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V0.8.0 add tools #41

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Tools allow the agent to perform actions and fetch data. Each tool defines a **schema** (what arguments it accepts) and an **execution function** (what it does). Tools allow the agent to perform actions and fetch data. Each tool defines a **schema** (what arguments it accepts) and an **execution function** (what it does).
## Quick Start
Add a new tool by creating a `.jl` file in `src/tools/`. The file must define a `getTool()` function that returns an `agentTool`:
```julia
# src/tools/my_tool.jl
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
city = args["city"]
return agentToolResult(
[textContent("Hello from $(city)!")],
Dict{Any,Any}(), nothing, false
)
end
function getTool()::agentTool
return agentTool(
name = "my_tool",
label = "My Tool",
description = "Says hello to a city.",
inputSchema = Dict{String,Any}(
"type" => "object",
"properties" => Dict(
"city" => Dict("type" => "string", "description" => "City name")
),
"required" => ["city"]
),
execute = executeTool,
prepareArguments = nothing,
validateRequiredArgs = nothing,
parallelToolExecute = false
)
end
```
When `loadTools()` or `registerTool()` is called, the tool becomes available to the agent.
## Tool Anatomy ## Tool Anatomy
Each tool has 3 main parts: Each tool has 3 main parts:
@@ -155,220 +118,177 @@ end
Return `nothing` to pass, or an error `String` to fail. The error is fed back to the LLM so it can retry with corrected arguments. Return `nothing` to pass, or an error `String` to fail. The error is fed back to the LLM so it can retry with corrected arguments.
## Tool Lifecycle — Framework Internals ## Tool Discovery and Lifecycle
This section traces the full code path from the moment the LLM returns tool calls to the final result being fed back into the conversation. All code references are to `agentCore.jl`. The agent iterates through tools via a **discover → execute → loop** cycle. Here is the complete flow from the framework author's perspective:
### Phase 1: Detect Tool Calls in LLM Response ### The Agent Loop
After the LLM returns an `assistantMessage`, the loop at `agentCore.jl:220-244` inspects each `content` block:
```julia ```julia
# agentCore.jl:217-244 # agentCore.jl:175 - _process_message()
has_tool_calls = false while true
# 1. Drain messages from inputChannel
while isready(agent.inputChannel)
raw_msg = take!(agent.inputChannel)
user_msg = OpenAiToUserMessage(raw_msg)
push!(agent._state.messages, user_msg)
end
# 2. Format messages for LLM
ctx = agent.prepareContext(agent._state)
formatted = agent.formatMsgForLLM(ctx)
# 3. Call LLM
response = agent.llmCall(formatted)
# 4. Check if LLM used tool calls
if has_tool_calls(response.content)
# 5. Execute tools, feed results back to LLM, loop
else
# 6. No tool calls — return final response
break
end
end
```
### Step 1: Tool Discovery
Tools are discovered from `agent._state.tools`, which is a `Vector{agentTool}` populated during agent creation:
```julia
# Loading tools
tools = loadTools("src/tools") # returns Vector{agentTool}
# Passing to agent
agent = yiemAgent(
systemPrompt = "...",
tools = tools, # ← tools stored in agent._state.tools
llmCall = my_llm_call,
agentEventSink = my_event_sink,
)
```
When the LLM response contains tool calls, the agent builds an `agentContext` with those tools:
```julia
context = agentContext(
agent._state.systemPrompt,
agent._state.messages,
agent._state.tools, # ← tools available for discovery
)
```
### Step 2: Extract Tool Calls from LLM Response
The agent inspects the `response.content` blocks for `tool_calls`:
```julia
# agentCore.jl:217-245
tool_call_list = agentToolCall[] tool_call_list = agentToolCall[]
for content_block in response.content for content_block in response.content
if content_block isa Dict if content_block isa Dict
# OpenAI-style: type == "tool_calls" with array of tool calls if get(content_block, :type, "") == "tool_calls"
if get(content_block, :type, "") == "tool_calls" # OpenAI format: {"type": "tool_calls", "tool_calls": [...]}
for tc_data in get(content_block, :tool_calls, []) for tc_data in get(content_block, :tool_calls, [])
tc = agentToolCall( tc = agentToolCall(
type="function", type = "function",
id=get(tc_data, :id, string(uuid4())), id = get(tc_data, :id, string(uuid4())),
name=get(tc_data, :function, Dict{String,Any}())[:name], name = get(tc_data, :function, Dict())[:name],
arguments=get(tc_data, :function, Dict{String,Any}())[:arguments], arguments = get(tc_data, :function, Dict())[:arguments],
) )
push!(tool_call_list, tc) push!(tool_call_list, tc)
end end
# Alternative style: type == "tool_call" single dict per block elseif get(content_block, :type, "") == "tool_call"
elseif get(content_block, :type, "") == "tool_call" # Alternative format: single tool_call block
tc = agentToolCall( tc = agentToolCall(
type="function", type = "function",
id=get(tc_data, :id, string(uuid4())), id = get(content_block, :id, string(uuid4())),
name=get(tc_data, :name, ""), name = get(content_block, :name, ""),
arguments=get(tc_data, :arguments, Dict{String,Any}()), arguments = get(content_block, :arguments, Dict()),
) )
push!(tool_call_list, tc) push!(tool_call_list, tc)
end
end
end
```
Each content block with `type == "tool_calls"` or `type == "tool_call"` extracts an `agentToolCall` (id, name, arguments dict) and collects them into a `Vector{agentToolCall}`.
### Phase 2: Dispatch to Sequential or Parallel Execution
At `agentCore.jl:247`, the framework checks if any tool calls exist and decides execution mode:
```julia
# agentCore.jl:247-265
context = agentContext(agent._state.systemPrompt, agent._state.messages, agent._state.tools)
config = agentLoopConfig(
agent._state.tools,
agent.beforeToolCall,
agent.afterToolCall,
agent.parallelToolExecute ? "parallel" : "sequential",
)
batch = executeToolCalls(context, response, tool_call_list, config, signal, emit)
```
`executeToolCalls` (`agentCore.jl:988-1015`) checks:
- `config.toolExecution == "sequential"` → sequential mode
- Any tool has `parallelToolExecute == false` → sequential mode
- Otherwise → parallel mode
### Phase 3: Per-Call Preparation (`prepareToolCall`)
Each tool call goes through `prepareToolCall` (`agentCore.jl:511-547`):
```
1. Look up tool by name: find(t -> t.name == tc.name, context.tools)
2. If not found → immediateOutcome("Tool X not found", true)
3. Run tool.prepareArguments (if defined) → transforms raw LLM args
4. Run validateToolArguments → validateRequiredArgs (hook or default)
→ if fails → throws ArgumentError → caught below
5. Run beforeToolCall hook (if defined) → can block execution
→ if blocked → immediateOutcome("Tool execution was blocked", true)
6. Return preparedToolCall(tool, tc, validatedArgs)
```
If any step throws (validation, prepareArguments, beforeToolCall), the catch block at `agentCore.jl:545` converts it to an `immediateOutcome`:
```julia
catch err
return immediateOutcome(createErrorToolResult(sprint(showerror, err)), true)
end
```
### Phase 4: Execution (`executePreparedToolCall`)
For each `preparedToolCall`, `executePreparedToolCall` (`agentCore.jl:589-617`) runs:
```julia
function executePreparedToolCall(prep::preparedToolCall, signal, emit)::executedOutcome
updateEvents = promise[]
accepting = true
try
result = prep.tool.execute(
prep.toolCall.id, prep.args, signal,
partialResult -> begin
if accepting
push!(updateEvents, emit(toolExecUpdateEvent(..., partialResult)))
end end
end
)
accepting = false
wait.(updateEvents)
return executedOutcome(result, false)
catch err
accepting = false
wait.(updateEvents)
return executedOutcome(createErrorToolResult(sprint(showerror, err)), true)
end
end
```
Key behaviors:
- Calls `tool.execute(id, args, signal, onPartialResult)` — your tool's `executeTool` function
- `signal` can be checked inside `executeTool` for cancellation
- `onPartialResult` is called for streaming updates, which are emitted as `toolExecutionUpdate` events
- `accepting` guard prevents emitting updates after the result is already captured
- `wait.(updateEvents)` ensures all streaming updates are delivered before returning
- Execution errors are caught and returned as `executedOutcome(isError=true)` — never thrown
### Phase 5: Finalization (`finalizeExecutedToolCall`)
After execution, `finalizeExecutedToolCall` (`agentCore.jl:675-706`) runs the `afterToolCall` hook:
```julia
function finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)::finalizedOutcome
result = executed.result
isError = executed.isError
if config.afterToolCall !== nothing
try
after = config.afterToolCall(afterCtx(assistantMsg, prep.toolCall, prep.args, result, isError, context), signal)
if after !== nothing
# Hook can mutate: content, details, usage, terminate, isError
result = merge(result, dict(...))
isError = get(after, :isError, isError)
end
catch err
result = createErrorToolResult(sprint(showerror, err))
isError = true
end end
end
return finalizedOutcome(prep.toolCall, result, isError)
end end
``` ```
The hook can: ### Step 3: Execute Each Tool Call
- Mask sensitive data from result content
- Normalize usage tracking
- Flip `terminate: true` based on business logic
- Wrap errors in friendlier messages for the LLM
If the hook itself throws, the error is caught and converted to an error outcome. For each tool call, the agent runs through the **prepare → execute → finalize** pipeline:
### Phase 6: Emit Events and Create Result Message
Each call emits `toolExecutionEnd`:
```julia ```julia
function emitToolExecutionEnd(finalized::finalizedOutcome, emit::Function) # agentCore.jl:247-302
emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name, finalized.result, finalized.isError)) if has_tool_calls && length(tool_call_list) > 0
context = agentContext(agent._state.systemPrompt, agent._state.messages, agent._state.tools)
config = agentLoopConfig(agent._state.tools, agent.beforeToolCall, agent.afterToolCall, execution_mode)
signal = nothing
emit = agent.agentEventSink
# Execute all tool calls (sequential or parallel)
batch = executeToolCalls(context, response, tool_call_list, config, signal, emit)
# Save results to conversation history
for tool_result in batch.messages
push!(agent._state.messages, tool_result)
end
# If any tool requested termination, break the loop
if batch.terminate
final_response = build_final_response(batch)
break
end
# Otherwise, loop back to step 2 (format + call LLM again)
end end
``` ```
Then creates the `toolResultMessage` for conversation history (`agentCore.jl:373-379`): ### Step 4: The Per-Call Pipeline
```julia Each tool call goes through three phases:
function createToolResultMessage(f::finalizedOutcome)::toolResultMessage
return toolResultMessage(
"toolResult", f.toolCall.id, f.toolCall.name,
f.result.content, f.result.details, f.result.usage,
get(f.result, :addedToolNames, string[]), f.isError, nowMillis()
)
end
```
### Phase 7: Batch Assembly and Loop Control
In `executeToolCallsSequential` (`agentCore.jl:795-829`) or `executeToolCallsParallel` (`agentCore.jl:888-936`), all results are collected:
```julia
messages = toolResultMessage[]
for finalized in finalizedCalls
push!(messages, createToolResultMessage(finalized))
end
return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
```
`shouldTerminate` (`agentCore.jl:409`) returns `true` only if ALL tools in the batch set `result.terminate == true`. If `false`, the agent loop at `agentCore.jl:176-308` feeds the tool results back to the LLM for another turn.
### Data Flow Summary
``` ```
response.content (Vector{Any}) ┌─────────────────────────────────────────────────────────────────┐
└── phase 1: parse content blocks │ PREPARE → prepareToolCall() │
└── tool_call_list :: Vector{agentToolCall} │ │
└── phase 2: dispatch to sequential/parallel │ 1. Find tool by name in context.tools │
└── phase 3: prepareToolCall │ 2. Transform args via tool.prepareArguments (if defined) │
└── preparedToolCall or immediateOutcome │ 3. Validate via tool.validateRequiredArgs (or default) │
└── phase 4: executePreparedToolCall │ 4. Run beforeToolCall hook (if defined) │
└── executedOutcome │ └── on any failure → return immediateOutcome (skip execution) │
└── phase 5: finalizeExecutedToolCall │ └── success → return preparedToolCall │
└── finalizedOutcome ├─────────────────────────────────────────────────────────────────┤
└── phase 6: createToolResultMessage │ EXECUTE → executePreparedToolCall() │
└── toolResultMessage │ │
└── phase 7: agentToolCallBatch │ 1. emit toolExecutionStart event │
└── pushed to agent._state.messages │ 2. call tool.execute(toolCallId, args, signal, onPartialResult)│
└── loop back to LLM │ 3. wait for all pending update events │
│ └── on error → return executedOutcome(isError=true) │
│ └── success → return executedOutcome(isError=false) │
├─────────────────────────────────────────────────────────────────┤
│ FINALIZE → finalizeExecutedToolCall() │
│ │
│ 1. Run afterToolCall hook (if defined) │
│ - can mutate content, details, usage, terminate, isError │
│ 2. emit toolExecutionEnd event │
│ 3. createToolResultMessage → adds to conversation history │
│ └── return finalizedOutcome │
└─────────────────────────────────────────────────────────────────┘
``` ```
### Step 5: Feed Results Back to LLM
Tool results are added to `agent._state.messages` as `toolResultMessage` objects. On the next loop iteration, `formatMsgForLLM()` converts them to OpenAI format and the LLM receives the results:
```
Conversation history after tool execution:
[system] "You are a helpful assistant."
[user] "What's the weather in Tokyo?"
[assistant] (tool_calls: getWeather(city="Tokyo"))
[tool] tool_call_id="call_1", tool_name="getWeather", content="Weather in Tokyo: Sunny, 22°C"
```
The LLM then decides: call another tool, or return a final text answer.
## Execution Modes ## Execution Modes
### Sequential ### Sequential
@@ -424,124 +344,53 @@ tool = getTool() # from your tool module
registerTool(tool) registerTool(tool)
``` ```
## Using Tools with an Agent ## Complete Lifecycle Example
Loading tools only registers them — you must pass them to the `yiemAgent` and provide an `llmCall` function. Here is the complete flow:
```julia ```julia
using .YiemAgent # ─── USER SENDS MESSAGE ───────────────────────────────────────────
using .toolRegistry
# 1. Load tools from the tools directory
tools = loadTools("src/tools")
# [toolRegistry] Loading tool from: src/tools/getTime.jl
# [toolRegistry] Loaded tool: getTime — Time Lookup
# [toolRegistry] Loading tool from: src/tools/getWeather.jl
# [toolRegistry] Loaded tool: getWeather — Weather Lookup
# 2. Define your LLM call function
function my_llm_call(messages::Dict)::assistantMessage
# Call your LLM API here (OpenAI, Anthropic, local model, etc.)
# Return an assistantMessage with the response content
# If the LLM wants to call a tool, include tool_call content blocks
...
end
# 3. Define your event sink (optional, for logging/debugging)
function my_event_sink(event)
if event isa toolExecStartEvent
println("[EVENT] Tool start: $(event.toolName)")
elseif event isa toolExecEndEvent
status = event.isError ? "ERROR" : "OK"
println("[EVENT] Tool end: $(event.toolName) — $status")
end
end
# 4. Create the agent with tools
agent = yiemAgent(
systemPrompt = "You are a helpful assistant that can check weather and time.",
model = my_model,
tools = tools, # pass loaded tools
llmCall = my_llm_call, # your LLM function
agentEventSink = my_event_sink, # event handler
)
# 5. Send a message and get a response
run_agent(agent, "What's the weather in Tokyo?") run_agent(agent, "What's the weather in Tokyo?")
response = take_response(agent)
# response.content contains the LLM's reply (with tool results if applicable) # ─── LOOP ITERATION 1 ─────────────────────────────────────────────
# Agent formats messages and calls LLM
formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
response = agent.llmCall(formatted)
# LLM returns: {"content": [{"type": "tool_calls", "tool_calls": [{"name": "getWeather", "arguments": {"city": "Tokyo"}}]}]}
# Agent extracts tool call, builds context
context = agentContext(systemPrompt, messages, agent._state.tools)
tool_call_list = [agentToolCall("call_1", "getWeather", Dict("city" => "Tokyo"))]
# PREPARE: find tool, validate args
tool = find(t -> t.name == "getWeather", context.tools) # found!
validateRequiredArgs(Dict("city" => "Tokyo"), tool.inputSchema) # passes
beforeToolCall_hook(agentMsgCtx, nothing) # nil, skipped
# EXECUTE: call tool.execute()
result = tool.execute("call_1", Dict("city" => "Tokyo"), nothing, onPartialResult)
# Returns: agentToolResult([textContent("Weather in Tokyo: Sunny, 22°C")], Dict(), nothing, false)
# FINALIZE: afterToolCall hook, emit events
finalized = finalizedOutcome(tc, result, false)
emit(toolExecEndEvent("call_1", "getWeather", result, false))
msg = createToolResultMessage(finalized) # toolResultMessage for conversation history
# Add result to conversation
push!(agent._state.messages, msg)
# Messages now: [user: "What's the weather?", assistant: {tool_calls: getWeather}, tool: "Sunny, 22°C"]
# ─── LOOP ITERATION 2 ─────────────────────────────────────────────
# LLM called again with tool result included
formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
response = agent.llmCall(formatted)
# LLM returns: {"content": [{"type": "text", "text": "The weather in Tokyo is sunny, 22°C."}]}
# No tool calls detected → break loop, return final response
return assistantMessage(content=[textContent("The weather in Tokyo is sunny, 22°C.")], ...)
# ─── USER RECEIVES RESPONSE ───────────────────────────────────────
response = take_response(agent)
println(response.content) println(response.content)
# => "[textContent(\"The weather in Tokyo is sunny, 22°C.\")]"
# 6. When done, stop the agent
stop_agent(agent)
```
### How It Works
1. **User sends a message** via `run_agent(agent, "What's the weather in Tokyo?")`. The message goes into `inputChannel`.
2. **Agent loop** (`_agent_loop`) picks it up, converts it to a `userMessage`, and adds it to `agent._state.messages`.
3. **LLM is called** via `agent.llmCall(formatted_messages)`. The LLM sees the system prompt, conversation history, and the tool definitions in the prompt (via `formatMsgForLLM`).
4. **If the LLM uses a tool**, it returns a response with `tool_call` content blocks. The agent:
- Extracts each tool call (name, arguments)
- Runs validation (`validateRequiredArgs` or default)
- Executes the tool (or returns an error if validation fails)
- Feeds the result back as a `toolResultMessage` in the conversation
5. **LLM is called again** with the tool results. This repeats until the LLM returns a text response with no tool calls.
6. **Final response** is sent to `outputChannel` — retrieve it with `take_response(agent)`.
### Minimal Working Example
```julia
using .YiemAgent
using .toolRegistry
# Load tools
tools = loadTools("src/tools")
# Mock LLM that echoes back a tool call, then a text response
call_count = 0
function mock_llm_call(messages::Dict)::assistantMessage
global call_count += 1
if call_count == 1
# First call: LLM decides to use getWeather
return assistantMessage(
content=[
Dict("type" => "tool_calls",
"tool_calls" => [Dict("id" => "call_1", "name" => "getWeather",
"arguments" => Dict("city" => "Tokyo"))])
],
model = "mock",
usage = llmUsage(0, 0)
)
else
# Second call: LLM returns text (after tool result)
return assistantMessage(
content = [textContent("The weather in Tokyo is sunny, 22°C.")],
model = "mock",
usage = llmUsage(0, 0)
)
end
end
# Create agent
agent = yiemAgent(
systemPrompt = "You are a helpful assistant.",
tools = tools,
llmCall = mock_llm_call,
agentEventSink = e -> nothing, # no events
)
# Run
run_agent(agent, "What's the weather in Tokyo?")
response = take_response(agent)
stop_agent(agent)
``` ```
## Available Tools ## Available Tools
@@ -550,19 +399,3 @@ stop_agent(agent)
|---|---|---| |---|---|---|
| `getWeather` | Fetch weather for a city | Default (JSON Schema required) | | `getWeather` | Fetch weather for a city | Default (JSON Schema required) |
| `getTime` | Get current time for a timezone or city | Custom (cross-field + format) | | `getTime` | Get current time for a timezone or city | Custom (cross-field + format) |
## Example: Error Flow
When the LLM calls a tool with invalid arguments:
```
User: "What's the weather?"
└── LLM: call getWeather() with no arguments
└── prepareToolCall → validateRequiredArgs → "Missing required arguments: city"
└── immediateOutcome → error tool result
└── LLM sees: "Missing required arguments: city"
└── LLM retries: call getWeather(city="Tokyo")
└── executeTool → "Weather in Tokyo: Sunny, 22°C"
```
The agent feeds the error back to the LLM as a tool result message, allowing it to self-correct.