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Author SHA1 Message Date
ton 4e592173a6 update 2026-08-12 14:37:11 +07:00
ton 0cacb5c94a update 2026-08-12 04:35:34 +07:00
ton 6c96409969 update 2026-08-12 04:33:19 +07:00
ton 06d51c1ee9 update 2026-08-12 04:00:09 +07:00
ton 2ad3d1df38 update 2026-08-11 19:10:37 +07:00
ton 83c7770877 update 2026-08-11 18:57:53 +07:00
ton bad14fbe7f update 2026-08-11 18:42:34 +07:00
ton 578e8f55bd update 2026-08-11 18:28:03 +07:00
ton ae3e432b02 update 2026-08-11 17:35:56 +07:00
ton 7c14390400 update 2026-08-11 17:28:25 +07:00
ton 89885c1583 update 2026-08-11 16:43:48 +07:00
ton 5a27630ccf update 2026-08-11 12:15:05 +07:00
ton ed91260468 update 2026-08-10 20:37:28 +07:00
ton c13aeb3a74 Merge pull request 'V0.8.0 verify tool use' (#43) from v0.8.0-verify_tool_use into v0.8.0
Reviewed-on: #43
2026-08-10 13:10:57 +00:00
ton 287704778f update 2026-08-10 20:07:15 +07:00
ton a9fa23f01b update 2026-08-10 19:19:58 +07:00
ton c5cb18f0f1 update 2026-08-10 16:10:04 +07:00
ton c78f4b023d update 2026-08-10 14:55:09 +07:00
ton 1b69f69c7d update 2026-08-10 13:33:45 +07:00
ton 3891099eaa update readme 2026-08-10 10:39:30 +07:00
ton 268d340e2f Merge pull request 'V0.8.0 use tool module' (#42) from v0.8.0-use_tool_module into v0.8.0
Reviewed-on: #42
2026-08-10 02:48:25 +00:00
16 changed files with 2362 additions and 1420 deletions
+2 -2
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@@ -6,7 +6,7 @@ Julia framework for building agents with tool use.
1. Install dependencies: `]add JSON, DataStructures, UUIDs, Dates, ...`
2. Create a `yiemAgent` with `loadTools("src/tools")`
3. Call `run_agent(agent, "message")` then `take_response(agent)`
3. Call `runAgent(agent, "message")` then `takeResponse(agent)`
## Architecture
@@ -16,7 +16,7 @@ src/
├── type.jl # Core types (messages, tools, agent state)
├── utils.jl # Message formatting, validation
├── agentCore.jl # Agent loop, tool execution pipeline
├── api.jl # Public API (run_agent, take_response, etc.)
├── api.jl # Public API (runAgent, takeResponse, etc.)
└── tools/
├── registry.jl # Tool registry (loadTools, registerTool, listTools)
├── getWeather.jl # Weather lookup tool
+1625
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File diff suppressed because it is too large Load Diff
+77 -2
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@@ -1,2 +1,77 @@
# ── executeToolCalls() Julia pseudo code ──────────────────────────
# Full call stack from runLoop → executeToolCalls → prepare → execute → finalize → emit
i am not sure that's the case. see my NATS message log:
<NATS debug message>
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "new user msg"
Info: debug
payload = "_process_message 3"
Info: debug
payload = "_process_message 5"
Info: debug
payload = "_process_message 6"
Info: debug
payload = "_process_message 7"
</NATS debug message>
my NATS receiver report the following for a long time
Info: debug
payload = "new user msg"
untill I Ctrl + d so shutdown the process then i got the following report
Info: debug
payload = "_process_message 3"
Info: debug
payload = "_process_message 5"
Info: debug
payload = "_process_message 6"
Info: debug
payload = "_process_message 7"
my point is if _process_message() actually run then this code in _process_message()
"raw_msg = take!(agent.inputChannel)"
should take the new msg message out of agent.inputChannel and there should be only one debug message showing
Info: debug
payload = "new user msg"
before reaching error("debug marker")
+1 -1
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@@ -13,7 +13,7 @@ module YiemAgent
include("utils.jl")
using .utils
include("tools/registry.jl")
include("toolRegistry.jl")
using .toolRegistry
# include("llmfunction.jl")
+203 -94
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@@ -1,14 +1,142 @@
module agentCore
export _agent_loop, OpenAiToUserMessage
export yiemAgent, _agent_loop, OpenAiToUserMessage
using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
DataFrames, Base.Threads
using GeneralUtils
using ..type, ..utils
using ..type, ..utils, ..toolRegistry
# ---------------------------------------------- 100 --------------------------------------------- #
"""
docstring
"""
mutable struct yiemAgent <: agent # High-level agent wrapper
_state::agentState # Current state (prompt, model, messages, tools, etc.)
# user sends prompt message to agent. if agent is idle, it process user message right away.
# if agent is running, it process user message after the current tool call finished.
inputChannel::Channel
# Buffers messages the user sends while the agent is busy. Processed after all inputChannel
# messages are handled and the agent is idle (not using a tool call).
followUpChannel::Channel
# agent sends response message to user after processing all user messages in inputChannel
# and all followUp messages.
outputChannel::Channel
_agent_loop::Union{Task, Nothing} # agent loop running in the background
# Preprocess/transform messages and context (modify, filter, prune, inject context from memory,
# reorder, ...) for a single LLM call in _process_message()'s loop.
# returns new Vector{agentMessage}
prepareContext::Union{Function, Nothing}
# Convert prepareContext()'s new Vector{agentMessage} to LLM message format
formatMsgForLLM::Function
# A callable struct. Actually invoke the LLM to get a completion response.
# The LLM response comes back as an assistantMessage whose content is an array of content blocks.
# Each block has a type — "text", "thinking", or "toolCall".
# The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
llmCall
# Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
beforeToolCall::Union{Function, Nothing}
# Callback invoked after executing a tool call to sanitize tools output so the output is ready
# to be converted into toolResults message
afterToolCall::Union{Function, Nothing}
# prepareNextTurn::Union{Function, Nothing} # Callback to prepare the next conversation turn
# prepareNextTurnWithContext::Union{Function, Nothing} # Same but receives context
sessionId::Union{String, Nothing} # Optional session identifier
maxRetryDelayMs::Union{Int64, Nothing} # Maximum delay between retries (ms)
parallelToolExecute::Bool # Default: false
agentEventSink # agent emits its status via this function
end
"""
Create a new yiemAgent instance with a background loop task.
Spawns a background `@spawn` task that runs the agent loop, listening
on `inputChannel` and `followUpChannel` channels concurrently.
# Keyword Arguments
- `systemPrompt::String`: System prompt for the agent
- `model`: LLM model to use
- `tools::OrderedDict{String, agentTool}`: Available tools keyed by name (default: empty)
- `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
- `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
- `llmCall::Function`: Function to invoke the LLM (required)
- `prepareContext::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
- `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
- `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
- `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
- `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
- `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
- `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
- `parallelToolExecute::Bool`: Run tool calls in parallel (default: `false`)
- `agentEventSink::Function`: Callback to receive agent events
# Returns
- A new `yiemAgent` instance with an active background task
"""
function yiemAgent(
toolsFolderPath::String,
llmCall,
;
systemPrompt::String="You are helpful assistant.",
model=nothing,
messages::Vector{agentMessage}=agentMessage[],
prepareContext::Function=prepareContext,
formatMsgForLLM::Function=formatMsgForLLM,
beforeToolCall::Function=beforeToolCall,
afterToolCall::Function=afterToolCall,
# prepareNextTurn::Union{Function, Nothing}=nothing,
# prepareNextTurnWithContext::Union{Function, Nothing}=nothing,
sessionId::Union{String, Nothing}=nothing,
maxRetryDelayMs::Union{Int64, Nothing}=nothing,
parallelToolExecute::Bool=false,
agentEventSink=agentEventSink,
)
# Create channels: input (user -> agent), followUp (async queue), output (agent -> user)
inputChannel = Channel(16)
followUp = Channel(32)
outputChannel = Channel(16)
# load tools from toolsFolderPath
toolStore1 = toolStore(name="myagent")
loadTools(toolStore1, toolsFolderPath)
# Create struct with a placeholder task, then spawn and replace it
agent = yiemAgent(
agentState(systemPrompt, model, getTools(toolStore1), messages),
inputChannel,
followUp,
outputChannel,
nothing, # placeholder — replaced below
prepareContext,
formatMsgForLLM,
llmCall,
beforeToolCall,
afterToolCall,
# prepareNextTurn,
# prepareNextTurnWithContext,
sessionId,
maxRetryDelayMs,
parallelToolExecute,
agentEventSink,
)
# Spawn the background loop and attach it
agent._agent_loop = @spawn _agent_loop(agent)
return agent
end
"""
Private agent loop. Runs in a background `@spawn` task.
@@ -81,7 +209,8 @@ function _agent_loop(agent::yiemAgent)
if isready(agent.inputChannel)
# message will be taken in _process_message()
msg = fetch!(agent.inputChannel)
msg = fetch(agent.inputChannel)
agent.agentEventSink("new user msg")
else
yield()
end
@@ -107,9 +236,11 @@ function _agent_loop(agent::yiemAgent)
# start _process_message loop
if agent._state.activeRun == false
agent.agentEventSink("_agent_loop 2")
# Dispatch message through the processing pipeline
processingTask = Threads.@spawn _process_message(agent)
processingTask = Threads.@spawn _process_message(agent)
agent._state.activeRun = true
agent.agentEventSink("_agent_loop 3")
end
# during agent runs, check followUp message after _process_message() is done
@@ -173,6 +304,7 @@ julia> # Currently returns a placeholder echo response
```
"""
function _process_message(agent::yiemAgent)::assistantMessage
agent.agentEventSink("_process_message 1")
# loop until llmCall() response didn't use tool calls
final_response = nothing
while true
@@ -185,21 +317,26 @@ function _process_message(agent::yiemAgent)::assistantMessage
Dict(
"type" => "image_url",
"image_url" => Dict("url" => "data:mime_type;base64,image2_base64_string")
)
),
]
),
)
"""
# Drain inputChannel and convert OpenAI-format messages to userMessage type
while isready(agent.inputChannel)
agent.agentEventSink("_process_message 2")
raw_msg = take!(agent.inputChannel)
agent.agentEventSink("_process_message 3")
if raw_msg === :shutdown
agent.agentEventSink("_process_message 4")
# Re-emit shutdown signal for the loop to handle
put!(agent.inputChannel, :shutdown)
break
end
agent.agentEventSink("_process_message 5")
user_msg = OpenAiToUserMessage(raw_msg)
push!(agent._state.messages, user_msg)
agent.agentEventSink("_process_message 6")
end
# call agent.prepareContext()
@@ -208,10 +345,11 @@ function _process_message(agent::yiemAgent)::assistantMessage
# Call agent.formatMsgForLLM(agent._state) to format for LLM
formatted_messages = agent.formatMsgForLLM(preparedContext)
agent.agentEventSink("_process_message 7")
# Call llmCall() (blocking — the task waits here)
error("debug marker")
response = agent.llmCall(formatted_messages)
error(5555555)
agent.agentEventSink("_process_message 8")
#WORKING Check if LLM used tool calls (inspect content for tool_call blocks)
has_tool_calls = false
@@ -509,41 +647,42 @@ prepareToolCall(context, msg, tc, config, abortedSignal)
```
"""
function prepareToolCall(
context::agentContext,
assistantMsg::assistantMessage,
toolCall::agentToolCall,
config::agentLoopConfig,
signal::Union{Nothing, abortSignal},
context::agentContext,
assistantMsg::assistantMessage,
toolCall::agentToolCall,
config::agentLoopConfig,
signal::Union{Nothing, abortSignal},
)::Union{preparedToolCall,immediateOutcome}
tool = get(context.tools, toolCall.name, nothing)
if tool === nothing
return immediateOutcome(createErrorToolResult("Tool $toolCall.name not found"), true)
tool = get(context.tools, toolCall.name, nothing)
if tool === nothing
return immediateOutcome(createErrorToolResult("Tool $toolCall.name not found"), true)
end
try
# 1. prepare arguments (tool-specific transform)
prepared = prepareToolCallArguments(tool, toolCall)
validatedArgs = validateToolArguments(tool, prepared)
# 2. beforeToolCall hook — can block
if config.beforeToolCall !== nothing
before = config.beforeToolCall(
beforeToolCallContext(assistantMsg, toolCall, validatedArgs, context),
signal
)
if signal !== nothing && signal.aborted
return immediateOutcome(createErrorToolResult("Operation aborted"), true)
end
if before !== nothing && before.block
return immediateOutcome(
createErrorToolResult(get(before, :reason, "Tool execution was blocked")), true)
end
end
try
# 1. prepare arguments (tool-specific transform)
prepared = prepareToolCallArguments(tool, toolCall)
validatedArgs = validateToolArguments(tool, prepared)
# 2. beforeToolCall hook — can block
if config.beforeToolCall !== nothing
before = config.beforeToolCall(
assistantMsgCtx(assistantMsg, toolCall, validatedArgs, context), signal
)
if signal !== nothing && signal.aborted
return immediateOutcome(createErrorToolResult("Operation aborted"), true)
end
if before !== nothing && before.block
return immediateOutcome(
createErrorToolResult(get(before, :reason, "Tool execution was blocked")), true)
end
end
return preparedToolCall(tool, toolCall, validatedArgs)
catch err
return immediateOutcome(createErrorToolResult(sprint(showerror, err)), true)
end
return preparedToolCall(tool, toolCall, validatedArgs)
catch err
return immediateOutcome(createErrorToolResult(sprint(showerror, err)), true)
end
end
# ── per-call execution ──────────────────────────────────────────
@@ -681,61 +820,28 @@ function finalizeExecutedToolCall(
signal::Union{Nothing,abortSignal},
)::finalizedOutcome
result = executed.result
isError = executed.isError
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
result = merge(result, dict(:content=>get(after,:content,result.content),
:details=>get(after,:details,result.details),
:usage=>get(after,:usage,result.usage),
:terminate=>get(after,:terminate,result.terminate)))
isError = get(after, :isError, isError)
end
catch err
result = createErrorToolResult(sprint(showerror, err))
isError = true
end
if config.afterToolCall !== nothing
try
after = config.afterToolCall(
afterToolCallContext(assistantMsg, prep.toolCall, prep.args, result, isError, context), signal
)
if after !== nothing
result = merge(result, dict(:content=>get(after,:content,result.content),
:details=>get(after,:details,result.details),
:usage=>get(after,:usage,result.usage),
:terminate=>get(after,:terminate,result.terminate)))
isError = get(after, :isError, isError)
end
catch err
result = createErrorToolResult(sprint(showerror, err))
isError = true
end
end
return finalizedOutcome(prep.toolCall, result, isError)
end
"""
emitToolExecutionEnd(finalized, emit)
Emits the `toolExecutionEnd` event with the finalized outcome,
signalling to listeners that the tool call has completed.
This event is part of the tool execution lifecycle:
`toolExecutionStart` → (zero or more `toolExecutionUpdate` events) →
`toolExecutionEnd`. Listeners (such as the TUI or logging systems)
use this lifecycle to track individual tool calls. The event carries
the final result so listeners have all the data they need without
requiring external state lookups.
# Arguments
- `finalized::finalizedOutcome`: The finalized outcome to report
- `emit::Function`: Event emitter
# Notes
- Part of a three-event lifecycle per tool call
- Carries the complete result so listeners need no external lookups
# Examples
```julia
# Emits a single event; returns nothing
emitToolExecutionEnd(finalized, emit)
# (emit receives toolExecEndEvent("call_1", "search_wine", result, false))
```
"""
function emitToolExecutionEnd(finalized::finalizedOutcome, emit::Function)
emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
finalized.result, finalized.isError))
return finalizedOutcome(prep.toolCall, result, isError)
end
# ── sequential execution ────────────────────────────────────────
@@ -816,7 +922,8 @@ function executeToolCallsSequential(
finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)
end
emitToolExecutionEnd(finalized, emit)
emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
finalized.result, finalized.isError))
push!(messages, createToolResultMessage(finalized))
push!(finalizedCalls, finalized)
@@ -903,13 +1010,15 @@ function executeToolCallsParallel(
if prep isa immediateOutcome
finalized = finalizedOutcome(tc, prep.result, prep.isError)
emitToolExecutionEnd(finalized, emit)
emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
finalized.result, finalized.isError))
push!(entries, finalized)
else
task = task() do
executed = executePreparedToolCall(prep, signal, emit)
finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)
emitToolExecutionEnd(finalized, emit)
emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
finalized.result, finalized.isError))
return finalized
end
schedule(task)
+14 -15
View File
@@ -5,12 +5,11 @@ export prompt
using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
DataFrames
using GeneralUtils
using ..type, ..utils
using ..type, ..utils, ..agentCore, ..toolRegistry
# ---------------------------------------------- 100 --------------------------------------------- #
"""
Send a message to the agent's input channel.
@@ -25,16 +24,16 @@ The agent processes messages from `inputChannel` in the background task.
- The same `agent` instance for chaining
# Notes
- Use `take_response(agent)` to receive the agent's response after sending a message.
- Use `follow_up(agent, msg)` to send messages while the agent is still processing.
- Use `takeResponse(agent)` to receive the agent's response after sending a message.
- Use `followUp(agent, msg)` to send messages while the agent is still processing.
# Examples
```jldoctest
julia> run_agent(agent, "Hello!")
julia> runAgent(agent, "Hello!")
yiemAgent(...)
```
"""
function run_agent(agent::yiemAgent, msg)
function runAgent(agent::yiemAgent, msg)
put!(agent.inputChannel, msg)
return agent
end
@@ -51,15 +50,15 @@ Blocks until the agent sends a response.
- An `assistantMessage` instance representing the agent's response
# Notes
- Use `run_agent(agent, msg)` to send a message before calling this function.
- Use `runAgent(agent, msg)` to send a message before calling this function.
# Examples
```jldoctest
julia> response = take_response(agent)
julia> response = takeResponse(agent)
assistantMessage(...)
```
"""
function take_response(agent::yiemAgent)
function takeResponse(agent::yiemAgent)
return take!(agent.outputChannel)
end
@@ -77,17 +76,17 @@ and before any tool call results are sent.
- The same `agent` instance for chaining
# Notes
- Use `run_agent(agent, msg)` for the primary message and `follow_up(agent, msg)` for additional
- Use `runAgent(agent, msg)` for the primary message and `followUp(agent, msg)` for additional
messages while the agent is processing.
- Follow-up messages are buffered in a separate channel (capacity 32 by default).
# Examples
```jldoctest
julia> follow_up(agent, "Also consider red wines")
julia> followUp(agent, "Also consider red wines")
yiemAgent(...)
```
"""
function follow_up(agent::yiemAgent, msg)
function followUp(agent::yiemAgent, msg)
put!(agent.followUpChannel, msg)
return agent
end
@@ -105,16 +104,16 @@ then closes all channels (`inputChannel`, `outputChannel`, `followUpChannel`).
- `nothing`
# Notes
- After calling `stop_agent`, the agent is no longer usable. A new agent must be created
- After calling `stopAgent`, the agent is no longer usable. A new agent must be created
for further interaction.
- If the background task throws a `TaskFailedException`, it is rethrown.
# Examples
```jldoctest
julia> stop_agent(agent)
julia> stopAgent(agent)
```
"""
function stop_agent(agent::yiemAgent)
function stopAgent(agent::yiemAgent)
put!(agent.inputChannel, :shutdown)
try
fetch(agent._agent_loop)
+271
View File
@@ -0,0 +1,271 @@
module toolRegistry
export toolStore, loadTools, registerTool, getTools, clearTools, listTool
using Dates
using JSON, DataStructures
using ..type
"""
Per-agent isolated tool storage.
Each agent gets its own `toolStore` so tool registration is independent —
`registerTool(store, tool)` only affects that agent's tool set.
# Fields
- `tools::OrderedDict{String, agentTool}` — keyed by name for O(1) lookup + ordered iteration
- `name::String` — identifier for debugging/logs
"""
struct toolStore
tools::OrderedDict{String, agentTool}
name::String
end
"""
toolStore(; name="default") -> toolStore
Create a new empty tool store.
# Keyword Arguments
- `name::String`: Display name for logging (default: `"default"`)
# Example
```julia
julia> store = toolStore(name="agent1")
toolStore(OrderedDict{String, agentTool}(), "agent1")
```
"""
function toolStore(; name::String="default")::toolStore
toolStore(OrderedDict{String, agentTool}(), name)
end
"""
listTool(store::toolStore) -> agentTool
Return an `agentTool` definition for listing registered tools.
Each call produces a **new** tool object that captures (closes over)
`store`. `loadTools` auto-registers one so the LLM can discover tools
at runtime.
# Arguments
- `store`: The tool store whose tools will be listed when the tool runs
# Example
```julia
julia> store = toolStore(name="agent1");
julia> loadTools(store, "src/tools") # auto-registers listTools
[toolRegistry:agent1] Loaded tool: getWeather (Weather Lookup)
[toolRegistry:agent1] Registered tool: listTools
julia> tools = getTools(store)
OrderedDict{String, agentTool} with 4 entries:
"getWeather" => agentTool(...)
"getTime" => agentTool(...)
"writeTool" => agentTool(...)
"listTools" => agentTool(...)
```
"""
function listTool(store::toolStore)::agentTool
return agentTool(
name = "listTools",
label = "List Tools",
description = "List all available tools with their names, labels, and descriptions. Use this before creating a new tool to check for name collisions.",
inputSchema = Dict{String,Any}(
"type" => "object",
"properties" => Dict{String,Any}(),
"required" => Any[]
),
execute = (toolCallId, args, signal, onPartialResult) -> begin
tools = getTools(store)
if isempty(tools)
result_text = "No tools registered."
else
lines = String["- $(t.name): $(t.label)$(t.description)" for (k, t) in tools]
result_text = "Available tools:\n" * join(lines, "\n")
end
return agentToolResult(
[textContent(result_text)],
Dict{Any,Any}("count" => length(tools)),
nothing, false
)
end,
prepareArguments = nothing,
validateRequiredArgs = nothing,
parallelToolExecute = false
)
end
"""
Load `.jl` tool files from `dir` into `store`, then auto-register
`listTool` so the LLM can discover available tools at runtime.
Each `.jl` file must define `function getTool()::agentTool ... end`.
Files are sorted alphabetically for deterministic registration order.
Each file is loaded into its own Julia submodule to avoid name collisions.
# Arguments
- `store`: Tool store to populate
- `dir`: Directory containing `.jl` tool files
# Returns
- The same `store.tools` dict (modified in place)
# Errors
- Throws `ArgumentError` if `dir` does not exist or a file lacks `getTool()`
# Example
```julia
julia> store = toolStore(name="agent1");
julia> loadTools(store, "src/tools")
[toolRegistry:agent1] Loaded tool: getWeather (Weather Lookup)
[toolRegistry:agent1] Loaded tool: getTime (Time Lookup)
[toolRegistry:agent1] Registered tool: listTools
OrderedDict{String, agentTool} with 3 entries:
"getWeather" => agentTool(...)
"getTime" => agentTool(...)
"listTools" => agentTool(...)
```
"""
function loadTools(store::toolStore, dir::String)::OrderedDict{String, agentTool}
if !isdir(dir)
throw(ArgumentError("Tool directory does not exist: $dir"))
end
jl_files = filter(f -> endswith(f, ".jl") && !occursin(r"(?i)registry", f), readdir(dir))
sort!(jl_files)
for filename in jl_files
filepath = joinpath(dir, filename)
# Derive a unique module name from the filename only (not full path).
# e.g. "getWeather.jl" -> "_tool_getWeather"
mod_name = Symbol("_tool_", replace(rstrip(filename, '.'), ".jl" => ""))
# Build the complete module as a string and eval the parsed code.
# Julia does not allow `module ... end` inside eval(quote ...),
# and constructing the module AST by hand is fragile.
# Instead, we generate the full module source as a string,
# parse it, and eval the resulting expression.
# Each tool file declares its own dependencies via `using` statements
# at the top of the file — the registry only injects `using ..type`
# to make core types (agentTool, textContent, etc.) available.
file_content = read(filepath, String)
module_code = """
module $(mod_name)
using ..type
$(file_content)
end
"""
mod = eval(Meta.parse(module_code))
# Call getTool() via Core.eval in the submodule's scope.
# This evaluates getTool() entirely within the new module's world,
# completely avoiding world-age issues — no invokelatest needed.
# Note: all uses of `tool` must be inside the `try` block because
# Julia 1.12's SSA form doesn't track `tool` as definitely assigned
# after a `try-catch` where it's only assigned inside `try`.
try
tool = Core.eval(mod, :(getTool()))
if !(tool isa agentTool)
throw(ArgumentError(
"getTool() in $(filepath) did not return an agentTool instance, got: $(typeof(tool))"
))
end
store.tools[tool.name] = tool
println("[$(store.name)] Loaded tool: $(tool.name) ($(tool.label))")
catch e
if e isa UndefVarError || occursin("getTool", sprint(showerror, e))
throw(ArgumentError(
"Tool file $(filepath) does not define a `getTool()` function in module $(mod_name). " *
"Each tool file must define: function getTool()::agentTool ... end"
))
end
rethrow(e)
end
end
registerTool(store, listTool(store))
return store.tools
end
"""
registerTool(store::toolStore, tool::agentTool) -> OrderedDict{String, agentTool}
Add `tool` to `store`, overwriting any existing tool with the same name.
# Arguments
- `store`: Tool store to modify
- `tool`: The `agentTool` to register
# Returns
- The same `store.tools` dict (modified in place)
# Example
```julia
julia> store = toolStore(name="agent1");
julia> registerTool(store, listTool(store))
[toolRegistry:agent1] Registered tool: listTools
OrderedDict{String, agentTool} with 1 entry:
"listTools" => agentTool(...)
```
"""
function registerTool(store::toolStore, tool::agentTool)::OrderedDict{String, agentTool}
store.tools[tool.name] = tool
println("[$(store.name)] Registered tool: $(tool.name)")
return store.tools
end
"""
Return the tools registered in `store`.
The returned dict is the **same object** stored inside `store` — mutations
to it (e.g. via `registerTool`) are visible through subsequent calls.
# Arguments
- `store`: Tool store to query
# Returns
- `OrderedDict{String, agentTool}`: Tools keyed by name, in registration order
# Example
```julia
julia> tools = getTools(store)
OrderedDict{String, agentTool} with 2 entries:
"getWeather" => agentTool(...)
"getTime" => agentTool(...)
```
"""
function getTools(store::toolStore)::OrderedDict{String, agentTool}
return store.tools
end
"""
Remove all tools from `store`.
# Arguments
- `store`: Tool store to clear
# Returns
- `nothing`
# Example
```julia
julia> clearTools(store)
[toolRegistry:agent1] Registry cleared
nothing
julia> getTools(store)
OrderedDict{String, agentTool} with 0 entries
```
"""
function clearTools(store::toolStore)::Nothing
empty!(store.tools)
println("[$(store.name)] Registry cleared")
return nothing
end
end # module
-525
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@@ -1,525 +0,0 @@
# Tools
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).
## Tool Anatomy
Each tool has 3 main parts:
### 1. Schema (`inputSchema`)
JSON Schema (MCP format) describing the tool's arguments. The `"required"` array lists mandatory fields:
```julia
inputSchema = Dict{String,Any}(
"type" => "object",
"properties" => Dict(
"city" => Dict("type" => "string", "description" => "City name"),
"units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
),
"required" => ["city"]
)
```
### 2. Execution Function (`execute`)
A function with the signature:
```julia
execute(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
```
- **`toolCallId`** — unique ID for this invocation (from the LLM's tool call)
- **`args`** — validated arguments provided by the LLM
- **`signal`** — abort signal for cancellable operations
- **`onPartialResult`** — callback for streaming progress updates
- **Returns** — `agentToolResult` with content, details, usage, and termination flag
```julia
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
# Optional: stream progress updates
onPartialResult(Dict("status" => "Fetching data..."))
# Do work
result = "Weather in $(args["city"]): Sunny, 22°C"
# Return result
return agentToolResult(
[textContent(result)],
Dict{Any,Any}(), # details
nothing, # usage
false # terminate (true to stop agent loop)
)
end
```
### 3. Tool Definition (`getTool()`)
Returns an `agentTool` struct:
| Field | Type | Description |
|---|---|---|
| `name` | `String` | Unique identifier (e.g. `"getWeather"`) |
| `label` | `String` | Human-readable name (e.g. `"Weather Lookup"`) |
| `description` | `String` | What the tool does (shown to the LLM) |
| `inputSchema` | `Any` | JSON Schema (MCP format) |
| `execute` | `Function` | The execution function |
| `prepareArguments` | `Union{Function,Nothing}` | Optional argument transform before validation |
| `validateRequiredArgs` | `Union{Function,Nothing}` | Optional custom validation |
| `parallelToolExecute` | `Bool` | Run this tool in parallel with others |
## Argument Validation
Validation happens **before** tool execution, in the `prepareToolCall` phase. Invalid calls return an error immediately without invoking `execute`, `beforeToolCall`, or logging `toolExecutionStart`.
### Default: JSON Schema Required Fields
Set `validateRequiredArgs = nothing` to use the default validator, which checks that all fields in `inputSchema["required"]` are present:
```julia
# src/tools/getWeather.jl — uses default validation
function getTool()::agentTool
return agentTool(
name = "getWeather",
# ...
validateRequiredArgs = nothing, # uses default
)
end
```
### Custom Validation Hook
Override `validateRequiredArgs` when you need:
- **Cross-field constraints** (e.g. "at least one of X or Y")
- **Format validation** (e.g. regex patterns, date parsing)
- **Domain rules** (e.g. value ranges, business logic)
The hook signature takes only `args`:
```julia
function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
tz = get(args, "timezone", nothing)
city = get(args, "city", "")
if !haskey(args, "timezone") && isempty(city)
return "Missing required argument: provide at least one of 'timezone' or 'city'"
end
if tz !== nothing
tz_str = string(tz)
if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York'"
end
end
return nothing
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.
## Tool Discovery and Lifecycle
The agent iterates through tools via a **discover → execute → loop** cycle. Here is the complete flow from the framework author's perspective:
### The Agent Loop
```julia
# agentCore.jl:175 - _process_message()
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[]
for content_block in response.content
if content_block isa Dict
if get(content_block, :type, "") == "tool_calls"
# OpenAI format: {"type": "tool_calls", "tool_calls": [...]}
for tc_data in get(content_block, :tool_calls, [])
tc = agentToolCall(
type = "function",
id = get(tc_data, :id, string(uuid4())),
name = get(tc_data, :function, Dict())[:name],
arguments = get(tc_data, :function, Dict())[:arguments],
)
push!(tool_call_list, tc)
end
elseif get(content_block, :type, "") == "tool_call"
# Alternative format: single tool_call block
tc = agentToolCall(
type = "function",
id = get(content_block, :id, string(uuid4())),
name = get(content_block, :name, ""),
arguments = get(content_block, :arguments, Dict()),
)
push!(tool_call_list, tc)
end
end
end
```
### Step 3: Execute Each Tool Call
For each tool call, the agent runs through the **prepare → execute → finalize** pipeline:
```julia
# agentCore.jl:247-302
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
```
### Step 4: The Per-Call Pipeline
Each tool call goes through three phases:
```
┌─────────────────────────────────────────────────────────────────┐
│ PREPARE → prepareToolCall() │
│ │
│ 1. Find tool by name in context.tools │
│ 2. Transform args via tool.prepareArguments (if defined) │
│ 3. Validate via tool.validateRequiredArgs (or default) │
│ 4. Run beforeToolCall hook (if defined) │
│ └── on any failure → return immediateOutcome (skip execution) │
│ └── success → return preparedToolCall │
├─────────────────────────────────────────────────────────────────┤
│ EXECUTE → executePreparedToolCall() │
│ │
│ 1. emit toolExecutionStart event │
│ 2. call tool.execute(toolCallId, args, signal, onPartialResult)│
│ 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
### Sequential
Tools execute one at a time in order. Required when:
- Tools have implicit dependencies
- Tools share state (e.g. writing to the same file)
- Tools have `parallelToolExecute = false`
Set globally via `agentLoopConfig.toolExecution = "sequential"`, or per-tool via `parallelToolExecute = false`.
### Parallel
Tools execute concurrently when all are independent. Reduces wall-clock time. Set `parallelToolExecute = true` on individual tools, or set `agentLoopConfig.toolExecution = "parallel"`.
## Streaming Partial Results
For long-running tools (API calls, file uploads, training), use `onPartialResult` to stream progress:
```julia
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
onPartialResult(Dict("status" => "Step 1: Fetching data..."))
sleep(1)
onPartialResult(Dict("status" => "Step 2: Processing..."))
sleep(1)
return agentToolResult(
[textContent("Done!")],
Dict{Any,Any}(), nothing, false
)
end
```
UI listeners and the TUI consume these events in real time via `toolExecutionUpdate`.
## Loading Tools
### Auto-load from Directory
```julia
using .toolRegistry
tools = loadTools("src/tools") # scans for *.jl files with getTool()
```
Files are loaded alphabetically for deterministic registration order.
### Manual Registration
```julia
tool = getTool() # from your tool module
registerTool(tool)
```
## Complete Lifecycle Example
```julia
# ─── USER SENDS MESSAGE ───────────────────────────────────────────
run_agent(agent, "What's the weather in Tokyo?")
# ─── 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)
# => "[textContent(\"The weather in Tokyo is sunny, 22°C.\")]"
```
## Self-Modifying Tools
The framework includes tools that allow the agent to create new tools at runtime.
### `writeTool` — Create New Tool Files
`writeTool` is a **file writer**, not a code generator. The LLM provides the tool logic as `executeCode` (the actual Julia code), and `writeTool` wraps it in the required boilerplate.
**How it works:**
The LLM constructs `writeTool` with:
- **`executeCode`** — the actual tool logic (Julia code body, NOT wrapped in a function)
- **`name`, `label`, `description`** — tool metadata
- **`inputSchema`** — parameter schema in MCP format
- **`validateCode`, `prepareCode`** (optional) — custom validation/preparation logic
`writeTool` produces `src/tools/<name>.jl` by:
1. Converting the `inputSchema` Dict into a Julia `Dict{String,Any}(...)` string literal
2. Indenting `executeCode` with 4 spaces
3. Wrapping it inside a `function executeTool(...)::agentToolResult ... end` template
4. Appending the `getTool()` definition that returns an `agentTool` struct
5. Writing the combined string to disk
**Workflow:**
```
LLM decides: "Need a searchWine tool. I'll provide the logic."
LLM calls writeTool:
name: "searchWine"
executeCode: "query = args[\"query\"]\nresult = search(query)\nreturn ..."
writeTool wraps it → src/tools/searchWine.jl:
function executeTool(...)::agentToolResult
query = args["query"] ← LLM code (indented 4 spaces)
result = search(query)
return agentToolResult(...)
end
function getTool()::agentTool
return agentTool(name="searchWine", ...)
end
Restart → loadTools("src/tools") loads searchWine.jl
```
**Example specification:**
```julia
Dict(
"name" => "searchWine",
"label" => "Wine Search",
"description" => "Search a wine database by name, region, or variety",
"inputSchema" => Dict(
"type" => "object",
"properties" => Dict(
"query" => Dict("type" => "string", "description" => "Search query"),
"maxResults" => Dict("type" => "integer", "default" => 10)
),
"required" => ["query"]
),
"executeCode" => """
query = args["query"]
max_results = get(args, "maxResults", 10)
# Perform search logic here
result = "Found 3 wines matching: $query"
return agentToolResult([textContent(result)], Dict{Any,Any}(), nothing, false)
""",
"parallel" => false
)
```
**Optional hooks:**
| Field | Description |
|---|---|
| `validateCode` | Custom validation Julia code (runs before execute). Return `nothing` to pass, or an error `String` to fail. |
| `prepareCode` | Argument preparation code (runs before validation). Return modified args dict. |
### `listTools` — Discover Available Tools
Returns all registered tools. Primarily useful for **collision detection** before creating a new tool via `writeTool` — the LLM checks existing names before picking a unique one.
```julia
# Result from listTools:
# Available tools:
# - getWeather: Weather Lookup — Fetch current weather and forecast for a given city.
# - getTime: Time Lookup — Get current local time for a timezone or city.
# - writeTool: Create Tool — Generate new tool files...
# - listTools: List Tools — List all available tools with their names and labels...
```
### Complete Self-Tooling Example
```
User: "I need to search for wines. Do you have a tool for that?"
# ─── LOOP: Agent realizes no wine search tool exists ─────────────────
# LLM generates the tool logic and calls writeTool to write it to disk
[Tool Call] writeTool(name="searchWine", label="Wine Search",
description="Search a wine database by name, region, or variety",
inputSchema={...},
executeCode="query = args[\"query\"]\nresult = \"Found wines...\"\nreturn agentToolResult([textContent(result)], ...)")
# writeTool generates src/tools/searchWine.jl
# ─── SYSTEM RESTARTS ─────────────────────────────────────────────────
# loadTools("src/tools") loads searchWine.jl alongside all other tools
# ─── Agent calls the new tool ─────────────────────────────────────────
[Tool Call] searchWine(query="cabernet", maxResults=5)
# Result: "Found 5 cabernet wines..."
# ─── Final response ──────────────────────────────────────────────────
"The search found 5 cabernet wines: ..."
```
## Available Tools
| Tool | Description | Validation |
|---|---|---|
| `getWeather` | Fetch weather for a city | Default (JSON Schema required) |
| `getTime` | Get current time for a timezone or city | Custom (cross-field + format) |
| `writeTool` | Create a new Julia tool module at runtime | Built-in (name + schema validation) |
| `listTools` | List all available tools with descriptions | None (no arguments) |
+4 -1
View File
@@ -1,3 +1,5 @@
using Dates
"""
Validate required arguments for the getTime tool.
@@ -41,7 +43,8 @@ Execute the getTime tool.
Returns mock time data for the given timezone or city.
"""
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal},
onPartialResult::Function)::agentToolResult
tz = get(args, "timezone", nothing)
city = get(args, "city", "")
if tz !== nothing
+2 -1
View File
@@ -3,7 +3,8 @@ Execute the getWeather tool.
Returns mock weather data for the given city and temperature units.
"""
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal},
onPartialResult::Function)::agentToolResult
city = get(args, "city", "")
units = get(args, "units", "celsius")
temp = units == "fahrenheit" ? "72" : "22"
-196
View File
@@ -1,196 +0,0 @@
module toolRegistry
export loadTools, registerTool, getTools, clearTools
using Dates
using JSON, DataStructures
using ..type
# Global registry — populated at runtime by loadTools() or registerTool()
const _registry = Vector{agentTool}()
# Module references — kept alive to prevent GC of tool code that closures depend on
const _tool_modules = Vector{Module}()
# Auto-register the built-in listTools tool
function __init__()
registerTool(_listTool())
end
"""
List tool definition — lets the agent query available tools for collision detection
when creating new tools via writeTool.
"""
function _listTool()::agentTool
return agentTool(
name = "listTools",
label = "List Tools",
description = "List all available tools with their names, labels, and descriptions. Use this before creating a new tool to check for name collisions.",
inputSchema = Dict{String,Any}(
"type" => "object",
"properties" => Dict{String,Any}(),
"required" => Any[]
),
execute = (toolCallId, args, signal, onPartialResult) -> begin
tools = getTools()
if isempty(tools)
result_text = "No tools registered."
else
lines = String["- $(t.name): $(t.label)$(t.description)" for t in tools]
result_text = "Available tools:\n" * join(lines, "\n")
end
return agentToolResult(
[textContent(result_text)],
Dict{Any,Any}("count" => length(tools)),
nothing, false
)
end,
prepareArguments = nothing,
validateRequiredArgs = nothing,
parallelToolExecute = false
)
end
"""
Load all tool modules from a directory.
Scans `dir` for `.jl` files. Each file must define a function named
`getTool()::agentTool`. Files are sorted alphabetically so tool
registration order is deterministic.
Each `.jl` file is loaded into its own **submodule** so that all functions
defined in the file (`validateRequiredArgs`, `prepareArguments`, `executeTool`,
and any helper functions) are namespaced and never collide with other tools.
# Tool file format
Each `.jl` file defines one function `getTool()` that returns an `agentTool`.
Inside the file you can freely define as many helper functions as you need —
they will all be scoped under the tool's submodule.
```julia
# src/tools/getWeather.jl
# These are namespaced — no collision with getTime.validateRequiredArgs, etc.
function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
...
end
function getTool()::agentTool
return agentTool(
name = "getWeather",
...
)
end
```
# Arguments
- `dir::String`: Directory path to scan for `.jl` tool files
# Returns
- `Vector{agentTool}`: All loaded tools
# Errors
- Throws `ArgumentError` if a tool file does not define a `getTool` function
"""
function loadTools(dir::String)::OrderedDict{String, agentTool}
if !isdir(dir)
throw(ArgumentError("Tool directory does not exist: $dir"))
end
tools = OrderedDict{String, agentTool}()
jl_files = filter(f -> endswith(f, ".jl") && !occursin(r"(?i)registry", f), readdir(dir))
sort!(jl_files)
for filename in jl_files
filepath = joinpath(dir, filename)
# Derive a unique module name from the filename only (not full path).
# e.g. "getWeather.jl" -> "_tool_getWeather"
mod_name = Symbol("_tool_", replace(rstrip(filename, '.'), ".jl" => ""))
# Build the complete module as a string and eval the parsed code.
# Julia does not allow `module ... end` inside eval(quote ...),
# and constructing the module AST by hand is fragile.
# Instead, we generate the full module source as a string,
# parse it, and eval the resulting expression.
# Also import Dates, UUIDs, DataStructures, JSON — common dependencies
# that tool files use (and that the ..type module transitively uses).
file_content = read(filepath, String)
module_code = """
module $(mod_name)
using ..type
using Dates, UUIDs, DataStructures, JSON
$(file_content)
end
"""
mod = eval(Meta.parse(module_code))
# Call getTool() via Core.eval in the submodule's scope.
# This evaluates getTool() entirely within the new module's world,
# completely avoiding world-age issues — no invokelatest needed.
# Note: all uses of `tool` must be inside the `try` block because
# Julia 1.12's SSA form doesn't track `tool` as definitely assigned
# after a `try-catch` where it's only assigned inside `try`.
try
tool = Core.eval(mod, :(getTool()))
if !(tool isa agentTool)
throw(ArgumentError(
"getTool() in $(filepath) did not return an agentTool instance, got: $(typeof(tool))"
))
end
# Keep module reference alive — closures in the agentTool (execute,
# validateRequiredArgs, prepareArguments) may reference module-scoped
# functions. Without this, GC could collect the module.
push!(_tool_modules, mod)
push!(_registry, tool)
tools[tool.name] = tool
println("[toolRegistry] Loaded tool: $(tool.name)$(tool.label)")
catch e
if e isa UndefVarError || occursin("getTool", sprint(showerror, e))
throw(ArgumentError(
"Tool file $(filepath) does not define a `getTool()` function in module $(mod_name). " *
"Each tool file must define: function getTool()::agentTool ... end"
))
end
rethrow(e)
end
end
return tools
end
"""
Register a single agentTool into the global registry.
# Arguments
- `tool::agentTool`: The tool to register
# Returns
- `Vector{agentTool}`: Updated registry
"""
function registerTool(tool::agentTool)::Vector{agentTool}
push!(_registry, tool)
println("[toolRegistry] Registered tool: $(tool.name)")
return _registry
end
"""
Get all registered tools.
# Returns
- `Vector{agentTool}`: Copy of the registry
"""
function getTools()::Vector{agentTool}
return deepcopy(_registry)
end
"""
Clear all registered tools from the global registry.
"""
function clearTools()::Nothing
empty!(_registry)
println("[toolRegistry] Registry cleared")
return nothing
end
end # module
+5 -3
View File
@@ -1,3 +1,5 @@
using JSON
"""
Tool that writes new Julia tool module files to disk.
@@ -5,7 +7,7 @@ The agent can use this tool when it encounters a task that no existing tool
can handle. Provide the tool's name, label, description, inputSchema, and
execute logic as Julia code. The tool is written to `src/tools/<name>.jl`.
After calling this tool, restart the agent so `loadTools("src/tools")` picks
After calling this tool, restart the agent so `loadTools(agent._tool_store, "src/tools")` picks
up the new file. The new tool is immediately available.
# Example
@@ -248,13 +250,13 @@ function getTool()::agentTool
tool_code = join(parts)
# Write the file — tool is loaded on next agent restart via loadTools()
# Write the file — tool is loaded on next agent restart via loadTools(store, "src/tools")
write(filepath, tool_code)
onPartialResult(Dict("status" => "Done"))
return agentToolResult(
[textContent("Tool '$(tool_name)' written to $filepath. Restart the agent so loadTools() picks it up, then call listTools to verify.")],
[textContent("Tool '$(tool_name)' written to $filepath. Restart the agent so loadTools(agent._tool_store, \"src/tools\") picks it up, then call listTools to verify.")],
Dict{Any,Any}(
"file" => filepath,
"name" => tool_name,
+26 -153
View File
@@ -13,8 +13,8 @@
# Context types
agentContext, agentState, agentToolCall, prepareNextTurnContext,
# Loop & execution types
agentLoopConfig, abortSignal, agentToolResult,
assistantMsgCtx, afterCtx,
agentLoopConfig, abortSignal, agentToolResult,beforeToolCallContext,
beforeToolCallResult, afterToolCallContext,
# Event types
toolExecStartEvent, toolExecUpdateEvent, toolExecEndEvent,
# Agent
@@ -23,7 +23,7 @@
preparedToolCall, immediateOutcome, executedOutcome, finalizedOutcome,
agentToolCallBatch,
# Functions (defined elsewhere)
run_agent, take_response, follow_up, stop_agent
runAgent, takeResponse, followUp, stopAgent
using Dates, UUIDs, DataStructures, JSON, NATS, Base.Threads
@@ -144,7 +144,7 @@ assistantMessage("assistant", [textContent("Hello!")], "", "", "gpt-4", ..., "en
```
"""
function assistantMessage(; role="assistant", content=Vector{messageContent}(),
api="", provider="", model="", usage=llmUsage(0, 0), stopReason="end_turn",
api="", provider="", model=nothing, usage=llmUsage(0, 0), stopReason="end_turn",
errorMessage=nothing, timestamp=now())
return assistantMessage(role, content, api, provider, model, usage, stopReason, errorMessage, timestamp)
end
@@ -309,7 +309,7 @@ end
mutable struct agentState # Mutable runtime state of an agent
systemPrompt::String # System prompt for the agent
model::llmModel # LLM model to use
model::Union{llmModel, Nothing} # LLM model to use
tools::OrderedDict{String, agentTool} # Available tools keyed by name, insertion-ordered
# messages history includes userMessage, assistantMessage, toolResultMessage. NO system prompt
@@ -341,21 +341,21 @@ julia> state = agentState(systemPrompt="You are a helpful assistant")
agentState("You are a helpful assistant", OrderedDict{String, agentTool}(), agentMessage[], String[], nothing)
"""
function agentState(
systemPrompt::String="",
model::llmModel=llmModel{String}("", "", "unknown", "unknown", "", false, String[],
modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
messages::Vector{agentMessage}=agentMessage[],
systemPrompt::String="",
model=llmModel("model_1", "unknown", "unknown", "", false, String[],
modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
messages::Vector{agentMessage}=agentMessage[],
)
agentState(
systemPrompt,
model,
deepcopy(tools),
deepcopy(messages),
Vector{String}(),
false,
nothing,
)
agentState(
systemPrompt,
model,
deepcopy(tools),
deepcopy(messages),
Vector{String}(),
false,
nothing,
)
end
@@ -442,13 +442,18 @@ Context passed to the `beforeToolCall` hook.
- `args::Dict{String,Any}`: Validated tool arguments
- `context::agentContext`: Current conversation context
"""
struct assistantMsgCtx
struct beforeToolCallContext
message::assistantMessage
toolCall::agentToolCall
args::Dict{String,Any}
context::agentContext
end
struct beforeToolCallResult
block::Bool
reason::String
end
"""
Context passed to the `afterToolCall` hook.
@@ -460,7 +465,7 @@ Context passed to the `afterToolCall` hook.
- `isError::Bool`: Whether execution resulted in an error
- `context::agentContext`: Current conversation context
"""
struct afterCtx
struct afterToolCallContext
message::assistantMessage
toolCall::agentToolCall
args::Dict{String,Any}
@@ -521,138 +526,6 @@ end
abstract type agent end
"""
docstring
"""
mutable struct yiemAgent <: agent # High-level agent wrapper
_state::agentState # Current state (prompt, model, messages, tools, etc.)
# user sends prompt message to agent. if agent is idle, it process user message right away.
# if agent is running, it process user message after the current tool call finished.
inputChannel::Channel
# Buffers messages the user sends while the agent is busy. Processed after all inputChannel
# messages are handled and the agent is idle (not using a tool call).
followUpChannel::Channel
# agent sends response message to user after processing all user messages in inputChannel
# and all followUp messages.
outputChannel::Channel
_agent_loop::Union{Task, Nothing} # agent loop running in the background
# Preprocess/transform messages and context (modify, filter, prune, inject context from memory,
# reorder, ...) for a single LLM call in _process_message()'s loop.
# returns new Vector{agentMessage}
prepareContext ::Union{Function, Nothing}
# Convert prepareContext()'s new Vector{agentMessage} to LLM message format
formatMsgForLLM::Function
# Actually invoke the LLM to get a completion response. The LLM response comes back as an
# assistantMessage whose content is an array of content blocks.
# Each block has a type — "text", "thinking", or "toolCall".
# The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
llmCall::Function
# Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
beforeToolCall::Union{Function, Nothing}
executeToolCalls::Function # execute tool calls ()
# Callback invoked after executing a tool call to sanitize tools output so the output is ready
# to be converted into toolResults message
afterToolCall::Union{Function, Nothing}
# prepareNextTurn::Union{Function, Nothing} # Callback to prepare the next conversation turn
# prepareNextTurnWithContext::Union{Function, Nothing} # Same but receives context
sessionId::Union{String, Nothing} # Optional session identifier
maxRetryDelayMs::Union{Int64, Nothing} # Maximum delay between retries (ms)
parallelToolExecute::Bool # Default: false
agentEventSink::Function # agent emits its status via this function
end
"""
Create a new yiemAgent instance with a background loop task.
Spawns a background `@spawn` task that runs the agent loop, listening
on `inputChannel` and `followUpChannel` channels concurrently.
# Keyword Arguments
- `systemPrompt::String`: System prompt for the agent
- `model`: LLM model to use
- `tools::OrderedDict{String, agentTool}`: Available tools keyed by name (default: empty)
- `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
- `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
- `llmCall::Function`: Function to invoke the LLM (required)
- `prepareContext::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
- `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
- `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
- `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
- `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
- `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
- `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
- `parallelToolExecute::Bool`: Run tool calls in parallel (default: `false`)
- `agentEventSink::Function`: Callback to receive agent events
# Returns
- A new `yiemAgent` instance with an active background task
# Examples
```julia
julia> tools = loadTools("src/tools")
julia> agent = yiemAgent(systemPrompt="You are a helpful assistant", model=my_model, tools=tools, llmCall=...)
yiemAgent(agentState(...), Channel(...), Channel(...), Channel(...), ..., ...)
"""
function yiemAgent(
; systemPrompt::String="You are helpful assistant.",
model=nothing,
tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
messages::Vector{agentMessage}=agentMessage[],
prepareContext::Union{Function, Nothing}=nothing,
formatMsgForLLM::Function=defaultformatMsgForLLM,
llmCall::Function,
beforeToolCall::Union{Function, Nothing}=nothing,
afterToolCall::Union{Function, Nothing}=nothing,
# prepareNextTurn::Union{Function, Nothing}=nothing,
# prepareNextTurnWithContext::Union{Function, Nothing}=nothing,
sessionId::Union{String, Nothing}=nothing,
maxRetryDelayMs::Union{Int64, Nothing}=nothing,
parallelToolExecute::Bool=false,
agentEventSink::Function,
)
# Create channels: input (user -> agent), followUp (async queue), output (agent -> user)
inputChannel = Channel(16)
followUp = Channel(32)
outputChannel = Channel(16)
# Create struct with a placeholder task, then spawn and replace it
agent = yiemAgent(
agentState(systemPrompt, model, tools, messages),
inputChannel,
followUp,
outputChannel,
nothing, # placeholder — replaced below
prepareContext,
formatMsgForLLM,
llmCall,
beforeToolCall,
afterToolCall,
# prepareNextTurn,
# prepareNextTurnWithContext,
sessionId,
maxRetryDelayMs,
parallelToolExecute,
agentEventSink,
)
# Spawn the background loop and attach it
agent._agent_loop = @spawn _agent_loop(agent)
return agent
end
"""
preparedToolCall(tool, toolCall, args)
+32 -2
View File
@@ -1,7 +1,9 @@
module utils
export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, validateToolArguments, _userMessageToOpenAI,
_assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks
export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs,
validateToolArguments, _userMessageToOpenAI,
_assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks,
beforeToolCall, afterToolCall, agentEventSink
using UUIDs, Dates, DataStructures, HTTP, JSON
using GeneralUtils
@@ -218,6 +220,34 @@ function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
return Dict("messages" => messages)
end
#TODO
function beforeToolCall(context::beforeToolCallContext, signal::abortSignal
)::beforeToolCallResult
# final context check
# seek user approval via UI
# other check
return beforeToolCallResult(false, "N/A")
end
#TODO
function afterToolCall(context::beforeToolCallContext, signal::abortSignal
)::Union{agentToolResult, Nothing}
# modify context.result if needed and return agentToolResult
return nothing
end
#TODO
function agentEventSink(x)
end
"""
Convert a userMessage to OpenAI message format.
-401
View File
@@ -1,401 +0,0 @@
using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
NATS, Base.Threads
using YiemAgent, GeneralUtils, msghandler
function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
payloads = [("msg", openai_msg, "dictionary")] # List of tuples
_, msg_envelope_json_str = msghandler.smartpack(
config["externalservice"]["servicesloadbalancer"]["nats"],
payloads;
sender_id=sender_id,
msg_purpose="text2text",
broker_url=config["nats_server_info"]["url"],
fileserver_url=config["externalservice"]["fileserver"]["url"])
reply = NATS.request(agent_conn,
config["externalservice"]["servicesloadbalancer"]["nats"],
msg_envelope_json_str, timeout=120)
incoming_env_json_str = String(reply.payload)
incoming_env = msghandler.smartunpack(incoming_env_json_str)
_llm_response = incoming_env["payloads"][1][2]
llm_response = _llm_response["choices"][1]["message"]["content"]
return llm_response
end
""" get a single text embedding from a LLM service
Example
text = ["hello"]
embedding = get_embedding(text)
"""
function get_embedding(text::AbstractArray{String})
documents_dict = Dict("documents" => text)
payloads = [("documents", documents_dict, "dictionary")]
_, msg_envelope_json_str = msghandler.smartpack(
config["externalservice"]["servicesloadbalancer"]["nats"],
payloads;
msg_purpose="embedding",
broker_url=config["nats_server_info"]["url"],
fileserver_url=config["externalservice"]["fileserver"]["url"])
reply = NATS.request(agent_conn,
config["externalservice"]["servicesloadbalancer"]["nats"],
msg_envelope_json_str, timeout=120)
incoming_env_json_str = String(reply.payload)
incoming_env = msghandler.smartunpack(incoming_env_json_str)
embedding_response = incoming_env["payloads"][1][2]
return embedding_response
end
""" sql = "SELECT * FROM wine;"
result = execute_sql_winedb(sql)
"""
function execute_sql_winedb(sql::T) where {T<:AbstractString}
host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
port = parse(Int, _port)
dbname = "winedb"
user = config["externalservice"]["sommpanion_db"]["user"]
password = config["externalservice"]["sommpanion_db"]["password"]
db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
result = nothing
try
result = LibPQ.execute(db_connection, sql)
catch e
LibPQ.close(db_connection)
end
LibPQ.close(db_connection)
return result
end
""" find similar sql from vector database
sql = "SELECT * FROM wine;"
result, distance = similar_sql_vectordb(sql)
"""
function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
tablename = "sqlllm_decision_repository"
# get embedding of the query
df = find_similar_text_from_vectordb(sql, tablename,
"function_input_embedding", execute_sql_vectordb)
# println(df[1, [:id, :function_output]])
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < maxdistance
# if there is usable SQL, return it.
output_b64 = df[1, :function_output_base64] # pick the closest match
output_str = String(base64decode(output_b64))
rowid = df[1, :id]
println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
pprintln(output_str)
return (result=output_str, distance=distance)
else
println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
return (result=nothing, distance=nothing)
end
end
""" insert query and sql into vector database
query = "get all wines from wine table"
sql = "SELECT * FROM wine;"
insert_sql_vectordb(query, sql)
"""
function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
) where {T1<:AbstractString, T2<:AbstractString}
tablename = "sqlllm_decision_repository"
# get embedding of the query
# query = state[:thoughtHistory][:question]
df = find_similar_text_from_vectordb(query, tablename,
"function_input_embedding", execute_sql_vectordb)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row == 0 || distance > maxdistance # no close enough SQL stored in the database
_query_embedding = get_embedding([query])
_query_embedding = GeneralUtils.dictify(_query_embedding)
# println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# println(_query_embedding)
# println("---\n")
query_embedding = _query_embedding["data"][1]["embedding"]
query = replace(query, "'" => "")
sql_base64 = base64encode(SQL)
sql_ = replace(SQL, "'" => "")
sql =
"""
INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
"""
# println("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# println(sql)
_ = execute_sql_vectordb(sql)
end
end
""" execute sql against vectordb
sql = "SELECT * FROM wine;"
result = execute_sql_vectordb(sql)
"""
function execute_sql_vectordb(sql::T) where {T<:AbstractString}
host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
port = parse(Int, _port)
dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
user = config["externalservice"]["sommpanion_vectordb"]["user"]
password = config["externalservice"]["sommpanion_vectordb"]["password"]
DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
result = LibPQ.execute(DBconnection, sql)
close(DBconnection)
return result
end
""" search similar decision llm made from vectordb
"""
function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
)::Union{AbstractDict, Nothing} where {T1<:AbstractString}
tablename = "sommelier_decision_repository"
# find similar
df = find_similar_text_from_vectordb(recentevents, tablename,
"function_input_embedding", execute_sql_vectordb)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < maxdistance
# if there is usable decision, return it.
rowid = df[1, :id]
println("\n--| found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
output_b64 = df[1, :function_output_base64] # pick the closest match
_output_str = String(base64decode(output_b64))
output = copy(JSON.read(_output_str))
return output
else
println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
return nothing
end
end
""" search similar text from vectordb
"""
function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
vectorDB::Function; limit::Integer=1
)::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
# get embedding from LLM service
_embedding = get_embedding([text])
_embedding = _embedding["data"][1]["embedding"]
_embedding = "$_embedding"
embedding = _embedding[4:end]
# check whether there is close enough vector already store in vectorDB. if no, add, else skip
sql = """
SELECT *, $embeddingColumnName <-> '$embedding' as distance
FROM $tablename
ORDER BY distance LIMIT $limit;
"""
response = vectorDB(sql)
df = DataFrame(response)
return df
end
""" insert decision llm made to vectordb
"""
function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
) where {T1<:AbstractString, T2<:AbstractDict}
tablename = "sommelier_decision_repository"
# find similar
df = find_similar_text_from_vectordb(recentevents, tablename,
"function_input_embedding", execute_sql_vectordb)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row == 0 || distance > maxdistance # no close enough SQL stored in the database
_embedding = get_embedding([recentevents])[1]
recentevents_embedding = _embedding["data"][1]["embedding"]
recentevents = replace(recentevents, "'" => "")
decision_json = JSON.json(decision)
decision_base64 = base64encode(decision_json)
decision = replace(decision_json, "'" => "")
sql =
"""
INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
"""
println("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
println(sql)
_ = execute_sql_vectordb(sql)
else
println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
end
end
config = JSON.parsefile("./appconfig.json")
sessionId = "0"
backend_session_topic = "sommpanion.testsubject"
agent_ch = Channel(8)
agent_conn = NATS.connect(config["nats_server_info"]["url"])
sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
put!(agent_ch, msg)
end
agent_context = YiemAgent.agentcontext(
text2text_instruct_llm,
get_embedding,
execute_sql_winedb,
similar_sql_vectordb,
insert_sql_vectordb,
similar_sommelier_decision,
insert_sommelier_decision
)
# can't instantiate
agent = YiemAgent.sommelier(
agent_context;
name="Janie",
id=sessionId, # agent instance id
retailername="Yiem Wine Ltd.",
llmFormatName=""
)
image1_path = "test/large_image.png"
image1_bytes = read(image1_path)
image1_base64_string = base64encode(image1_bytes)
mime_type = "image/png"
data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
# 1. Read local file and encode to base64 string
image2_path = "test/small_image.png"
image2_bytes = read(image2_path)
image2_base64_string = base64encode(image2_bytes)
mime_type = "image/png"
data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
# 3. Construct payload with the Data URI
message = Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data1_uri)
)
]
)
result = YiemAgent.conversation(agent; userinput=message)
println("\n$result")
# message = Dict(
# "role" => "user",
# "content" => [
# Dict("type" => "text", "text" =>
# "
# เป็นงานเลี้ยงทั่วไป
# "),
# ]
# )
# result = YiemAgent.conversation(agent; userinput=message)
# println("\n$result")
# message = Dict(
# "role" => "user",
# "content" => [
# Dict("type" => "text", "text" => "no thanks. that's all"),
# ]
# )
# result = YiemAgent.conversation(agent; userinput=message)
# println("\n$result")
# message = Dict(
# "role" => "user",
# "content" => [
# Dict("type" => "text", "text" => "What about this wine?"),
# Dict(
# "type" => "image_url",
# "image_url" => Dict("url" => data2_uri)
# )
# ]
# )
# result = YiemAgent.conversation(agent; userinput=message)
# println("\n$result")
+100 -24
View File
@@ -6,12 +6,13 @@ using YiemAgent.type
# Path to the real tools directory
TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
@testset "loadTools" begin
@testset "loadTools with toolStore" begin
# ------------------------------------------------------------------ #
# 1. loadTools throws on non-existent directory #
# ------------------------------------------------------------------ #
@test_throws ArgumentError loadTools("/nonexistent/dir/that/does/not/exist")
store = toolStore(name="test1")
@test_throws ArgumentError loadTools(store, "/nonexistent/dir/that/does/not/exist")
# ------------------------------------------------------------------ #
# 2. loadTools throws if a .jl file does not define getTool() #
@@ -20,28 +21,30 @@ TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
# ------------------------------------------------------------------ #
bad_dir = mktempdir()
write(joinpath(bad_dir, "noTool.jl"), "x = 42\n")
@test_throws ArgumentError loadTools(bad_dir)
@test_throws ArgumentError loadTools(store, bad_dir)
# ------------------------------------------------------------------ #
# 3. loadTools loads actual tool files from src/tools/ #
# ------------------------------------------------------------------ #
loaded = loadTools(TOOLS_DIR)
store2 = toolStore(name="test2")
loaded = loadTools(store2, TOOLS_DIR)
@test !isempty(loaded)
@test length(loaded) == 3
@test length(loaded) == 4 # 3 files + auto-registered listTools
names = [k for k in keys(loaded)]
@test "getTime" in names
@test "getWeather" in names
@test "writeTool" in names
@test "listTools" in names
# ------------------------------------------------------------------ #
# 4. loadTools returns tools sorted alphabetically by filename #
# (getTime.jl < getWeather.jl < writeTool.jl) #
# because 'T' < 'W' in ASCII #
# (getTime.jl < getWeather.jl < writeTool.jl) + listTools at end #
# ------------------------------------------------------------------ #
@test collect(keys(loaded))[1] == "getTime"
@test collect(keys(loaded))[2] == "getWeather"
@test collect(keys(loaded))[3] == "writeTool"
@test collect(keys(loaded))[4] == "listTools"
# ------------------------------------------------------------------ #
# 5. Verify loaded tool fields are correct #
@@ -98,15 +101,29 @@ TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
@test occursin("72°F", result_w2.content[1].text)
# ------------------------------------------------------------------ #
# 7. getTools / registerTool / clearTools #
# 7. getTools / registerTool / clearTools (per-store isolation) #
# ------------------------------------------------------------------ #
registry_tools = getTools()
@test !isempty(registry_tools)
@test any(t -> t.name == "getTime", registry_tools)
@test any(t -> t.name == "getWeather", registry_tools)
store3 = toolStore(name="test3")
registry_tools = getTools(store3)
@test isempty(registry_tools)
clearTools()
@test isempty(getTools())
# listTool is not auto-registered anymore — each store starts empty
# Register tools manually
registerTool(store3, loaded["getTime"])
registerTool(store3, loaded["getWeather"])
registerTool(store3, loaded["writeTool"])
reg = getTools(store3)
@test !isempty(reg)
@test "getTime" in keys(reg)
@test "getWeather" in keys(reg)
@test "writeTool" in keys(reg)
@test collect(keys(reg))[1] == "getTime"
@test collect(keys(reg))[2] == "getWeather"
@test collect(keys(reg))[3] == "writeTool"
clearTools(store3)
@test isempty(getTools(store3))
test_tool = agentTool(
name = "manualTool",
@@ -119,18 +136,77 @@ TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
validateRequiredArgs = nothing,
parallelToolExecute = true
)
registerTool(test_tool)
reg = getTools()
@test any(t -> t.name == "manualTool", reg)
@test count(t -> t.name == "manualTool", reg) == 1
@test reg[1].parallelToolExecute == true
registerTool(store3, test_tool)
reg = getTools(store3)
@test haskey(reg, "manualTool")
@test length(reg) == 1
@test reg["manualTool"].parallelToolExecute == true
# ------------------------------------------------------------------ #
# 8. getTools returns deep copy (mutations don't affect registry) #
# 8. getTools returns direct reference (mutations affect registry) #
# ------------------------------------------------------------------ #
copy1 = getTools()
copy2 = getTools()
@test copy1 !== copy2
copy1 = getTools(store3)
copy2 = getTools(store3)
@test copy1 === copy2 # same reference, not a deep copy
empty!(copy1)
@test !isempty(getTools())
@test isempty(getTools(store3)) # mutation propagates
# ------------------------------------------------------------------ #
# 9. Per-store isolation — two stores don't share tools #
# ------------------------------------------------------------------ #
storeA = toolStore(name="isolationA")
storeB = toolStore(name="isolationB")
registerTool(storeA, loaded["getTime"])
registerTool(storeB, loaded["getWeather"])
regA = getTools(storeA)
regB = getTools(storeB)
@test "getTime" in keys(regA)
@test "getWeather" keys(regA)
@test "getWeather" in keys(regB)
@test "getTime" keys(regB)
clearTools(storeA)
@test isempty(getTools(storeA))
@test !isempty(getTools(storeB)) # storeB unaffected
end
@testset "listTool" begin
store = toolStore(name="test_list")
loaded = loadTools(store, TOOLS_DIR) # auto-registers getWeather, getTime, writeTool + listTools
# loadTools auto-registers listTool
@test "listTools" in keys(loaded)
# listTool returns an agentTool, not a string or array
list_t = listTool(store)
@test list_t isa agentTool
@test list_t.name == "listTools"
@test list_t.label == "List Tools"
@test isempty(list_t.inputSchema["required"])
# Verify all tools appear (3 loaded + listTools = 4)
result = list_t.execute("call-1", Dict{String,Any}(), nothing, x -> x)
@test result isa agentToolResult
@test result.content[1] isa textContent
@test occursin("listTools", result.content[1].text)
@test occursin("getWeather", result.content[1].text)
@test occursin("getTime", result.content[1].text)
@test occursin("writeTool", result.content[1].text)
@test result.details["count"] == 4
# Each listTool call creates an independent closure
storeB = toolStore(name="test_listB")
registerTool(storeB, loaded["getWeather"])
list_tB = listTool(storeB)
resultA = list_t.execute("call-3", Dict{String,Any}(), nothing, x -> x)
resultB = list_tB.execute("call-4", Dict{String,Any}(), nothing, x -> x)
@test occursin("getWeather", resultA.content[1].text)
@test occursin("getWeather", resultB.content[1].text)
@test occursin("getTime", resultA.content[1].text)
@test occursin("getTime", resultB.content[1].text) == false # storeB only has getWeather
end