if info.kind == "file" || info.kind == "directory"
return info.kind
end
canonical_path = canonicalPath(env, info.path, nothing)
if !canonical_path.ok
if canonical_path.error.code != "not_found"
push!(diagnostics, PromptTemplateDiagnostic(
"warning",
"file_info_failed",
canonical_path.error.message,
info.path,
))
end
return nothing
end
target = fileInfo(env, canonical_path.value, nothing)
if !target.ok
if target.error.code != "not_found"
push!(diagnostics, PromptTemplateDiagnostic(
"warning",
"file_info_failed",
target.error.message,
info.path,
))
end
return nothing
end
if target.value.kind == "file" || target.value.kind == "directory"
return target.value.kind
end
return nothing
end
# ============================================================================
# Load templates from directory
# ============================================================================
function loadTemplatesFromDir(
env::ExecutionEnv,
dir::String,
)::Tuple{Vector{PromptTemplate}, Vector{PromptTemplateDiagnostic}}
prompt_templates::Vector{PromptTemplate} = PromptTemplate[]
diagnostics::Vector{PromptTemplateDiagnostic} = PromptTemplateDiagnostic[]
entries_result = listDir(env, dir, nothing)
if !entries_result.ok
push!(diagnostics, PromptTemplateDiagnostic(
"warning",
"list_failed",
entries_result.error.message,
dir,
))
return prompt_templates, diagnostics
end
entries = entries_result.value
for entry in sort(entries, by=e -> e.name)
kind = getFileKind(env, entry, diagnostics)
if kind != "file" || !endswith(entry.name, ".md")
continue
end
result = loadTemplateFromFile(env, entry.path)
if !isnothing(result.prompt_template)
push!(prompt_templates, result.prompt_template)
end
append!(diagnostics, result.diagnostics)
end
return prompt_templates, diagnostics
end
# ============================================================================
# Load template from file
# ============================================================================
function loadTemplateFromFile(
env::ExecutionEnv,
file_path::String,
)::Tuple{Union{PromptTemplate, Nothing}, Vector{PromptTemplateDiagnostic}}
diagnostics::Vector{PromptTemplateDiagnostic} = PromptTemplateDiagnostic[]
raw_content = readTextFile(env, file_path, nothing)
if !raw_content.ok
push!(diagnostics, PromptTemplateDiagnostic(
"warning",
"read_failed",
raw_content.error.message,
file_path,
))
return nothing, diagnostics
end
# TODO: Parse frontmatter
# parsed = parseFrontmatter<PromptTemplateFrontmatter>(rawContent.value);
# if !parsed.ok {
# diagnostics.push({
# type: "warning",
# code: "parse_failed",
# message: parsed.error.message,
# path: filePath,
# });
# return { promptTemplate: null, diagnostics };
# }
# const { frontmatter, body } = parsed.value;
# const firstLine = body.split("\n").find((line) => line.trim());
# let description = typeof frontmatter.description === "string" ? frontmatter.description : "";
# if (!description && firstLine) {
# description = firstLine.slice(0, 60);
# if (firstLine.length > 60) description += "...";
# }
# return {
# promptTemplate: {
# name: basenameEnvPath(filePath).replace(/\.md$/i, ""),
# description,
# content: body,
# },
# diagnostics,
# };
return nothing, diagnostics
end
# ============================================================================
# Parse command arguments
# ============================================================================
function parseCommandArgs(args_string::String)::Vector{String}
args::Vector{String} = String[]
current::String = ""
in_quote::Union{String, Nothing} = nothing
for i in 1:length(args_string)
char = args_string[i]
if !isnothing(in_quote)
if char == in_quote
in_quote = nothing
else
current *= char
end
elseif char == '"' || char == '\''
in_quote = char
elseif char == ' ' || char == '\t'
if !isempty(current)
push!(args, current)
current = ""
end
else
current *= char
end
end
if !isempty(current)
push!(args, current)
end
return args
end
# ============================================================================
# Substitute arguments
# ============================================================================
function substituteArgs(content::String, args::Vector{String})::String
result = content
# Substitute $1, $2, etc.
result = replace(result, r"\$(\d+)" => s -> begin
idx = parse(Int, s[1])
if idx > 0 && idx <= length(args)
return args[idx]
end
return ""
end)
# Substitute ${@:N} and ${@:N:L}
result = replace(result, r"\$\{@:(\d+)(?::(\d+))?\}" => s -> begin
m = match(r"\$\{@:(\d+)(?::(\d+))?\}", s)
if !isnothing(m)
start = parse(Int, m.captures[1]) - 1
if start < 0
start = 0
end
if !isnothing(m.captures[2])
length = parse(Int, m.captures[2])
return join(args[start+1:start+length], " ")
end
return join(args[start+1:end], " ")
end
return s
end)
# Substitute $ARGUMENTS and $@
all_args = join(args, " ")
result = replace(result, "$ARGUMENTS" => all_args)
result = replace(result, "$@" => all_args)
return result
end
# ============================================================================
# Format prompt template invocation
# ============================================================================
function formatPromptTemplateInvocation(template::PromptTemplate, args::Vector{String}=String[])::String
return substituteArgs(template.content, args)
end
# ============================================================================
# Helper functions
# ============================================================================
function basenameEnvPath(path::String)::String
normalized = rtrim(path, '/')
slash_index = findlast('/', normalized)
if isnothing(slash_index)
return normalized
end
return normalized[slash_index+1:end]
end
function findlast(pattern::Char, s::String)::Union{Int64, Nothing}
for i in length(s):-1:1
if s[i] == pattern
return i
end
end
return nothing
end
function rtrim(s::String, chars::String)::String
idx = length(s)
while idx >= 1 && s[idx] in chars
idx -= 1
end
return s[1:idx]
end
end
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"""
session/jsonl_repo.jl - JSONL session repository
This module provides a JSONL-based session repository implementation.
"""
module JsonlRepo
using ..Types: *
using ..SessionStorage: SessionStorage, SessionMetadata
using ..JsonlStorage: JsonlSessionStorage, headerToSessionMetadata
using ..MemoryRepo: createSessionId, createTimestamp, getEntriesToFork, toSession
using ..HarnessTypes: SessionRepo, SessionForkOptions
# ============================================================================
# JSONL session repository
# ============================================================================
mutable struct JsonlSessionRepo <: SessionRepo{
JsonlSessionMetadata,
JsonlSessionCreateOptions,
JsonlSessionListOptions
}
fs::Any
sessions_root_input::String
sessions_root::Union{String, Nothing}
function JsonlSessionRepo(; sessions_root::String, fs::Any)
new(fs, sessions_root, nothing)
end
end
# ============================================================================
# Session repo methods
# ============================================================================
function create(repo::JsonlSessionRepo, options::JsonlSessionCreateOptions)::Session
id = if haskey(options, :id) && !isnothing(options[:id])
options[:id]
else
createSessionId()
end
created_at = createTimestamp()
session_dir = getSessionDir(repo, options.cwd)
file_path = createSessionFilePath(repo, options.cwd, id, created_at)
storage = JsonlSessionStorage(
file_path,
SessionHeader(
"session",
3,
id,
created_at,
options.cwd,
get(options, :parentSessionPath, nothing),
get(options, :metadata, nothing),
),
SessionTreeEntry[],
nothing,
)
return toSession(storage)
end
function open(repo::JsonlSessionRepo, metadata::JsonlSessionMetadata)::Session
# TODO: Open existing file
return toSession(JsonlSessionStorage(
metadata.path,
SessionHeader(
"session",
3,
metadata.id,
metadata.created_at,
metadata.cwd,
metadata.parent_session_path,
metadata.metadata,
),
SessionTreeEntry[],
nothing,
))
end
function list(repo::JsonlSessionRepo, options::JsonlSessionListOptions=JsonlSessionListOptions())::Vector{JsonlSessionMetadata}
# TODO: List sessions
return JsonlSessionMetadata[]
end
function delete(repo::JsonlSessionRepo, metadata::JsonlSessionMetadata)::Nothing
# TODO: Delete session file
return nothing
end
function fork(repo::JsonlSessionRepo, source::JsonlSessionMetadata, options::Dict{String, Any})::Session
# TODO: Fork session
return create(repo, JsonlSessionCreateOptions(
cwd=get(options, "cwd", ""),
id=get(options, "id", createSessionId()),
))
end
# ============================================================================
# Helper functions
# ============================================================================
function getSessionsRoot(repo::JsonlSessionRepo)::String
if isnothing(repo.sessions_root)
repo.sessions_root = getFileSystemResultOrThrow(
absolutePath(repo.fs, repo.sessions_root_input),
"Failed to resolve sessions root $(repo.sessions_root_input)",
)
end
return repo.sessions_root
end
function getSessionDir(repo::JsonlSessionRepo, cwd::String)::String
return getFileSystemResultOrThrow(
joinPath(repo.fs, [getSessionsRoot(repo), encodeCwd(cwd)]),
"Failed to resolve session directory for $(cwd)",
)
end
function encodeCwd(cwd::String)::String
result = replace(cwd, r"^[/\\]" => "")
result = replace(result, r"[/\\:]" => "-")
return "--$(result)--"
end
function createSessionFilePath(repo::JsonlSessionRepo, cwd::String, session_id::String, timestamp::String)::String
return getFileSystemResultOrThrow(
joinPath(repo.fs, [
getSessionDir(repo, cwd),
"$(replace(timestamp, r"[:.]" => "-"))_$(session_id).jsonl",
]),
"Failed to resolve session file path for $(session_id)",
)
end
function getFileSystemResultOrThrow(result::Result, message::String)
if !result.ok
code = result.error.code == "not_found" ? "not_found" : "storage"
throw(SessionError(code, "$(message): $(result.error.message)", result.error))
end
return result.value
end
end
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"""
session/jsonl_storage.jl - JSONL session storage
This module provides JSONL-based session storage implementation.
"""
module JsonlStorage
using ..Types: *
using ..SessionStorage: SessionStorage, SessionMetadata
# ============================================================================
# Session header
# ============================================================================
mutable struct SessionHeader
type::String
version::Int64
id::String
timestamp::String
cwd::String
parent_session::Union{String, Nothing}
metadata::Union{Dict{String, Any}, Nothing}
end
# ============================================================================
# JSONL session storage
# ============================================================================
mutable struct JsonlSessionStorage{T<:SessionMetadata} <: SessionStorage{T}
file_path::String
metadata::T
entries::Vector{SessionTreeEntry}
by_id::Dict{String, SessionTreeEntry}
labels_by_id::Dict{String, String}
current_leaf_id::Union{String, Nothing}
function JsonlSessionStorage{T}(
file_path::String,
header::SessionHeader,
entries::Vector{SessionTreeEntry},
leaf_id::Union{String, Nothing},
) where T
by_id = Dict{String, SessionTreeEntry}((e.id, e) for e in entries)
labels_by_id = Dict{String, String}()
for entry in entries
if entry isa LabelEntry && !isnothing(entry.label)
labels_by_id[entry.target_id] = entry.label
end
end
new(
file_path,
header,
entries,
by_id,
labels_by_id,
leaf_id,
)
end
end
# ============================================================================
# Session storage methods
# ============================================================================
function getMetadata(storage::JsonlSessionStorage)::T
return storage.metadata
end
function getLeafId(storage::JsonlSessionStorage)::Union{String, Nothing}
if !isnothing(storage.current_leaf_id) && !haskey(storage.by_id, storage.current_leaf_id)
throw(SessionError("invalid_session", "Entry $(storage.current_leaf_id) not found"))
end
return storage.current_leaf_id
end
function setLeafId(storage::JsonlSessionStorage, leaf_id::Union{String, Nothing})::Nothing
if !isnothing(leaf_id) && !haskey(storage.by_id, leaf_id)
throw(SessionError("not_found", "Entry $(leaf_id) not found"))
end
entry = LeafEntry(
"leaf",
generateEntryId(storage.by_id),
storage.current_leaf_id,
create_timestamp(),
leaf_id,
)
# TODO: Write to file
# getFileSystemResultOrThrow(
# await this.fs.appendFile(this.filePath, `${JSON.stringify(entry)}\n`),
# `Failed to append session leaf ${entry.id}`,
# );
push!(storage.entries, entry)
storage.by_id[entry.id] = entry
storage.current_leaf_id = leaf_id
return nothing
end
function createEntryId(storage::JsonlSessionStorage)::String
return generateEntryId(storage.by_id)
end
function appendEntry(storage::JsonlSessionStorage, entry::SessionTreeEntry)::Nothing
# TODO: Write to file
# getFileSystemResultOrThrow(
# await this.fs.appendFile(this.filePath, `${JSON.stringify(entry)}\n`),
# `Failed to append session entry ${entry.id}`,
# );
push!(storage.entries, entry)
storage.by_id[entry.id] = entry
if entry isa LabelEntry
updateLabelCache(storage.labels_by_id, entry)
end
storage.current_leaf_id = leafIdAfterEntry(entry)
return nothing
end
function getEntry(storage::JsonlSessionStorage, id::String)::Union{SessionTreeEntry, Nothing}
return get(storage.by_id, id, nothing)
end
function findEntries(storage::JsonlSessionStorage, type::String)::Vector{SessionTreeEntry}
return filter(entry -> entry.type == type, storage.entries)
end
function getLabel(storage::JsonlSessionStorage, id::String)::Union{String, Nothing}
return get(storage.labels_by_id, id, nothing)
end
function getSessionName(storage::JsonlSessionStorage)::Union{String, Nothing}
entries = findEntries(storage, "session_info")
if isempty(entries)
return nothing
end
return strip(entries[end].name)
end
function getSessionStats(storage::JsonlSessionStorage)::SessionStats
message_count = 0
cached_tokens = 0
uncached_tokens = 0
total_tokens = 0
cost_total = 0.0
for entry in storage.entries
if entry isa MessageEntry
message_count += 1
end
usage = if entry isa MessageEntry && entry.message.role == "assistant"
entry.message.usage
elseif entry isa CompactionEntry || entry isa BranchSummaryEntry
entry.usage
else
nothing
end
if !isnothing(usage) &&
usage.input isa Int64 &&
usage.output isa Int64 &&
usage.cache_read isa Int64 &&
usage.cache_write isa Int64 &&
usage.cost.total isa Float64
cached_tokens += usage.cache_read
uncached_tokens += usage.input + usage.cache_write
total_tokens += usage.input + usage.output + usage.cache_read + usage.cache_write
cost_total += usage.cost.total
end
end
return SessionStats(
message_count,
cached_tokens,
uncached_tokens,
total_tokens,
cost_total,
)
end
function getPathToRootOrCompaction(storage::JsonlSessionStorage, leaf_id::Union{String, Nothing})::Vector{SessionTreeEntry}
if isnothing(leaf_id)
return SessionTreeEntry[]
end
path::Vector{SessionTreeEntry} = SessionTreeEntry[]
stop_at_entry_id::Union{String, Nothing} = nothing
current = get(storage.by_id, leaf_id, nothing)
if isnothing(current)
throw(SessionError("not_found", "Entry $(leaf_id) not found"))
end
while !isnothing(current)
unshift!(path, current)
if !isnothing(stop_at_entry_id) && current.id == stop_at_entry_id
break
end
if current isa CompactionEntry
if !isnothing(current.retained_tail)
break
end
stop_at_entry_id = current.first_kept_entry_id
end
if isnothing(current.parent_id)
break
end
parent = get(storage.by_id, current.parent_id, nothing)
if isnothing(parent)
throw(SessionError("invalid_session", "Entry $(current.parent_id) not found"))
end
current = parent
end
return path
end
function getEntries(storage::JsonlSessionStorage, options::Dict{String, Any})::Vector{SessionTreeEntry}
start = get(options, "afterEntrySeq", 0)
end_idx = if haskey(options, "limit")
start + options["limit"]
else
nothing
end
if isnothing(end_idx)
return copy(storage.entries[start+1:end])
end
return copy(storage.entries[start+1:end_idx])
end
# ============================================================================
# Helper functions
# ============================================================================
function updateLabelCache(labels_by_id::Dict{String, String}, entry::SessionTreeEntry)::Nothing
if entry isa LabelEntry
label = strip(get(entry, :label, nothing))
if !isnothing(label) && !isempty(label)
labels_by_id[entry.target_id] = label
else
delete!(labels_by_id, entry.target_id)
end
end
return nothing
end
function generateEntryId(by_id::Dict{String, SessionTreeEntry})::String
for i in 1:100
id = uuidv7()[end-7:end]
if !haskey(by_id, id)
return id
end
end
return uuidv7()
end
function leafIdAfterEntry(entry::SessionTreeEntry)::Union{String, Nothing}
if entry isa LeafEntry
return entry.target_id
end
return entry.id
end
function headerToSessionMetadata(header::SessionHeader, path::String)::JsonlSessionMetadata
return JsonlSessionMetadata(
header.id,
header.timestamp,
header.cwd,
path,
header.parent_session,
header.metadata,
)
end
end
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"""
session/memory_repo.jl - In-memory session repository
This module provides an in-memory session repository implementation for testing.
"""
module MemoryRepo
using ..Types: *
using ..SessionStorage: SessionStorage, SessionMetadata
using ..MemoryStorage: InMemorySessionStorage
# ============================================================================
# In-memory session repository
# ============================================================================
mutable struct InMemorySessionRepo <: SessionRepo{SessionMetadata, Dict{String, Any}, Nothing}
sessions::Dict{String, Session}
function InMemorySessionRepo()
new(Dict{String, Session}())
end
end
# ============================================================================
# Session repo methods
# ============================================================================
function create(repo::InMemorySessionRepo, options::Dict{String, Any}=Dict{String, Any}())::Session
metadata = SessionMetadata(
if haskey(options, :id) && !isnothing(options[:id])
options[:id]
else
createSessionId()
end,
createTimestamp(),
)
storage = InMemorySessionStorage{SessionMetadata}(metadata=metadata)
session = toSession(storage)
repo.sessions[metadata.id] = session
return session
end
function open(repo::InMemorySessionRepo, metadata::SessionMetadata)::Session
session = get(repo.sessions, metadata.id, nothing)
if isnothing(session)
throw(SessionError("not_found", "Session not found: $(metadata.id)"))
end
return session
end
function list(repo::InMemorySessionRepo)::Vector{SessionMetadata}
return [getMetadata(session) for session in values(repo.sessions)]
end
function delete(repo::InMemorySessionRepo, metadata::SessionMetadata)::Nothing
delete!(repo.sessions, metadata.id)
return nothing
end
function fork(repo::InMemorySessionRepo, source::SessionMetadata, options::Dict{String, Any})::Session
source_session = open(repo, source)
forked_entries = getEntriesToFork(getStorage(source_session), options)
metadata = SessionMetadata(
if haskey(options, :id) && !isnothing(options[:id])
options[:id]
else
createSessionId()
end,
createTimestamp(),
)
storage = InMemorySessionStorage{SessionMetadata}(
entries=forked_entries,
metadata=metadata,
)
session = toSession(storage)
repo.sessions[metadata.id] = session
return session
end
# ============================================================================
# Helper functions
# ============================================================================
function createSessionId()::String
return uuidv7()
end
function createTimestamp()::String
return create_timestamp()
end
function toSession(storage::SessionStorage)::Session
return Session(storage)
end
function getEntriesToFork(storage::SessionStorage, options::Dict{String, Any})::Vector{SessionTreeEntry}
if !haskey(options, :entryId) || isnothing(options[:entryId])
return getEntries(storage, Dict{String, Any}())
end
target = getEntry(storage, options[:entryId])
if isnothing(target)
throw(SessionError("invalid_fork_target", "Entry $(options[:entryId]) not found"))
end
effective_leaf_id::Union{String, Nothing}
position = get(options, "position", "before")
if position == "at"
effective_leaf_id = target.id
else
if target isa MessageEntry && target.message.role != "user"
throw(SessionError("invalid_fork_target", "Entry $(options[:entryId]) is not a user message"))
end
effective_leaf_id = target.parent_id
end
return getPathToRootOrCompaction(storage, effective_leaf_id)
end
function getStorage(session::Session)::SessionStorage
return session.storage
end
end
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"""
session/memory_storage.jl - In-memory session storage
This module provides an in-memory session storage implementation for testing and temporary use.
"""
module MemoryStorage
using ..Types: *
using ..SessionStorage: SessionStorage, SessionMetadata
using ..JsonlStorage: updateLabelCache, generateEntryId, leafIdAfterEntry
# ============================================================================
# In-memory session storage
# ============================================================================
mutable struct InMemorySessionStorage{T<:SessionMetadata} <: SessionStorage{T}
metadata::T
entries::Vector{SessionTreeEntry}
by_id::Dict{String, SessionTreeEntry}
labels_by_id::Dict{String, String}
leaf_id::Union{String, Nothing}
function InMemorySessionStorage{T}(;
entries::Vector{SessionTreeEntry}=SessionTreeEntry[],
metadata::Union{T, Nothing]=nothing,
) where T
by_id = Dict{String, SessionTreeEntry}((e.id, e) for e in entries)
labels_by_id = Dict{String, String}()
leaf_id = nothing
for entry in entries
if entry isa LabelEntry
updateLabelCache(labels_by_id, entry)
end
leaf_id = leafIdAfterEntry(entry)
end
if !isnothing(leaf_id) && !haskey(by_id, leaf_id)
throw(SessionError("invalid_session", "Entry $(leaf_id) not found"))
end
new(
if isnothing(metadata)
T(uuidv7(), create_timestamp())
else
metadata
end,
copy(entries),
by_id,
labels_by_id,
leaf_id,
)
end
end
# ============================================================================
# Session storage methods
# ============================================================================
function getMetadata(storage::InMemorySessionStorage)::T
return storage.metadata
end
function getLeafId(storage::InMemorySessionStorage)::Union{String, Nothing}
if !isnothing(storage.leaf_id) && !haskey(storage.by_id, storage.leaf_id)
throw(SessionError("invalid_session", "Entry $(storage.leaf_id) not found"))
end
return storage.leaf_id
end
function setLeafId(storage::InMemorySessionStorage, leaf_id::Union{String, Nothing})::Nothing
if !isnothing(leaf_id) && !haskey(storage.by_id, leaf_id)
throw(SessionError("not_found", "Entry $(leaf_id) not found"))
end
entry = LeafEntry(
"leaf",
generateEntryId(storage.by_id),
storage.leaf_id,
create_timestamp(),
leaf_id,
)
push!(storage.entries, entry)
storage.by_id[entry.id] = entry
storage.leaf_id = leaf_id
return nothing
end
function createEntryId(storage::InMemorySessionStorage)::String
return generateEntryId(storage.by_id)
end
function appendEntry(storage::InMemorySessionStorage, entry::SessionTreeEntry)::Nothing
push!(storage.entries, entry)
storage.by_id[entry.id] = entry
if entry isa LabelEntry
updateLabelCache(storage.labels_by_id, entry)
end
storage.leaf_id = leafIdAfterEntry(entry)
return nothing
end
function getEntry(storage::InMemorySessionStorage, id::String)::Union{SessionTreeEntry, Nothing}
return get(storage.by_id, id, nothing)
end
function findEntries(storage::InMemorySessionStorage, type::String)::Vector{SessionTreeEntry}
return filter(entry -> entry.type == type, storage.entries)
end
function getLabel(storage::InMemorySessionStorage, id::String)::Union{String, Nothing}
return get(storage.labels_by_id, id, nothing)
end
function getSessionName(storage::InMemorySessionStorage)::Union{String, Nothing}
entries = findEntries(storage, "session_info")
if isempty(entries)
return nothing
end
return strip(entries[end].name)
end
function getSessionStats(storage::InMemorySessionStorage)::SessionStats
message_count = 0
cached_tokens = 0
uncached_tokens = 0
total_tokens = 0
cost_total = 0.0
for entry in storage.entries
if entry isa MessageEntry
message_count += 1
end
usage = if entry isa MessageEntry && entry.message.role == "assistant"
entry.message.usage
elseif entry isa CompactionEntry || entry isa BranchSummaryEntry
entry.usage
else
nothing
end
if !isnothing(usage) &&
usage.input isa Int64 &&
usage.output isa Int64 &&
usage.cache_read isa Int64 &&
usage.cache_write isa Int64 &&
usage.cost.total isa Float64
cached_tokens += usage.cache_read
uncached_tokens += usage.input + usage.cache_write
total_tokens += usage.input + usage.output + usage.cache_read + usage.cache_write
cost_total += usage.cost.total
end
end
return SessionStats(
message_count,
cached_tokens,
uncached_tokens,
total_tokens,
cost_total,
)
end
function getPathToRootOrCompaction(storage::InMemorySessionStorage, leaf_id::Union{String, Nothing})::Vector{SessionTreeEntry}
if isnothing(leaf_id)
return SessionTreeEntry[]
end
path::Vector{SessionTreeEntry} = SessionTreeEntry[]
stop_at_entry_id::Union{String, Nothing} = nothing
current = get(storage.by_id, leaf_id, nothing)
if isnothing(current)
throw(SessionError("not_found", "Entry $(leaf_id) not found"))
end
while !isnothing(current)
unshift!(path, current)
if !isnothing(stop_at_entry_id) && current.id == stop_at_entry_id
break
end
if current isa CompactionEntry
if !isnothing(current.retained_tail)
break
end
stop_at_entry_id = current.first_kept_entry_id
end
if isnothing(current.parent_id)
break
end
parent = get(storage.by_id, current.parent_id, nothing)
if isnothing(parent)
throw(SessionError("invalid_session", "Entry $(current.parent_id) not found"))
end
current = parent
end
return path
end
function getEntries(storage::InMemorySessionStorage, options::Dict{String, Any})::Vector{SessionTreeEntry}
start = get(options, "afterEntrySeq", 0)
end_idx = if haskey(options, "limit")
start + options["limit"]
else
nothing
end
if isnothing(end_idx)
return copy(storage.entries[start+1:end])
end
return copy(storage.entries[start+1:end_idx])
end
end
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"""
session/repo_utils.jl - Session repository utilities
This module provides shared utilities for session repository implementations.
"""
module RepoUtils
using ..Types: *
using ..SessionStorage: SessionStorage, SessionMetadata
using ..Session: Session
# ============================================================================
# Helper functions
# ============================================================================
function createSessionId()::String
return uuidv7()
end
function createTimestamp()::String
return create_timestamp()
end
function toSession{T<:SessionMetadata}(storage::SessionStorage{T})::Session{T}
return Session(storage)
end
function getFileSystemResultOrThrow{TValue}(result::Result{TValue, FileError}, message::String)::TValue
if !result.ok
code = result.error.code == "not_found" ? "not_found" : "storage"
throw(SessionError(code, "$(message): $(result.error.message)", result.error))
end
return result.value
end
function getEntriesToFork(
storage::SessionStorage,
options::Dict{String, Any},
)::Vector{SessionTreeEntry}
if !haskey(options, :entryId) || isnothing(options[:entryId])
return getEntries(storage, Dict{String, Any}())
end
target = getEntry(storage, options[:entryId])
if isnothing(target)
throw(SessionError("invalid_fork_target", "Entry $(options[:entryId]) not found"))
end
effective_leaf_id::Union{String, Nothing}
position = get(options, "position", "before")
if position == "at"
effective_leaf_id = target.id
else
if target isa MessageEntry && target.message.role != "user"
throw(SessionError("invalid_fork_target", "Entry $(options[:entryId]) is not a user message"))
end
effective_leaf_id = target.parent_id
end
return getPathToRootOrCompaction(storage, effective_leaf_id)
end
end
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"""
session/session.jl - Session management
This module provides the Session class for managing conversation history with branch support.
"""
module Session
using ..Types: *
using ..SessionStorage: SessionStorage
using ..Messages: *
using ..HarnessTypes: *
# ============================================================================
# Session context build options
# ============================================================================
mutable struct SessionContextBuildOptions
entry_transforms::Union{Vector{Function}, Nothing}
entry_projectors::Union{Dict{String, Function}, Nothing}
end
# ============================================================================
# Default context entry transform
# ============================================================================
function defaultContextEntryTransform(path_entries::Vector{SessionTreeEntry})::Vector{SessionTreeEntry}
compaction = nothing
for entry in path_entries
if entry isa CompactionEntry
compaction = entry
break
end
end
if isnothing(compaction)
return copy(path_entries)
end
entries::Vector{SessionTreeEntry} = [compaction]
compaction_idx = findfirst(
(entry) -> entry isa CompactionEntry && entry.id == compaction.id,
path_entries,
)
if !isnothing(compaction.retained_tail)
for i in compaction_idx+1:length(path_entries)
push!(entries, path_entries[i])
end
return entries
end
if !isnothing(compaction.first_kept_entry_id)
found_first_kept = false
for i in 1:compaction_idx-1
entry = path_entries[i]
if entry.id == compaction.first_kept_entry_id
found_first_kept = true
end
if found_first_kept
push!(entries, entry)
end
end
end
for i in compaction_idx+1:length(path_entries)
push!(entries, path_entries[i])
end
return entries
end
# ============================================================================
# Build context entries
# ============================================================================
function buildContextEntries(
path_entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::Vector{SessionTreeEntry}
entries = defaultContextEntryTransform(path_entries)
if !isnothing(options.entry_transforms)
for transform in options.entry_transforms
entries = transform(entries)
end
end
return entries
end
# ============================================================================
# Session entry to context messages
# ============================================================================
function sessionEntryToContextMessages(
entry::SessionTreeEntry,
index::Int64,
entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::Vector{AgentMessage}
if entry isa MessageEntry
return [entry.message]
end
if entry isa CustomMessageEntry
return [createCustomMessage(
entry.custom_type,
entry.content,
entry.display,
entry.details,
entry.timestamp,
)]
end
if entry isa CompactionEntry
messages = [createCompactionSummaryMessage(
entry.summary,
entry.tokens_before,
entry.timestamp,
)]
if !isnothing(entry.retained_tail)
append!(messages, entry.retained_tail)
end
return messages
end
if entry isa BranchSummaryEntry
return [createBranchSummaryMessage(
entry.summary,
entry.from_id,
entry.timestamp,
)]
end
if entry isa CustomEntry
if !isnothing(options.entry_projectors) && haskey(options.entry_projectors, entry.custom_type)
projector = options.entry_projectors[entry.custom_type]
return projector(entry, index, entries)
end
return AgentMessage[]
end
return AgentMessage[]
end
# ============================================================================
# Build session context
# ============================================================================
function buildSessionContext(
path_entries::Vector{SessionTreeEntry},
options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)::SessionContext
state = deriveSessionContextState(path_entries)
context_entries = buildContextEntries(path_entries, options)
messages = SessionTreeEntry[]
for (i, entry) in enumerate(context_entries)
append!(messages, sessionEntryToContextMessages(entry, i, context_entries, options))
end
return SessionContext(messages, state.thinking_level, state.model, state.active_tool_names)
end
function deriveSessionContextState(path_entries::Vector{SessionTreeEntry})::Dict{String, Any}
thinking_level = "off"
model = nothing
active_tool_names = nothing
for entry in path_entries
if entry isa ThinkingLevelChangeEntry
thinking_level = entry.thinking_level
elseif entry isa ModelChangeEntry
model = Dict{String, String}("provider" => entry.provider, "modelId" => entry.model_id)
elseif entry isa MessageEntry && entry.message.role == "assistant"
model = Dict{String, String}("provider" => entry.message.provider, "modelId" => entry.message.model)
elseif entry isa ActiveToolsChangeEntry
active_tool_names = copy(entry.active_tool_names)
end
end
return Dict{String, Any}(
"thinking_level" => thinking_level,
"model" => model,
"active_tool_names" => active_tool_names,
)
end
# ============================================================================
# Session class
# ============================================================================
mutable struct Session{T<:SessionMetadata}
storage::SessionStorage{T}
context_build_options::SessionContextBuildOptions
function Session(
storage::SessionStorage,
context_build_options::SessionContextBuildOptions=SessionContextBuildOptions(nothing, nothing),
)
new{typeof(storage.metadata)}(storage, context_build_options)
end
end
# ============================================================================
# Session methods
# ============================================================================
function getMetadata(session::Session)::T
return getMetadata(session.storage)
end
function getStorage(session::Session)::SessionStorage
return session.storage
end
function getLeafId(session::Session)::Union{String, Nothing}
return getLeafId(session.storage)
end
function getEntry(session::Session, id::String)::Union{SessionTreeEntry, Nothing}
return getEntry(session.storage, id)
end
function getEntries(session::Session, options::Dict{String, Any}=Dict{String, Any}())::Vector{SessionTreeEntry}
return getEntries(session.storage, options)
end
function getBranch(session::Session, from_id::Union{String, Nothing}=nothing)::Vector{SessionTreeEntry}
leaf_id = if isnothing(from_id)
getLeafId(session.storage)
else
from_id
end
return getPathToRootOrCompaction(session.storage, leaf_id)
end
function buildContextEntries(session::Session, options::SessionContextBuildOptions=SessionContextBuildOptions())::Vector{SessionTreeEntry}
return buildContextEntries(getBranch(session), mergeContextBuildOptions(session, options))
end
function buildContext(session::Session, options::SessionContextBuildOptions=SessionContextBuildOptions())::SessionContext
return buildSessionContext(getBranch(session), mergeContextBuildOptions(session, options))
end
function mergeContextBuildOptions(session::Session, options::SessionContextBuildOptions)::SessionContextBuildOptions
return SessionContextBuildOptions(
vcat(
isnothing(session.context_build_options.entry_transforms) ? [] : session.context_build_options.entry_transforms,
isnothing(options.entry_transforms) ? [] : options.entry_transforms,
),
merge(
isnothing(session.context_build_options.entry_projectors) ? Dict{String, Any}() : session.context_build_options.entry_projectors,
isnothing(options.entry_projectors) ? Dict{String, Any}() : options.entry_projectors,
promote=true,
),
)
end
function getLabel(session::Session, id::String)::Union{String, Nothing}
return getLabel(session.storage, id)
end
function getSessionStats(session::Session)::SessionStats
return getSessionStats(session.storage)
end
function getSessionName(session::Session)::Union{String, Nothing}
return getSessionName(session.storage)
end
function appendMessage(session::Session, message::AgentMessage)::String
return appendTypedEntry(session, MessageEntry(
"message",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
message,
))
end
function appendThinkingLevelChange(session::Session, thinking_level::String)::String
return appendTypedEntry(session, ThinkingLevelChangeEntry(
"thinking_level_change",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
thinking_level,
))
end
function appendModelChange(session::Session, provider::String, model_id::String)::String
return appendTypedEntry(session, ModelChangeEntry(
"model_change",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
provider,
model_id,
))
end
function appendActiveToolsChange(session::Session, active_tool_names::Vector{String})::String
return appendTypedEntry(session, ActiveToolsChangeEntry(
"active_tools_change",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
active_tool_names,
))
end
function appendCompaction(
session::Session,
summary::String,
first_kept_entry_id::Union{String, Nothing},
tokens_before::Int64,
details::Union{Any, Nothing}=nothing,
from_hook::Bool=false,
usage::Union{Usage, Nothing}=nothing,
retained_tail::Union{Vector{AgentMessage}, Nothing}=nothing,
)::String
return appendTypedEntry(session, CompactionEntry(
"compaction",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
summary,
first_kept_entry_id,
tokens_before,
retained_tail,
details,
usage,
from_hook,
))
end
function appendCustomEntry(session::Session, custom_type::String, data::Union{Any, Nothing}=nothing)::String
return appendTypedEntry(session, CustomEntry(
"custom",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
custom_type,
data,
))
end
function appendCustomMessageEntry(
session::Session,
custom_type::String,
content::String,
display::Bool,
details::Union{Any, Nothing}=nothing,
)::String
return appendTypedEntry(session, CustomMessageEntry(
"custom_message",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
custom_type,
content,
details,
display,
))
end
function appendLabel(session::Session, target_id::String, label::Union{String, Nothing})::String
if isnothing(getEntry(session, target_id))
throw(SessionError("not_found", "Entry $(target_id) not found"))
end
return appendTypedEntry(session, LabelEntry(
"label",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
target_id,
label,
))
end
function appendSessionName(session::Session, name::String)::String
sanitizedName = replace(name, r"[\r\n]+" => " ")
return appendTypedEntry(session, SessionInfoEntry(
"session_info",
createEntryId(session.storage),
getLeafId(session.storage),
create_timestamp(),
sanitizedName,
))
end
function moveTo(
session::Session,
entry_id::Union{String, Nothing},
summary::Union{Dict{String, Any}, Nothing}=nothing,
)::Union{String, Nothing
if !isnothing(entry_id) && isnothing(getEntry(session, entry_id))
throw(SessionError("not_found", "Entry $(entry_id) not found"))
end
setLeafId(session.storage, entry_id)
if isnothing(summary)
return nothing
end
return appendTypedEntry(session, BranchSummaryEntry(
"branch_summary",
createEntryId(session.storage),
entry_id,
create_timestamp(),
entry_id,
summary["summary"],
get(summary, "details", nothing),
get(summary, "usage", nothing),
get(summary, "from_hook", false),
))
end
function appendTypedEntry(session::Session, entry::SessionTreeEntry)::String
appendEntry(session.storage, entry)
return entry.id
end
end
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"""
skills.jl - Skill loading and formatting
This module provides utilities for loading skills from SKILL.md files and formatting skill invocations.
"""
module Skills
using ..Types: *
using ..HarnessTypes: Skill, ExecutionEnv, FileSystem, toError, FileError, Result, ok, err
const MAX_NAME_LENGTH = 64
const MAX_DESCRIPTION_LENGTH = 1024
const IGNORE_FILE_NAMES = [".gitignore", ".ignore", ".fdignore"]
# ============================================================================
# Skill diagnostic types
# ============================================================================
const SkillDiagnosticCode = String
const SKILL_DIAGNOSTIC_FILE_INFO_FAILED = "file_info_failed"
const SKILL_DIAGNOSTIC_LIST_FAILED = "list_failed"
const SKILL_DIAGNOSTIC_READ_FAILED = "read_failed"
const SKILL_DIAGNOSTIC_PARSE_FAILED = "parse_failed"
const SKILL_DIAGNOSTIC_INVALID_METADATA = "invalid_metadata"
mutable struct SkillDiagnostic
type::String
code::SkillDiagnosticCode
message::String
path::String
end
# ============================================================================
# Skill frontmatter
# ============================================================================
mutable struct SkillFrontmatter
name::Union{String, Nothing}
description::Union{String, Nothing}
disable_model_invocation::Union{Bool, Nothing}
extra::Dict{String, Any}
end
# ============================================================================
# Format skill invocation
# ============================================================================
function formatSkillInvocation(skill::Skill, additional_instructions::Union{String, Nothing})::String
skill_block = "<skill name=\"$(skill.name)\" location=\"$(skill.filePath)\">\nReferences are relative to $(dirnameEnvPath(skill.filePath)).\n\n$(skill.content)\n</skill>"
if isnothing(additional_instructions)
return skill_block
end
return "$(skill_block)\n\n$(additional_instructions)"
end
# ============================================================================
# Load skills from directories
# ============================================================================
function loadSkills(env::ExecutionEnv, dirs::Union{String, Vector{String}})::Tuple{Vector{Skill}, Vector{SkillDiagnostic}}
skills::Vector{Skill} = Skill[]
diagnostics::Vector{SkillDiagnostic} = SkillDiagnostic[]
dir_list = if dirs isa String
[dirs]
else
dirs
end
for dir in dir_list
root_info_result = fileInfo(env, dir, nothing)
if !root_info_result.ok
if root_info_result.error.code != "not_found"
push!(diagnostics, SkillDiagnostic(
"warning",
"file_info_failed",
root_info_result.error.message,
dir,
))
end
continue
end
root_info = root_info_result.value
if !isDirectory(env, root_info, diagnostics)
continue
end
result = loadSkillsFromDirInternal(env, root_info.path, true, Dict{String, Any}(), root_info.path)
append!(skills, result.skills)
append!(diagnostics, result.diagnostics)
end
return skills, diagnostics
end
function isDirectory(env::ExecutionEnv, info::FileInfo, diagnostics::Vector{SkillDiagnostic})::Bool
return info.kind == "directory"
end
function loadSkillsFromDirInternal(
env::ExecutionEnv,
dir::String,
include_root_files::Bool,
ignore_matcher::Dict{String, Any},
root_dir::String,
)::Tuple{Vector{Skill}, Vector{SkillDiagnostic}}
skills::Vector{Skill} = Skill[]
diagnostics::Vector{SkillDiagnostic} = SkillDiagnostic[]
dir_info_result = fileInfo(env, dir, nothing)
if !dir_info_result.ok
if dir_info_result.error.code != "not_found"
push!(diagnostics, SkillDiagnostic(
"warning",
"file_info_failed",
dir_info_result.error.message,
dir,
))
end
return skills, diagnostics
end
dir_info = dir_info_result.value
if !isDirectory(env, dir_info, diagnostics)
return skills, diagnostics
end
# TODO: Implement ignore rules
# await addIgnoreRules(env, ignoreMatcher, dir, rootDir, diagnostics);
entries_result = listDir(env, dir, nothing)
if !entries_result.ok
push!(diagnostics, SkillDiagnostic(
"warning",
"list_failed",
entries_result.error.message,
dir,
))
return skills, diagnostics
end
entries = entries_result.value
# Look for SKILL.md
for entry in entries
if entry.name != "SKILL.md"
continue
end
full_path = entry.path
if !isFile(env, entry, diagnostics)
continue
end
result = loadSkillFromFile(env, full_path)
if !isnothing(result.skill)
push!(skills, result.skill)
end
append!(diagnostics, result.diagnostics)
return skills, diagnostics
end
# Process other files
for entry in sort(entries, by=e -> e.name)
if startswith(entry.name, ".") || entry.name == "node_modules"
continue
end
full_path = entry.path
kind = getFileKind(env, entry, diagnostics)
if isnothing(kind)
continue
end
rel_path = relativeEnvPath(root_dir, full_path)
ignore_path = kind == "directory" ? "$(rel_path)/" : rel_path
if !isnothing(ignore_matcher) && haskey(ignore_matcher, ignore_path)
continue
end
if kind == "directory"
result = loadSkillsFromDirInternal(env, full_path, false, ignore_matcher, root_dir)
append!(skills, result.skills)
append!(diagnostics, result.diagnostics)
continue
end
if kind != "file" || !include_root_files || !endswith(entry.name, ".md")
continue
end
result = loadSkillFromFile(env, full_path)
if !isnothing(result.skill)
push!(skills, result.skill)
end
append!(diagnostics, result.diagnostics)
end
return skills, diagnostics
end
function isFile(env::ExecutionEnv, info::FileInfo, diagnostics::Vector{SkillDiagnostic})::Bool
return info.kind == "file"
end
function getFileKind(env::ExecutionEnv, info::FileInfo, diagnostics::Vector{SkillDiagnostic})::Union{String, Nothing}
if info.kind == "file" || info.kind == "directory"
return info.kind
end
canonical_path = canonicalPath(env, info.path, nothing)
if !canonical_path.ok
if canonical_path.error.code != "not_found"
push!(diagnostics, SkillDiagnostic(
"warning",
"file_info_failed",
canonical_path.error.message,
info.path,
))
end
return nothing
end
target = fileInfo(env, canonical_path.value, nothing)
if !target.ok
if target.error.code != "not_found"
push!(diagnostics, SkillDiagnostic(
"warning",
"file_info_failed",
target.error.message,
info.path,
))
end
return nothing
end
if target.value.kind == "file" || target.value.kind == "directory"
return target.value.kind
end
return nothing
end
# ============================================================================
# Load skill from file
# ============================================================================
function loadSkillFromFile(env::ExecutionEnv, file_path::String)::Tuple{Union{Skill, Nothing}, Vector{SkillDiagnostic}}
diagnostics::Vector{SkillDiagnostic} = SkillDiagnostic[]
raw_content = readTextFile(env, file_path, nothing)
if !raw_content.ok
push!(diagnostics, SkillDiagnostic(
"warning",
"read_failed",
raw_content.error.message,
file_path,
))
return nothing, diagnostics
end
# TODO: Parse frontmatter
# parsed = parseFrontmatter<SkillFrontmatter>(rawContent.value);
# if !parsed.ok {
# diagnostics.push({ type: "warning", code: "parse_failed", message: parsed.error.message, path: filePath });
# return { skill: null, diagnostics };
# }
# const { frontmatter, body } = parsed.value;
# const skillDir = dirnameEnvPath(filePath);
# const parentDirName = basenameEnvPath(skillDir);
# const description = typeof frontmatter.description === "string" ? frontmatter.description : undefined;
# for (const error of validateDescription(description)) {
# diagnostics.push({ type: "warning", code: "invalid_metadata", message: error, path: filePath });
# }
# const frontmatterName = typeof frontmatter.name === "string" ? frontmatter.name : undefined;
# const name = frontmatterName || parentDirName;
# for (const error of validateName(name, parentDirName)) {
# diagnostics.push({ type: "warning", code: "invalid_metadata", message: error, path: filePath });
# }
# if (!description || description.trim() === "") {
# return { skill: null, diagnostics };
# }
# return {
# skill: {
# name,
# description,
# content: body,
# filePath,
# disableModelInvocation: frontmatter["disable-model-invocation"] === true,
# },
# diagnostics,
# };
return nothing, diagnostics
end
# ============================================================================
# Path utility functions
# ============================================================================
function joinEnvPath(base::String, child::String)::String
return "$(rtrim(base, '/'))/$(ltrim(child, '/'))"
end
function dirnameEnvPath(path::String)::String
normalized = rtrim(path, '/')
slash_index = findlast('/', normalized)
if isnothing(slash_index) || slash_index <= 1
return "/"
end
return normalized[1:slash_index-1]
end
function basenameEnvPath(path::String)::String
normalized = rtrim(path, '/')
slash_index = findlast('/', normalized)
if isnothing(slash_index)
return normalized
end
return normalized[slash_index+1:end]
end
function relativeEnvPath(root::String, path::String)::String
normalized_root = rtrim(root, '/')
normalized_path = rtrim(path, '/')
if normalized_path == normalized_root
return ""
end
if startswith(normalized_path, "$(normalized_root)/")
return normalized_path[length(normalized_root)+2:end]
end
return lstrip(normalized_path, '/')
end
# ============================================================================
# Helper functions
# ============================================================================
function lstrip(s::String, chars::String)::String
idx = 1
while idx <= length(s) && s[idx] in chars
idx += 1
end
return s[idx:end]
end
function rtrim(s::String, chars::String)::String
idx = length(s)
while idx >= 1 && s[idx] in chars
idx -= 1
end
return s[1:idx]
end
function findlast(pattern::Char, s::String)::Union{Int64, Nothing}
for i in length(s):-1:1
if s[i] == pattern
return i
end
end
return nothing
end
end
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"""
stream_fn.jl - Stream function utilities
This module provides the default stream function configuration for AgentCore.
"""
module StreamFn
using ..Types: StreamFn
let default_stream_fn::Union{StreamFn, Nothing} = nothing
"""
setDefaultStreamFn(stream_fn)
Configure the fallback used by Agent and low-level loops when callers omit stream_fn.
# Arguments
- `stream_fn`: The stream function to set as default
"""
function setDefaultStreamFn(stream_fn::Union{StreamFn, Nothing})
global default_stream_fn = stream_fn
end
"""
getDefaultStreamFn()
Get the configured default stream function, or throw an error if none is configured.
# Returns
- The configured stream function
# Throws
- ErrorException if no default stream function is configured
"""
function getDefaultStreamFn()::StreamFn
if isnothing(default_stream_fn)
throw(ErrorException(
"No default stream function configured. Pass stream_fn explicitly or call setDefaultStreamFn()."
))
end
return default_stream_fn
end
end
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"""
system_prompt.jl - System prompt formatting
This module provides utilities for formatting skills in the system prompt.
"""
module SystemPrompt
using ..Types: Skill
"""
formatSkillsForSystemPrompt(skills)
Format skills for inclusion in the system prompt using XML-formatted blocks.
"""
function formatSkillsForSystemPrompt(skills::Vector{Skill})::String
visible_skills = filter(s -> !s.disableModelInvocation, skills)
if isempty(visible_skills)
return ""
end
lines = String[
"The following skills provide specialized instructions for specific tasks.",
"Read the full skill file when the task matches its description.",
"When a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.",
"",
"<available_skills>",
]
for skill in visible_skills
push!(lines, " <skill>")
push!(lines, " <name>$(escapeXml(skill.name))</name>")
push!(lines, " <description>$(escapeXml(skill.description))</description>")
push!(lines, " <location>$(escapeXml(skill.filePath))</location>")
push!(lines, " </skill>")
end
push!(lines, "</available_skills>")
return join(lines, "\n")
end
"""
escapeXml(value)
Escape special characters in a string for XML.
"""
function escapeXml(value::String)::String
result = replace(value, "&" => "&amp;")
result = replace(result, "<" => "&lt;")
result = replace(result, ">" => "&gt;")
result = replace(result, "\"" => "&quot;")
result = replace(result, "'" => "&apos;")
return result
end
end
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"""
tools/bash.jl - Bash execution tool
This module provides the bash execution tool for AgentCore.
"""
module Bash
using ..Types: *
struct BashExecution
command::String
cwd::String
env::Dict{String, String}
inherit_env::Bool
end
mutable struct BashPrepare{TContext}
function::Function
context::TContext
signal::Union{Any, Nothing}
end
mutable struct BashToolOptions{TContext}
command_prefix::Union{String, Nothing}
prepare::Union{BashPrepare{TContext}, Nothing}
end
mutable struct BashToolDetails
truncation::Union{Any, Nothing}
full_output_path::Union{String, Nothing}
end
function createBashTool{TContext}(options::Union{BashToolOptions{TContext}, Nothing}=nothing) where TContext
return AgentTool(
"bash",
"bash",
"Execute a bash command in the current working directory.",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# TODO: Implement bash execution
return AgentToolResult([TextContent("Command executed successfully")], nothing, nothing, nothing, nothing)
end,
nothing,
nothing,
)
end
end
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"""
tools/edit.jl - File edit tool
This module provides the file edit tool for AgentCore.
"""
module Edit
using ..Types: *
mutable struct EditToolDetails
diff::String
patch::String
first_changed_line::Union{Int64, Nothing}
end
function createEditTool{TContext}() where TContext
return AgentTool(
"edit",
"edit",
"Edit a single file using exact text replacement.",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# TODO: Implement edit execution
return AgentToolResult([TextContent("File edited successfully")], nothing, nothing, nothing, nothing)
end,
nothing,
nothing,
)
end
end
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"""
tools/edit_diff.jl - Edit diff utilities
This module provides shared diff computation utilities for the edit tool.
"""
module EditDiff
using ..Types: *
function detectLineEnding(content::String)::String
crlf_idx = findfirst("\r\n", content)
lf_idx = findfirst("\n", content)
if isnothing(lf_idx)
return "\n"
end
if isnothing(crlf_idx)
return "\n"
end
return crlf_idx < lf_idx ? "\r\n" : "\n"
end
function normalizeToLF(text::String)::String
return replace(text, "\r\n" => "\n", "\r" => "\n")
end
function restoreLineEndings(text::String, ending::String)::String
if ending == "\r\n"
return replace(text, "\n" => "\r\n")
end
return text
end
function normalizeForFuzzyMatch(text::String)::String
# TODO: Implement fuzzy matching normalization
return text
end
function splitLinesWithEndings(content::String)::Vector{String}
# TODO: Implement line splitting with endings
return split(content, "\n")
end
function applyEditsToNormalizedContent(
normalized_content::String,
edits::Vector{Any},
path::String,
)::Tuple{String, String}
# TODO: Implement edit application
return normalized_content, normalized_content
end
function generateUnifiedPatch(path::String, old_content::String, new_content::String, context_lines::Int64=4)::String
# TODO: Implement unified patch generation
return ""
end
function generateDiffString(
old_content::String,
new_content::String,
context_lines::Int64=4,
)::Tuple{String, Union{Int64, Nothing}}
# TODO: Implement diff string generation
return "", nothing
end
end
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"""
tools/file_mutation_queue.jl - File mutation queue
This module provides file mutation serialization for safe concurrent file writes.
"""
module FileMutationQueue
using ..Types: *
using ..HarnessTypes: ExecutionEnv, getOrThrow, FileError, Result
# ============================================================================
# Mutation queue state
# ============================================================================
mutable struct MutationQueueState
queues::Dict{String, Any}
registration::Any
end
# Global state
const states = Dict{ExecutionEnv, MutationQueueState}()
function getState(env::ExecutionEnv)::MutationQueueState
if !haskey(states, env)
states[env] = MutationQueueState(Dict{String, Any}(), nothing)
end
return states[env]
end
# ============================================================================
# File mutation queue helpers
# ============================================================================
async function getMutationQueueKey(env::ExecutionEnv, path::String)::String
absolute_path = getOrThrow(getOrThrow(absolutePath(env, path), "Failed to get absolute path"))
canonical_path = canonicalPath(env, absolute_path, nothing)
if canonical_path.ok
return canonical_path.value
end
if canonical_path.error.code in ("not_found", "not_supported")
return absolute_path
end
throw(canonical_path.error)
end
# ============================================================================
# Main function - serialize file mutations
# ============================================================================
function withFileMutationQueue{T}(env::ExecutionEnv, path::String, fn::Function)::T
state = getState(env)
# TODO: Implement proper async queueing
# This is a simplified version
return fn()
end
end
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"""
tools/image.jl - Image utilities
This module provides image detection and encoding utilities.
"""
module Image
using ..Types: *
function detectSupportedImageMimeType(buffer::Vector{UInt8})::Union{String, Nothing}
if length(buffer) >= 3 && buffer[1:3] == [0xff, 0xd8, 0xff]
if buffer[4] == 0xf7
return nothing
end
return "image/jpeg"
end
if length(buffer) >= 8 && buffer[1:8] == [0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a]
return "image/png"
end
if length(buffer) >= 3 && buffer[1:3] == [0x47, 0x49, 0x46]
return "image/gif"
end
if length(buffer) >= 12 && buffer[1:4] == [0x52, 0x49, 0x46, 0x46] && buffer[9:12] == [0x57, 0x45, 0x42, 0x50]
return "image/webp"
end
if length(buffer) >= 2 && buffer[1:2] == [0x42, 0x4d]
return "image/bmp"
end
return nothing
end
function encodeBase64(bytes::Vector{UInt8})::String
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
output = ""
for i in 1:3:length(bytes)
first_byte = i <= length(bytes) ? bytes[i] : 0
second_byte = i+1 <= length(bytes) ? bytes[i+1] : 0
third_byte = i+2 <= length(bytes) ? bytes[i+2] : 0
output *= alphabet[first_byte >> 2 + 1]
output *= alphabet[(((first_byte & 0x03) << 4) | ((second_byte >> 4) & 0x0f)) + 1]
if i+1 <= length(bytes)
output *= alphabet[(((second_byte & 0x0f) << 2) | ((third_byte >> 6) & 0x03)) + 1]
else
output *= "="
end
if i+2 <= length(bytes)
output *= alphabet[third_byte & 0x3f + 1]
else
output *= "="
end
end
return output
end
end
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"""
tools/index.jl - Tool exports
This module exports all tools.
"""
module ToolsIndex
using ..Tools.Bash: createBashTool
using ..Tools.Read: createReadTool
using ..Tools.Write: createWriteTool
using ..Tools.Edit: createEditTool
using ..Tools.Edit: EditToolDetails, EditToolInput
using ..Tools.Read: ReadToolDetails, ReadToolInput, ReadToolOptions, ReadImageProcessor, ReadImageProcessorResult
export
createBashTool,
createReadTool,
createWriteTool,
createEditTool,
BashExecution,
BashPrepare,
BashToolDetails,
BashToolInput,
BashToolOptions,
EditToolDetails,
EditToolInput,
ReadToolDetails,
ReadToolInput,
ReadToolOptions,
ReadImageProcessor,
ReadImageProcessorResult,
WriteToolInput
end
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"""
tools/path_utils.jl - Path resolution utilities
This module provides path resolution utilities for tools.
"""
module PathUtils
using ..Types: *
using ..HarnessTypes: ExecutionEnv, getOrThrow, FileError, Result
function normalizeToolPath(path::String)::String
normalized = replace(path, r"[\u00A0\u2000-\u200A\u202F\u205F\u3000]" => " ")
if startswith(normalized, "@")
return normalized[2:end]
end
return normalized
end
function resolveToolPath(env::ExecutionEnv, path::String, signal::Union{Any, Nothing}=nothing)::String
return getOrThrow(getOrThrow(absolutePath(env, normalizeToolPath(path), signal), "Failed to resolve path"))
end
function resolveReadToolPath(env::ExecutionEnv, path::String, signal::Union{Any, Nothing}=nothing)::String
resolved = getOrThrow(getOrThrow(absolutePath(env, normalizeToolPath(path), signal), "Failed to resolve path"))
variants = String[
resolved,
replace(resolved, r" (AM|PM)\."i => " $1."),
normalized = replace(resolved, NFC => NFD),
replace(resolved, "'" => "\u2019"),
replace(replace(resolved, NFC => NFD), "'" => "\u2019"),
]
for variant in variants
if getOrThrow(getOrThrow(exists(env, variant, signal), "Failed to check existence"), "Not found")
return variant
end
end
return resolved
end
end
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"""
tools/read.jl - File read tool
This module provides the file read tool for AgentCore.
"""
module Read
using ..Types: *
mutable struct ReadToolDetails
truncation::Union{Any, Nothing}
end
mutable struct ReadToolOptions
auto_resize_images::Bool
image_processor::Union{Any, Nothing}
end
function createReadTool{TContext}(options::Union{ReadToolOptions, Nothing}=nothing) where TContext
return AgentTool(
"read",
"read",
"Read the contents of a file.",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# TODO: Implement read execution
return AgentToolResult([TextContent("File read successfully")], nothing, nothing, nothing, nothing)
end,
nothing,
nothing,
)
end
end
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"""
tools/write.jl - File write tool
This module provides the file write tool for AgentCore.
"""
module Write
using ..Types: *
function createWriteTool{TContext}() where TContext
return AgentTool(
"write",
"write",
"Write content to a file.",
Dict{String, Any}(),
(tool_call_id, params, signal, on_update, context) -> begin
# TODO: Implement write execution
return AgentToolResult([TextContent("File written successfully")], nothing, nothing, nothing, nothing)
end,
nothing,
nothing,
)
end
end
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module type
export agent, sommelier, companion, virtualcustomer, agentcontext
using Dates, UUIDs, DataStructures, JSON, NATS
using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- #
mutable struct agentcontext
text2textInstructLLM::Function
getTextEmbedding::Function
executeSQL::Function
similarSQLVectorDB::Function
insertSQLVectorDB::Function
similarSommelierDecision::Function
insertSommelierDecision::Function
find_related_tables_for_user_question::Function
pg_conn_str::String
agentconfig::AbstractDict
end
abstract type agent end
mutable struct sommelier <: agent
name::String # agent name
id::String # agent id
retailername::String
retailerid::String
tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
chathistory::Vector{Dict{String, Any}}
memory::Dict{String, Any}
context::agentcontext
llmFormatName::String
end
""" A sommelier agent.
# Arguments
- `context::agentcontext`
Application context containing shared functions for LLM, SQL, and vector database operations.
# Keyword Arguments
- `name::String`
Agent's name. Default: `"Assistant"`
- `id::String`
Agent's ID. Default: generated UUID string.
- `retailername::String`
Retailer name associated with the sommelier. Default: `"retailer_name"`
- `maxHistoryMsg::Integer`
Maximum history messages. Default: `20`
- `chathistory::Vector{Dict{String, String}}`
Chat history. Default: empty vector.
- `llmFormatName::String`
LLM format name. Default: `"granite3"`
# Return
- `sommelier`: An instantiated sommelier agent.
# Example
```julia
julia> using YiemAgent
julia> context = agentcontext(
text2textInstructLLM,
getTextEmbedding,
executeSQL,
similarSQLVectorDB,
insertSQLVectorDB,
similarSommelierDecision,
insertSommelierDecision
)
julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyWineShop")
```
"""
function sommelier(
context::agentcontext, # agent functions, db connect and other context
;
name::String= "Assistant",
id::String= string(uuid4()),
retailername::String= "not specified",
retailerid::String= "not specified",
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
llmFormatName::String= "granite3"
)
tools = Dict( # update input format
"chatbox"=> Dict(
"description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
"input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
"output" => "" ,
),
"winestock"=> Dict(
"description" => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
"input" => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
"output" => """<output>Output are wines that match the search query in JSON format.""",
),
)
""" Memory
Chat history use openai format as follow:
image1_path = "test/large_image.png" ---
image1_bytes = read(image1_path) | this part must be done
image1_base64_string = base64encode(image1_bytes) | in frontend
mime_type = "image/png" | not in agent code
data1_uri = "data:<mime_type>;base64,<image1_base64_string>" ---
chathistory= [
Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => "You are a helpful assistant"),
]
),
Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "<internal_context_for_assistant>
LLM context here...
</internal_context_for_assistant>
Do you know this wine? Just give me brief intro."
),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data1_uri)
),
]
),
]
shortmem = Dict(
"1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
"2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
...
)
"""
memory = Dict{String, Any}(
"shortmem"=> OrderedDict{String, Any}(),
"scratchpad"=> "",
"recap"=> OrderedDict{String, Any}(),
)
newAgent = sommelier(
name,
id,
retailername,
retailerid,
tools,
maxHistoryMsg,
chathistory,
memory,
context,
llmFormatName
)
systemmsg =
"""
# store_policy
- Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
- If you found wines in the store's database, they are in stock.
- You can only recommend wines that are currently in our inventory
- Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
- Ask the user one question at a time.
- Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
- Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
- Spicy foods should be paired only with light red wines.
- We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
- Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
# store_guidelines
- Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
- Customer may provide images for you to look up.
- Encourage the customer to explore different options and try new things.
- If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
- Your store carries only wine.
- Vintage 0 means non-vintage.
- Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
- User usually ask for something similar. This means you should use the search term based on the profile they like.
# situation
You are having conversation with a customer.
# your role
Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
# objective
- Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
- Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
# your responsibility includes
- According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
- Keep the conversation with the customer going smoothly
# your responsibility does NOT includes
- Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
- Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
- Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
# you should then respond to the user with interleaving think, action_name, action_input in JSON format
1) "think", Your step-by-step reasoning process. Explain why you are choosing this action.
2) "action_name", Can be one of the available actions. Typically corresponds to the execution of the first step in your thought
3) "action_input", The input to the action you are about to perform.
After the action is executed you gets "action_result". It is the output from the action you selected.
# available actions
"CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to say with the user.
"SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
Example query 2: "Red or white wine, medium tannin, price under 700 USD"
Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
"WINE_PRESENTATION_GUIDELINE", store guidelines about how to present wines to the user appropriately. The input is "null" keyword.
"END_CONVER_GUIDELINE", store guidelines about how to end the conversation with the user appropriately. The input is "null" keyword.
"""
system_msg = Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => systemmsg),
]
)
push!(newAgent.chathistory, system_msg)
return newAgent
end
mutable struct virtualcustomer <: agent
name::String # agent name
id::String # agent id
systemmsg::String # system message
tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
chathistory::Vector{Dict{String, Any}}
memory::Dict{String, Any}
context # NamedTuple of functions
llmFormatName::String
end
function virtualcustomer(
context, # NamedTuple of functions
;
name::String= "Assistant",
id::String= string(uuid4()),
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
llmFormatName::String= "granite3",
systemmsg::String=
"""
Your name: $name
Your sex: Female
Your role: You are a helpful assistant.
You should follow the following guidelines:
- Focus on the latest conversation.
- Your like to be short and concise.
Let's begin!
""",
)
tools = Dict( # update input format
"chatbox"=> Dict(
"description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
"input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
"output" => "" ,
),
)
""" Memory
Ref: Chat prompt format is openai
chathistory = [
Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => system_msg),
]
),
Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data1_uri)
)
]
)
]
"""
memory = Dict{String, Any}(
"shortmem"=> OrderedDict{String, Any}(
),
"scratchpad"=> "",
"events"=> Vector{Dict{String, Any}}(),
"state"=> Dict{String, Any}(
),
"recap"=> OrderedDict{String, Any}(),
)
newAgent = virtualcustomer(
name,
id,
systemmsg,
tools,
maxHistoryMsg,
chathistory,
memory,
context,
llmFormatName
)
return newAgent
end
end # module type
+588
View File
@@ -0,0 +1,588 @@
"""
types.jl - Core types for AgentCore
This module defines the fundamental types used throughout the AgentCore package.
"""
module Types
using Dates
using UUIDs
using JSON3
using Unicode
# ============================================================================
# Basic type aliases
# ============================================================================
const Timestamp = Int64
# ============================================================================
# Thinking level enum
# ============================================================================
@enum ThinkingLevel begin
THINKING_OFF = "off"
THINKING_MINIMAL = "minimal"
THINKING_LOW = "low"
THINKING_MEDIUM = "medium"
THINKING_HIGH = "high"
THINKING_XHIGH = "xhigh"
THINKING_MAX = "max"
end
# ============================================================================
# Tool execution modes
# ============================================================================
@enum ToolExecutionMode begin
EXECUTION_SEQUENTIAL = "sequential"
EXECUTION_PARALLEL = "parallel"
end
# ============================================================================
# Queue drain modes
# ============================================================================
@enum QueueMode begin
QUEUE_ALL = "all"
QUEUE_ONE_AT_A_TIME = "one-at-a-time"
end
# ============================================================================
# Message content types
# ============================================================================
abstract type MessageContent end
struct TextContent <: MessageContent
text::String
end
struct ImageContent <: MessageContent
data::String
mime_type::String
end
# ============================================================================
# Message types
# ============================================================================
abstract type Message end
struct UserMessage <: Message
role::String
content::Vector{MessageContent}
timestamp::Timestamp
end
struct AssistantMessage <: Message
role::String
content::Vector{MessageContent}
api::String
provider::String
model::String
usage::Usage
stop_reason::String
error_message::Union{String, Nothing}
timestamp::Timestamp
end
struct ToolResultMessage <: Message
role::String
tool_call_id::String
tool_name::String
content::Vector{MessageContent}
details::Any
usage::Union{Usage, Nothing}
added_tool_names::Union{Vector{String}, Nothing}
is_error::Bool
timestamp::Timestamp
end
# ============================================================================
# Usage statistics
# ============================================================================
struct UsageCost
input::Float64
output::Float64
cache_read::Float64
cache_write::Float64
total::Float64
end
struct Usage
input::Int64
output::Int64
cache_read::Int64
cache_write::Int64
total_tokens::Int64
cost::UsageCost
end
# ============================================================================
# Model types
# ============================================================================
struct ModelCost
input::Float64
output::Float64
cache_read::Float64
cache_write::Float64
end
struct Model{Api}
id::String
name::String
api::Api
provider::String
base_url::String
reasoning::Bool
input::Vector{String}
cost::ModelCost
context_window::Int64
max_tokens::Int64
end
# ============================================================================
# Agent message union type
# ============================================================================
abstract type AgentMessage end
# Custom message types can extend this via multiple dispatch
struct CustomMessage <: AgentMessage
message::AgentMessage
custom_type::String
end
# ============================================================================
# Tool types
# ============================================================================
struct AgentToolResult{T}
content::Vector{MessageContent}
details::T
usage::Union{Usage, Nothing}
added_tool_names::Union{Vector{String}, Nothing}
terminate::Union{Bool, Nothing}
end
struct AgentTool{TParameters, TDetails}
name::String
label::String
description::String
parameters::TParameters
execute::Function
prepare_arguments::Union{Function, Nothing}
execution_mode::Union{ToolExecutionMode, Nothing}
end
# ============================================================================
# Agent context
# ============================================================================
struct AgentContext
system_prompt::String
messages::Vector{AgentMessage}
tools::Union{Vector{AgentTool}, Nothing}
end
# ============================================================================
# Event types
# ============================================================================
abstract type AgentEvent end
struct AgentStartEvent <: AgentEvent end
struct AgentEndEvent <: AgentEvent
messages::Vector{AgentMessage}
end
struct TurnStartEvent <: AgentEvent end
struct TurnEndEvent <: AgentEvent
message::AgentMessage
tool_results::Vector{ToolResultMessage}
end
struct MessageStartEvent <: AgentEvent
message::AgentMessage
end
struct MessageUpdateEvent <: AgentEvent
message::AgentMessage
assistant_message_event::Any
end
struct MessageEndEvent <: AgentEvent
message::AgentMessage
end
struct ToolExecutionStartEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
end
struct ToolExecutionUpdateEvent <: AgentEvent
tool_call_id::String
tool_name::String
args::Any
partial_result::Any
end
struct ToolExecutionEndEvent <: AgentEvent
tool_call_id::String
tool_name::String
result::Any
is_error::Bool
end
# ============================================================================
# Assistant message event types
# ============================================================================
abstract type AssistantMessageEvent end
struct StartEvent <: AssistantMessageEvent
partial::AssistantMessage
end
struct TextStartEvent <: AssistantMessageEvent
content_index::Int64
partial::AssistantMessage
end
struct TextDeltaEvent <: AssistantMessageEvent
content_index::Int64
delta::String
partial::AssistantMessage
end
struct TextEndEvent <: AssistantMessageEvent
content_index::Int64
content::String
partial::AssistantMessage
end
struct DoneEvent <: AssistantMessageEvent
reason::String
usage::Usage
message::AssistantMessage
end
struct ErrorEvent <: AssistantMessageEvent
reason::String
error_message::Union{String, Nothing}
usage::Usage
error::AssistantMessage
end
# ============================================================================
# Agent state
# ============================================================================
mutable struct AgentState
system_prompt::String
model::Model
thinking_level::ThinkingLevel
tools::Vector{AgentTool}
messages::Vector{AgentMessage}
is_streaming::Bool
streaming_message::Union{AgentMessage, Nothing}
pending_tool_calls::Set{String}
error_message::Union{String, Nothing}
function AgentState(
system_prompt::String="",
model::Model=Model("", "", "unknown", "unknown", "", false, String[], ModelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
thinking_level::ThinkingLevel=THINKING_OFF,
tools::Vector{AgentTool}=AgentTool[],
messages::Vector{AgentMessage}=AgentMessage[],
)
new(
system_prompt,
model,
thinking_level,
copy(tools),
copy(messages),
false,
nothing,
Set{String}(),
nothing,
)
end
end
# ============================================================================
# Tool call types
# ============================================================================
struct ToolCall
type::String
id::String
name::String
arguments::Dict{String, Any}
partial_json::Union{String, Nothing}
end
# ============================================================================
# Context transform types
# ============================================================================
struct PrepareNextTurnContext
message::AssistantMessage
tool_results::Vector{ToolResultMessage}
context::AgentContext
new_messages::Vector{AgentMessage}
end
struct AgentLoopTurnUpdate
context::Union{AgentContext, Nothing}
model::Union{Model, Nothing}
thinking_level::Union{ThinkingLevel, Nothing}
end
# ============================================================================
# Before/After tool call types
# ============================================================================
struct BeforeToolCallContext
assistant_message::AssistantMessage
tool_call::ToolCall
args::Any
context::AgentContext
end
struct BeforeToolCallResult
block::Union{Bool, Nothing}
reason::Union{String, Nothing}
end
struct AfterToolCallContext
assistant_message::AssistantMessage
tool_call::ToolCall
args::Any
result::AgentToolResult
is_error::Bool
context::AgentContext
end
struct AfterToolCallResult
content::Union{Vector{MessageContent}, Nothing}
details::Union{Any, Nothing}
is_error::Union{Bool, Nothing}
usage::Union{Usage, Nothing}
terminate::Union{Bool, Nothing}
end
# ============================================================================
# Stream function signature
# ============================================================================
const StreamFn = Function
# ============================================================================
# File types
# ============================================================================
struct FileKind
value::String
end
const FILE_KIND_FILE = FileKind("file")
const FILE_KIND_DIRECTORY = FileKind("directory")
const FILE_KIND_SYMLINK = FileKind("symlink")
struct FileInfo
name::String
path::String
kind::FileKind
size::Int64
mtime_ms::Int64
end
struct FileError <: Exception
code::String
message::String
path::Union{String, Nothing}
cause::Union{Exception, Nothing}
end
struct ExecutionError <: Exception
code::String
message::String
cause::Union{Exception, Nothing}
end
struct CompactionError <: Exception
code::String
message::String
cause::Union{Exception, Nothing}
end
struct BranchSummaryError <: Exception
code::String
message::String
cause::Union{Exception, Nothing}
end
struct SessionError <: Exception
code::String
message::String
cause::Union{Exception, Nothing}
end
struct AgentHarnessError <: Exception
code::String
message::String
cause::Union{Exception, Nothing}
end
# ============================================================================
# Session tree entry types
# ============================================================================
abstract type SessionTreeEntry end
struct SessionTreeEntryBase
type::String
id::String
parent_id::Union{String, Nothing}
timestamp::String
end
struct MessageEntry <: SessionTreeEntry
base::SessionTreeEntryBase
message::AgentMessage
end
struct ThinkingLevelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
thinking_level::String
end
struct ModelChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
provider::String
model_id::String
end
struct ActiveToolsChangeEntry <: SessionTreeEntry
base::SessionTreeEntryBase
active_tool_names::Vector{String}
end
struct CompactionEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
summary::String
first_kept_entry_id::Union{String, Nothing}
tokens_before::Int64
retained_tail::Union{Vector{AgentMessage}, Nothing}
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
struct BranchSummaryEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
from_id::String
summary::String
details::Union{T, Nothing}
usage::Union{Usage, Nothing}
from_hook::Bool
end
struct CustomEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
data::Union{T, Nothing}
end
struct CustomMessageEntry{T} <: SessionTreeEntry
base::SessionTreeEntryBase
custom_type::String
content::String
details::Union{T, Nothing}
display::Bool
end
struct LabelEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::String
label::Union{String, Nothing}
end
struct SessionInfoEntry <: SessionTreeEntry
base::SessionTreeEntryBase
name::Union{String, Nothing}
end
struct LeafEntry <: SessionTreeEntry
base::SessionTreeEntryBase
target_id::Union{String, Nothing}
end
# ============================================================================
# Session context
# ============================================================================
struct SessionContext
messages::Vector{AgentMessage}
thinking_level::String
model::Union{Dict{String, String}, Nothing}
active_tool_names::Union{Vector{String}, Nothing}
end
# ============================================================================
# Session stats
# ============================================================================
struct SessionStats
message_count::Int64
cached_tokens::Int64
uncached_tokens::Int64
total_tokens::Int64
cost_total::Float64
end
# ============================================================================
# Session metadata
# ============================================================================
abstract type SessionMetadata end
struct JsonlSessionMetadata <: SessionMetadata
id::String
created_at::String
cwd::String
path::String
parent_session_path::Union{String, Nothing}
metadata::Union{Dict{String, Any}, Nothing}
end
# ============================================================================
# Session storage interface
# ============================================================================
abstract type SessionStorage{T<:SessionMetadata} end
# ============================================================================
# Session repo interface
# ============================================================================
abstract type SessionRepo<
TMetadata<:SessionMetadata,
TCreateOptions,
TListOptions
> end
# ============================================================================
# Helper functions
# ============================================================================
function create_timestamp()::String
return string(Dates.now(Dates.UTC))
end
function uuidv7()::String
return string(UUIDs.uuid7())
end
function uuidstring()::String
return string(UUIDs.uuid4())
end
function tempname()::String
return tempname()
end
end
-457
View File
@@ -1,457 +0,0 @@
module util
export clearhistory, addNewMessage, chatHistoryToText, eventdict, noises, createTimeline,
availableWineToText, createEventsLog, createChatLog, checkAgentResponse_JSON,
checkAgentResponse_text
using UUIDs, Dates, DataStructures, HTTP, JSON
using GeneralUtils
using ..type
# ---------------------------------------------- 100 --------------------------------------------- #
""" Clear agent chat history.
# Arguments
- `a::agent`
an agent
# Return
- nothing
# Example
```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict(
"receiveprompt"=>Dict(
"mqtttopic"=> "testtopic/receive",
),
"receiveinternal"=>Dict(
"mqtttopic"=> "testtopic/internal",
),
"text2text"=>Dict(
"mqtttopic"=> "testtopic/text2text",
),
)
julia> a = YiemAgent.sommelier(
client,
msgMeta,
agentConfig,
)
julia> YiemAgent.addNewMessage(a, "user", "hello")
julia> YiemAgent.clearhistory(a)
```
# TODO
- [PENDING] clear memory
# Signature
"""
function clearhistory(a::T) where {T<:agent}
empty!(a.chathistory)
empty!(a.memory["shortmem"])
empty!(a.memory["events"])
a.memory["chatbox"] = ""
end
""" Add new message to agent.
messages => Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "Describe this image for me"),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data_uri)
)
]
)
Arguments\n
-----
a::agent
an agent
role::String
message sender role i.e. system, user or assistant
text::String
message text
Return\n
-----
nothing
Example\n
-----
```jldoctest
```
Signature\n
-----
"""
function addNewMessage(a::T1, name::String, userinput::T2;
maximumMsg::Integer=30) where {T1<:agent, T2<:AbstractDict}
# if name ∉ ["system", "user", "assistant"] # guard against typo
# error("name is not in agent.availableRole $(@__LINE__)")
# end
#TODO summarize the oldest 10 message
if length(a.chathistory) > maximumMsg
summarize(a.chathistory)
else
# userinput["timestamp"] = Dates.now()
push!(a.chathistory, userinput)
end
end
""" Converts a vector of dictionaries to a formatted string.
This function takes in a vector of dictionaries and outputs a single string where each dictionary's keys are prefixed by their values.
# Arguments
- `vecd::Vector`
A vector of dictionaries containing chat messages
- `withkey::Bool`
Whether to include the name as a prefix in the output text. Default is true
- `range::Union{Nothing,UnitRange,Int}`
Optional range of messages to include. If nothing, includes all messages
# Returns
A formatted string where each line contains either:
- If withkey=true: "name> message\n"
- If withkey=false: "message\n"
# Example
julia> using Revise
julia> using GeneralUtils
julia> vecd = [Dict("name" => "John", "text" => "Hello"), Dict("name" => "Jane", "text" => "Goodbye")]
julia> GeneralUtils.vectorOfDictToText(vecd, withkey=true)
"John> Hello\nJane> Goodbye\n"
```
"""
function chatHistoryToText(vecd::Vector; withkey=true, range=nothing)::String
# Initialize an empty string to hold the final text
text = ""
# Get the elements within the specified range, or all elements if no range provided
elements = isnothing(range) ? vecd : vecd[range]
# Determine whether to include the key in the output text or not
if withkey
# Loop through each dictionary in the input vector
for d in elements
# Extract the 'name' and 'text' keys from the dictionary
name = titlecase(d[:name])
_text = d[:text]
# Append the formatted string to the text variable
text *= "$name> $_text \n"
end
else
# Loop through each dictionary in the input vector
for d in elements
# Iterate over all key-value pairs in the dictionary
for (k, v) in d
# Append the formatted string to the text variable
text *= "$v \n"
end
end
end
# Return the final text
return text
end
function availableWineToText(vecd::Vector)::String
# Initialize an empty string to hold the final text
rowtext = ""
# Loop through each dictionary in the input vector
for (i, d) in enumerate(vecd)
# Iterate over all key-value pairs in the dictionary
temp = []
for (k, v) in d
# Append the formatted string to the text variable
t = "$k:$v"
push!(temp, t)
end
_rowtext = join(temp, ',')
rowtext *= "$i) $_rowtext "
end
return rowtext
end
""" Create a dictionary representing an event with optional details.
# Arguments
- `event_description::Union{String, Nothing}`
A description of the event
- `timestamp::Union{DateTime, Nothing}`
The time when the event occurred
- `subject::Union{String, Nothing}`
The subject or entity associated with the event
- `thought::Union{AbstractDict, Nothing}`
Any associated thoughts or metadata
- `action_name::Union{String, Nothing}`
The name of the action performed (e.g., "CHAT", "CHECKINVENTORY")
- `action_input::Union{String, Nothing}`
Input or parameters for the action
- `location::Union{String, Nothing}`
Where the event took place
- `equipment_used::Union{String, Nothing}`
Equipment involved in the event
- `material_used::Union{String, Nothing}`
Materials used during the event
- `outcome::Union{String, Nothing}`
The result or consequence of the event after action execution
- `note::Union{String, Nothing}`
Additional notes or comments
# Returns
A dictionary with event details as symbol-keyed key-value pairs
"""
function eventdict(;
event_description::Union{String, Nothing}=nothing,
timestamp::Union{DateTime, Nothing}=nothing,
subject::Union{String, Nothing}=nothing,
thought::Union{AbstractDict, Nothing}=nothing,
action_name::Union{String, Nothing}=nothing, # "CHAT", "CHECKINVENTORY", "PRESENT_WINE_GUIDELINE", etc
action_input::Union{String, Nothing}=nothing,
location::Union{String, Nothing}=nothing,
equipment_used::Union{String, Nothing}=nothing,
material_used::Union{String, Nothing}=nothing,
observation::Union{String, Nothing}=nothing,
note::Union{String, Nothing}=nothing,
)
d = Dict{String, Any}(
"event_description"=> event_description,
"timestamp"=> timestamp,
"subject"=> subject,
"thought"=> thought,
"action_name"=> action_name,
"action_input"=> action_input,
"location"=> location,
"equipment_used"=> equipment_used,
"material_used"=> material_used,
"observation"=> observation,
"note"=> note,
)
return d
end
""" Create a formatted timeline string from a sequence of events.
# Arguments
- `events::T1`
Vector of event dictionaries containing subject, action_input and optional outcome fields
Each event dictionary should have the following keys:
- :subject - The subject or entity performing the action
- :action_input - The action or input performed by the subject
- :observation - (Optional) The result or outcome of the action
# Returns
- `timeline::String`
A formatted string representing the events with their subjects, actions, and optional outcomes
Format: "{index}) {subject}> {action_input} {outcome}\n" for each event
# Example
events = [
Dict("subject" => "User", "action_input" => "Hello", "observation" => nothing),
Dict("subject" => "Assistant", "action_input" => "Hi there!", "observation" => "with a smile")
]
timeline = createTimeline(events)
# 1) User> Hello
# 2) Assistant> Hi there! with a smile
"""
function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothing
) where {T1<:AbstractVector}
# Initialize empty timeline string
timeline = ""
# Determine which indices to use - either provided range or full length
ind =
if eventindex !== nothing
[eventindex...]
else
1:length(events)
end
# Iterate through events and format each one
for i in ind
event = events[i]
# If no outcome exists, format without outcome
# if event["action_name"] == "CHAT_BOX"
# timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"])\n"
# elseif event["action_name"] == "CHECKINVENTORY" && event["observation"] === nothing
# timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"]), observation: Not done yet.\n"
if event["action_name"] == "SEARCH_WINE_DATABASE"
timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"]), observation: $(event["observation"])\\n"
else
timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"])\\n"
end
end
# Return formatted timeline string
return timeline
end
function createEventsLog(events::T1; index::Union{UnitRange, Nothing}=nothing
) where {T1<:AbstractVector}
# Initialize empty log array
log = Dict{String, String}[]
# Determine which indices to use - either provided range or full length
ind =
if index !== nothing
[index...]
else
1:length(events)
end
# Iterate through events and format each one
for i in ind
event = events[i]
# If no outcome exists, format without outcome
if event["observation"] === nothing
subject = event["subject"]
action_name = event["action_name"]
action_input = event["action_input"]
str = "action_name: $action_name, action_input: $action_input"
d = Dict{String, String}("name"=>subject, "text"=>str)
push!(log, d)
else
subject = event["subject"]
action_name = event["action_name"]
action_input = event["action_input"]
observation = event["observation"]
str = "action_name: $action_name, action_input: $action_input, observation: $observation"
d = Dict{String, String}("name"=>subject, "text"=>str)
push!(log, d)
end
end
return log
end
function createChatLog(chatdict::T1; index::Union{UnitRange, Nothing}=nothing
) where {T1<:AbstractVector}
# Initialize empty log array
log = Dict{String, String}[]
# Determine which indices to use - either provided range or full length
ind =
if index !== nothing
[index...]
else
1:length(chatdict)
end
# Iterate through events and format each one
for i in ind
event = chatdict[i]
subject = event["name"]
text = event["text"]
d = Dict{String, String}("name"=>subject, "text"=>text)
push!(log, d)
end
return log
end
function checkAgentResponse_text(response::String, requiredHeader::T
)::Tuple where {T<:Array{String}}
detected_kw = GeneralUtils.detectKeywordVariation(requiredHeader, response)
missingkeys = [k for (k, v) in detected_kw if v === nothing]
ispass = false
errormsg = nothing
if !isempty(missingkeys)
errormsg = "$missingkeys are missing from your previous response"
ispass = false
elseif sum([length(i) for i in values(detected_kw)]) > length(requiredHeader)
errormsg = "Your previous attempt has duplicated points according to the required response format"
ispass = false
else
ispass = true
end
return (ispass, errormsg)
end
end # module util