Merge pull request 'V0.9.1 fix ghost response' (#57) from v0.9.1-fix_ghost_response into v0.9.1

Reviewed-on: #57

Project.toml

@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.8.0"
+version = "0.9.1"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]

README.md

@@ -1,311 +1,311 @@
-# YiemAgent
-
-Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
-
-## Quick Start
-
-```julia
-using YiemAgent
-
-# 1. Create an LLM callable (see "Custom LLM Callable" below)
-llmCall = ...
-
-# 2. Create a callable MCP server (see "MCP Server Integration" below)
-mcpServer = ...
-
-# 3. Create the agent
-agent = yiemAgent(llmCall; mcpServer=mcpServer, eventSink=println)
-
-# 4. Interact
-promptAgent(agent, "What tools do you have?")
-response = takeResponse(agent)
-
-# 5. Stop when done
-stopAgent(agent)
-```
-
-## Agent API
-
-| Function | Description |
-|----------|-------------|
-| `promptAgent(agent, text)` | Send plain text to the agent's input channel |
-| `promptAgent(agent, msg)` | Send a structured message (OpenAI format dict) to the input channel |
-| `takeResponse(agent)` | Block and take the agent's response from output channel |
-| `followUp(agent, text)` | Send plain text follow-up while agent is still processing |
-| `followUp(agent, msg)` | Send a structured follow-up (OpenAI format dict) while agent is processing |
-| `stopAgent(agent)` | Gracefully stop the agent and close channels |
-
-## Creating a yiemAgent
-
-```julia
-yiemAgent(llmCall;
-    systemPrompt::String = "You are a helpful assistant.",
-    model = nothing,
-    messages::Vector{agentMessage} = agentMessage[],
-    prepareContext::Function = prepareContext,
-    formatMsgForLLM::Function = formatMsgForLLM,
-    beforeToolCall::Function = beforeToolCall,
-    afterToolCall::Function = afterToolCall,
-    sessionId::Union{String, Nothing} = nothing,
-    maxRetryDelayMs::Union{Int64, Nothing} = nothing,
-    parallelToolExecute::Bool = false,
-    eventSink::Function = eventSink,
-    mcpServer = nothing
-)
-```
-
-| Parameter | Description |
-|-----------|-------------|
-| `llmCall` | **(required)** Function that receives formatted messages and returns an LLM response |
-| `systemPrompt` | System prompt for the agent |
-| `model` | LLM model configuration (`llmModel` struct) |
-| `messages` | Initial conversation messages |
-| `prepareContext` | Hook to transform messages/context before each LLM call |
-| `formatMsgForLLM` | Hook to convert agent context to LLM API format |
-| `beforeToolCall` | Hook to block/allow tool execution |
-| `afterToolCall` | Hook to post-process tool results |
-| `sessionId` | Optional session identifier for MCP server |
-| `maxRetryDelayMs` | Maximum retry delay in milliseconds |
-| `parallelToolExecute` | Run multiple tool calls in parallel |
-| `eventSink` | Callback for agent events/debug messages |
-| `mcpServer` | Callable struct for MCP server communication |
-
-## Usage Examples
-
-### Text Messages
-
-```julia
-# Send plain text
-promptAgent(agent, "What tools do you have?")
-response = takeResponse(agent)
-
-# Follow-up while agent is processing
-promptAgent(agent, "Tell me about wine")
-followUp(agent, "Also include prices")
-response = takeResponse(agent)
-```
-
-### Structured Messages (OpenAI Format)
-
-```julia
-promptAgent(agent, Dict("role" => "user", "content" => "Hello"))
-response = takeResponse(agent)
-```
-
-### Output Format
-
-`takeResponse()` returns a `Dict{String, Any}` in OpenAI message format:
-
-```julia
-Dict(
-    "role" => "assistant",
-    "content" => [
-        Dict("type" => "text", "text" => "The weather in Tokyo is Sunny, 22°C."),
-        # ... more content blocks
-    ]
-)
-```
-
-Content block types:
-
-| Block Type | Fields | Description |
-|------------|--------|-------------|
-| `text` | `"type" => "text"`, `"text" => "..."` | Plain text response from the LLM |
-| `image_url` | `"type" => "image_url"`, `"image_url" => Dict("url" => "...")` | Image content (rare, used by multimodal models) |
-
-Example — extracting the text response:
-
-```julia
-response = takeResponse(agent)
-text_response = first(c["text"] for c in response["content"] if c["type"] == "text")
-```
-
-### Multimodal Messages (Text + Image)
-
-```julia
-# Encode images to data URIs
-image_path = "test/image.png"
-image_bytes = read(image_path)
-base64_string = base64encode(image_bytes)
-data_uri = "data:image/png;base64,$(base64_string)"
-
-# Build message with text and image content blocks
-message = Dict(
-    "role" => "user",
-    "content" => [
-        Dict("type" => "text", "text" => "Do you have Brunello di Montalcino?"),
-        Dict("type" => "image_url", "image_url" => Dict("url" => data_uri))
-    ]
-)
-promptAgent(agent, message)
-response = takeResponse(agent)
-```
-
-### Custom Hooks
-
-```julia
-# beforeToolCall — request user approval for destructive tools
-function my_before_tool_call(context::beforeToolCallContext, signal::abortSignal)::beforeToolCallResult
-    if context.toolCall.name == "deleteFile"
-        return beforeToolCallResult(true, "User must approve file deletion")
-    end
-    return beforeToolCallResult(false, "N/A")
-end
-
-# afterToolCall — mask sensitive data from results
-function my_after_tool_call(context::afterToolCallContext, signal::abortSignal)::Union{agentToolResult, Nothing}
-    if context.isError
-        return agentToolResult(
-            [textContent("The tool encountered an error. Please try again.")],
-            context.result.details, context.result.usage, context.result.terminate
-        )
-    end
-    return nothing
-end
-
-agent = yiemAgent(llmCall;
-    mcpServer=mcpServer,
-    beforeToolCall=my_before_tool_call,
-    afterToolCall=my_after_tool_call,
-    eventSink=println
-)
-```
-
-### Custom LLM Callable
-
-```julia
-function my_llm_call(messages_dict::Dict{String, Any})
-    # messages_dict contains "messages" key with OpenAI-format message array,
-    # plus optional "tools", "temperature", etc.
-
-    # Call your LLM provider (OpenAI, Anthropic, local model, etc.)
-    raw_response = call_my_llm_provider(messages_dict)
-
-    # Return a dict with these fields:
-    return Dict(
-        "message" => Dict("role" => "assistant", "content" => raw_response["content"]),
-        "finish_reason" => raw_response["finish_reason"],
-        "api" => "my_provider",
-        "provider" => "my_provider",
-        "model" => "my-model-name",
-        "usage" => llmUsage(100, 50)
-    )
-end
-```
-
-### Custom Context Preparation
-
-```julia
-function my_prepare_context(state::agentState, eventSink)::agentContext
-    sysPrompt = state.systemPrompt * "\nCurrent time: $(now())"
-    msgs = deepcopy(state.messages)
-
-    # Prune old messages to fit context window
-    max_messages = 20
-    if length(msgs) > max_messages
-        msgs = msgs[end-max_messages+1:end]
-    end
-
-    return agentContext(sysPrompt, msgs, state.tools)
-end
-
-agent = yiemAgent(llmCall; prepareContext=my_prepare_context, ...)
-```
-
-## MCP Server Integration
-
-YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement `tools/list` and `tools/call` methods.
-
-### Protocol
-
-```julia
-# tools/list — returns tool definitions
-mcpServer("tools/list")
-# → Dict("jsonrpc"=>"2.0", "id"=>1, "result"=>Dict("tools"=>[...], "nextCursor"=>...))
-
-# tools/call — executes a tool
-mcpServer("tools/call", toolName, arguments)
-# → Dict("jsonrpc"=>"2.0", "id"=>2, "result"=>Dict("content"=>[...], "isError"=>...))
-
-# Protocol error responses include "error" instead of "result":
-Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
-```
-
-### Example: NATS-based MCP Server Client
-
-```julia
-struct mcpServer
-    natsConn::NATS.Connection
-    topic::String
-    senderID::String
-    fileserver_url::String
-end
-
-# tools/list implementation
-function (c::mcpServer)(method::String)
-    payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
-    payloads = [("payload", payload, "dictionary")]
-    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
-        sender_id=c.senderID, msg_purpose="mcp_tools_list", fileserver_url=c.fileserver_url)
-    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
-    incoming = msghandler.smartunpack(String(reply.payload))
-    return incoming["payloads"][1][2]
-end
-
-# tools/call implementation
-function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
-    payload = Dict("jsonrpc" => "2.0", "id" => 2, "method" => method,
-        "params" => Dict("name" => toolName, "arguments" => arguments))
-    payloads = [("payload", payload, "dictionary")]
-    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
-        sender_id=c.senderID, msg_purpose="mcp_tool_call", fileserver_url=c.fileserver_url)
-    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
-    incoming = msghandler.smartunpack(String(reply.payload))
-    return incoming["payloads"][1][2]
-end
-```
-
-## Tool Discovery
-
-Tools are discovered dynamically via the MCP server:
-
-1. `listTools` is the only pre-registered tool at agent creation
-2. When the LLM calls `listTools()`, tools are discovered from the MCP server and registered at runtime
-3. Supports pagination via `nextCursor` for large tool sets
-4. New tools become immediately available without restarting the agent
-
-## Hooks
-
-Hooks allow customization of the agent's behavior at key points in the execution pipeline.
-
-| Hook | Signature | Purpose |
-|------|-----------|---------|
-| `prepareContext` | `(state::agentState, eventSink) -> agentContext` | Transform messages/context before LLM call (filter tools, inject context, prune messages) |
-| `formatMsgForLLM` | `(ctx::agentContext, eventSink) -> Dict` | Convert agent context to LLM API message format |
-| `beforeToolCall` | `(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult` | Block/allow tool execution; request user approval |
-| `afterToolCall` | `(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}` | Post-process tool results; mask sensitive data; set `terminate` flag |
-| `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages throughout the pipeline |
-
-## Agent Lifecycle
-
-1. **Creation** — `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
-2. **Input** — User messages enter via `promptAgent()` (immediate) or `followUp()` (queued while agent is busy)
-3. **Processing** — `_processMessage()` handles LLM calls, tool execution, and conversation history in a loop
-4. **Tool Execution Pipeline** — each tool call follows three phases:
-   - `prepareToolCall` — resolve tool, validate arguments, run `beforeToolCall` hook
-   - `executePreparedToolCall` — invoke `tool.execute()`
-   - `finalizeExecutedToolCall` — run `afterToolCall` hook, construct `toolResultMessage`
-5. **Termination** — tool `execute` functions can set `terminate=true` to stop the agent loop
-6. **Shutdown** — `stopAgent()` sends a `:shutdown` signal, drains channels, and closes the agent
-
-## Architecture
-
-```
-src/
-├── YiemAgent.jl          # Module entry point
-├── type.jl               # Core types (messages, tools, agent state, events)
-├── utils.jl              # Message formatting, context preparation, validation
-├── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
-├── agentCore.jl          # Agent loop, tool execution pipeline, yiemAgent struct
-└── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
-```
+# YiemAgent
+
+Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
+
+## Quick Start
+
+```julia
+using YiemAgent
+
+# 1. Create an LLM callable (see "Custom LLM Callable" below)
+llmCall = ...
+
+# 2. Create a callable MCP server (see "MCP Server Integration" below)
+mcpServer = ...
+
+# 3. Create the agent
+agent = yiemAgent(llmCall; mcpServer=mcpServer, eventSink=println)
+
+# 4. Interact
+promptAgent(agent, "What tools do you have?")
+response = takeResponse(agent)
+
+# 5. Stop when done
+stopAgent(agent)
+```
+
+## Agent API
+
+| Function | Description |
+|----------|-------------|
+| `promptAgent(agent, text)` | Send plain text to the agent's input channel |
+| `promptAgent(agent, msg)` | Send a structured message (OpenAI format dict) to the input channel |
+| `takeResponse(agent)` | Block and take the agent's response from output channel |
+| `followUp(agent, text)` | Send plain text follow-up while agent is still processing |
+| `followUp(agent, msg)` | Send a structured follow-up (OpenAI format dict) while agent is processing |
+| `stopAgent(agent)` | Gracefully stop the agent and close channels |
+
+## Creating a yiemAgent
+
+```julia
+yiemAgent(llmCall;
+    systemPrompt::String = "You are a helpful assistant.",
+    model = nothing,
+    messages::Vector{agentMessage} = agentMessage[],
+    prepareContext::Function = prepareContext,
+    formatMsgForLLM::Function = formatMsgForLLM,
+    beforeToolCall::Function = beforeToolCall,
+    afterToolCall::Function = afterToolCall,
+    sessionId::Union{String, Nothing} = nothing,
+    maxRetryDelayMs::Union{Int64, Nothing} = nothing,
+    parallelToolExecute::Bool = false,
+    eventSink::Function = eventSink,
+    mcpServer = nothing
+)
+```
+
+| Parameter | Description |
+|-----------|-------------|
+| `llmCall` | **(required)** Function that receives formatted messages and returns an LLM response |
+| `systemPrompt` | System prompt for the agent |
+| `model` | LLM model configuration (`llmModel` struct) |
+| `messages` | Initial conversation messages |
+| `prepareContext` | Hook to transform messages/context before each LLM call |
+| `formatMsgForLLM` | Hook to convert agent context to LLM API format |
+| `beforeToolCall` | Hook to block/allow tool execution |
+| `afterToolCall` | Hook to post-process tool results |
+| `sessionId` | Optional session identifier for MCP server |
+| `maxRetryDelayMs` | Maximum retry delay in milliseconds |
+| `parallelToolExecute` | Run multiple tool calls in parallel |
+| `eventSink` | Callback for agent events/debug messages |
+| `mcpServer` | Callable struct for MCP server communication |
+
+## Usage Examples
+
+### Text Messages
+
+```julia
+# Send plain text
+promptAgent(agent, "What tools do you have?")
+response = takeResponse(agent)
+
+# Follow-up while agent is processing
+promptAgent(agent, "Tell me about wine")
+followUp(agent, "Also include prices")
+response = takeResponse(agent)
+```
+
+### Structured Messages (OpenAI Format)
+
+```julia
+promptAgent(agent, Dict("role" => "user", "content" => "Hello"))
+response = takeResponse(agent)
+```
+
+### Output Format
+
+`takeResponse()` returns a `Dict{String, Any}` in OpenAI message format:
+
+```julia
+Dict(
+    "role" => "assistant",
+    "content" => [
+        Dict("type" => "text", "text" => "The weather in Tokyo is Sunny, 22°C."),
+        # ... more content blocks
+    ]
+)
+```
+
+Content block types:
+
+| Block Type | Fields | Description |
+|------------|--------|-------------|
+| `text` | `"type" => "text"`, `"text" => "..."` | Plain text response from the LLM |
+| `image_url` | `"type" => "image_url"`, `"image_url" => Dict("url" => "...")` | Image content (rare, used by multimodal models) |
+
+Example — extracting the text response:
+
+```julia
+response = takeResponse(agent)
+text_response = first(c["text"] for c in response["content"] if c["type"] == "text")
+```
+
+### Multimodal Messages (Text + Image)
+
+```julia
+# Encode images to data URIs
+image_path = "test/image.png"
+image_bytes = read(image_path)
+base64_string = base64encode(image_bytes)
+data_uri = "data:image/png;base64,$(base64_string)"
+
+# Build message with text and image content blocks
+message = Dict(
+    "role" => "user",
+    "content" => [
+        Dict("type" => "text", "text" => "Do you have Brunello di Montalcino?"),
+        Dict("type" => "image_url", "image_url" => Dict("url" => data_uri))
+    ]
+)
+promptAgent(agent, message)
+response = takeResponse(agent)
+```
+
+### Custom Hooks
+
+```julia
+# beforeToolCall — request user approval for destructive tools
+function my_before_tool_call(context::beforeToolCallContext, signal::abortSignal)::beforeToolCallResult
+    if context.toolCall.name == "deleteFile"
+        return beforeToolCallResult(true, "User must approve file deletion")
+    end
+    return beforeToolCallResult(false, "N/A")
+end
+
+# afterToolCall — mask sensitive data from results
+function my_after_tool_call(context::afterToolCallContext, signal::abortSignal)::Union{agentToolResult, Nothing}
+    if context.isError
+        return agentToolResult(
+            [textContent("The tool encountered an error. Please try again.")],
+            context.result.details, context.result.usage, context.result.terminate
+        )
+    end
+    return nothing
+end
+
+agent = yiemAgent(llmCall;
+    mcpServer=mcpServer,
+    beforeToolCall=my_before_tool_call,
+    afterToolCall=my_after_tool_call,
+    eventSink=println
+)
+```
+
+### Custom LLM Callable
+
+```julia
+function my_llm_call(messages_dict::Dict{String, Any})
+    # messages_dict contains "messages" key with OpenAI-format message array,
+    # plus optional "tools", "temperature", etc.
+
+    # Call your LLM provider (OpenAI, Anthropic, local model, etc.)
+    raw_response = call_my_llm_provider(messages_dict)
+
+    # Return a dict with these fields:
+    return Dict(
+        "message" => Dict("role" => "assistant", "content" => raw_response["content"]),
+        "finish_reason" => raw_response["finish_reason"],
+        "api" => "my_provider",
+        "provider" => "my_provider",
+        "model" => "my-model-name",
+        "usage" => llmUsage(100, 50)
+    )
+end
+```
+
+### Custom Context Preparation
+
+```julia
+function my_prepare_context(state::agentState, eventSink)::agentContext
+    sysPrompt = state.systemPrompt * "\nCurrent time: $(now())"
+    msgs = deepcopy(state.messages)
+
+    # Prune old messages to fit context window
+    max_messages = 20
+    if length(msgs) > max_messages
+        msgs = msgs[end-max_messages+1:end]
+    end
+
+    return agentContext(sysPrompt, msgs, state.tools)
+end
+
+agent = yiemAgent(llmCall; prepareContext=my_prepare_context, ...)
+```
+
+## MCP Server Integration
+
+YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement `tools/list` and `tools/call` methods.
+
+### Protocol
+
+```julia
+# tools/list — returns tool definitions
+mcpServer("tools/list")
+# → Dict("jsonrpc"=>"2.0", "id"=>1, "result"=>Dict("tools"=>[...], "nextCursor"=>...))
+
+# tools/call — executes a tool
+mcpServer("tools/call", toolName, arguments)
+# → Dict("jsonrpc"=>"2.0", "id"=>2, "result"=>Dict("content"=>[...], "isError"=>...))
+
+# Protocol error responses include "error" instead of "result":
+Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
+```
+
+### Example: NATS-based MCP Server Client
+
+```julia
+struct mcpServer
+    natsConn::NATS.Connection
+    topic::String
+    senderID::String
+    fileserver_url::String
+end
+
+# tools/list implementation
+function (c::mcpServer)(method::String)
+    payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
+    payloads = [("payload", payload, "dictionary")]
+    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
+        sender_id=c.senderID, msg_purpose="mcp_tools_list", fileserver_url=c.fileserver_url)
+    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
+    incoming = msghandler.smartunpack(String(reply.payload))
+    return incoming["payloads"][1][2]
+end
+
+# tools/call implementation
+function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
+    payload = Dict("jsonrpc" => "2.0", "id" => 2, "method" => method,
+        "params" => Dict("name" => toolName, "arguments" => arguments))
+    payloads = [("payload", payload, "dictionary")]
+    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
+        sender_id=c.senderID, msg_purpose="mcp_tool_call", fileserver_url=c.fileserver_url)
+    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
+    incoming = msghandler.smartunpack(String(reply.payload))
+    return incoming["payloads"][1][2]
+end
+```
+
+## Tool Discovery
+
+Tools are discovered dynamically via the MCP server:
+
+1. `listTools` is the only pre-registered tool at agent creation
+2. When the LLM calls `listTools()`, tools are discovered from the MCP server and registered at runtime
+3. Supports pagination via `nextCursor` for large tool sets
+4. New tools become immediately available without restarting the agent
+
+## Hooks
+
+Hooks allow customization of the agent's behavior at key points in the execution pipeline.
+
+| Hook | Signature | Purpose |
+|------|-----------|---------|
+| `prepareContext` | `(state::agentState, eventSink) -> agentContext` | Transform messages/context before LLM call (filter tools, inject context, prune messages) |
+| `formatMsgForLLM` | `(ctx::agentContext, eventSink) -> Dict` | Convert agent context to LLM API message format |
+| `beforeToolCall` | `(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult` | Block/allow tool execution; request user approval |
+| `afterToolCall` | `(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}` | Post-process tool results; mask sensitive data; set `terminate` flag |
+| `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages throughout the pipeline |
+
+## Agent Lifecycle
+
+1. **Creation** — `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
+2. **Input** — User messages enter via `promptAgent()` (immediate) or `followUp()` (queued while agent is busy)
+3. **Processing** — `_processMessage()` handles LLM calls, tool execution, and conversation history in a loop
+4. **Tool Execution Pipeline** — each tool call follows three phases:
+   - `prepareToolCall` — resolve tool, validate arguments, run `beforeToolCall` hook
+   - `executePreparedToolCall` — invoke `tool.execute()`
+   - `finalizeExecutedToolCall` — run `afterToolCall` hook, construct `toolResultMessage`
+5. **Termination** — tool `execute` functions can set `terminate=true` to stop the agent loop
+6. **Shutdown** — `stopAgent()` sends a `:shutdown` signal, drains channels, and closes the agent
+
+## Architecture
+
+```
+src/
+├── YiemAgent.jl          # Module entry point
+├── type.jl               # Core types (messages, tools, agent state, events)
+├── utils.jl              # Message formatting, context preparation, validation
+├── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
+├── agentCore.jl          # Agent loop, tool execution pipeline, yiemAgent struct
+└── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
+```

appconfig.json

@@ -0,0 +1,54 @@
+{
+  "nats_server_info": {
+    "description": "nats server",
+    "url": "nats.yiem.cc"
+  },
+  "testingOrProduction": "testing",
+  "agentId": "2b74b87a-5413-4fe2-a4d3-405891051680",
+  "agentCentralConfigSubject": "/yiem/hq/agent/sommelier/backend/config/api/v1.1",
+  "this_service_name": "agent_backend",
+  "this_service_input_channel": {
+    "mqtt": [
+        "/yiem/hq/agent/sommpanion/backend/db/api_v1"
+    ],
+    "nats": [
+      "sommpanion.backend.agentbackend.v1.inbox"
+    ]
+  },
+  "agentRole": "sommelier",
+  "organization": "yiem_hq",
+  "externalservice": {
+    "servicesloadbalancer": {
+      "nats": "sommpanion.backend.servicesloadbalancer.v1.inbox" 
+    },
+    "textembedding": {
+        "url": "textembedding.api.v1"
+    },
+    "textimage_to_text_llm": {
+        "url": "https://llmcoder.yiem.cc/v1/chat/completions",
+        "modelname": "Qwen3.6-35B-A3B-UD-Q4_K_M"
+    },
+    "virtualWineCustomer_1": {
+        "serviceSubject": "",
+        "modelName": "qwen3:8b"
+    },
+    "sommpanion_db" : {
+      "description": "A database connection info for LibPQ client",
+      "url": "192.168.88.106:5432",
+      "dbname": "winedb",
+      "user": "admin",
+      "password": "admin@Sommpanion_0.0"
+    },
+    "sommpanion_vectordb" : {
+      "description": "A wine database connection info for LibPQ client",
+      "url": "192.168.88.106:5433",
+      "dbname": "vectordb",
+      "user": "admin",
+      "password": "admin@Sommpanion_0.0"
+    },
+    "fileserver": {
+    "description": "temporary file server",
+    "url": "https://fileserver.yiem.cc"
+    }
+  }
+}

docs/1-requirements.md

@@ -0,0 +1,155 @@
+# YiemAgent: Agile Agent Framework
+
+## 1. Business Context & Success Metrics
+
+### Business Goal
+
+YiemAgent is a Julia-based AI agent framework that enables building chatbot assistants with tool-calling capabilities, model-agnostic LLM integration, and runtime tool discovery via MCP (Model Context Protocol). It provides an asynchronous agent loop with channel-based message passing, supporting both single-turn and multi-turn conversations with automatic tool call resolution.
+
+### User Stories
+
+- **US-001**: As a developer, I want to create an AI agent with a simple constructor so that I can quickly prototype chatbot functionality in Julia
+- **US-002**: As a developer, I want the agent to discover tools automatically from an MCP server so that I don't need to manually register tool APIs
+- **US-003**: As a developer, I want the agent to handle tool calls automatically in a loop so that the LLM can use tools without external orchestration
+- **US-004**: As a developer, I want to intercept tool calls with before/after hooks so that I can add user approval, logging, or result sanitization
+- **US-005**: As a developer, I want the framework to support multiple LLM models and providers through a pluggable `llmCall` interface so that I can swap models without changing agent logic
+
+### KPIs & Targets
+
+- **KPI-001**: Agent loop processes messages with <500ms overhead for sequential tool calls (validates US-003)
+- **KPI-002**: MCP tool discovery completes within 2 seconds for 50+ tools (validates US-002)
+- **KPI-003**: 100% of message types have OpenAI format conversion (validates US-001)
+
+## 2. Technical Boundaries
+
+### In Scope
+
+- Julia module implementing an AI agent with asynchronous message processing
+- Channel-based input (user→agent), follow-up (async queue), and output (agent→user) interfaces
+- LLM model abstraction with tool call extraction from multiple response formats (OpenAI content blocks, LMStudio/vLLM format)
+- Tool registry with MCP server integration (tools/list, tools/call via JSON-RPC 2.0)
+- Agent loop with before/after tool call hooks for lifecycle management
+- Message type system with OpenAI format conversion utilities
+- Abort signal and termination flag support via tool implementations
+
+### Out of Scope
+
+- GUI/CLI interface (exposed via channels only; UI layer is external)
+- Persistent chat history or long-term memory (TODO comments in code)
+- Multi-agent orchestration (single agent instance per `yiemAgent` struct)
+- Embedding generation or retrieval-augmented generation (RAG)
+- Natural language-to-code generation or execution sandboxes
+
+### Dependencies
+
+- **Julia Standard Library**: `Dates`, `UUIDs`, `Base.Threads`
+- **External Packages**: `JSON`, `DataStructures`, `HTTP`, `PrettyPrinting`, `Serialization`, `DataFrames`
+- **Internal Package**: `GeneralUtils` (project-specific utility library)
+- **Optional**: `NATS` (MCP server communication), `LibPQ` (PostgreSQL)
+
+### Deployment Constraints
+
+- **NFR-001**: Library designed for embedded use in Docker containers (MicroK8s cluster, per `NFR-501` from template)
+- **NFR-002**: Must support Julia 1.9+ for module include/export semantics compatibility
+- **NFR-003**: Thread-safe channel operations; single-agent instance processes messages sequentially on background `@spawn` task
+
+## 3. Functional Requirements (FR)
+
+### FR-001: Agent Message Processing
+
+The agent shall process user messages through an asynchronous loop that:
+- Accepts messages via `inputChannel` (capacity 16) and `followUpChannel` (capacity 32)
+- Prioritizes `inputChannel` messages over `followUpChannel` messages
+- Feeds responses to `outputChannel` (capacity 16) in OpenAI message format
+- Exits cleanly on `:shutdown` signal
+
+**Traceability**: US-001, US-003
+
+### FR-002: LLM Model Agnosticism
+
+The agent shall accept a pluggable `llmCall` function that:
+- Receives formatted messages and returns an LLM response dict
+- The response dict may contain `message.tool_calls` (LMStudio format) or `content` blocks (OpenAI format)
+- The framework extracts tool calls regardless of response format
+
+**Traceability**: US-005
+
+### FR-003: Tool Call Execution Loop
+
+The agent shall automatically execute tool calls returned by the LLM:
+- Extract tool calls via `_extractToolCalls()` which supports both `message.tool_calls` and `content[{type="tool_calls"}]` formats
+- Build `agentContext` via `prepareContext()` (pluggable hook)
+- Format messages via `formatMsgForLLM()` to OpenAI format
+- Extract tool calls → loop: execute tool calls → append results to history → call LLM again
+- Break loop when LLM response has no tool calls
+- Support `terminate` flag: if `toolResultBatch.terminate == true`, break loop and return final response
+
+**Traceability**: US-003
+
+### FR-004: Tool Discovery and Registration
+
+The agent shall:
+- Query MCP server via `mcpserver("tools/list")` (JSON-RPC 2.0) to discover tools
+- Support pagination via `nextCursor` field
+- Wrap each MCP tool definition as an `agentTool` with `execute` hook that calls `mcpserver("tools/call", name, args, sessionId)`
+- Register a builtin `listTools` tool that triggers runtime discovery and lists all available tools
+- Skip tools already registered (idempotent)
+
+**Traceability**: US-002
+
+### FR-005: Tool Call Lifecycle Hooks
+
+The agent shall support two lifecycle hooks:
+- `beforeToolCall(beforeToolCallContext, signal)` — can block execution by returning `beforeToolCallResult(true, reason)`
+- `afterToolCall(afterToolCallContext)` — can transform result by returning modified `agentToolResult`
+- Errors from hooks are caught and converted to `immediateOutcome`/error tool result messages (never throw)
+
+**Traceability**: US-004
+
+### FR-006: Message Type Conversion
+
+The agent shall:
+- Convert internal message types (`userMessage`, `assistantMessage`, `assistantMessageToolCall`, `toolResultMessage`) to OpenAI format via `_userMessageToOpenAI()`, `_assistantMessageToOpenAI()`, `_assistantMessageToolCallToOpenAI()`, `_toolResultMessageToOpenAI()`
+- Convert internal `messageContent` types (`textContent`, `imageContent`, `reasoningContent`) to OpenAI content blocks via `_messageContentToBlocks()`
+- Convert `agentTool` definitions to OpenAI function tool format via `_toolsToOpenAI()`
+
+**Traceability**: US-001
+
+## 4. Non-Functional Requirements (NFRs)
+
+### 4.1 Performance & Scalability
+
+- **NFR-101**: Agent loop message processing overhead <500ms per sequential tool call (measured KPI: KPI-001)
+- **NFR-102**: MCP tool discovery for 50+ tools completes within 2 seconds (measured KPI: KPI-002)
+- **NFR-103**: Channel buffers prevent unbounded memory growth — `inputChannel` (16), `followUpChannel` (32), `outputChannel` (16)
+
+### 4.2 Availability & Reliability
+
+- **NFR-201**: Agent loop catches all errors via `try/catch` in `_agentLoop()` and calls `eventSink(errMsg)` before exit (measured by event sink logging)
+- **NFR-202**: `prepareToolCall()` never throws errors — validation failures return `immediateOutcome` with error tool result
+
+### 4.3 Privacy & Security
+
+- **Data Classification**: Tool arguments and results may contain PII (user messages); MCP tool results treated as untrusted input
+- **No built-in auth/authz**: Responsibility of `mcpServer` implementation and `beforeToolCall` hook
+- **Input validation**: JSON schema validation via `validateToolArguments()` checks required fields only (tools need custom validation via `validateRequiredArgs` hook)
+
+### 4.4 Observability & Telemetry
+
+- **Required Logs**: `agentLoop` events (via `eventSink`) emit state machine steps (`_agentLoop 1` through `_agentLoop 6`), `_processMessage` steps (1 through 18), `prepareToolCall` steps (1 through 10)
+- **Critical Metrics**:
+  - `agent._state.messages.length` — conversation history size (logged at each step)
+  - `toolExecutionStart`/`toolExecutionEnd` events (via `toolExecStartEvent`, `toolExecEndEvent` structs)
+- **Retention**: No built-in log retention; agent is a library, does not manage external logging
+
+## 5. Acceptance Conditions
+
+- [ ] **FR-001**: Agent processes messages through channel system with priority ordering (inputChannel > followUpChannel)
+- [ ] **FR-002**: `llmCall` is pluggable; response formats supported include `message.tool_calls` dict and `content` array blocks
+- [ ] **FR-003**: Agent loops on tool calls until LLM returns no tool calls or terminate flag is set
+- [ ] **FR-004**: MCP tools are discovered via `tools/list` with pagination; all registered tools are callable via `tools/call`
+- [ ] **FR-005**: `beforeToolCall` hook can block; `afterToolCall` hook can transform result; errors return error tool result
+- [ ] **FR-006**: All message types (userMessage, assistantMessage, assistantMessageToolCall, toolResultMessage) convert to OpenAI format
+- [ ] **NFR-101**: Message processing overhead <500ms per sequential tool call
+- [ ] **NFR-102**: MCP discovery for 50+ tools completes <2 seconds
+- [ ] **NFR-201**: Agent loop catches errors and exits gracefully without panics

docs/2-solution_design.md

@@ -0,0 +1,119 @@
+# Solution Design: YiemAgent Agent Framework
+
+## 1. Problem Decomposition
+
+| Problem | Description | User Impact |
+|---------|-------------|-------------|
+| P-001 | Developers need a chatbot framework in Julia that handles LLM calls and tool calls without writing boilerplate | Must write separate request/response handling, tool call parsing, and loop management code for each agent |
+| P-002 | Tool discovery is manual and repetitive | Must register every tool individually; no automatic discovery from MCP-compatible servers |
+| P-003 | Multiple LLM providers have different response formats | Must handle `message.tool_calls` (LMStudio/vLLM) vs `content` blocks (OpenAI API) separately |
+| P-004 | Tool execution needs lifecycle hooks for observability and control | No built-in before/after hooks for authorization, logging, or result sanitization |
+| P-005 | Agent state management is error-prone | Must handle message history, tool registration, and pending tool call tracking manually |
+
+## 2. Solution Approach
+
+**Approach**: "Build a channel-based agent system where the LLM talks to tools automatically, tools talk to MCP servers automatically, and you compose agents by injecting `llmCall` and `mcpServer` functions — the framework owns the loop, the state, and the format conversion."
+
+**Key Principles**:
+- **Channel-based async processing**: Each agent owns three channels (`inputChannel`, `followUpChannel`, `outputChannel`); a background `@spawn` task runs the agent loop, processing messages in priority order
+- **Tool call loop**: The agent calls `llmCall()`, extracts tool calls, executes them (sequentially or in parallel), appends results, and calls `llmCall()` again — repeating until no tool calls remain
+- **Pluggable hooks**: `prepareContext`, `formatMsgForLLM`, `beforeToolCall`, `afterToolCall`, and `llmCall` are all injectable functions — the framework provides sensible defaults, but you override what you need
+- **MCP-native tool discovery**: Tools are discovered at startup via MCP `tools/list` and can be discovered at runtime via the `listTools` builtin tool (idempotent registration with pagination support)
+- **Format-agnostic LLM responses**: `_extractToolCalls()` handles both OpenAI content blocks and LMStudio/vLLM nested message format, allowing the same agent to work with any model provider
+
+## 3. Decision Rationale
+
+| Decision ID | Decision | Rationale | Alternatives Rejected |
+|-------------|----------|-----------|----------------------|
+| SD-001 | Use channel-based async processing with `@spawn` task | Provides non-blocking message queues; `inputChannel` blocks if full (backpressure); `followUpChannel` allows async follow-ups while agent is busy | Threading with shared state — race conditions; callback-based async — hard to compose |
+| SD-002 | Background `_agentLoop` task runs message dispatch loop | Isolates message processing from construction; agent can be created and used concurrently | Synchronous agent.run(message) — blocks caller; manual task management — boilerplate |
+| SD-003 | `_processMessage` runs `llmCall()` in an inner loop (while loop) until no tool calls remain | Handles multi-turn tool use transparently — agent decides when to stop based on LLM response | External loop in caller code — duplicates agent loop logic; single-turn only — no multi-step tool use |
+| SD-004 | MCP server integration via callable struct `mcpServer(method, args...)` | MCP is model-agnostic protocol; callable struct allows type-stable single dispatch; supports NATS transport (struct has `natsConn`, `topic`, `senderID` fields) | Ad-hoc HTTP client for MCP — breaks NATS-only transports; tight coupling to specific transport |
+| SD-005 | Tool call lifecycle: `prepareToolCall()` → `executePreparedToolCall()` → `finalizeExecutedToolCall()` — three phases, never throw errors | Preparation validates args and runs `beforeToolCall` hook (can block); execution calls `tool.execute()`; finalization runs `afterToolCall` hook. Each phase returns a result type, not exceptions — agent loop catches all and builds error tool result | `try/catch` in loop — loses context; throwing errors breaks tool result message contract |
+| SD-006 | Message types are Julia structs with keyword constructors (`userMessage(content=[...])`) | Type stable; keyword constructors provide defaults (`role="user"`, `timestamp=now()`); supports both positional and keyword usage | `Dict{String, Any}` for messages — lose type safety, docgen, IDE support |
+| SD-007 | `eventSink` function passed to all deep lifecycle calls | Provides hooks for external monitoring (logging, metrics, NATS pub/sub) without modifying core logic | Built-in `println` — can't disable or redirect; no metrics integration |
+| SD-008 | `abortSignal` struct with `.aborted::Bool` field passed to `prepareToolCall()` and hooks | Simple mutable state for interrupting long-running tool calls (no async cancellation — just flag check) | Threading with `Task.cancel()` — Julia thread model doesn't support it; `@async` spawn — complex cleanup |
+| SD-009 | `_extractToolCalls()` supports both `message.tool_calls` and `content[{type="tool_calls"}]` formats | LMStudio.jl/vLLM vs OpenAI API have different response shapes; framework must work with both | Separate parser per provider — maintenance burden; require provider wrapper — adds indirection |
+
+## 4. High-Level Component Diagram
+
+```mermaid
+flowchart TD
+    User[External Caller] -->|promptAgent / followUp| InputCh[inputChannel]
+    
+    subagent AgentLoop [Agent Loop Task]
+        InputCh -->|take!| Disp[Message Dispatcher]
+        Disp -->|spawn| Proc[_processMessage]
+        Proc -->|Channel| PrCh[processMessageInputCh]
+    end
+    
+    subagent Message Pipeline
+        PrCh -->|put!| DMsg[Drain input, convert to userMessage]
+        DMsg --> PC[prepareContext hook]
+        PC --> FM[formatMsgForLLM hook]
+        FM --> LLM[llmCall]
+        LLM --> EC[_extractToolCalls]
+        EC -->|hasToolCalls| TC[executeToolCalls]
+        TC -->|toolResultBatch| TM[toolResultMessage]
+        TM -->|push!| MH[messageHistory]
+        TC -->|no tool calls break| BRK[break loop]
+        BRK --> End[exit]
+    end
+    
+    subagent Tool System
+        TC --> Prep[prepareToolCall]
+        Prep --> Exec[executePreparedToolCall]
+        Exec --> Term[finalizeExecutedToolCall]
+        Term --> CRM[createToolResultMessage]
+        CRM --> TM
+    end
+    
+    subagent MCP Integration
+        Prep --> ToolLookup[context.tools lookup]
+        ToolLookup --> MCP[MCP tools/list + tools/call]
+        MCP --> Wr[_wrap_mcp_tool]
+        Wr --> Reg[toolRegistry.registerAllTools]
+    end
+    
+    OutputCh[outputChannel] --> User
+    Proc -->|final response| OutputCh
+```
+
+**Component Descriptions**:
+- **Agent Loop Task** (`SD-001`, `SD-002`): Runs `@spawn _agentLoop()`; manages three channels; dispatches messages to `_processMessage`
+- **Message Pipeline** (`SD-003`, `SD-004`): `prepareContext` → `formatMsgForLLM` → `llmCall` → extract tool calls → loop
+- **Tool System** (`SD-005`): Three-phase lifecycle (prepare → execute → finalize); never throws; returns error tool result on failure
+- **MCP Integration** (`SD-004`): `tools/list` discovery with pagination; `_wrap_mcp_tool` creates executable `agentTool` with MCP JSON-RPC calls
+
+## 5. Alternatives Considered
+
+| Alternative | Pros | Cons | Decision |
+|-------------|------|------|----------|
+| Synchronous `agent.process(message)` | Simpler API; no channels or tasks | Blocks caller; no follow-up support; harder to test concurrency | **Rejected** — async channels needed for real-time chat UX |
+| JSON dicts for messages everywhere | Flexible; no type definitions | No static checking; no docgen; no IDE autocomplete; harder to serialize/deserialize | **Rejected** — Julia advantages of structs (type-stable, documented) outweigh cost |
+| Single `onToolCall` hook (pre-execute only) | Simpler hook interface | Can't sanitize results post-execution; can't set terminate flag after tool completes | **Rejected** — `afterToolCall` needed for masking secrets, normalizing usage, inspecting error status |
+| Direct HTTP calls to MCP servers (no NATS) | Simpler dependency (no NATS) | Breaks if MCP uses NATS (common in microservices); no single transport | **Rejected** — callable struct abstracts transport; NATS is one implementation option |
+| External orchestrator runs tool call loop | Caller has full control | Duplicates logic; every caller must implement same loop; harder to maintain | **Rejected** — loop is agent framework responsibility (single source of truth) |
+
+## 6. Risk Assessment
+
+| Risk | Impact | Probability | Mitigation |
+|------|--------|-------------|------------|
+| `llmCall` throws (network timeout, API error) | Agent loop exits with error in `eventSink` | Medium | `try/catch` in `_processMessage` and `_agentLoop`; eventSink receives error string |
+| Tool `prepareArguments` or `validateRequiredArgs` hook throws | `prepareToolCall` catches and returns `immediateOutcome` error | Low | Hook errors caught; error tool result sent to LLM for recovery |
+| MCP server unreachable | Tool discovery returns 0 tools; `listTools` still available | Medium | `register_mcp_tools` returns 0 on error; tool execution calls catch errors |
+| Long-running tool call hangs | Caller blocks on `outputChannel`; no built-in timeout | Medium | `eventSink` passes abort signal; caller sets `signal.aborted = true` externally |
+| Multiple agents share same `mcpServer` struct | `mcpServer` must be thread-safe for concurrent tool calls | Low | Each thread gets own `mcpServer` instance; NATS handles concurrency |
+| Message history grows unbounded | Memory exhaustion in long sessions | Medium | No built-in pruning; `prepareContext` can filter/prune messages (TODO in code) |
+
+## 7. Requirements Traceability
+
+| Solution Component | Requirement ID | Decision ID | Description |
+|-------------------|----------------|-------------|-------------|
+| Channel-based async processing | FR-001 | SD-001, SD-002 | `inputChannel`, `followUpChannel`, `outputChannel` with priority dispatch |
+| LLM call loop | FR-003 | SD-003 | `_processMessage` while loop; `llmCall()` → extract → execute → append → repeat |
+| MCP tool discovery | FR-004 | SD-004, SD-009 | `tools/list` with pagination; `tools/call` execution |
+| Lifecycle hooks | FR-005 | SD-005 | `beforeToolCall` can block; `afterToolCall` transforms; never throw |
+| Message type conversion | FR-006 | SD-006 | Julia structs with keyword constructors; `_userMessageToOpenAI`, `_messageContentToBlocks` |
+| Format-agnostic extraction | FR-002 | SD-009 | `_extractToolCalls` handles both `message.tool_calls` and `content` blocks |
+| Event sink observability | NFR-201, NFR-103 | SD-007 | `eventSink` passed to all lifecycle steps; logs state machine transitions |

docs/3-walkthrough.md

@@ -0,0 +1,318 @@
+# Walkthrough: YiemAgent Agent Framework
+
+## Structural Guidance
+
+- One **overview sequence diagram** per participant (high-level flow)
+- **Detailed sub-diagrams** for complex interaction points (tool call loop, MCP discovery)
+- Use sequence diagrams for request/response patterns; lifecycle states for multi-step operations
+
+---
+
+## External Caller
+
+_The Developer calling `YiemAgent.jl` via `promptAgent`, `takeResponse`, `followUp`, and `stopAgent`._
+
+### Session Start: Create Agent
+
+**WT-001**: Developer creates an `yiemAgent` instance with `llmCall` and `mcpServer`
+
+> Cross-reference: `2-solution_design.md:SD-001` (channel-based async), `SD-004` (MCP struct)
+
+**Details**:
+```julia
+mcp = mcpServer(conn, topic, senderID)
+agent = yiemAgent(llmCall, mcpServer=mcp, systemPrompt="You are a helpful assistant")
+```
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent YiemAgent Constructor
+    Dev->>agent: yiemAgent(llmCall, ; mcpServer=mcp)
+    agent->>agent: create channels (input=16, followUp=32, output=16)
+    agent->>agent: registerAllTools(tools, mcp)
+    agent->>mcp: mcp("tools/list", sessionId)
+    mcp-->>agent: response (tools array + nextCursor)
+    agent->>agent: _wrap_mcp_tool for each tool
+    agent->>agent: registerTool(tools, listTools_tool)
+    agent->>agent: println("[toolRegistry] Discovered N MCP tools")
+    agent->>agent: yiemAgent struct with placeholder task
+    agent->>agent: @spawn _agentLoop(agent)
+    agent-->>Dev: yiemAgent instance
+```
+
+> **Traceability**: FR-004 (tool discovery), US-001 (agent creation)
+
+### Send Message: User Prompt
+
+**WT-002**: Developer sends a user message to the agent
+
+> Cross-reference: `2-solution_design.md:SD-002` (background task dispatch)
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent AgentLoop
+    Dev->>agent: promptAgent(agent, text="What's the weather?")
+    agent->>agent: put!(inputChannel, msg_dict)
+    Dev->>agent: takeResponse(agent)  # blocks
+    agent->>agent: inputChannel isready
+    agent->>agent: take!(inputChannel)
+    agent->>Dev: (returns agent for chaining)
+```
+
+> **Traceability**: FR-001 (channel processing), PR-001, SD-001, SD-002
+
+### Receive Response
+
+**WT-003**: Agent returns response on `outputChannel`
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent AgentLoop
+    AgentLoop->>AgentLoop: _processMessage runs
+    AgentLoop->>AgentLoop: while loop, llmCall, tool calls...
+    AgentLoop->>AgentLoop: no more tool calls → exit loop
+    AgentLoop->>AgentLoop: filter reasoningContent from final result
+    AgentLoop->>AgentLoop: _assistantMessageToOpenAI(result)
+    AgentLoop->>AgentLoop: put!(outputChannel, respondToUI)
+    AgentLoop->>Dev: take!(outputChannel)  # unblocks
+    Dev->>Dev: receive OpenAI dict response
+```
+
+> **Traceability**: FR-001, FR-003, FR-006, PR-001
+
+### Send Follow-up While Agent Busy
+
+**WT-004**: Developer sends a follow-up message while agent is processing a tool call
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent AgentLoop
+    Dev->>agent: promptAgent(text="What's the weather in Tokyo?")  # agent starts processing
+    AgentLoop->>AgentLoop: _processMessage started (processingTask = Task)
+    Dev->>agent: followUp(text="Also check Seoul")  # while busy
+    agent->>agent: put!(followUpChannel, msg_dict)
+    AgentLoop->>AgentLoop: _processMessage completes (tool calls done)
+    AgentLoop->>AgentLoop: outputChannel has message → put!(outputChannel, response), take!(next msg)
+    AgentLoop->>AgentLoop: no inputChannel → check followUpChannel
+    AgentLoop->>AgentLoop: isready(followUpChannel) → take!(followUpMsg), put!(inputChannel, followUpMsg)
+    AgentLoop->>AgentLoop: processingTask = nothing → reset
+    AgentLoop->>AgentLoop: loop back → take!(inputChannel) from followUp
+    Dev->>agent: takeResponse() → blocks
+    AgentLoop->>Dev: next response via outputChannel
+```
+
+> **Traceability**: FR-001 (follow-up priority), PR-001, SD-001, SD-002
+
+### Stop Agent
+
+**WT-005**: Developer stops the agent gracefully
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent AgentLoop
+    Dev->>agent: stopAgent(agent)
+    agent->>agent: put!(inputChannel, :shutdown)
+    AgentLoop->>AgentLoop: check newUserMsg === :shutdown
+    AgentLoop->>AgentLoop: drain inputChannel (while isready)
+    AgentLoop->>AgentLoop: drain followUpChannel (while isready)
+    AgentLoop->>AgentLoop: break, catch, fetch(_agentLoop)
+    Dev->>agent: close(inputChannel, outputChannel, followUpChannel)
+    Dev-->>Dev: nothing (agent destroyed)
+```
+
+> **Traceability**: FR-001, NFR-201 (graceful exit), PR-001
+
+---
+
+## LLM Provider
+
+_The external LLM API (e.g. Anthropic, OpenAI, LMStudio) receiving formatted messages and returning responses._
+
+### Receive Formatted Message
+
+**WT-010**: Agent sends LLM request in OpenAI format
+
+> Cross-reference: `2-solution_design.md:SD-003` (formatMsgForLLM)
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    part agent AgentLoop
+    AgentLoop->>AgentLoop: preparedContext = prepareContext(state)
+    AgentLoop->>AgentLoop: formattedMessages = formatMsgForLLM(preparedContext)
+    Note over AgentLoop: messages: [system, user, assistant, tool, ...]<br/>tools: [{"function": {"name": "getWeather", "parameters": {...}}}]
+    agentLoop->>llmCall: llmCall(formattedMessages, eventSink=eventSink)
+    llmCall->>Provider: HTTP POST /v1/chat/completions (JSON)
+    Provider-->>llmCall: JSON response
+    llmCall-->>AgentLoop: response = JSON.Object{String, Any}(...)
+```
+
+> **Traceability**: FR-002, SD-003, SD-009
+
+### Receive Response: No Tool Calls
+
+**WT-11**: Agent receives text-only response
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    llmCall-->>AgentLoop: response = {finish_reason=> "stop", message=> {role=> "assistant", content=> "It's 22°C in Bangkok"}}
+    AgentLoop->>AgentLoop: _extractToolCalls(response)
+    AgentLoop->>AgentLoop: hasToolCalls = false
+    AgentLoop->>AgentLoop: assistant_msg = assistantMessage(content=[textContent("It's 22°C")], ...)
+    AgentLoop->>AgentLoop: push!(agentMsgHistory, assistant_msg)
+    AgentLoop->>AgentLoop: break (no tool calls)
+    AgentLoop->>AgentLoop: filter reasoningContent (if any)
+    AgentLoop->>AgentLoop: create final assistantMessage
+    AgentLoop->>AgentLoop: put!(outputChannel, OpenAI format dict)
+```
+
+> **Traceability**: FR-003 (loop exit), FR-006 (OpenAI conversion), PR-003
+
+### Receive Response: With Tool Calls
+
+**WT-012**: Agent receives tool call response
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    llmCall-->>AgentLoop: response = {finish_reason=> "tool_calls",<br/>message=> {role=> "assistant",<br/>reasoning_content=> "I need to call getWeather...",<br/>tool_calls=> [{"id"=>"call_1", "function"=>{name=>"getWeather", arguments=>"{\"city\":\"Bangkok\"}"}}, ...]}}
+    AgentLoop->>AgentLoop: _extractToolCalls(response)
+    AgentLoop->>AgentLoop: hasToolCalls = true<br/>toolCallList = [agentToolCall("getWeather", ...)]
+    AgentLoop->>AgentLoop: assistant_msg = assistantMessageToolCall(toolCalls, reasoningContent, ...)
+    AgentLoop->>AgentLoop: push!(agentMsgHistory, assistant_msg)
+    AgentLoop->>AgentLoop: config = agentLoopConfig(beforeToolCall, afterToolCall, parallel="sequential")
+    AgentLoop->>AgentLoop: toolResultBatch = executeToolCalls(...)
+    ...
+```
+
+> **Traceability**: FR-002, FR-003, PR-003, SD-003, SD-005, SD-009
+
+---
+
+## MCP Server
+
+_The Model Context Protocol server providing tools via JSON-RPC 2.0 (transport via NATS or HTTP).
+
+### Receive tools/list Request
+
+**WT-020**: Agent discovers tools on startup
+
+> Cross-reference: `2-solution_design.md:SD-004` (MCP integration)
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    part agent AgentLoop
+    agent->>mcp: mcp("tools/list", sessionId; eventSink=eventSink)
+    Note over mcp: JSON-RPC 2.0: {"jsonrpc"=>"2.0", method=>"tools/list", "id"=>..., "params"=>{...}}
+    MCP_server-->>agent: response = {<br/>  "result" => {<br/>    "tools" => [name, label, description, inputSchema], ...<br/>    , "nextCursor" => 123<br/>  }<br/>}
+    agent->>agent: for tool_def in tools_array<br/>  _wrap_mcp_tool(mcp, tool_def)
+    agent->>agent: while cursor !== nothing → paginate
+    agent->>agent: println("[toolRegistry] Discovered N MCP tools. Total: $(length(tools))")
+```
+
+> **Traceability**: FR-004 (MCP discovery), PR-004
+
+### Receive tools/call Request
+
+**WT-021**: Agent executes tool via MCP
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    part agent Tool System
+    agent->>mcp: mcp("tools/call", "getWeather", args={"city" => "Bangkok"}, sessionId; eventSink=eventSink)
+    Note over mcp: JSON-RPC 2.0: {"jsonrpc"=>"2.0", method=>"tools/call", "params"=>{name, arguments, sessionId}}
+    MCP_server-->>agent: response = {<br/>  "result" => {<br/>    "content" => [{"type"=>"text", "text"=>"Sunny, 22°C"}],<br/>    "isError" => false<br/>  }<br/>}
+    agent->>agent: check haskey(response, "error") → no
+    agent->>agent: result_data = response["result"]
+    agent->>agent: content_text = _extract_text_content(result_data)
+    agent->>agent: return agentToolResult([textContent(content_text)], details={isError=>false}, nothing, false)
+```
+
+> **Traceability**: FR-004 (tool execution), PR-004, SD-004, SD-005
+
+### Receive Error from MCP Server
+
+**WT-022**: MCP server returns error
+
+**Sequence**:
+```mermaid
+sequenceDiagram
+    MCP_server-->>agent: response = {<br/>  "error" => {<br/>    "code" => -32601,<br/>    "message" => "Method not found"<br/>  }<br/>}
+    agent->>agent: haskey(response, "error") → true
+    agent->>agent: return agentToolResult(<br/>    [textContent("MCP error: Method not found")],<br/>    details={"error" => "Method not found"},<br/>    nothing, false<br/>)
+    agent->>AgentLoop: finalOutcome(error=true)
+    agent->>AgentLoop: push!(agentMsgHistory, toolResultMessage(...))
+    agent->>AgentLoop: next LLM turn receives error result
+```
+
+> **Traceability**: FR-004 (error handling), NFR-202 (never throw), PR-004, SD-005
+
+---
+
+## Error Paths and Edge Cases
+
+**WT-030**: LLM times out (network error)
+
+> Same participant POV — from caller's perspective, they block on `takeResponse()` until task exits
+
+```mermaid
+sequenceDiagram
+    participant Dev as Developer
+    part agent AgentLoop
+    Dev->>agent: takeResponse(agent)  # blocks
+    agent->>llmCall: llmCall(messages)  # calls external API
+    lltimout
+    LLM provider-->>AgentLoop: ERROR (timeout)
+    AgentLoop->>AgentLoop: @error "Agent loop failed"
+    AgentLoop->>AgentLoop: eventSink("error: ...")
+    AgentLoop->>Dev: fetch(_agentLoop) throws TaskFailedException
+    Dev->>agent: stopAgent(agent)  # cleanup channels
+```
+
+**Traceability**: PR-001, NFR-201 (catch error and exit), SD-001
+
+**WT-031**: Tool execution throws exception
+
+```mermaid
+sequenceDiagram
+    agent->>agent: executePreparedToolCall
+    agent->>tool: tool.execute(toolCallId, args, signal, eventSink)
+    note over tool: tool raises exception (e.g., network error, invalid JSON)
+    tool-->>agent: throw Exception
+    agent->>agent: catch e
+    agent->>agent: executedOutcome(createErrorToolResult("error message"), true)
+    agent->>AgentLoop: finalizeExecutedToolCall
+    AgentLoop->>AgentLoop: createToolResultMessage(error=true)
+    AgentLoop->>AgentLoop: push!(history, toolResultMessage)
+    AgentLoop->>AgentLoop: next llmCall with error in message history
+```
+
+**Traceability**: FR-005 (hook errors caught), NFR-202 (never throw), SD-005
+
+**WT-032**: beforeToolCall hook blocks execution
+
+```mermaid
+sequenceDiagram
+    agent->>agent: beforeToolCall(beforeCtx, signal, eventSink)
+    agent->>agent: returns beforeToolCallResult(true, "User must approve")
+    agent->>agent: return immediateOutcome(createErrorToolResult("Tool blocked"), true)
+    agent->>AgentLoop: executeToolCalls → immediateOutcome
+    AgentLoop->>AgentLoop: finalizeExecutedToolCall
+    AgentLoop->>AgentLoop: createToolResultMessage(error=true)
+    AgentLoop->>AgentLoop: push! to history
+```
+
+**Traceability**: FR-005 (beforeToolCall can block), SD-005

docs/4-specification.md

@@ -0,0 +1,518 @@
+# Specification: YiemAgent Agent Framework
+
+## 1. Message Type System (`src/type.jl`)
+
+### SP-001: userMessage
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `role::String` | `String` | Yes | Always `"user"` |
+| `content::Vector{messageContent}` | `Vector{messageContent}` | No | Plain text and/or image content |
+| `timestamp::Timestamp` | `DateTime` | Yes | When the message was sent |
+
+**Keyword Constructor**: `userMessage(; role="user", content=[...], timestamp=now())`
+
+**Traceability**:
+- Requirement: FR-006 (message type conversion)
+- Solution Design: SD-006 (Julia structs for messages)
+
+### SP-002: assistantMessage
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `role::String` | `String` | Yes | Always `"assistant"` |
+| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Text and/or image content |
+| `api::String` | `String` | Yes | API name used (e.g., `"openai"`) |
+| `provider::String` | `String` | Yes | Provider name (e.g., `"anthropic"`) |
+| `model::String` | `String` | Yes | Model identifier |
+| `usage::llmUsage` | `llmUsage` | Yes | Token usage (in/out) |
+| `stopReason::String` | `String` | Yes | Why generation stopped (e.g., `"end_turn"`) |
+| `errorMessage::Union{String, Nothing}` | `Union{String, Nothing}` | Yes | Error if generation failed |
+| `timestamp::Timestamp` | `DateTime` | Yes | When the message was received |
+
+**Keyword Constructor**: `assistantMessage(; role="assistant", content=[...], api="", provider="", model=nothing, usage=llmUsage(0,0), stopReason="end_turn", errorMessage=nothing, timestamp=now())`
+
+**Traceability**:
+- Requirement: FR-006 (message type conversion)
+- Solution Design: SD-006
+
+### SP-003: assistantMessageToolCall
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `role::String` | `String` | Yes | Always `"assistant"` |
+| `toolCalls::Vector{agentToolCall}` | `Vector{agentToolCall}` | Yes | Tool calls to execute |
+| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Reasoning/thinking content blocks |
+| `api::String` | `String` | Yes | API name used |
+| `provider::String` | `String` | Yes | Provider name |
+| `model::String` | `String` | Yes | Model identifier |
+| `usage::llmUsage` | `llmUsage` | Yes | Token usage |
+| `stopReason::String` | `String` | Yes | Why generation stopped (e.g., `"tool_calls"`) |
+| `errorMessage::Union{String, Nothing}` | `Union{String, Nothing}` | Yes | Error if generation failed |
+| `timestamp::Timestamp` | `DateTime` | Yes | When the message was received |
+
+**Keyword Constructor**: `assistantMessageToolCall(; role="assistant", toolCalls=[...], content=[], api="", provider="", model=nothing, usage=llmUsage(0,0), stopReason="tool_calls", errorMessage=nothing, timestamp=now())`
+
+**Traceability**:
+- Requirement: FR-003 (tool call loop)
+- Solution Design: SD-005 (lifecycle phases), SD-009 (format-agnostic LLM responses)
+
+### SP-004: toolResultMessage
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `role::String` | `String` | Yes | Always `"toolResult"` (note: `"tool"` in example) |
+| `toolCallId::String` | `String` | Yes | ID matching the tool call |
+| `toolName::String` | `String` | Yes | Name of the executed tool |
+| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Tool output content |
+| `details::Any` | `Any` | Yes | Additional tool-specific details |
+| `usage::Union{llmUsage, Nothing}` | `Union{llmUsage, Nothing}` | Yes | Token usage if applicable |
+| `addedToolNames::Union{Vector{String}, Nothing}` | `Union{Vector{String}, Nothing}` | Yes | Tools added during execution |
+| `isError::Bool` | `Bool` | Yes | Whether the tool call resulted in an error |
+| `timestamp::Timestamp` | `DateTime` | Yes | When the result was recorded |
+
+**Keyword Constructor**: `toolResultMessage(; role="tool", toolCallId="", toolName="", content=[], details=nothing, usage=nothing, addedToolNames=nothing, isError=false, timestamp=now())`
+
+**Traceability**:
+- Requirement: FR-005 (tool lifecycle)
+- Solution Design: SD-005
+
+### SP-005: agentToolCall
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `type::String` | `String` | Yes | Always `"function"` |
+| `id::String` | `String` | Yes | Unique tool call identifier |
+| `name::String` | `String` | Yes | Tool name |
+| `arguments::Dict{String, Any}` | `Dict{String, Any}` | Yes | Parsed tool arguments |
+
+**Traceability**:
+- Requirement: FR-003 (tool call extraction)
+- Solution Design: SD-009 (extract tool calls from LLM response)
+
+### SP-006: messageContent Types
+
+**textContent** (`SP-006.1`):
+```julia
+struct textContent <: messageContent
+    text::String
+end
+```
+
+**imageContent** (`SP-006.2`):
+```julia
+struct imageContent <: messageContent
+    data::String                   # Base64-encoded image data
+    mimeType::String               # MIME type (e.g., "image/png")
+end
+```
+
+**reasoningContent** (`SP-006.3`):
+```julia
+struct reasoningContent <: messageContent
+    text::String
+end
+```
+
+**Traceability**:
+- Requirement: FR-006 (message content conversion)
+- Solution Design: SD-006
+
+## 2. LLM Model Configuration (`src/type.jl`)
+
+### SP-007: llmModel
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `id::String` | `String` | Yes | Unique model identifier |
+| `name::String` | `String` | Yes | Human-readable model name |
+| `provider::String` | `String` | Yes | Provider name (e.g., `"anthropic"`, `"openai"`) |
+| `baseUrl::String` | `String` | Yes | API endpoint base URL |
+| `reasoning::Bool` | `Bool` | Yes | Whether the model supports chain-of-thought |
+| `input::Vector{String}` | `Vector{String}` | Yes | Supported input modalities (e.g., `"text"`, `"image"`) |
+| `cost::modelCost` | `modelCost` | Yes | Pricing information |
+| `contextWindow::Int64` | `Int64` | Yes | Maximum context length in tokens |
+| `maxTokens::Int64` | `Int64` | Yes | Maximum output tokens per completion |
+
+**Traceability**:
+- Requirement: FR-002 (LLM agnosticism)
+- Solution Design: SD-003
+
+### SP-008: modelCost
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `input::Float64` | `Float64` | Yes | Price per 1M input tokens |
+| `output::Float64` | `Float64` | Yes | Price per 1M output tokens |
+| `cache_read::Float64` | `Float64` | Yes | Price per 1M cached read tokens |
+| `cache_write::Float64` | `Float64` | Yes | Price per 1M cache write tokens |
+
+**Traceability**:
+- Requirement: N/A (internal tracking data)
+- Solution Design: SD-003
+
+## 3. MCP Tool Integration (`src/toolRegistry.jl`)
+
+### SP-009: tools/list Response Format
+
+**Request**: `mcp("tools/list", sessionId; eventSink=eventSink)`
+
+**Response** (JSON-RPC 2.0):
+```json
+{
+  "result": {
+    "tools": [
+      {
+        "name": "tool_name",
+        "title"?: "Human-readable title",
+        "label"?: "Fallback title",
+        "description": "What the tool does",
+        "inputSchema": {
+          "type": "object",
+          "properties": {
+            "field": { "type": "string", "description": "..." }
+          },
+          "required": ["field"]
+        }
+      }
+    ],
+    "nextCursor"?: "pagination_cursor_or_empty"
+  }
+}
+```
+
+**Response error**:
+```json
+{
+  "error": {
+    "code": -32601,
+    "message": "Method not found"
+  }
+}
+```
+
+**Traceability**:
+- Requirement: FR-004 (MCP discovery)
+- Solution Design: SD-004
+
+### SP-010: tools/call Request/Response Format
+
+**Request**: `mcp("tools/call", toolName, args, sessionId; eventSink=eventSink)`
+
+**Response** (JSON-RPC 2.0):
+```json
+{
+  "result": {
+    "content": [
+      {"type": "text", "text": "Tool output text"}
+    ],
+    "isError": false
+  }
+}
+```
+
+**Response error**:
+```json
+{
+  "result": {
+    "content": [
+      {"type": "text", "text": "Error message"}
+    ],
+    "isError": true
+  }
+}
+```
+
+**Traceability**:
+- Requirement: FR-004 (tool execution)
+- Solution Design: SD-004 (`_wrap_mcp_tool`)
+
+## 4. OpenAI Message Format (`src/utils.jl`)
+
+### SP-011: system message format
+
+```julia
+Dict(
+  "role" => "system",
+  "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
+)
+```
+
+**Traceability**:
+- Requirement: FR-006
+- Solution Design: SD-006
+
+### SP-012: userMessage → OpenAI format
+
+```julia
+Dict(
+  "role" => "user",
+  "content" => _messageContentToBlocks(msg.content)
+)
+```
+
+Where `_messageContentToBlocks` converts:
+- `textContent(t)` → `Dict("type" => "text", "text" => t)`
+- `imageContent(data, mime)` → `Dict("type" => "image_url", "image_url" => Dict("url" => "data:$mime;base64,$data"))`
+
+**Traceability**:
+- Requirement: FR-006
+- Solution Design: SD-006
+
+### SP-013: assistantMessageToolCall → OpenAI format
+
+```julia
+Dict(
+  "role" => "assistant",
+  "content" => nothing,
+  "tool_calls" => [
+    Dict(
+      "id" => tc.id,
+      "type" => tc.type,
+      "function" => Dict(
+        "name" => tc.name,
+        "arguments" => JSON.json(tc.arguments)  # serialized as JSON string
+      )
+    )
+  ]
+)
+```
+
+**Traceability**:
+- Requirement: FR-003
+- Solution Design: SD-009
+
+### SP-014: assistantMessage → OpenAI format
+
+```julia
+Dict(
+  "role" => "assistant",
+  "content" => _messageContentToBlocks(msg.content)
+)
+```
+
+**Traceability**:
+- Requirement: FR-006
+- Solution Design: SD-006
+
+### SP-015: toolResultMessage → OpenAI format
+
+```julia
+Dict(
+  "role" => "tool",
+  "tool_call_id" => msg.toolCallId,
+  "content" => _messageContentToBlocks(msg.content)
+)
+```
+
+**Traceability**:
+- Requirement: FR-005, FR-006
+- Solution Design: SD-005
+
+### SP-016: agentTool → OpenAI tools format
+
+```julia
+Dict(
+  "type" => "function",
+  "function" => Dict(
+    "name" => tool.name,
+    "description" => tool.description,
+    "parameters" => tool.inputSchema
+  )
+)
+```
+
+**Traceability**:
+- Requirement: FR-004
+- Solution Design: SD-004, SD-006
+
+## 5. LLM Response Format Agnostic Extraction (`src/agentCore.jl`)
+
+### SP-017: format 1 — LMStudio.jl / vLLM style (dict with nested `message.tool_calls`)
+
+**Response dict** (key: `message::Dict`):
+```json
+{
+  "finish_reason": "tool_calls",
+  "message": {
+    "role": "assistant",
+    "content": "",
+    "reasoning_content": "I need to call getWeather...",
+    "tool_calls": [
+      {
+        "type": "function",
+        "function": {
+          "name": "getWeather",
+          "arguments": "{\"city\":\"Bangkok\"}"  // JSON string
+        },
+        "id": "call_1"
+      }
+    ]
+  }
+}
+```
+
+**When no tool calls**:
+```json
+{
+  "finish_reason": "stop",
+  "message": {
+    "role": "assistant",
+    "content": "It's 22°C in Bangkok"
+  }
+}
+```
+
+**Traceability**:
+- Requirement: FR-002
+- Solution Design: SD-009
+
+### SP-018: format 2 — OpenAI API style (content blocks array)
+
+**Content blocks with tool calls** (`SP-018.1`):
+```json
+{
+  "content": [
+    {
+      "type": "tool_calls",
+      "tool_calls": [
+        {
+          "type": "function",
+          "function": {
+            "name": "getWeather",
+            "arguments": {"city": "Bangkok"}  // already parsed dict
+          },
+          "id": "call_1"
+        }
+      ]
+    }
+  ]
+}
+```
+
+**Content blocks with tool_call (already parsed)** (`SP-018.2`):
+```json
+{
+  "content": [
+    {
+      "type": "tool_call",
+      "name": "getWeather",
+      "arguments": {"city": "Bangkok"},
+      "id": "call_1"
+    }
+  ]
+}
+```
+
+**Content blocks with text only** (`SP-018.3`):
+```json
+{
+  "content": [
+    {"type": "text", "text": "It's 22°C in Bangkok"}
+  ]
+}
+```
+
+**Response dict extras** (common across all formats, `SP-018.4`):
+```json
+{
+  "api": "openai",
+  "provider": "anthropic",
+  "model": "gpt-4",
+  "usage": {"inputTokens": 100, "outputTokens": 50},
+  "error_message": "Optional error"
+}
+```
+
+**Traceability**:
+- Requirement: FR-002
+- Solution Design: SD-009
+
+## 6. Agent Loop Configuration (`src/type.jl` + `src/agentCore.jl`)
+
+### SP-019: agentContext
+
+```julia
+struct agentContext
+    systemPrompt::String
+    messages::Vector{agentMessage}
+    tools::Union{OrderedDict{String, agentTool}, Nothing}  # O(1) lookup by name
+end
+```
+
+**Traceability**:
+- Requirement: FR-003 (context preparation)
+- Solution Design: SD-003, SD-004
+
+### SP-020: agentState
+
+```julia
+mutable struct agentState
+    systemPrompt::String
+    model::Union{llmModel, Nothing}
+    tools::OrderedDict{String, agentTool}      # insertion-ordered
+    messages::Vector{agentMessage}             # history (no system prompt)
+    pendingToolCalls::Vector{String}           # tool call IDs waiting for results
+    errorMessage::Union{String, Nothing}       # last error message
+end
+```
+
+**Traceability**:
+- Requirement: FR-001 (agent state management)
+- Solution Design: SD-006
+
+### SP-021: agentLoopConfig
+
+```julia
+struct agentLoopConfig
+    beforeToolCall::Union{Function, Nothing}
+    afterToolCall::Union{Function, Nothing}
+    toolExecution::String  # "sequential" or "parallel"
+end
+```
+
+**Traceability**:
+- Requirement: FR-005 (lifecycle hooks)
+- Solution Design: SD-005
+
+### SP-022: abortSignal
+
+```julia
+struct abortSignal
+    aborted::Bool
+end
+```
+
+**Traceability**:
+- Requirement: NFR-101 (timeout handling)
+- Solution Design: SD-008
+
+## 7. Tool Validation Hooks (`src/utils.jl`)
+
+### SP-023: validateRequiredArgs (default hook)
+
+**Signature**: `validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any})::Union{Nothing, String}`
+
+**Behavior**:
+- Extract `required = get(inputSchema, "required", Any[])`
+- Check each required field exists in `keys(args)`
+- Return `nothing` if all present
+- Return `"Missing required arguments: $(join(missing, ", "))"` if any missing
+
+**Traceability**:
+- Requirement: NFR-301 (input validation)
+- Solution Design: SD-005
+
+### SP-024: validateToolArguments (wrapper hook)
+
+**Signature**: `validateToolArguments(tool::agentTool, prepared::agentToolCall)::AbstractDict{String, Any}`
+
+**Behavior**:
+- If `tool.validateRequiredArgs !== nothing`: call `tool.validateRequiredArgs(prepared.arguments)`
+- Else: call `validateRequiredArgs(prepared.arguments, tool.inputSchema)`
+- If returns `!nothing`: throw `ArgumentError(result)`
+- Caller (`prepareToolCall`) catches and returns `immediateOutcome`
+
+**Traceability**:
+- Requirement: FR-005 (tool validation)
+- Solution Design: SD-005

docs/6-validation.md

@@ -0,0 +1,192 @@
+# Validation: YiemAgent Agent Framework
+
+## Test Architecture Overview
+
+Because YiemAgent is a Julia library (not a web service), validation tests focus on:
+- **Unit tests**: message type conversion, tool execution logic, format extraction
+- **Integration tests**: full agent loop with stubbed LLM and MCP
+- **Contract tests**: response format compatibility with OpenAI/LMStudio specs
+- **Performance tests**: agent loop overhead for sequential tool calls
+
+Every specification rule (SP-XXX), walkthrough journey (WT-XXX), and requirement (FR-XXX) must have a corresponding test.
+
+---
+
+## Test Types Mapping to Source Documents
+
+| Test Type | Source Document | Example Coverage |
+|-----------|-----------------|------------------|
+| **Unit tests** | `4-specification.md` (SP-001..SP-024), `src/utils.jl` (converters) | Message type → OpenAI format conversion; tool argument validation |
+| **Integration tests** | `3-walkthrough.md` (WT-001..WT-032) | Full agent loop: prompt → llmCall → tool extract → execute → response |
+| **Contract tests** | `4-specification.md` SP-017, SP-018 | Response format 1 (LMStudio/vLLM) and format 2 (OpenAI) parsed correctly |
+| **Performance tests** | `1-requirements.md` FR-003, NFR-101 | Agent loop overhead <500ms per sequential tool call |
+| **Penetration tests** | `1-requirements.md` NFR-301 | Tool argument injection; MCP spoofing |
+| **Schema validation** | `4-specification.md` SP-023, SP-024 | Required fields, type checking on tool arguments |
+
+---
+
+## Specification-to-Test Execution Trace
+
+Every traceable item — specification rules (SP-XXX), walkthrough journeys (WT-XXX), and requirements (FR-XXX) — must have a runnable test. This trace maps each item to its corresponding test file and test function.
+
+**Format**:
+
+| Requirement/Spec ID | Participant | Source Path | Unit Test File | Test Function | Additional Test File(s) | CI Gate | Status |
+|---------------------|-------------|-------------|----------------|---------------|------------------------|---------|--------|
+| FR-001 | — | `1-requirements.md:3.1` | `tests/unit/channel-handling.jl` | `test_input_channel_priority` | `tests/integration/full-agent-loop.jl` | `ci/julia-unit.yaml` | **TODO** |
+| FR-002 | — | `1-requirements.md:3.2` | `tests/unit/llm-format-extraction.jl` | `test_format1_tool_calls` | `tests/contract/response-format.jl` | `ci/julia-unit.yaml` | **TODO** |
+| FR-003 | — | `1-requirements.md:3.3` | `tests/integration/tool-call-loop.jl` | `test_loop_exits_on_no_tool_calls` | — | `ci/julia-integration.yaml` | **TODO** |
+| FR-004 | — | `1-requirements.md:3.4` | `tests/unit/mcp-tool-discovery.jl` | `test_tools_list_with_pagination` | `tests/contract/mcp-json-rpc.jl` | `ci/julia-unit.yaml` | **TODO** |
+| FR-005 | — | `1-requirements.md:3.5` | `tests/unit/tool-hooks.jl` | `test_beforeToolCall_blocks` | `tests/integration/agent-loop.jl` | `ci/julia-unit.yaml` | **TODO** |
+| FR-006 | — | `1-requirements.md:3.6` | `tests/unit/message-conversion.jl` | `test_userMessage_to_OpenAI` | — | `ci/julia-unit.yaml` | **TODO** |
+| WT-001 | Caller | `3-walkthrough.md:#agent-creation` | `integration/agent-creation.jl` | `test_agent_construction_and_lifecycle` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-002 | Caller | `3-walkthrough.md:#send-message` | `integration/message-flow.jl` | `test_promptAgent_and_takeResponse` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-003 | Caller | `3-walkthrough.md:#receive-response` | `integration/response-format.jl` | `test_output_channel_delivery` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-004 | Caller | `3-walkthrough.md:#follow-up` | `integration/followup.jl` | `test_followUp_while_busy` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-005 | Caller | `3-walkthrough.md:#stop-agent` | `integration/shutdown.jl` | `test_stopAgent_channel_cleanup` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-010 | LLM Provider | `3-walkthrough.md:#receive-formatted` | `integration/llm-call.jl` | `test_formatMsgForLLM_output_structure` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-011 | LLM Provider | `3-walkthrough.md:#response-no-tool-calls` | `integration/non-tool-response.jl` | `test_lmm_response_no_tool_calls_breaks_loop` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-012 | LLM Provider | `3-walkthrough.md:#response-with-tool` | `integration/tool-response.jl` | `test_lmm_response_with_tool_calls` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-020 | MCP Server | `3-walkthrough.md:#discover-tools` | `unit/mcp-discovery.jl` | `test_mcp_tools_list_parsed` | `contract/mcp-json-rpc.jl` | `ci/julia-unit.yaml` | **TODO** |
+| WT-021 | MCP Server | `3-walkthrough.md:#execute-tool` | `unit/mcp-tool-execute.jl` | `test_mcp_tools_call_response_parsed` | — | `ci/julia-unit.yaml` | **TODO** |
+| WT-022 | MCP Server | `3-walkthrough.md:#mcp-error` | `unit/mcp-error-handling.jl` | `test_mcp_response_error_returned` | — | `ci/julia-unit.yaml` | **TODO** |
+| WT-030 | Error | `3-walkthrough.md:#llm-timeout` | `integration/llm-timeout.jl` | `test_LLM_timeout_agent_loop_exits` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-031 | Error | `3-walkthrough.md:#tool-throw` | `integration/tool-error.jl` | `test_tool_execute_throws_returns_error_result` | — | `ci/julia-integration.yaml` | **TODO** |
+| WT-032 | Error | `3-walkthrough.md:#beforeToolCall-block` | `unit/beforeToolCall-block.jl` | `test_beforeToolCall_blocks_execution` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-001 | — | `4-specification.md:#SP-001:userMessage` | `unit/message-types.jl` | `test_userMessage_struct_default_roles` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-002 | — | `4-specification.md:#SP-002:assistantMessage` | `unit/message-types.jl` | `test_assistantMessage_struct_default_stopReason_end_turn` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-003 | — | `4-specification.md:#SP-003:assistantMessageToolCall` | `unit/message-types.jl` | `test_assistantMessageToolCall_struct_default_stopReason_tool_calls` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-004 | — | `4-specification.md:#SP-004:toolResultMessage` | `unit/message-types.jl` | `test_toolResultMessage_struct_defaults` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-005 | — | `4-specification.md:#SP-005:agentToolCall` | `unit/message-types.jl` | `test_agentToolCall_type_is_always_function` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-006.1,2,3 | — | `4-specification.md:#SP-006:content-types` | `unit/content-types.jl` | `test_textContent_imageContent_reasoningContent` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-017 | — | `4-specification.md:#SP-017` | `contract/response-format.jl` | `test_lmStudio_vLLM_format_parsed` | — | `ci/julia-contract.yaml` | **TODO** |
+| SP-018.1,2,3,4 | — | `4-specification.md:#SP-018` | `contract/response-format.jl` | `test_openAI_format_blocks_parsed` | — | `ci/julia-contract.yaml` | **TODO** |
+| SP-023 | — | `4-specification.md:#SP-023` | `unit/tool-validation.jl` | `test_validateRequiredArgs_missing_fields` | — | `ci/julia-unit.yaml` | **TODO** |
+| SP-024 | — | `4-specification.md:#SP-024` | `unit/tool-validation.jl` | `test_validateToolArguments_throws_on_missing` | — | `ci/julia-unit.yaml` | **TODO** |
+| NFR-101 | — | `1-requirements.md:4.1` | `perf/agent-loop.jl` | `perf_agent_loop_overhead_<500ms` | `tests/perf/concurrency.jl` | `ci/julia-perf.yaml` | **TODO** |
+| NFR-102 | — | `1-requirements.md:4.1` | `perf/mcp-discovery.jl` | `perf_mcp_discovery_50_tools_<2s` | — | `ci/julia-perf.yaml` | **TODO** |
+| NFR-201 | — | `1-requirements.md:4.2` | `unit/error-handling.jl` | `test_agent_loop_catches_and_logs_errors` | `integration/llm-timeout.jl` | `ci/julia-unit.yaml` | **TODO** |
+
+---
+
+## Trace Rules
+
+### Unit Test Rules
+
+- **Every specification struct** (SP-001..SP-008, SP-019..SP-022) must have a unit test validating:
+  - Default values for keyword arguments are set correctly
+  - Type field matches spec (e.g., `agentToolCall.type` is always `"function"`)
+  - Constructor works with both positional and keyword args
+- **Every converter function** (`_userMessageToOpenAI`, `_assistantMessageToOpenAI`, etc. SP-011..SP-016) must have a unit test:
+  - Input Julia struct → output `Dict{String, Any}` matching OpenAI spec
+  - Round-trip conversion preserves data (e.g., `textContent` text is preserved)
+- **Every tool validation function** (SP-023, SP-024) must have unit tests:
+  - Missing required field → returns error string (not throws)
+  - All required present → returns `nothing`
+  - Custom `validateRequiredArgs` hook on tool takes precedence over default
+
+### Integration Test Rules
+
+- **Every walkthrough journey** (WT-XXX) must have at least one integration test:
+  - `_agentLoop` processes messages through the full pipeline with stubbed LLM/MCP
+  - `promptAgent` → `takeResponse` round-trip works
+  - `followUp` while busy queues to `followUpChannel`, processes after current work
+  - `stopAgent` sends `:shutdown`, drains channels, closes all 3 channels
+- **Tool call lifecycle** (FR-003, FR-005):
+  - `llmCall` returns tool calls → loop calls `executeToolCalls` → results appended
+  - Tool calls → no tool calls on next `llmCall` → loop exits
+  - `toolResultBatch.terminate == true` → loop exits immediately
+- **Hook tests**:
+  - `beforeToolCall` returning `beforeToolCallResult(true, reason)` blocks execution → error tool result
+  - `afterToolCall` returning modified `agentToolResult` replaces result
+
+### Contract Test Rules
+
+- **Every response format** (SP-017, SP-018) must be parsed:
+  - Format 1: `_extractToolCalls({message => {tool_calls => [...]}})` extracts `assistantMessageToolCall`
+  - Format 2: `_extractToolCalls({content => [{type=>"tool_calls", tool_calls=[...]}}])` extracts `assistantMessageToolCall`
+  - Format 2 (no tools): `_extractToolCalls({content => [{type=>"text", text=>...}]})` extracts `assistantMessage`
+  - `reasoning_content` field → added to `assistantMessageToolCall.content`
+  - `api`, `provider`, `model`, `usage` → copied to message fields
+
+### Performance Test Rules
+
+- **NFR-101**: Agent loop overhead measurement:
+  - Stub LLM returns tool call → execute tool (1ms) → stub returns no tool calls → measure elapsed
+  - Subtract stub costs (llm call 1ms + tool 1ms)
+  - Overhead = elapsed - stub costs
+  - Must be <500ms per tool call (p95)
+- **NFR-102**: MCP discovery performance:
+  - Mock MCP server returns 200 tools (simulated, no real network)
+  - Measure `_wrap_mcp_tool` + `registerTool` for all 200 tools
+  - Must complete <2 seconds
+
+### Security/Validation Rules
+
+- **NFR-301 (input validation)**:
+  - Tool arguments with injection patterns (e.g., `{"city" => "; rm -rf /"}`) → passed through to MCP
+  - `beforeToolCall` can block dangerous tools (e.g., `toolCall.name == "delete_file"`)
+- **Penetration test**: MCP server spoofing
+  - Attacker sends `{"result" => {"tools" => [{"name" => "malicious", ...}]}}` from untrusted MCP endpoint
+  - Agent executes tool via MCP `tools/call` with attacker-controlled arguments
+  - Mitigation: `beforeToolCall` hook can block unknown tools; app should validate MCP server URL
+
+---
+
+## Gap Check (Validation Review)
+
+Per ASG Framework §6, verify:
+
+- ✓ Every `SP-XXX` specification item has at least one unit test (listed in trace table above)
+- ✓ Every `WT-XXX` walkthrough step has at least one integration test (listed in trace table above)
+- ✓ Every NFR threshold has at least one performance test (NFR-101, NFR-102 in trace table)
+- ✓ Security NFRs have penetration test scenarios (NFR-301 in trace rules above)
+- ✓ Every `SP-017, SP-018` response format has contract tests (listed in trace table)
+- ✓ Every `SP-011..SP-016, SP-023, SP-024` converter/validation rule has unit tests (listed in trace table)
+
+**Status**: All rows have **TODO** status. Tests must be written before integration into production CI pipeline.
+
+---
+
+## CI Pipeline
+
+```yaml
+# ci/julia-pipeline.yaml (placeholder)
+name: Julia YiemAgent CI
+on: [push, pull_request]
+
+jobs:
+  unit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/julia-buildpkg@latest
+      - run: julia --project=. -e 'using Pkg; Pkg.instantiate()'
+      - run: julia --project=./tests -e 'using Test; Test.@testset "Unit tests" # include tests/unit/run_tests.jl"'
+  
+  integration:
+    runs-on: ubuntu-latest
+    needs: unit
+    steps:
+      - run: julia --project=./tests -e 'using Test; Test.@testset "Integration" # include tests/integration/run_tests.jl"'
+  
+  contract:
+    runs-on: ubuntu-fail
+    needs: unit
+    steps:
+      - run: julia --project=./tests -e 'using Test; Test.@testset "Contract" # include tests/contract/run_tests.jl"'
+  
+  perf:
+    runs-on: ubuntu-latest
+    needs: [unit, integration]
+    steps:
+      - run: julia --project=./tests -e 'using Test; Test.@testset "Performance" # include tests/perf/run_tests.jl"'
+    # Fail if NFR-101 or NFR-102 thresholds breached
+  
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - run: julia --eval 'using Aqua; Aqua.test_all(YiemAgent)'
+```
+
+**Gap Check**: Per ASG §5, every spec item maps to a CI gate. If a `SP-XXX` or `WT-XXX` row has no test file/function, the trace table row must be filled before merging.

docs/7-implementation-plan.md

@@ -0,0 +1,217 @@
+# Implementation Plan: YiemAgent Agent Framework
+
+## 1. Implementation Phases
+
+### Phase 1: Type System & Core Structures (COMPLETED)
+
+**Files**: `src/type.jl`
+
+**Completed Tasks**:
+- `IMP-001`: Define Julia struct types for messages (`userMessage`, `assistantMessage`, `assistantMessageToolCall`, `toolResultMessage`) with keyword constructors — **COMPLETED** (see `src/type.jl:95-249`)
+- `IMP-002`: Define message content types (`textContent`, `imageContent`, `reasoningContent`) with abstract `messageContent` base type — **COMPLETED** (see `src/type.jl:74-87`)
+- `IMP-003`: Define tool types (`agentTool`, `agentToolCall`) with lifecycle hooks (`prepareArguments`, `validateRequiredArgs`) — **COMPLETED** (see `src/type.jl:319-328`)
+- `IMP-004`: Define agent context/state types (`agentContext`, `agentState`) — **COMPLETED** (see `src/type.jl:357-415`)
+- `IMP-005`: Define LLM model/usage/cost types (`llmModel`, `modelCost`, `llmUsage`) — **COMPLETED** (see `src/type.jl:45-67`)
+- `IMP-006`: Define tool call lifecycle result types (`preparedToolCall`, `immediateOutcome`, `executedOutcome`, `finalizedOutcome`) — **COMPLETED** (see `src/type.jl:600-710`)
+- `IMP-007`: Define agent loop config and event types (`agentLoopConfig`, `abortSignal`, `agentToolResult`, `toolExecStartEvent`, etc.) — **COMPLETED** (see `src/type.jl:450-567`)
+- `IMP-008`: Export all types from `type` module — **COMPLETED** (see `src/type.jl:2-26`)
+
+**Validation Tests**: Unit tests for struct construction with keyword args (SP-001..SP-008, SP-019..SP-022)
+
+**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005, FR-006
+
+---
+
+### Phase 2: Utility & Formatting Functions (COMPLETED)
+
+**Files**: `src/utils.jl`
+
+**Completed Tasks**:
+- `IMP-010`: Implement `prepareContext(state, eventSink) -> agentContext` — **COMPLETED** (see `src/utils.jl:112-126`)
+  - TODO: Filter tools based on user intent and tool description (`src/utils.jl:114`)
+  - TODO: Inject system prompt context (e.g., time, session state) (`src/utils.jl:118`)
+  - TODO: Prune/reorder messages (`src/utils.jl:121`)
+- `IMP-011`: Implement `formatMsgForLLM(ctx, eventSink) -> Dict{String, Any}` — **COMPLETED** (see `src/utils.jl:159-244`)
+  - Converts system prompt to system message
+  - Converts conversation messages via `_userMessageToOpenAI`, `_assistantMessageToOpenAI`, `_assistantMessageToolCallToOpenAI`, `_toolResultMessageToOpenAI`
+  - Converts `ctx.tools` to OpenAI function tool format via `_toolsToOpenAI`
+- `IMP-012`: Implement message converters (`_userMessageToOpenAI`, `_assistantMessageToOpenAI`, `_assistantMessageToolCallToOpenAI`, `_toolResultMessageToOpenAI`) — **COMPLETED** (see `src/utils.jl:321-387`)
+- `IMP-013`: Implement `_messageContentToBlocks` — converts `textContent`/`imageContent` to OpenAI content blocks — **COMPLETED** (see `src/utils.jl:396-413`)
+- `IMP-014`: Implement `_toolsToOpenAI` — converts `OrderedDict{String, agentTool}` to OpenAI tools array — **COMPLETED** (see `src/utils.jl:429-447`)
+- `IMP-015`: Implement `beforeToolCall` hook default (returns `beforeToolCallResult(false, "N/A")`) — **COMPLETED** (see `src/utils.jl:278-284`)
+- `IMP-016`: Implement `afterToolCall` hook default (returns `nothing`) — **COMPLETED** (see `src/utils.jl:310-316`)
+- `IMP-017`: Implement `validateRequiredArgs(args, inputSchema)` — validates required fields in tool arguments — **COMPLETED** (see `src/utils.jl:479-497`)
+- `IMP-018`: Implement `validateToolArguments(tool, prepared)` — wrapper that calls tool-specific or default hook — **COMPLETED** (see `src/utils.jl:533-546`)
+
+**Validation Tests**: Unit tests for converters, validation hooks, and OpenAI format output structure (SP-011..SP-016, SP-023, SP-024)
+
+**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005, FR-006
+
+---
+
+### Phase 3: Tool Registry & MCP Integration (COMPLETED)
+
+**Files**: `src/toolRegistry.jl`
+
+**Completed Tasks**:
+- `IMP-020`: Implement `_extract_text_content(result)` — extracts text from MCP tool result content array — **COMPLETED** (see `src/toolRegistry.jl:19-33`)
+- `IMP-021`: Implement `_wrap_mcp_tool(mcpserver, tool_def)` — wraps MCP tool as `agentTool` with `execute` calling `mcpserver("tools/call", ...)` — **COMPLETED** (see `src/toolRegistry.jl:41-106`)
+  - Handles MCP error responses (`haskey(response, "error")`)
+  - Extracts text and `isError` from result
+- `IMP-022`: Implement `register_mcp_tools(tools, mcpserver)` — queries `tools/list` with pagination (`nextCursor`) — **COMPLETED** (see `src/toolRegistry.jl:118-186`)
+  - Handles both `result.tools` and `result.error` response shapes
+  - Skips already registered tools (idempotent)
+- `IMP-023`: Implement `registerTool(tools, tool)` — adds tool to OrderedDict — **COMPLETED** (see `src/toolRegistry.jl:200-205`)
+- `IMP-024`: Implement `listTool(tools, mcpserver)` — builtin tool that triggers runtime discovery and lists all tools — **COMPLETED** (see `src/toolRegistry.jl:235-271`)
+- `IMP-025`: Implement `registerAllTools(tools, mcpserver)` — discovers existing tools + registers `listTools` — **COMPLETED** (see `src/toolRegistry.jl:286-289`)
+
+**Validation Tests**: Unit tests for MCP tools/list parsing, pagination, tool registration idempotency; contract tests for JSON-RPC 2.0 format (SP-009, SP-010)
+
+**Traceability**: FR-002, FR-004
+
+---
+
+### Phase 4: Agent Core & Loop (COMPLETED)
+
+**Files**: `src/agentCore.jl`
+
+**Completed Tasks**:
+- `IMP-030`: Define `yiemAgent` struct with channels, callbacks, MCP server — **COMPLETED** (see `src/agentCore.jl:17-95`)
+- `IMP-031`: Implement `yiemAgent` constructor — creates channels, discovers MCP tools, spawns `_agentLoop` task — **COMPLETED** (see `src/agentCore.jl:126-199`)
+- `IMP-032`: Implement `_agentLoop(agent)` — background `@spawn` task managing `inputChannel`/`followUpChannel` priority dispatch — **COMPLETED** (see `src/agentCore.jl:225-364`)
+  - Handles shutdown signal: drains all channels, breaks loop
+  - On error: catches, calls `eventSink(errMsg)`, exits loop
+- `IMP-033`: Implement `_processMessage(...)` — drains input, calls `llmCall`, extracts tool calls, executes, loops until no tool calls — **COMPLETED** (see `src/agentCore.jl:394-544`)
+  - `while true` loop: `llmCall()` → `_extractToolCalls()` → if `hasToolCalls && length > 0` execute → append results → repeat
+  - On `toolResultBatch.terminate == true`: build final response and break
+  - Catches all errors and calls `eventSink(err_msg)`
+- `IMP-034`: Implement `_extractToolCalls(response)` — handles **both** formats:
+  - Format 1: `response["message"]["tool_calls"]` (LMStudio/vLLM style) — **COMPLETED** (see `src/agentCore.jl:671-683`)
+  - Format 2: `response.content` blocks with `type => "tool_calls"` or `type => "tool_call"` (OpenAI API style) — **COMPLETED** (see `src/agentCore.jl:685-719`)
+  - Extracts `reasoning_content`, `content` text blocks, `api`, `provider`, `model`, `usage`, `finish_reason` — **COMPLETED** (see `src/agentCore.jl:723-797`)
+  - Returns `(hasToolCalls, toolCallList, assistant_message)` — **COMPLETED** (see `src/agentCore.jl:799-827`)
+- `IMP-035`: Implement `prepareToolCall(context, assistantMsg, toolCall, config, signal, eventSink)` — resolves tool, validates args, runs `beforeToolCall` hook, never throws — **COMPLETED** (see `src/agentCore.jl:974-1035`)
+- `IMP-036`: Implement `executePreparedToolCall(prep, signal, eventSink)` — calls `tool.execute(toolCallId, args, ...)` — **COMPLETED** (see `src/agentCore.jl:1078-1100`)
+- `IMP-037`: Implement `finalizeExecutedToolCall(context, msg, prep, executed, config, signal, eventSink)` — runs `afterToolCall` hook, catches errors — **COMPLETED** (see `src/agentCore.jl:1159-1198`)
+- `IMP-038`: Implement `prepareToolCallArguments(tool, toolCall)` — calls `tool.prepareArguments` if present — **COMPLETED** (see `src/agentCore.jl:908-917`)
+- `IMP-039`: Implement `createErrorToolResult(msg)` — builds `agentToolResult` with error content — **COMPLETED** (see `src/agentCore.jl:572-574`)
+- `IMP-040`: Implement `createToolResultMessage(f)` — builds `toolResultMessage` from `finalizedOutcome` — **COMPLETED** (see `src/agentCore.jl:606-612`)
+
+**Note**: Sequential and parallel execution implementations continue beyond line 1207 of `src/agentCore.jl` — implementation incomplete, requires review to ensure execution methods properly call hooks and emit lifecycle events. See IMP-041.
+
+**Validation Tests**: Integration tests for full agent loop with mocked LLM and MCP; contract tests for response format extraction (WT-001..WT-032, SP-017, SP-018)
+
+**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005
+
+---
+
+### Phase 5: Public API (COMPLETED)
+
+**Files**: `src/api.jl`
+
+**Completed Tasks**:
+- `IMP-050`: Implement `promptAgent(agent, msg::AbstractDict)` — put to `inputChannel`, returns agent for chaining — **COMPLETED** (see `src/api.jl:36-39`)
+- `IMP-051`: Implement `promptAgent(agent, text::String)` — wraps text as OpenAI `Dict` and put to `inputChannel` — **COMPLETED** (see `src/api.jl:41-44`)
+- `IMP-052`: Implement `takeResponse(agent)` — blocks on `outputChannel` `take!`, returns OpenAI dict response — **COMPLETED** (see `src/api.jl:66-68`)
+- `IMP-053`: Implement `followUp(agent, msg::AbstractDict)` — put to `followUpChannel`, returns agent — **COMPLETED** (see `src/api.jl:94-97`)
+- `IMP-054`: Implement `followUp(agent, text::String)` — wraps text as OpenAI `Dict` and put to `followUpChannel` — **COMPLETED** (see `src/api.jl:99-102`)
+- `IMP-055`: Implement `stopAgent(agent)` — puts `:shutdown`, `fetch(_agentLoop)`, closes all channels — **COMPLETED** (see `src/api.jl:126-139`)
+
+**Validation Tests**: Integration tests for send/receive/stop lifecycle (WT-001..WT-005)
+
+**Traceability**: FR-001, FR-006
+
+---
+
+## 2. Module Structure
+
+```
+src/
+├── YiemAgent.jl     # Parent module, includes submodules in dependency order
+├── type.jl           # All Julia struct types (exports 30+ types)
+├── utils.jl          # Formatting, validation, hooks (default implementations)
+├── toolRegistry.jl   # MCP tool discovery, registration, listTools builtin
+├── agentCore.jl      # yiemAgent struct, _agentLoop, _processMessage, extractToolCalls
+└── api.jl            # Public: promptAgent, takeResponse, followUp, stopAgent
+```
+
+**Include order** (per `src/YiemAgent.jl` lines 6-19):
+1. `type.jl` (no dependencies)
+2. `utils.jl` (depends on `type`)
+3. `toolRegistry.jl` (depends on `type`)
+4. `agentCore.jl` (depends on `type`, `utils`, `toolRegistry`)
+5. `api.jl` (depends on `type`, `utils`, `agentCore`, `toolRegistry`)
+
+---
+
+## 3. Task Dependencies
+
+| Task ID | Title | Depends On | Status |
+|---------|-------|------------|--------|
+| IMP-001..IMP-008 | Type system | — | **COMPLETED** |
+| IMP-010..IMP-018 | Utils (formatMsgForLLM, converters, hooks) | IMP-001..IMP-008 | **COMPLETED** |
+| IMP-020..IMP-025 | Tool registry (MCP discovery) | IMP-003, IMP-017 | **COMPLETED** |
+| IMP-030..IMP-040 | Agent core (agent loop, tool call execution) | IMP-001..IMP-008, IMP-010..IMP-018, IMP-020..IMP-025 | **INCOMPLETE*** (see note) |
+| IMP-050..IMP-055 | Public API | IMP-030..IMP-040 | **COMPLETED** |
+
+**NOTE on IMP-030..IMP-040**: The sequential and parallel execution functions (`executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls`) begin after line 1207 of `src/agentCore.jl` but the full implementation is truncated in file reads and requires review. The exported functions (`executePreparedToolCall`, `prepareToolCall`, `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls`) are declared (`src/agentCore.jl:4-5`) but their full bodies may be incomplete. **Requires manual validation.**
+
+**Traceability**: IMP-001..IMP-008 → SP-001..SP-008, IMP-020..IMP-025 → SP-009, SP-010; IMP-010..IMP-018 → SP-011..SP-016, SP-023, SP-024; IMP-030..IMP-040 → SP-017, SP-018, SP-019..SP-022
+
+---
+
+## 4. Remaining Work (TODOs)
+
+| File | Line | Description | Priority |
+|------|------|-------------|----------|
+| `src/utils.jl` | 114 | Filter tools from `state.tools` based on user intent and tool description | High |
+| `src/utils.jl` | 118 | Add system prompt context: time, session state, user preferences | Medium |
+| `src/utils.jl` | 121 | Prune/reorder/conversation messages before sending to LLM | Medium |
+| `src/agentCore.jl` | post-1207 | Complete and validate `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls` implementations (currently exported but body may be incomplete) | **Critical** |
+| `tests/unit/...` | — | Write unit tests for all struct types (IMP-001..IMP-008) and converters (SP-011..SP-016) | High |
+| `tests/unit/...` | — | Write unit tests for tool validation hooks (SP-023, SP-024) | High |
+| `tests/integration/...` | — | Write integration tests for agent loop with mocked LLM/MCP (WT-001..WT-032) | High |
+| `tests/contract/...` | — | Write contract tests for response formats (SP-017, SP-018) | Medium |
+| `tests/perf/...` | — | Write performance tests (NFR-101, NFR-102) | Medium |
+
+---
+
+## 5. Build & Deployment
+
+### Build
+
+```bash
+cd /home/ton/docker-apps/sommpanion/YiemAgent
+julia --project=. -e 'using Pkg; Pkg.instantiate(); using Pkg; Pkg.precompile()'
+```
+
+### Test
+
+```bash
+julia --project=./tests -e 'using Pkg; Pkg.instantiate()'
+julia --project=./tests -e 'using Test; Test.@testset "Unit" begin include("tests/unit/run_tests.jl") end'
+julia --project=./tests -e 'using Test; Test.@testset "Integration" begin include("tests/integration/run_tests.jl") end'
+```
+
+### CI/CD Preparation
+
+- Add `.github/workflows/julia.yaml` referencing `ci/julia-pipeline.yaml` steps
+- Add `Project.toml` dependencies:
+  ```toml
+  [deps]
+  JSON = "682c8566-3430-5439-8f4e-b64832e8aaba"
+  DataStructures = "892a3cad-33e6-51e5-808c-f3d90072be70"
+  HTTP = "cd6eee30-4dd5-5329-8bad-3259ec48d0f4"
+  # plus: UUIDs, Dates, PrettyPrinting, Serialization, DataFrames, NATS, LibPQ
+  ```
+
+---
+
+## 6. Risk Mitigation
+
+| Risk | Mitigation |
+|------|------------|
+| Line 1207+ of `agentCore.jl` (sequential/parallel execution) is incomplete or broken | Manually review and validate `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls` against spec SP-021 (agentLoopConfig) and trace to test `test_tool_call_execution_sequence` |
+| `GeneralUtils` internal package unavailable in CI | Ensure `GeneralUtils` is installed as dependency (via `Pkg.develop` or registry) |
+| NATS `mcpServer` struct is abstract — no concrete implementation | Provide at least one mock `mcpServer` for tests that returns canned JSON responses |
+| Message history grows unbounded | Implement `prepareContext` pruning (TODO in code); add NFR-104 for max history tokens |

docs/8-runbook.md

@@ -0,0 +1,445 @@
+# Runbook: YiemAgent Agent Framework
+
+**Severity**: N/A (library — not a deployed service)
+**Estimated Time**: N/A
+
+This runbook documents operational guidance for developers working with the YiemAgent agent framework. It is not an operations runbook for a production service, but a developer handbook for debugging, troubleshooting, and extending the framework.
+
+---
+
+## 1. Agent Lifecycle
+
+### 1.1: Create an Agent
+
+**Steps**:
+```julia
+using YiemAgent
+
+# 1. Implement llmCall function
+function my_llmCall(messages; eventSink=nothing)
+    # Call LLM API (e.g., OpenAI, Anthropic, LMStudio)
+    # Return dict with keys: "message", "content", "finish_reason", "api", "provider", "model", "usage"
+    eventSink("my_llmCall: sending $(length(messages["messages"])) messages"; publish=true)
+    # ... API call ...
+    return response_dict
+end
+
+# 2. Implement mcpServer callable struct
+struct MyMCPClient
+    conn::NATS.Connection   # or HTTP.Client
+    topic::String
+    senderID::String
+end
+
+function (mcp::MyMCPClient)(method::String, args...; eventSink=nothing)
+    # JSON-RPC 2.0 calls:
+    #   mcp("tools/list", sessionId; eventSink=eventSink)
+    #   mcp("tools/call", toolName, args, sessionId; eventSink=eventSink)
+    # ... NATS/HTTP call ...
+    return jsonrpc_response
+end
+
+# 3. Create agent
+mcp = MyMCPClient(nats_conn, "yiemagent:tools", "agent_1")
+agent = yiemAgent(
+    my_llmCall;
+    systemPrompt="You are a wine assistant.\nYou have tools for searching wine inventory.",
+    tools=mcp_tools,              # optional: pass pre-discovered tools
+    prepareContext=my_prepareContext,   # optional: custom context preparation
+    formatMsgForLLM=my_formatMsgForLLM, # optional: custom formatting for provider
+    beforeToolCall=my_beforeToolCall,   # optional: approval gate
+    afterToolCall=my_afterToolCall,     # optional: result sanitization
+    mcpServer=mcp,
+    parallelToolExecute=false,
+    eventSink=my_eventSink,
+)
+```
+
+> **Reference**: `2-solution_design.md:SD-001` (channel-based async), `SD-002` (background task), `SD-004` (MCP struct)
+
+### 1.2: Send a Message
+
+```julia
+# Option 1: Send a string message (automatically wrapped as OpenAI dict)
+promptAgent(agent, "What wines have notes of oak and vanilla?")
+
+# Option 2: Send a full OpenAI-format dict with images
+msg = Dict(
+    "role" => "user",
+    "content" => [
+        Dict("type" => "text", "text" => "What wines match these notes?"),
+        Dict("type" => "image_url", "image_url" => Dict("url" => "data:image/png;base64,..."))
+    ]
+)
+promptAgent(agent, msg)
+```
+
+### 1.3: Receive a Response
+
+```julia
+# Blocks until agent finishes processing (no tool calls or tool calls complete)
+response = takeResponse(agent)
+# response is a Dict in OpenAI format:
+#   Dict("role" => "assistant", "content" => [Dict("type" => "text", "text" => "...")])
+
+# For text-only response:
+if response["content"][1]["type"] == "text"
+    println(response["content"][1]["text"])
+end
+```
+
+### 1.4: Send Follow-up While Agent is Busy
+
+```julia
+# While the agent is processing tool calls (between promptAgent(...) and takeResponse(...))
+followUp(agent, "Also check European wines")
+```
+
+### 1.5: Stop the Agent
+
+```julia
+stopAgent(agent)
+# Sends :shutdown, closes all channels. Agent object is destroyed.
+```
+
+---
+
+## 2. Troubleshooting
+
+### 2.1: Agent Loop Exits Silently
+
+**Symptoms**:
+- `takeResponse(agent)` hangs indefinitely
+- No output in eventSink
+
+**Steps**:
+1. Check if `eventSink` is receiving error messages:
+   ```julia
+   global error_count = 0
+   my_eventSink(msg; publish=false) = begin
+       if occursin("error", lowercase(msg)) || occursin("failed", lowercase(msg))
+           println("ERROR EVENT: $msg")
+           global error_count += 1
+       end
+   end
+   ```
+2. Check network connectivity to LLM API endpoint
+3. Verify `formatMsgForLLM` produces valid OpenAI-format message structure (`4-specification.md:SP-011..SP-016`)
+
+> **Reference**: `4-specification.md` (OpenAI format specs), `3-walkthrough.md:WT-030` (LLM timeout)
+
+### 2.2: Tool Call Not Executing
+
+**Symptoms**:
+- LLM returns `tool_calls` but agent returns text response immediately
+- `beforeToolCall` hook is missing or incorrectly implemented
+
+**Steps**:
+1. Verify `prepareContext` returns `agentContext` with `tools` field (not `nothing`) (`src/utils.jl:123`)
+2. Check `beforeToolCall` hook is not blocking execution:
+   ```julia
+   function my_beforeToolCall(ctx, signal, eventSink)
+       # Returning nothing → allows execution
+       # Returning beforeToolCallResult(true, "blocked") → blocks
+       eventSink("beforeToolCall for $(ctx.toolCall.name)"; publish=true)
+       nothing  # ← must return nothing (not beforeToolCallResult(false, "N/A"))
+   end
+   ```
+3. Verify tool exists in `context.tools[toolCall.name]` (`src/agentCore.jl:985`)
+4. Check `validateToolArguments` is not throwing (catches and returns error tool result) (`src/utils.jl:533`)
+
+> **Reference**: `4-specification.md:SP-021` (agentLoopConfig with beforeToolCall hook), `3-walkthrough.md:WT-032` (beforeToolCall blocks)
+
+### 2.3: MCP Server Returns No Tools
+
+**Symptoms**:
+- `println("[toolRegistry] Discovered 0 MCP tools."` on startup
+- Agent has no tools available at all times
+
+**Steps**:
+1. Verify `mcpServer` callable struct correctly handles `mcp("tools/list", sessionId; eventSink)`
+2. Check JSON-RPC 2.0 response shape matches `4-specification.md:SP-009`:
+   ```json
+   {
+     "result": {
+       "tools": [{"name": "...", ...}],
+       "nextCursor": ""  # empty string = no more pages
+     }
+   }
+   ```
+3. Verify `tools/list` error response uses `haskey(response, "error")` check (`src/toolRegistry.jl:128`)
+
+> **Reference**: `4-specification.md:SP-009` (tools/list format), `3-walkthrough.md:WT-020` (MCP discovery)
+
+### 2.4: LLM Response Format Not Parsed
+
+**Symptoms**:
+- Agent always treats LLM response as text, never extracts tool calls
+- LLM correctly returns `tool_calls` but agent ignores them
+
+**Steps**:
+1. Verify `llmCall` returns a dict containing `message` (LMStudio/vLLM format, `4-specification.md:SP-017`) or `content` (OpenAI format, `4-specification.md:SP-018`)
+2. Check for both format variations:
+   - Format 1: `response["message"]["tool_calls"]` (LMStudio style, see `src/agentCore.jl:671-683`)
+   - Format 2: `response.content[{type => "tool_calls"}]` (OpenAI style, see `src/agentCore.jl:700-705`)
+3. Verify `finish_reason` field matches: `"tool_calls"` triggers tool execution, `"stop"`/`"end_turn"` breaks loop (`src/agentCore.jl:723-724`)
+
+> **Reference**: `4-specification.md:SP-017, SP-018`, `3-walkthrough.md:WT-011, WT-012` (LLM response handling)
+
+### 2.5: Tool Execution Hangs
+
+**Symptoms**:
+- `tool.execute(...)` starts but never returns
+- EventSink receives `toolExecutionStart` but never `toolExecutionEnd`
+
+**Steps**:
+1. Implement `abortSignal` checking in tool execute function:
+   ```julia
+   function my_tool_execute(toolCallId, args, signal, eventSink)
+       while !signal.aborted && !all_conditions_met
+           # Long-running operation with periodic abort checks
+           sleep(0.1)
+       end
+       if signal.aborted
+           return agentToolResult([textContent("Cancelled")], Dict(), nothing, false)
+       end
+       return agentToolResult([textContent("Done")], Dict(), nothing, false)
+   end
+   ```
+2. External caller sets `signal.aborted = true` (if `abortSignal` is shared via closure)
+3. Add timeout logic to tool execute function:
+   ```julia
+   start_time = time()
+   timeout_seconds = 30.0
+   while time() - start_time < timeout_seconds && !signal.aborted
+       # operation...
+       sleep(0.5)
+   end
+   ```
+
+> **Reference**: `4-specification.md:SP-022` (abortSignal), `3-walkthrough.md:WT-030` (LLM timeout)
+
+### 2.6: Message History Grows Unbounded
+
+**Symptoms**:
+- Memory usage grows linearly with conversation length
+- LLM context window exceeded for long sessions
+
+**Steps**:
+1. Implement pruning in `prepareContext` (`src/utils.jl:112-126`):
+   ```julia
+   function my_prepareContext(state::agentState, eventSink)
+       # Prune old messages, keep last N turns
+       max_turns = 20
+       recent_msgs = isempty(state.messages) ? state.messages :
+                     state.messages[max(1, end-2*max_turns):end]
+       
+       # Optionally inject system context (time, user prefs)
+       preparedMessages = recent_msgs
+       preparedSystemPrompt = state.systemPrompt * "\nCurrent time: $(now())"
+       
+       # Filter tools (optional: keep only relevant subset)
+       filteredTools = state.tools  # or filter by intent...
+       
+       return agentContext(preparedSystemPrompt, preparedMessages, filteredTools)
+   end
+   ```
+2. Add hard limit in `_agentLoop`:
+   ```julia
+   if length(agent._state.messages) > 200
+       # Truncate oldest messages
+       agent._state.messages = agent._state.messages[end-100:end]
+   end
+   ```
+
+> **Reference**: `3-walkthrough.md` (agent message flow), `1-requirements.md:NFR-103` (channel buffer limits)
+
+---
+
+## 3. Extending the Framework
+
+### 3.1: Add a Custom LLM Provider
+
+**Steps**:
+1. Implement `llmCall` function that accepts `FormattedMessages::Dict{String, Any}` and returns response dict
+2. Ensure response dict contains at least one of:
+   - `message.tool_calls` format (LMStudio/vLLM) — `4-specification.md:SP-017`
+   - `content[{type=>"tool_calls"}]` format (OpenAI) — `4-specification.md:SP-018`
+3. Response dict may optionally contain: `api`, `provider`, `model`, `usage` (`llmUsage` or dict), `finish_reason`, `error_message` (`4-specification.md:SP-017`, `SP-018.4`)
+
+```julia
+function my_openai_llmCall(messages::Dict{String, Any}; eventSink=nothing)
+    # messages["messages"] contains OpenAI-format messages
+    # messages["temperature"] (default 0.7)
+    # messages["tools"] (if tools are set)
+    
+    body = Dict(
+        "model" => "gpt-4",
+        "messages" => messages["messages"],
+        "tools" => get(messages, "tools", Any[])
+    )
+    
+    resp = HTTP.post("https://api.openai.com/v1/chat/completions", 
+                     headers, JSON.json(body))
+    json_resp = JSON.parse(String(resp.body))
+    
+    # Extract first completion
+    choice = json_resp["choices"][1]["message"]
+    
+    # Build uniform response
+    return Dict(
+        "api" => "openai",
+        "provider" => "openai",
+        "model" => "gpt-4",
+        "message" => choice,
+        "usage" => json_resp["usage"],
+        "finish_reason" => json_resp["choices"][1]["finish_reason"]
+    )
+end
+```
+
+> **Reference**: `1-requirements.md:FR-002`, `4-specification.md:SP-017, SP-018`
+
+### 3.2: Add a Custom Tool
+
+**Steps**:
+1. Define tool schema in `inputSchema` (JSON Schema format):
+   ```julia
+   schema = Dict{String, Any}(
+       "type" => "object",
+       "properties" => Dict(
+           "query" => Dict("type" => "string", "description" => "Search query"),
+           "max_results" => Dict("type" => "integer", "default" => 5)
+       ),
+       "required" => ["query"]
+   )
+   ```
+2. Implement `execute` function:
+   ```julia
+   tool = agentTool(
+       name="searchWine",
+       label="Search Wine Inventory",
+       description="Search for wines matching a query",
+       inputSchema=schema,
+       execute=(toolCallId, args, signal, eventSink) -> begin
+           query = args["query"]
+           max_results = get(args, "max_results", Dict{Any,Any}()["max_results"] => 5...)  # handle default
+           
+           wines = run_search(query, max_results)
+           
+           result_text = join(["- $(w.name) (\$$(w.price))" for w in wines], "\n")
+           return agentToolResult(
+               [textContent("Found $(length(wines)) wines:\n$result_text")],
+               Dict("count" => length(wines)),
+               nothing, false  # content, details, usage, terminate
+           )
+       end,
+       prepareArguments = nothing,
+       validateRequiredArgs = nothing,  # uses default validator
+       parallelToolExecute = false
+   )
+   
+   # Register: push!(tools, tool.name => tool)
+   ```
+3. Optionally override validation:
+   ```julia
+   function my_validate(args)
+       if haskey(args, "query") && length(args["query"]) < 3
+           return "Query must be at least 3 characters"
+       end
+       return nothing
+   end
+   
+   agentTool(
+       ...,
+       validateRequiredArgs = my_validate,  # replaces default validator
+   )
+   ```
+
+> **Reference**: `1-requirements.md:FR-004`, `4-specification.md:SP-009, SP-010`
+
+### 3.3: Implement User Approval Gate (beforeToolCall Hook)
+
+**Steps**:
+1. Implement `beforeToolCall` that blocks expensive/destructive operations:
+   ```julia
+   function my_approval_toolCall(ctx::beforeToolCallContext, signal::abortSignal, eventSink)
+       if ctx.toolCall.name in ("delete_wine", "update_price_bulk")
+           eventSink("Approval required for $(ctx.toolCall.name) with args $(ctx.args)")
+           # In production, send to UI and wait for user input
+           approved = ask_user_for_approval(ctx.toolCall.name, ctx.args)  # blocking call
+           if !approved
+               return beforeToolCallResult(true, "User denied approval for $(ctx.toolCall.name)")
+           end
+       end
+       return nothing  # allow execution
+   end
+   ```
+
+> **Reference**: `2-solution_design.md:SD-005` (lifecycle hooks), `3-walkthrough.md:WT-032` (beforeToolCall blocks)
+
+### 3.4: Implement Result Sanitization (afterToolCall Hook)
+
+**Steps**:
+1. Implement `afterToolCall` to mask sensitive data:
+   ```julia
+   function my_sanitize_toolCall(ctx::afterToolCallContext, signal::abortSignal, eventSink)
+       if ctx.toolCall.name == "getCustomerInfo"
+           # Mask sensitive fields before LLM sees result
+           result = ctx.result
+           for (i, block) in enumerate(result.content)
+               if block isa textContent
+                   masked = replace(block.text, r"\d{12}" => "***-***-****")  # mask SSN
+                   result = agentToolResult(
+                       [replace(block.text, r"\d{12}" => "***-***-****") for block in result.content],
+                       result.details,
+                       result.usage,
+                       result.terminate
+                   )
+               end
+           end
+           return result
+       end
+       return nothing  # pass through for other tools
+   end
+   ```
+
+> **Reference**: `2-solution_design.md:SD-005` (lifecycle hooks)
+
+---
+
+## 4. Common Error Codes
+
+| Error Condition | Response Code | Description |
+|-----------------|---------------|-------------|
+| Tool not found | `immediateOutcome("Tool <name> not found", true)` | `context.tools[toolCall.name]` returns `nothing` |
+| Missing required args | `immediateOutcome("Missing required arguments: <fields>", true)` | `validateRequiredArgs` returns error string |
+| Custom validation failed | `immediateOutcome("<tool.validateRequiredArgs result>", true)` | Tool-specific validation hook returns error |
+| beforeToolCall blocked | `immediateOutcome("Tool execution was blocked: <reason>", true)` | `beforeToolCall` returns `beforeToolCallResult(true, reason)` |
+| Operation aborted | `immediateOutcome("Operation aborted", true)` | `signal.aborted == true` after `beforeToolCall` |
+| Tool execution crashed | `immediateOutcome("<exception message>", true)` | `preptool.execute()` throws exception |
+| MCP error | `agentToolResult(["MCP error: <err_msg>"], {..., "error"=>err_msg}, ...)` | Server returns `response["error"]` JSON-RPC envelope |
+| MCP network error | `agentToolResult(["MCP call error: <err_msg>"], {..., "error"=>err_msg}, ...)` | Exception during `mcp("tools/call", ...)` |
+
+> **Reference**: `4-specification.md:SP-009, SP-010`, `src/agentCore.jl:989, 1013, 1019, 1033, 1097`
+
+---
+
+## 5. Debugging Checklist
+
+Per ASG Framework §Pre-Code Debugging Habit:
+
+| Stage | Debug Question |
+|-------|----------------|
+| **Implementation** | If a tool is not found, does `prepareToolCall` return `immediateOutcome` with "Tool not found"? |
+| **Testing** | If an LLM response has `tool_calls`, does `_extractToolCalls` return `(true, toolCallList, assistantMessageToolCall)`? |
+| **Deploying** | If the agent loop exits, does `eventSink` contain the error message? |
+
+---
+
+## 6. References
+
+- **API contract**: [`4-specification.md`](4-specification.md) — message types, MCP JSON-RPC format, response extraction formats
+- **Solution decisions**: [`2-solution_design.md`](2-solution_design.md) — component architecture, lifecycle hooks, format-agnostic LLM responses
+- **Walkthroughs**: [`3-walkthrough.md`](3-walkthrough.md) — end-to-end agent flow, error paths, hook sequences
+- **Validation tests**: [`6-validation.md`](6-validation.md) — test traceability matrix mapping requirements/specs to test files

etc.jl

@@ -0,0 +1,10 @@
+# check if this column has vector embedding. if there is one, seach vector version instead
+              column_name_embedding = column_name * "_embedding"
+              if occursin(column_name_embedding, tables_schema[column_name_embedding])
+                vector_column = Dict(
+                  "table_name"=> table_name,
+                  "column_name"=> column_name_embedding,
+                  "operator"=> "vector_similarity",
+                  "value"=> column_obj["value"]
+                )
+              end

etc.md

@@ -1,11 +0,0 @@
-check my understanding
-1) if LLM didn't use tool calls, assistantMessage get pushed into agent._state.messages and it will be the latest message in agent._state.messages. then _agentLoop() can pick it as the output to outputChannel
-2) if LLM use tool calls, assistantMessageToolCall get pushed into agent._state.messages. then toolResult get pushed into agent._state.messages. if toolResultBatch.terminate is false then _processMessage() loop continue
-3) if LLM use tool calls, assistantMessageToolCall get pushed into agent._state.messages. then toolResult get pushed into agent._state.messages. if toolResultBatch.terminate is true then final_response message get pushed into agent._state.messages. _processMessage() loop exit. then _agentLoop() can pick it as the output to outputChannel
-
-Is my understanding correct?
-
-
-the user can provide NATS connection to MCP server by adding agent.mcpserver (a callable struct) for communication with MCP server just like agent.llmCall (also a callable struct). I think communicating with MCP server is just send/receive JSON text right?
-Moreover, for simplicity I want to all tools into an MCP server so an agent can be instantiated with only listTools() in tool store then populate tools from MCP server later.
-what do you think?

example/agent_chat_virtualCustomer.jl

@@ -0,0 +1,585 @@
+using Revise
+using JSON, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures
+using YiemAgent, GeneralUtils
+using Base.Threads
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+
+# load config
+config = JSON.parsefile("/appfolder/app/dev/YiemAgent/test/config.json")
+# config = copy(JSON.parsefile("../mountvolume/config.json"))
+
+
+function executeSQL(sql::T) where {T<:AbstractString}
+  host = config[:externalservice][:wineDB][:host]
+  port = config[:externalservice][:wineDB][:port]
+  dbname = config[:externalservice][:wineDB][:dbname]
+  user = config[:externalservice][:wineDB][:user]
+  password = config[:externalservice][:wineDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function executeSQLVectorDB(sql)
+  host = config[:externalservice][:SQLVectorDB][:host]
+  port = config[:externalservice][:SQLVectorDB][:port]
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+  user = config[:externalservice][:SQLVectorDB][:user]
+  password = config[:externalservice][:SQLVectorDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function text2textInstructLLM(prompt::String; maxattempt::Integer=10, modelsize::String="medium",
+    senderId=GeneralUtils.uuid4snakecase(), timeout=90,
+    llmkwargs=Dict(
+      :num_ctx => 32768,
+      :temperature => 0.5,
+    )
+  )
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="inference",
+    senderName="yiemagent",
+    senderId=senderId,
+    receiverName="text2textinstruct_$modelsize",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => prompt,
+      :kwargs => llmkwargs
+    )
+  )
+
+  response = nothing
+  for attempts in 1:maxattempt
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; responsetimeout=timeout, responsemaxattempt=maxattempt)
+    payload = _response[:response]
+    if _response[:success] && payload[:text] !== nothing
+      response = _response[:response][:text]
+      break
+    else
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(outgoingMsg)
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      sleep(3)
+    end
+  end
+
+  return response
+end
+
+# get text embedding from a LLM service
+function getEmbedding(text::T) where {T<:AbstractString}
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="embedding",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="textembedding",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => [text] # must be a vector of string
+    )
+  )
+
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; responsetimeout=120, responsemaxattempt=3)
+  embedding = response[:response][:embeddings]
+  return embedding
+end
+
+function findSimilarTextFromVectorDB(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 = getEmbedding(text)[1]
+  # 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
+
+function similarSQLVectorDB(query; maxdistance::Integer=100)
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  # println(df[1, [:id, :function_output]])
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]  
+  # distance = 100  # CHANGE this is for testing only
+  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~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=output_str, distance=distance)
+  else
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=nothing, distance=nothing)
+  end
+end
+
+function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  # query = state[:thoughtHistory][:question]
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  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 = getEmbedding(query)[1]
+    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)
+    _ = executeSQLVectorDB(sql)
+  end
+end
+
+
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  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.parsefile(_output_str))
+    return output
+  else
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    return nothing
+  end
+end
+
+
+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  ) where {T1<:AbstractString, T2<:AbstractDict}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    recentevents_embedding = getEmbedding(recentevents)[1]
+    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)
+    _ = executeSQLVectorDB(sql)
+  else
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+  end
+end
+
+
+sessionId = GeneralUtils.uuid4snakecase()
+
+externalFunction = (
+    getEmbedding=getEmbedding,
+    text2textInstructLLM=text2textInstructLLM,
+    executeSQL=executeSQL,
+    similarSQLVectorDB=similarSQLVectorDB,
+    insertSQLVectorDB=insertSQLVectorDB,
+    similarSommelierDecision=similarSommelierDecision,
+    insertSommelierDecision=insertSommelierDecision,
+  )
+
+
+# s = "full-bodied red wine, budget 1500 USD"
+# r = YiemAgent.extractWineAttributes_1(agent, s)
+# println(r)
+
+
+# --------------------------- generating scenario and customer profile --------------------------- #
+
+function rolegenerator()
+  rolegenerator_systemmsg =
+  """
+    Your role:
+      - You are a helpful assistant 
+    Your mission:
+      - Create one random role of a potential customer of an internet wine store.
+    You must follow the following guidelines:
+     - the user only need the role, do not add your own words.
+     - the role should be detailed and realistic.
+    You should then respond to the user with:
+      Name: a name of the potential customer
+      Situation: a situation that the potential customer may be facing
+      Mission: a mission of the potential customer
+      Profile: a profile of the potential customer, including their age, gender, occupation, and other relevant information
+    You should only respond in format as described below:
+      Name: ...
+      Situation: ...
+      Mission: ...
+      Profile: ...
+      Additional_information: ...
+
+    Here are some examples:
+    Name: Jimmy
+    Situation:
+      - Your relationship with your boss is not that good. You need to improve your relationship with your boss.
+      - Your boss's wedding anniversary is coming up.
+      - You are at a wine store and start talking with the store's sommelier.
+    Mission:
+      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
+    Profile:
+      - You are a young professional in a big company.
+      - You are avid party goer
+      - You like beer.
+      - You know nothing about wine.
+      - You have a budget of 1500usd.
+    Additional_information:
+      - your boss like spicy food.
+      - your boss is a middle-aged man.
+      - your boss likes Australian wine.
+
+    Name: Kate
+    Situation:
+      - Your husband asked you to get him a bottle of wine. He will gift the wine to his business client while dining at a German restaurant.
+      - Your husband is a business client and he will gift the wine to his business
+      - You are at a wine store and start talking with the store's sommelier.
+    Mission:
+      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
+    Profile:
+      - You are a CEO in a startup company.
+      - You are a nerd
+      - You don't like alcohol.
+      - You have a budget of 150usd.
+      - You don't care about organic, sulfite, gluten-free, or sustainability certified wines
+    Additional_information:
+      - your husband like spicy food.
+      - your husband is a middle-aged man.
+
+    Name: John
+    Situation: 
+      - A local newspaper club wants to have a scoop about wine with local food in the U.S.
+      - You are at a wine store and start talking with the store's sommelier.
+    Mission:
+      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
+    Profile:
+      - I'm a young guy.
+      - I prefer to express my ideas in a succinct and clear manner.
+    Additional_information:
+      - N/A
+
+    Name: Jane
+    Situation:
+      - You have catering a dinner party with French cuisine.
+      - You want to serve wine with your guests.
+      - You are at a wine store and start talking with the store's sommelier.
+    Mission:
+      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
+    Profile:
+      - You are a young French restaurant owner.
+      - You like dry, full-bodied red wine with high tannin
+      - You don't care about organic, sulfite, gluten-free, or sustainability certified wines.
+      - You have a budget of 200 usd.
+    Additional_information:
+      - N/A
+
+    Let's begin!
+  """
+
+  header = ["Name:", "Situation:", "Mission:", "Profile:", "Additional_information:"]
+  dictkey = ["name", "situation", "mission", "profile", "additional_information"]
+  errornote = "N/A"
+
+  for attempt in 1:10
+    _prompt =
+      [
+        Dict(:name => "system", :text => rolegenerator_systemmsg),
+      ]
+    prompt = GeneralUtils.formatLLMtext(_prompt, "qwen3")
+    
+    response = text2textInstructLLM(prompt) # generated role
+    response = GeneralUtils.deFormatLLMtext(response, "qwen3")
+    think, response = GeneralUtils.extractthink(response)
+
+    # check whether response has all header
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    kwvalue = [i for i in values(detected_kw)]
+    zeroind = findall(x -> x == 0, kwvalue)
+    missingkeys = [header[i] for i in zeroind]
+    if 0 ∈ values(detected_kw)
+      errornote = "$missingkeys are missing from your previous response"
+      println("\nERROR YiemAgent rolegenerator() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "\nYour previous attempt has duplicated points according to the required response format"
+      println("\nERROR YiemAgent rolegenerator() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                          dictKey=dictkey, symbolkey=true)
+    responsedict[:id] = GeneralUtils.uuid4snakecase()
+
+    responsedict[:systemmsg] = 
+    """
+    You are role playing as a CUSTOMER of a wine store and you are currently talking with a sommelier of a wine store.
+    Your profile is as follows:
+      Situation: $(responsedict[:situation])
+      Mission: $(responsedict[:mission])
+      Profile: $(responsedict[:profile])
+      Additional_information: $(responsedict[:additional_information])
+
+    You should follow the following guidelines:
+    - Focus on the lastest conversation
+    - Your like to be short and concise
+    - If you don't know an answer to sommelier's question, you should say: I don't know.
+    - If you think the store can't provide what you seek, you can leave.
+
+    You should then respond to the user with:
+      Dialogue: what you want to say to the user
+      Role: Verify that the dialogue is intended for the customer of a wine store. Can be "yes" or "no"
+    You should only respond in format as described below:
+    Dialogue: ...
+    Role: ...
+
+    Let's begin!
+    """
+
+    println("\nrolegenerator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    println(responsedict)
+    return responsedict
+  end
+  error("ERROR rolegenerator() failed to generate customer role: ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+end
+
+
+# Define the external functions for the customer agent in named tuple format
+customer_externalFunction = ( 
+    text2textInstructLLM=text2textInstructLLM,
+  )
+
+
+
+
+function main()
+  agent = YiemAgent.sommelier(
+    externalFunction;
+    name="Jane",
+    id=sessionId,  # agent instance id
+    retailername="Yiem",
+    llmFormatName="qwen3"
+  )
+
+  customerDict = rolegenerator()
+  customer = YiemAgent.virtualcustomer(
+    customer_externalFunction;
+    systemmsg=customerDict[:systemmsg],
+    name=customerDict[:name],
+    id=sessionId,  # agent instance id
+    llmFormatName="qwen3"
+  )
+
+  # customer_chat = "hello"
+  
+  # YiemAgent.addNewMessage(customer, "assistant", customer_chat)
+  # # add user activity to events memory
+  # push!(customer.memory[:events],
+  #   YiemAgent.eventdict(;
+  #     event_description="the assistant talks to the user.",
+  #     timestamp=Dates.now(),
+  #     subject="assistant",
+  #     action_name="CHAT_BOX",
+  #     action_input=customer_chat,
+  #   )
+  # )
+  # println("\ncustomer respond:\n $customer_chat")
+  agent_response = YiemAgent.conversation(agent; maximumMsg=50)
+  println("\nagent respond:\n $agent_response")
+  while true
+    customer_chat = nothing
+    while customer_chat === nothing
+      customer_response = YiemAgent.conversation(customer, Dict(:text=> agent_response); 
+                                    converPartnerName=agent.name,
+                                    maximumMsg=50)
+      customer_response = GeneralUtils.deFormatLLMtext(customer_response, customer.llmFormatName)
+      customer_chat = customer_response
+
+      #[WORKING] check whether customer response the same before
+    end
+    
+    println("\ncustomer respond:\n $customer_chat")
+
+    agent_response = YiemAgent.conversation(agent;
+                                            userinput=Dict(:text=> customer_chat),
+                                            maximumMsg=50)
+    println("\nagent respond:\n $agent_response")
+
+    if haskey(agent.memory[:events][end], :thought)
+      lastAssistantAction = agent.memory[:events][end][:thought][:action_name]
+      if lastAssistantAction == "END_CONVER_GUIDELINE"    # store thoughtDict
+
+        # save a.memory[:shortmem][:decisionlog] to disk using JSON
+        println("\nsaving agent.memory[:shortmem][:decisionlog] to disk")
+        date = "$(Dates.now())"
+        date = replace(date, ':'=>'.')
+        filename = "agent_decision_log_$(date)_$(agent.id).json"
+        filepath = "/appfolder/mountvolume/appdata/log/$filename"
+        open(filepath, "w") do io
+          JSON.pretty(io, agent.memory[:shortmem][:decisionlog])
+        end
+
+        # check how many file in /appfolder/mountvolume/appdata/log/ folder now
+        logfilesnumber = length(readdir("/appfolder/mountvolume/appdata/log/"))
+        println("\nCaching conversation process done. Total $logfilesnumber files in /appfolder/mountvolume/appdata/log/ folder now.\n")
+        break
+      end
+    end
+  end
+end
+
+for i in 1:100
+  main()
+  println("\n Round $i/100 done.")
+end
+
+println("done")
+
+# prompt = 
+# """
+# <|im_start|>system
+# You are a role playing agent acting as:
+#   Name: Emily
+#   Situation: - Emily is planning her upcoming birthday party and wants to make it extra special. She has invited close friends and family, and she's looking for a unique wine that will impress them.
+#   Mission: - Emily needs to find a rare and high-quality wine that matches the theme of her party, which is a mix of classic and modern flavors. She also wants to ensure that the wine is not too expensive so that it won't break her budget.
+#   Profile: - Emily is in her late 20s, works as a marketing executive for a tech company, and has a passion for trying new things. She's organized and detail-oriented but can be spontaneous when it comes to planning events.
+#   Additional_information: - Emily loves experimenting with different types of food and wine pairings.
+
+# Your are currently talking with a sommelier.
+
+# You should follow the following guidelines:
+# - Focus on the lastest conversation
+# - If you satisfy with the sommelier's recommendation for bottle of wine(s), you should say: Thanks for you help. I will buy the wine you recommended.
+# - If you don't satisfy with the sommelier's questions or can't get a good wine recommendation, you can continue the conversation.
+
+# Let's begin!
+
+# <|im_end|>
+# <|im_start|>Jane
+# Hello! Welcome to Yiem's Wine Store. I'm Jane, your friendly sommelier. How can I assist you today? What type of wine are you in the mood for, and is there a special occasion or event on your mind?
+# <|im_end|>
+# <|im_start|>Emily
+# Hi Jane! Thank you so much for welcoming me. For my birthday party, I'm looking for something that combines classic and modern flavors. It's a mix of guests who enjoy both traditional tastes and more contemporary ones. Also, I want to make sure it won't break the bank. Any suggestions?
+# <|im_end|>
+# <|im_start|>Jane
+# Thank you for sharing your preferences, Jane! To better assist you, could you please let me know if there are any specific characteristics of wine you're looking for, such as tannin, sweetness, intensity, or acidity? Additionally, do you have any food items in mind that this wine should pair well with?
+# <|im_end|>
+# <|im_start|>Emily
+# """
+
+# llmkwargs=Dict(
+#       :num_ctx => 32768,
+#       :temperature => 0.3,
+#     )
+# r = text2textInstructLLM(prompt, llmkwargs=llmkwargs)
+# println(r)
+# println(555)
+
+# response = YiemAgent.conversation(agent, Dict(:text=> "I want to get a French red wine under 100."))
+
+
+# while true
+#   println("your respond: ")
+#   user_answer = readline()
+#   response = YiemAgent.conversation(agent, Dict(:text=> user_answer))
+#   println("\n$response")
+# end
+
+
+
+
+
+
+
+
+
+
+
+
+# """
+# Hello
+
+# I would like to get a bottle of wine for my boss but I don't know much about wine. Can you help me?
+
+# well actually, my boss is going to offer the wine to his client as a gift in a business meeting. All I know is his client like spicy food and French wine. I have a budget about 1000.
+
+# """
+
+# input = "French wine, bordeaux, under USD100, pairs with spicy food"
+# r = YiemAgent.extractWineAttributes_1(a, input)
+
+# inventory_order = "French Syrah, Viognier, full bodied, under 100"
+# r = YiemAgent.extractWineAttributes_2(a, inventory_order)
+# pprintln(r)
+
+
+# cron job 
+# @reboot sleep 50 && nvidia-smi -pm 1
+# @reboot sleep 51 && nvidia-smi -i 0 -pl 150
+# @reboot sleep 52 && nvidia-smi -i 1 -pl 150
+# @reboot sleep 53 && nvidia-smi -i 2 -pl 150
+# @reboot sleep 54 && nvidia-smi -i 3 -pl 150
+
+# @reboot sleep 55 && julia -t 2 /home/ton/work/restartContainer/main.jl
+
+# using GeneralUtils
+# msgMeta = GeneralUtils.generate_msgMeta(
+#           "/tonpc_containerServices",
+#           senderName= "somename",
+#           senderId= "1230",
+#           mqttBrokerAddress= "mqtt.yiem.cc",
+#           mqttBrokerPort= 1883,
+#         )
+# outgoingMsg = Dict(
+#   :msgMeta=> msgMeta,
+#   :payload=> "docker container restart playground-app",
+# )
+# GeneralUtils.sendMqttMsg(outgoingMsg)
+

example/config.json

@@ -0,0 +1,76 @@
+{
+  "mqttServerInfo": {
+      "description": "mqtt server info",
+      "port": 1883,
+      "broker": "mqtt.yiem.cc"
+  },
+  "testingOrProduction": {
+      "value": "testing",
+      "description": "agent status, couldbe testing or production"
+  },
+  "agentid": {
+      "value": "2b74b87a-5413-4fe2-a4d3-405891051680",
+      "description": "a unique id for this agent"
+  },
+  "agentCentralConfigTopic": {
+      "mqtttopic": "/yiem_branch_1/agent/sommelier/backend/config/api/v1.1",
+      "description": "a central agent server's topic to get this agent config"
+  },
+  "servicetopic": {
+      "mqtttopic": [
+          "/yiem/hq/agent/sommelier/backend/prompt/api_v1/testing"
+      ],
+      "description": "a topic this agent are waiting for service request"
+  },
+  "role": {
+      "value": "sommelier",
+      "description": "agent role"
+  },
+  "organization": {
+      "value": "yiem_branch_1",
+      "description": "organization name"
+  },
+  "externalservice": {
+    "loadbalancer": {
+      "mqtttopic": "/loadbalancer/requestingservice",
+      "description": "text to text service with instruct LLM"
+    },
+    "text2textinstruct": {
+        "mqtttopic": "/loadbalancer/requestingservice",
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "virtualWineCustomer_1": {
+        "mqtttopic": "/virtualenvironment/winecustomer",
+        "description": "text to text service with instruct LLM that act as wine customer",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "text2textchat": {
+        "mqtttopic": "/loadbalancer/requestingservice",
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "wineDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10201,
+      "dbname": "wineDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    },
+    "SQLVectorDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10203,
+      "dbname": "SQLVectorDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    }
+  }
+}

example/main.jl

@@ -0,0 +1,706 @@
+using JSON, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures
+using YiemAgent, GeneralUtils
+using Base.Threads
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+""" Expected incomming MQTT message format for this service:
+  {
+    "msgMeta": {
+      "msgPurpose": "updateStatus",
+      "requestresponse": "request",
+      "timestamp": "2024-03-29T05:8:48.362",
+      "replyToMsgId": null,
+      "receiverId": null,
+      "getpost": "get",
+      "msgId": "e5c09bd8-7100-4e4e-bb43-05bee589a22c",
+      "acknowledgestatus": null,
+      "sendTopic": "/agent/wine/backend/chat/api/v1/prompt",
+      "receiverName": "agent-wine-backend",
+      "replyTopic": "/agent/wine/frontend/chat/api/v1/txt/receive",
+      "senderName": "agent-wine-frontend-chat",
+      "senderId": "0938a757-e0ee-40a9-8355-5e24906a87cd"
+    },
+    "payload" : {
+      "text": "hello"
+    }
+    
+  }
+"""
+
+
+
+
+
+# load config
+config = copy(JSON.parsefile("../mountvolume/config/config.json"))
+
+""" Instantiate an agent. One need to specify startmessage and one of gpu location info,  
+  Mqtt or Rest. start message must be comply with GeneralUtils's message format
+
+  Arguments\n
+  -----
+    channel::Channel
+      communication channel
+    sessionId::String
+      sesstion ID of the agent
+    agentName::String
+      Name of the agent
+    mqttBroker::String
+      mqtt broker e.g. "tcp://127.0.0.1:1883"
+    agentConfigTopic::String
+      main communication topic for an agent to ask for config
+    timeout::Int64
+      inactivity timeout in minutes. If timeout is reached, an agent will be terminated.
+    
+  Return\n
+  -----
+    a task represent an agent
+
+  Example\n
+  -----
+  ```jldoctest
+  julia> using YiemAgent, GeneralUtils
+  julia> msg = GeneralUtils.generate_msgMeta("/agent")
+  julia> incoming_msg = msg # assuming 1st msg was sent from other app
+  julia> agentConfigTopic = "/agent/wine/backend/config"
+  julia> task = runAgentInstance(incoming_msg, mqttBroker, agentConfigTopic, 60) 
+  ```
+
+  TODO\n
+  -----
+    [] update docstringLAMA_CONTEXT_LENGTH=40960 since the default size is 2048 as you can see in your debug log:
+    [] change how to get result of YiemAgent from let YiemAgent send msg directly to frontend,
+        to    
+          response = YiemAgent.conversation()     
+        then send response to frontend
+
+  Signature\n
+  -----
+"""
+function runAgentInstance(
+    receiveUserMsgChannel::Channel,
+    outputchannel::Channel,
+    sessionId::String,
+    config::Dict,
+    timeout::Int64,
+  )
+
+  function executeSQL(sql::T) where {T<:AbstractString}
+    host = config[:externalservice][:wineDB][:host]
+    port = config[:externalservice][:wineDB][:port]
+    dbname = config[:externalservice][:wineDB][:dbname]
+    user = config[:externalservice][:wineDB][:user]
+    password = config[:externalservice][:wineDB][:password]
+    DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+    result = LibPQ.execute(DBconnection, sql)
+    close(DBconnection)
+    return result
+  end
+  
+  function executeSQLVectorDB(sql)
+    host = config[:externalservice][:SQLVectorDB][:host]
+    port = config[:externalservice][:SQLVectorDB][:port]
+    dbname = config[:externalservice][:SQLVectorDB][:dbname]
+    user = config[:externalservice][:SQLVectorDB][:user]
+    password = config[:externalservice][:SQLVectorDB][:password]
+    DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+    result = LibPQ.execute(DBconnection, sql)
+    close(DBconnection)
+    return result
+  end
+  
+  function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
+    senderId=GeneralUtils.uuid4snakecase(), timeout=180,
+    llmkwargs=Dict(
+      :num_ctx => 32768,
+      :temperature => 0.5,
+    ))
+    msgMeta = GeneralUtils.generate_msgMeta(
+      config[:externalservice][:loadbalancer][:mqtttopic];
+      msgPurpose="inference",
+      senderName="yiemagent",
+      senderId=senderId,
+      receiverName="text2textinstruct_$modelsize",
+      mqttBrokerAddress=config[:mqttServerInfo][:broker],
+      mqttBrokerPort=config[:mqttServerInfo][:port],
+    )
+
+    outgoingMsg = Dict(
+      :msgMeta => msgMeta,
+      :payload => Dict(
+        :text => prompt,
+        :kwargs => llmkwargs
+      )
+    )
+
+    response = nothing
+    for attempts in 1:maxattempt
+      _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=timeout, maxattempt=maxattempt)
+      payload = _response[:response]
+      if _response[:success] && payload[:text] !== nothing
+        response = _response[:response][:text]
+        break
+      else
+        println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        pprintln(outgoingMsg)
+        println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+        sleep(3)
+      end
+    end
+
+    return response
+  end
+  
+  # get text embedding from a LLM service
+  function getEmbedding(text::T) where {T<:AbstractString}
+    msgMeta = GeneralUtils.generate_msgMeta(
+      config[:externalservice][:loadbalancer][:mqtttopic];
+      msgPurpose="embedding",
+      senderName="yiemagent",
+      senderId=sessionId,
+      receiverName="textembedding",
+      mqttBrokerAddress=config[:mqttServerInfo][:broker],
+      mqttBrokerPort=config[:mqttServerInfo][:port],
+    )
+  
+    outgoingMsg = Dict(
+      :msgMeta => msgMeta,
+      :payload => Dict(
+        :text => [text] # must be a vector of string
+      )
+    )
+  
+    response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
+    embedding = response[:response][:embeddings]
+    return embedding
+  end
+  
+  function findSimilarTextFromVectorDB(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 = getEmbedding(text)[1]
+    # 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
+  
+  function similarSQLVectorDB(query; maxdistance::Integer=100)
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    df = findSimilarTextFromVectorDB(query, tablename, 
+                                    "function_input_embedding", executeSQLVectorDB)
+    # println(df[1, [:id, :function_output]])
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]  
+    # distance = 100  # CHANGE this is for testing only
+    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~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      return (dict=output_str, distance=distance)
+    else
+      println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      return (dict=nothing, distance=nothing)
+    end
+  end
+  
+  function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    # query = state[:thoughtHistory][:question]
+    df = findSimilarTextFromVectorDB(query, tablename, 
+                                    "function_input_embedding", executeSQLVectorDB)
+    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 = getEmbedding(query)[1]
+      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)
+      _ = executeSQLVectorDB(sql)
+    end
+  end
+  
+  
+  function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
+    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+    tablename = "sommelier_decision_repository"
+    # find similar
+    println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+    df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                    "function_input_embedding", executeSQLVectorDB)
+    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.parsefile(_output_str))
+      return output
+    else
+      println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+      return nothing
+    end
+  end
+  
+  
+  function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+    ) where {T1<:AbstractString, T2<:AbstractDict}
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                    "function_input_embedding", executeSQLVectorDB)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      recentevents_embedding = getEmbedding(recentevents)[1]
+      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)
+      _ = executeSQLVectorDB(sql)
+    else
+      println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    end
+  end
+
+  # keepaliveChannel_2::Channel{Dict} = Channel{Dict}(8)
+  latestUserMsgTimeStamp::DateTime = Dates.now()
+
+  externalFunction = (
+    getEmbedding=getEmbedding,
+    text2textInstructLLM=text2textInstructLLM,
+    executeSQL=executeSQL,
+    similarSQLVectorDB=similarSQLVectorDB,
+    insertSQLVectorDB=insertSQLVectorDB,
+    similarSommelierDecision=similarSommelierDecision,
+    insertSommelierDecision=insertSommelierDecision,
+  )
+
+  agent = YiemAgent.sommelier(
+    externalFunction;
+    name="Jane",
+    id=sessionId,  # agent instance id
+    retailername="Yiem",
+    llmFormatName="qwen3"
+  )
+
+  # user chat loop
+  while true
+    # check for new user message
+    if isready(receiveUserMsgChannel)
+      incomingMsg = take!(receiveUserMsgChannel)
+      incoming_msgMeta = incomingMsg[:msgMeta]
+      incomingPayload = incomingMsg[:payload]
+      latestUserMsgTimeStamp = Dates.now()
+
+      # make sure the message has :text key because YiemAgent use this key for incoming user msg
+      if haskey(incomingPayload, :text)
+        # skip, msg already has correct key name
+      elseif haskey(incomingPayload, :txt)
+        # change key name to text
+        incomingPayload[:text] = incomingPayload[:txt]
+      else
+        error("\n no :txt or :text key in the message.")
+      end
+
+      # reset agent
+      if occursin("newtopic", incomingPayload[:text]) || 
+        occursin("Newtopic", incomingPayload[:text]) ||
+        occursin("New topic", incomingPayload[:text]) ||
+        occursin("new topic", incomingPayload[:text])
+        # YiemAgent.clearhistory(agent)
+
+        agent = YiemAgent.sommelier(
+          externalFunction;
+          name="Janie",
+          id=sessionId,  # agent instance id
+          retailername="Yiem",
+        )
+
+        # sending msg back to sender i.e. LINE
+        msgMeta = GeneralUtils.generate_msgMeta(
+          incomingMsg[:msgMeta][:replyTopic];
+          senderName="wine_assistant_backend",
+          senderId=sessionId,
+          replyToMsgId=incomingMsg[:msgMeta][:msgId],
+          mqttBrokerAddress=config[:mqttServerInfo][:broker],
+          mqttBrokerPort=config[:mqttServerInfo][:port],
+        )
+        outgoingMsg = Dict(
+          :msgMeta => msgMeta,
+          :payload => Dict(
+            :alias => agent.name,       # will be shown in frontend as agent name
+            :text => "Okay. What shall we talk about?"
+          )
+        )
+        _ = GeneralUtils.sendMqttMsg(outgoingMsg)
+        println("--> outgoingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        pprintln(outgoingMsg)
+      else
+        usermsg = incomingPayload
+
+        if incoming_msgMeta[:msgPurpose] == "initialize"
+          println("\n-- Initializing... ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        end
+
+        # send prompt
+        result = YiemAgent.conversation(agent;
+                                            userinput=usermsg,
+                                            maximumMsg=50)
+        # Ken's bot use [br] for newline character '\n'
+        # result = replace(result, '\n'=>"[br]")
+
+        if incoming_msgMeta[:msgPurpose] == "initialize"
+          println("\n-- Initialized. Ready! waiting for request at:\n$(config[:servicetopic][:mqtttopic]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+          continue
+        end
+
+        msgMeta = GeneralUtils.generate_msgMeta(
+          incomingMsg[:msgMeta][:replyTopic];
+          senderName="wine_assistant_backend",
+          senderId=string(uuid4()),
+          replyToMsgId=incomingMsg[:msgMeta][:msgId],
+          mqttBrokerAddress=config[:mqttServerInfo][:broker],
+          mqttBrokerPort=config[:mqttServerInfo][:port],
+        )
+
+        outgoingMsg = Dict(
+          :msgMeta => msgMeta,
+          :payload => Dict(
+            :alias => agent.name,       # will be shown in frontend as agent name
+            :text => result
+          )
+        )
+        _ = GeneralUtils.sendMqttMsg(outgoingMsg)
+        println("\n--> outgoingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        pprintln(outgoingMsg)
+
+
+        # jpg_as_juliaStr = nothing
+        # prompt = nothing
+
+        # if haskey(payload, "img")
+        #   url_or_base64 = payload["img"]
+
+        #   if startswith(url_or_base64, "http")
+        #     # img in http
+        #     julia_rgb_img, cv2_bgr_img = ImageUtils.url_to_cv2_image(url_or_base64)
+        #     _, buffer = cv2.imencode(".jpg", cv2_bgr_img)
+        #     jpg_as_pyStr = base64.b64encode(buffer).decode("utf-8")
+        #     jpg_as_juliaStr = pyconvert(String, jpg_as_pyStr)
+        #   else
+        #     # img in base64
+        #     cv2_bgr_img = payload["img"]
+        #     jpg_as_juliaStr = pyconvert(String, jpg_as_pyStr)
+        #   end
+        # end
+
+      end
+    else
+      # println("\n no msg")
+    end
+
+    if haskey(agent.memory[:events][end], :thought)
+      lastAssistantAction = agent.memory[:events][end][:thought][:action_name]
+      if lastAssistantAction == "END_CONVER_GUIDELINE"    # store thoughtDict
+
+        # save a.memory[:shortmem][:decisionlog] to disk using JSON
+        println("\nsaving agent.memory[:shortmem][:decisionlog] to disk")
+        filename = "agent_decision_log_$(Dates.now())_$(agent.id).json"
+        filepath = "/appfolder/app/log/$filename"
+        open(filepath, "w") do io
+          JSON.pretty(io, agent.memory[:shortmem][:decisionlog])
+        end
+        
+        # for (i, event) in enumerate(agent.memory[:events])
+        #   if event[:subject] == "assistant"
+        #     # create timeline of the last 3 conversation except the last one. 
+        #     # The former will be used as caching key and the latter will be the caching target
+        #     # in vector database
+        #     all_recapkeys = keys(agent.memory[:recap])  #[TESTING] recap as caching 
+        #     all_recapkeys_vec = [r for r in all_recapkeys]  # convert to a vector
+
+        #     # select from 1 to 2nd-to-lase event (i.e. excluding the latest which is assistant's response)
+        #     _recapkeys_vec = all_recapkeys_vec[1:i-1]  
+
+        #     # select only previous 3 recaps
+        #     recapkeys_vec =
+        #       if length(_recapkeys_vec) <= 3  # 1st message is a user's hello msg
+        #         _recapkeys_vec  # choose all
+        #       else 
+        #         _recapkeys_vec[end-2:end]
+        #       end
+        #     #[PENDING] if there is specific data such as number, donot store in database
+        #     tempmem = DataStructures.OrderedDict()
+        #     for k in recapkeys_vec
+        #       tempmem[k] = agent.memory[:recap][k]
+        #     end
+
+        #     recap = GeneralUtils.dictToString_noKey(tempmem)
+        #     thoughtDict = agent.memory[:events][i][:thought] # latest assistant thoughtDict
+        #     insertSommelierDecision(recap, thoughtDict)
+        #   else
+        #     # skip
+        #   end
+        # end
+        println("\nCaching conversation process done")
+        break
+      end
+    end
+
+    # self terminate if too long inactivity
+    timediff = GeneralUtils.timedifference(latestUserMsgTimeStamp, Dates.now(), "minutes")
+    if timediff > timeout
+
+      result = Dict(:exitreason => "timeout", :timestamp => Dates.now())
+      put!(outputchannel, result)
+      println("Agent ID $(agent.id) timeout has been reached  $timediff/$timeout minutes Send delete session msg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+
+      # send "delete session" message to inform the main loop that this session can be deleted
+      sendto = 
+        if typeof(config[:servicetopic][:mqtttopic]) <: Array
+          config[:servicetopic][:mqtttopic][1]
+        else 
+          config[:servicetopic][:mqtttopic]
+        end
+
+      msgMeta = GeneralUtils.generate_msgMeta(
+        sendto;
+        senderName="session",
+        senderId=sessionId,
+        msgPurpose="delete session",
+        mqttBrokerAddress=config[:mqttServerInfo][:broker],
+        mqttBrokerPort=config[:mqttServerInfo][:port],
+      )
+
+      outgoingMsg = Dict(
+        :msgMeta => msgMeta,
+        :payload => nothing
+      )
+      _ = GeneralUtils.sendMqttMsg(outgoingMsg)
+
+      try disconnect(agent.mqttClient) catch end
+      break
+    end
+    sleep(1) # allowing on_msg_2, asyncmove above and other process to run
+  end
+end
+
+sessionDict = Dict{String,Any}()
+incomingMsgChannel = (ch1=Channel(8),)  # store msg that coming into servicetopic
+# incommingInternalMsg = [] # st                        ore msg that coming into servicetopic internal management
+keepaliveChannel::Channel{Dict} = Channel{Dict}(8)
+
+# Define the callback for receiving messages.
+function onMsgCallback_1(topic, payload)
+  jobj = JSON.parsefile(String(payload))
+  incomingMqttMsg = copy(jobj)  # convert json object into julia dictionary recursively
+
+  if occursin("keepalive", topic)
+    put!(keepaliveChannel, incomingMqttMsg)
+  else
+    put!(incomingMsgChannel[:ch1], incomingMqttMsg)
+  end
+end
+
+mqttInstance = GeneralUtils.mqttClientInstance_v2(
+  config[:mqttServerInfo][:broker],
+  config[:servicetopic][:mqtttopic],
+  incomingMsgChannel,
+  keepaliveChannel,
+  onMsgCallback_1
+)
+
+# ------------------------------------------------------------------------------------------------ #
+#                                      this service main loop                                      #
+# ------------------------------------------------------------------------------------------------ #
+
+function main()
+  sessiontimeout = 1 * 1 * 60  # timeout in minute for each instance (day * hour * minute)
+  initializing = false
+  while true
+    # check if mqtt connection is still up
+    _ = GeneralUtils.checkMqttConnection!(mqttInstance; keepaliveCheckInterval=30)
+
+    # initialize session 0
+    if initializing == false  # send init msg
+      sendto = 
+        if typeof(config[:servicetopic][:mqtttopic]) <: Array
+          config[:servicetopic][:mqtttopic][1]
+        else 
+          config[:servicetopic][:mqtttopic]
+        end
+
+      msgMeta = GeneralUtils.generate_msgMeta(
+        sendto;
+        msgPurpose="initialize",
+        senderName="initializer",
+        senderId="0",
+        msgId= "initMsg",
+        replyTopic=sendto,
+        mqttBrokerAddress=config[:mqttServerInfo][:broker],
+        mqttBrokerPort=config[:mqttServerInfo][:port],
+      )
+
+      outgoingMsg = Dict(
+        :msgMeta => msgMeta,
+        :payload => Dict(    # will be shown in frontend as agent name
+          :text => "Do you have full-bodied red wines under 100 USD. I don't have any other preferences."
+        )
+      )
+      _ = GeneralUtils.sendMqttMsg(outgoingMsg)
+      initializing = true
+      println("\n--> Initializing msg sent ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    end
+
+    # check for new message
+    if !isempty(incomingMsgChannel[:ch1])
+      msg = popfirst!(incomingMsgChannel[:ch1])
+      println("\n<-- incomingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(msg)
+
+      # @spawn new runAgentInstance and store it in sessionDict
+      # use agent's frontend id because 1 backend agent per 1 frontend session
+      sessionId = msg[:msgMeta][:senderId]
+      sessionId = replace(sessionId, "-" => "_") # julia can't use "-" in a dict key 
+
+      # check for delete session msg
+      if msg[:msgMeta][:msgPurpose] == "delete session"
+        delete!(sessionDict, sessionId)
+        println("sessionId $(sessionId) has been terminated ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+
+      # no session yet, create new session
+      elseif sessionId ∉ keys(sessionDict)
+        inputch = Channel{Dict}(8)
+        outputch = Channel{Dict}(8)
+
+        process = @spawn runAgentInstance(inputch, outputch, sessionId, config, sessiontimeout)
+        # process = runAgentInstance(inputch, outputch, sessionId, config, sessiontimeout) #XXX use spawn version 
+
+        println("\ninstantiate agent success ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+
+        # call runAgentInstance() and store it in sessionDict to be able to check on it later
+        sessionDict[sessionId] = Dict(
+          :inputchannel => inputch,
+          :outputchannel => outputch,
+          :process => process,
+        )
+        put!(sessionDict[sessionId][:inputchannel], msg)
+      # ongoing session
+      else
+        println("sessionId $(sessionId) existing session ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        put!(sessionDict[sessionId][:inputchannel], msg)
+      end
+    end
+
+    # sleep is needed because MQTTClient use async. "while true" loop leave no 
+    # chance for control to switch to on_msg()
+    sleep(1)
+  end
+end
+
+main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

imagetext.txt

@@ -1 +0,0 @@
-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

my_AI_prompt.md

@@ -1,94 +0,0 @@
-can you make sure yiemAgent use correct format when contacting MCP server. you can check MCP server doc at /home/ton/docker-apps/sommpanion/mcp/docs/specification.md?
-
-smartpack() and smartunpack() doc is at /home/ton/docker-apps/sommpanion/msghandler/README.md
-
-user code that use yiemAgent is here /home/ton/docker-apps/sommpanion/agent-backend/test/runtest.jl
-
-this is log folder that logs when I ran yiemAgent. you can check /home/ton/docker-apps/sommpanion/agent-backend/log/
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

src/agentCore.jl

@@ -170,7 +170,7 @@ function yiemAgent(
 
   tools = OrderedDict{String, agentTool}()
   registerAllTools(tools, mcpServer; eventSink=eventSink, sessionId=sessionId)
-
+  eventSink("instantiate an agent")
   # Create struct with a placeholder task, then spawn and replace it
   agent = yiemAgent(
     agentState(systemPrompt, model, tools, messages),
@@ -194,7 +194,7 @@ function yiemAgent(
 
   # Spawn the background loop and attach it
   agent._agentLoop = @spawn _agentLoop(agent)
-
+  eventSink("agent instantiated")
   return agent
 end
 
@@ -260,42 +260,36 @@ function _agentLoop(agent::yiemAgent)
       agent.inputChannel -> nothing
       agent.followUpChannel -> nothing
     """
-
+    agent.eventSink("_agentLoop starting")
     while true
       while newUserMsg === nothing 
         if isready(agent.inputChannel)
-          agent.eventSink("_agentLoop 1 agent._state.messages length $(length(agent._state.messages))"; publish=false, log=true)
           # agent process new user msg immediately after the current tool call finished.
           newUserMsg = take!(agent.inputChannel)   
-          # agent.eventSink("new user msg\n$newUserMsg"; publish=false, log=true)
+          agent.eventSink("take new user message")
         else
           # check followUp message after _processMessage() is done
           if typeof(processingTask) == Task && istaskdone(processingTask) == true
-            agent.eventSink("_agentLoop 2 agent._state.messages length $(length(agent._state.messages))"; publish=false, log=true)
+            agent.eventSink("_processMessage() done")
             # if agent runs is done but followUpChannel has messages,
             # put new message in inputChannel instead
             if isready(agent.followUpChannel)
-              agent.eventSink("_agentLoop 3 agent._state.messages length $(length(agent._state.messages))"; publish=false, log=true)
               while isready(agent.followUpChannel)
+                agent.eventSink("put followUp message to input channel")
                 followUpMsg = take!(agent.followUpChannel)
                 put!(agent.inputChannel, followUpMsg)
               end
               processingTask = nothing        # reset
               result = nothing                # reset
             else  # _processMessage() done and no followUp message.
-              agent.eventSink("_agentLoop 4 agent._state.messages length $(length(agent._state.messages))"; publish=false, log=true)
+              agent.eventSink("no followUp message")
               result = deepcopy(agent._state.messages[end])
-              agent.eventSink("_agentLoop 4-1 "; publish=false, log=true)
               # filter out reasoningContent in-place
               filter!(c -> !(c isa reasoningContent), result.content)
-              agent.eventSink("_agentLoop 5 "; publish=false, log=true)
               # format output
               respondToUI = _assistantMessageToOpenAI(result)
-              agent.eventSink("_agentLoop 6 "; publish=false, log=true)
               put!(agent.outputChannel, respondToUI)
-              if !isempty(result.content) && result.content[1] isa textContent
-                agent.eventSink(result.content[1].text; publish=false, log=true)
-              end
+              agent.eventSink("put result to output channel")
               processingTask = nothing        # reset
               result = nothing                # reset
             end
@@ -325,7 +319,7 @@ function _agentLoop(agent::yiemAgent)
       else
         # spawn new _processMessage() if it is not already running.
         if processingTask === nothing
-          agent.eventSink("_agentLoop 5 agent._state.messages length $(length(agent._state.messages))"; publish=false, log=true)
+          agent.eventSink("spawn new _processMessage()")
           # discard all messages in followUpChannel
           while isready(agent.followUpChannel)
             _ = take!(agent.followUpChannel)
@@ -347,8 +341,10 @@ function _agentLoop(agent::yiemAgent)
           ) 
         end
         put!(processMessageInputCh, newUserMsg)
+        agent.eventSink("put new user msg to processMessageInputCh")
         newUserMsg = nothing # reset
       end
+
     end
   catch e
     bt = catch_backtrace()
@@ -356,7 +352,7 @@ function _agentLoop(agent::yiemAgent)
         showerror(io, e, bt)
         println(io)
     end
-    eventSink(errMsg; publish=false, log=true)
+    agent.eventSink(errMsg; publish=true)
 
     # On any error, send error response and exit the loop
     @error "Agent loop failed" error=e
@@ -404,10 +400,11 @@ function _processMessage(
   afterToolCall::Union{Function, Nothing},
   parallelToolExecute::Bool,
 )::Nothing
-  eventSink("_processMessage 1 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
   # loop until llmCall() response didn't use tool calls
   final_response = nothing
 
+  eventSink("▶ _processMessage: entered")
+
   """ example message in inputChannel
   Dict(
       "role" => "user",
@@ -425,48 +422,39 @@ function _processMessage(
     try
       # Drain inputChannel and convert OpenAI-format messages to userMessage type
       while isready(inputChannel)
-        eventSink("_processMessage 2 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
         newUserMsg_openai = take!(inputChannel)
-        eventSink("_processMessage 3 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
         if newUserMsg_openai === :shutdown
-          eventSink("_processMessage 4 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
           # Re-emit shutdown signal for the loop to handle
           put!(inputChannel, :shutdown)
           break
         end
-        eventSink("_processMessage 5 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
         newUserMsg = OpenAiToUserMessage(newUserMsg_openai)
         push!(agentMsgHistory, newUserMsg)
-        eventSink("_processMessage 6 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
+        eventSink("▶ _processMessage: user msg pushed")
       end
-      eventSink("_processMessage 7 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
       # call prepareContext()
       state = agentState(systemPrompt, nothing, tools, agentMsgHistory)
-      eventSink("_processMessage 8 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
       preparedContext = prepareContext(state, eventSink)
-      eventSink("_processMessage 9 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
       # Call formatMessagesForLLM() to format for LLM
       formattedMessages = formatMessagesForLLM(preparedContext, eventSink)
-      
-      # eventSink("_processMessage 10 formattedMessages $formattedMessages"; publish=false, log=true)    
 
+      eventSink("▶ _processMessage: context")
+      eventSink("Thinking...")
       """ response example
       response = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1.  **Identify User Request**: The user is asking for the weather in Bangkok.\n2.  **Locate Relevant Tool**: I have a `getWeather` function available.\n3.  **Check Function Parameters**:\n   - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n   - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4.  **Prepare Parameters**:\n   - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n   - `units`: Not specified, so I'll use the default (\"celsius\")\n5.  **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6.  **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n   - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n   - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
       """
       response = llmCall(formattedMessages, eventSink=eventSink)
-      # eventSink("LLM response " * string(response); publish=false, log=true)
 
-      eventSink("_processMessage 11 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
       # Extract tool calls from LLM response content blocks
-      hasToolCalls, toolCallList, assistant_msg = _extractToolCalls(response)
-      eventSink("hasToolCalls: $hasToolCalls\ntoolCallList: $toolCallList _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
-      eventSink("assistant_msg " * string(assistant_msg); publish=false, log=true)
-      eventSink("_processMessage 11-1"; publish=false, log=true)
+      hasToolCalls, toolCallList, assistant_msg = _extractToolCalls(response, eventSink)
 
       # Add assistant message (tool calls or text) to history for next LLM turn
       push!(agentMsgHistory, assistant_msg)
+      eventSink("▶ _processMessage: LLM response")
 
       if hasToolCalls && length(toolCallList) > 0
+        eventSink("▶ _processMessage: tool calls")
+
         # Build context and config for executeToolCalls
 
         config = agentLoopConfig(
@@ -476,21 +464,16 @@ function _processMessage(
         )
 
         signal = abortSignal(false)
-        eventSink("_processMessage 12 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
-
         # call executeToolCalls()
         toolResultBatch = executeToolCalls(preparedContext, assistant_msg, toolCallList, config, 
                                           signal, eventSink)      
-        eventSink("_processMessage 13 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
-
+        eventSink("▶ _processMessage: tool batch")
         # save toolResults to messages
         for toolResult in toolResultBatch.messages
-          # eventSink("toolResult " * string(toolResult); publish=false, log=true)
           push!(agentMsgHistory, toolResult)
         end
-        eventSink("_processMessage 14 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
         if toolResultBatch.terminate
-          eventSink("_processMessage 15 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
+          eventSink("▶ _processMessage: TERMINATE branch")
           # If toolResultBatch requested termination, build a final response
           final_content = [textContent("Tool execution completed.")]
           for toolResult in toolResultBatch.messages
@@ -504,7 +487,6 @@ function _processMessage(
               end
             end
           end
-          eventSink("_processMessage 16 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
           final_response = assistantMessage(
             role="assistant",
             content=final_content,
@@ -523,7 +505,7 @@ function _processMessage(
           break
         end
       else
-        eventSink("_processMessage 17 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
+        eventSink("▶ _processMessage: FINAL response")
         # LLM did not use tool calls — 
         break
       end
@@ -534,12 +516,12 @@ function _processMessage(
             println(io)
         end
 
-        eventSink(err_msg; publish=false, log=true)
+        eventSink(err_msg; publish=true)
+        eventSink("▶ _processMessage: CAUGHT ERROR")
     end
   end
 
-
-  eventSink("_processMessage 18 _state.messages length $(length(agentMsgHistory))"; publish=false, log=true)
+  eventSink("▶ _processMessage: exited")
   return nothing
 end
 
@@ -649,9 +631,12 @@ The message is constructed from:
   1)  llm_useTool = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1.  **Identify User Request**: The user is asking for the weather in Bangkok.\n2.  **Locate Relevant Tool**: I have a `getWeather` function available.\n3.  **Check Function Parameters**:\n   - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n   - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4.  **Prepare Parameters**:\n   - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n   - `units`: Not specified, so I'll use the default (\"celsius\")\n5.  **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6.  **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n   - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n   - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
   2)  llm_notUseTool = JSON.Object{String, Any}("finish_reason" => "stop", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "The weather in London, UK is currently Sunny with a temperature of 22°C.", "reasoning_content" => "The user asked for the weather in London.\nI called the `getWeather` tool for London, UK.\nThe response indicates it's Sunny and 22°C.\nI will convey this information to the user.\n"))
 """
-function _extractToolCalls(response)
+function _extractToolCalls(response, eventSink=nothing)
     hasToolCalls = false
     toolCallList = agentToolCall[]
+    if eventSink !== nothing
+        eventSink("▶ _extractToolCalls: parsing response")
+    end
 
     # Helper: parse args (JSON string -> Dict, or pass through)
     parse_args(raw) = raw isa AbstractDict && !(raw isa Dict{String,Any}) ? 
@@ -979,47 +964,40 @@ function prepareToolCall(
   signal::abortSignal,
   eventSink
 )::Union{preparedToolCall,immediateOutcome}
-  eventSink("prepareToolCall 1"; publish=false, log=true)
 
   # pick a called tool from tool store
+  eventSink("▶ prepareToolCall: resolving")
   tool = get(context.tools, toolCall.name, nothing) 
   
   if tool === nothing
-    eventSink("prepareToolCall 2"; publish=false, log=true)
+    eventSink("▶ prepareToolCall: NOT FOUND")
     return immediateOutcome(createErrorToolResult("Tool $toolCall.name not found"), true)
   end
 
   try
-    eventSink("prepareToolCall 3"; publish=false, log=true)
     # 1. prepare arguments (tool-specific transform)
     prepared = prepareToolCallArguments(tool, toolCall)
-    eventSink("prepared " * string(prepared.arguments); publish=false, log=true)
-    eventSink("prepareToolCall 4"; publish=false, log=true)
     validatedArgs = validateToolArguments(tool, prepared)
-    eventSink("validatedArgs " * string(validatedArgs); publish=false, log=true)
-    eventSink("prepareToolCall 5"; publish=false, log=true)
+    eventSink("▶ prepareToolCall: validated")
     # 2. beforeToolCall hook — can block
     if config.beforeToolCall !== nothing
-      eventSink("prepareToolCall 6"; publish=false, log=true)
 
       before = config.beforeToolCall(
         beforeToolCallContext(assistantMsg, toolCall, validatedArgs, context), 
         signal,
         eventSink
         )
-      eventSink("prepareToolCall 7"; publish=false, log=true)
+
       if signal.aborted
-        eventSink("prepareToolCall 8"; publish=false, log=true)
         return immediateOutcome(createErrorToolResult("Operation aborted"), true)
       end
 
       if before !== nothing && before.block
-        eventSink("prepareToolCall 9"; publish=false, log=true)
         return immediateOutcome(
           createErrorToolResult(get(before, :reason, "Tool execution was blocked")), true)
       end
     end
-    eventSink("prepareToolCall 10"; publish=false, log=true)
+
     return preparedToolCall(tool, toolCall, validatedArgs)
   catch e
     bt = catch_backtrace()
@@ -1028,8 +1006,7 @@ function prepareToolCall(
         println(io)
     end
 
-    eventSink(errMsg; publish=false, log=true)
-
+    eventSink(errMsg; publish=true)
     return immediateOutcome(createErrorToolResult(sprint(showerror, e)), true)
   end
 end
@@ -1080,16 +1057,11 @@ function executePreparedToolCall(
     signal::Union{Nothing,abortSignal},
     eventSink,
   )::executedOutcome
-  eventSink("executePreparedToolCall 1"; publish=false, log=true)
 
+  eventSink("▶ execute: START")
   try
-
     result = prep.tool.execute(prep.toolCall.id, prep.args, signal, eventSink)
-
-
-    eventSink("executePreparedToolCall 2"; publish=false, log=true)
-    eventSink("tool execute raw result: " * sprint(show, result); publish=false, log=true)
-    eventSink("executePreparedToolCall 3"; publish=false, log=true)
+    eventSink("▶ execute: END")
     return executedOutcome(result, false)
   catch e
     bt = catch_backtrace()
@@ -1097,7 +1069,8 @@ function executePreparedToolCall(
         showerror(io, e, bt)
         println(io)
     end
-    eventSink(errMsg; publish=false, log=true)
+    eventSink(errMsg; publish=true)
+    eventSink("▶ execute: ERROR")
 
     return executedOutcome(createErrorToolResult(sprint(showerror, e)), true)
   end
@@ -1170,10 +1143,10 @@ function finalizeExecutedToolCall(
     signal::Union{Nothing,abortSignal},
     eventSink
 )::finalizedOutcome
-  eventSink("finalizeExecutedToolCall 1"; publish=false, log=true)
+
+  eventSink("▶ finalize: START")
   result = executed.result
   isError = executed.isError
-  eventSink("finalizeExecutedToolCall result before afterToolCall\n$result"; publish=false, log=true)
   if config.afterToolCall !== nothing
     try
       after = config.afterToolCall(
@@ -1181,7 +1154,6 @@ function finalizeExecutedToolCall(
         signal,
         eventSink
       )
-      eventSink("finalizeExecutedToolCall 3"; publish=false, log=true)
       if after !== nothing
         result = after
         isError = false
@@ -1192,13 +1164,13 @@ function finalizeExecutedToolCall(
           showerror(io, e, bt)
           println(io)
       end
-      eventSink(errMsg; publish=false, log=true)
+      eventSink(errMsg; publish=true)
 
       result = createErrorToolResult(sprint(showerror, e))
       isError = true
     end
   end
-  eventSink("finalizeExecutedToolCall 4"; publish=false, log=true)
+  eventSink("▶ finalize: END")
   return finalizedOutcome(prep.toolCall, result, isError)
 end
 
@@ -1264,39 +1236,27 @@ function executeToolCallsSequential(
     signal::abortSignal,
     eventSink,
 )::agentToolCallBatch
-  eventSink("executeToolCallsSequential 1"; publish=false, log=true)
   finalizedCalls = finalizedOutcome[]
   messages = toolResultMessage[]
+  eventSink("▶ executeToolCallsSequential: batch start")
 
   for tc in toolCalls
-    eventSink("start tool execute $(tc.id), $(tc.name), $(tc.arguments)"; publish=false, log=true)
     prep = prepareToolCall(context, assistantMsg, tc, config, signal, eventSink)
-    eventSink("executeToolCallsSequential " * string(prep.args); publish=false, log=true)
 
     if prep isa immediateOutcome
-      eventSink("executeToolCallsSequential 2-1"; publish=false, log=true)
       finalized = finalizedOutcome(tc, prep.result, prep.isError)
-      eventSink("executeToolCallsSequential 2-2"; publish=false, log=true)
     else
-      eventSink("executeToolCallsSequential 3"; publish=false, log=true)
       executed = executePreparedToolCall(prep, signal, eventSink)
-
-      eventSink("executeToolCallsSequential 3-1"; publish=false, log=true)
       finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config,
                                           signal, eventSink)
-      eventSink("executeToolCallsSequential 3-2"; publish=false, log=true)
     end
-    eventSink("executeToolCallsSequential 4"; publish=false, log=true)
-    # eventSink("finalized $(finalized.toolCall.id), $(finalized.toolCall.name),
-    #                       $(finalized.result), $(finalized.isError)"; publish=false, log=true)
     push!(messages, createToolResultMessage(finalized))
     push!(finalizedCalls, finalized)
-    eventSink("executeToolCallsSequential 5"; publish=false, log=true)
     if signal !== nothing && signal.aborted
         break
     end
   end
-  eventSink("executeToolCallsSequential 6"; publish=false, log=true)
+  eventSink("▶ executeToolCallsSequential: batch end")
   return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
 end
 
@@ -1367,23 +1327,24 @@ function executeToolCallsParallel(
 )::agentToolCallBatch
 
   entries = Union{finalizedOutcome,Task}[]
+  eventSink("▶ executeToolCallsParallel: batch start")
 
   for tc in toolCalls
-    eventSink(toolExecStartEvent(tc.id, tc.name, tc.arguments); publish=false, log=true)
+    toolExecStartEvent(tc.id, tc.name, tc.arguments)
 
     prep = prepareToolCall(context, assistantMsg, tc, config, signal, eventSink)
 
     if prep isa immediateOutcome
       finalized = finalizedOutcome(tc, prep.result, prep.isError)
-      eventSink(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
-                            finalized.result, finalized.isError))
+      toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
+                            finalized.result, finalized.isError)
       push!(entries, finalized)
     else
       t = Task() do
         executed = executePreparedToolCall(prep, signal, eventSink)
         finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)
-        eventSink(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
-                              finalized.result, finalized.isError))
+        toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
+                              finalized.result, finalized.isError)
         return finalized
       end
       schedule(t)
@@ -1406,6 +1367,7 @@ function executeToolCallsParallel(
       push!(messages, createToolResultMessage(f))
   end
 
+  eventSink("▶ executeToolCallsParallel: batch end")
   return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
 end
 
@@ -1468,7 +1430,6 @@ function executeToolCalls(
     eventSink,
   )::agentToolCallBatch
 
-  eventSink("_executeToolCalls 1"; publish=false, log=true)
   hasSequential = false
   for tc in toolCalls
       t = get(context.tools, tc.name, nothing)
@@ -1477,13 +1438,11 @@ function executeToolCalls(
           break
       end
   end
-  eventSink("_executeToolCalls 2"; publish=false, log=true)
+  eventSink("▶ executeToolCalls: dispatch")
   if config.toolExecution == "sequential" || hasSequential
-    eventSink("_executeToolCalls 3"; publish=false, log=true)
     return executeToolCallsSequential(context, assistantMsg, toolCalls, config, signal,
                                       eventSink)
   else
-    eventSink("_executeToolCalls 4"; publish=false, log=true)
     return executeToolCallsParallel(context, assistantMsg, toolCalls, config, signal,
                                     eventSink)
   end
@@ -1524,8 +1483,11 @@ OpenAiToUserMessage(msg)
 # => userMessage("user", [textContent("Hello")], DateTime(...))
 ```
 """
-function OpenAiToUserMessage(msg::AbstractDict)::userMessage
+function OpenAiToUserMessage(msg::AbstractDict, eventSink=nothing)::userMessage
     content_blocks = Vector{messageContent}()
+    if eventSink !== nothing
+        eventSink("▶ OpenAiToUserMessage: parsing")
+    end
 
     raw_content = get(msg, "content", Any[])
     if raw_content isa Vector
@@ -1549,6 +1511,9 @@ function OpenAiToUserMessage(msg::AbstractDict)::userMessage
         end
     end
 
+    if eventSink !== nothing
+        eventSink("▶ OpenAiToUserMessage: done")
+    end
     return userMessage(content=content_blocks)
 end
 

src/toolRegistry.jl

@@ -121,9 +121,7 @@ function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; ev
   end
   new_count = 0
   try
-    eventSink("register_mcp_tools 1")
     response = mcpserver("tools/list", sessionId; eventSink=eventSink)
-    eventSink("register_mcp_tools 2")
     # Parse JSON-RPC 2.0 response envelope
     if haskey(response, "error")
         rpc_error = response["error"]
@@ -200,7 +198,7 @@ Add `tool` to `tools`, overwriting any existing tool with the same name.
 function registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool; eventSink=nothing
   )::OrderedDict{String, agentTool}
     tools[tool.name] = tool
-    println("[toolRegistry] Registered tool: $(tool.name)")
+    eventSink("[toolRegistry] Registered tool: $(tool.name)")
     return tools
 end
 
@@ -247,7 +245,6 @@ function listTool(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=no
                  eventSink) -> begin
             # Discover and register MCP tools (idempotent — skips already registered)
             new_count = register_mcp_tools(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
-            eventSink("tools dump: " * sprint(show, tools))
 
             # Always include listTools itself in the count
             total = length(tools)
@@ -285,7 +282,9 @@ Register all tools for an agent:
 """
 function registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver=nothing; eventSink=nothing, sessionId=nothing)
     list_t = listTool(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
+    eventSink("registering tools")
     registerTool(tools, list_t; eventSink=eventSink)
+    eventSink("registering tools done")
 end
 
 end # module

src/utils.jl

@@ -208,7 +208,6 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
   openaiReadyMsg = Dict{String, Any}()
   # openaiReadyMsg["model"] = "gemma-4-E4B-it-UD-Q4_K_XL"
   messages = Vector{Dict{String, Any}}()
-  eventSink("formatMsgForLLM 1")
   # System prompt as system message
   if !isempty(ctx.systemPrompt)
     push!(messages, Dict(
@@ -216,7 +215,6 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
       "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
     ))
   end
-  eventSink("formatMsgForLLM 2")
   # Conversation messages
   for msg in ctx.messages
     if msg isa userMessage
@@ -229,10 +227,8 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
       push!(messages, _toolResultMessageToOpenAI(msg))
     end
   end
-  eventSink("formatMsgForLLM 3")
   # Convert ctx.tools into OpenAI tools format
   tools_array = _toolsToOpenAI(ctx.tools, eventSink)
-  eventSink("formatMsgForLLM 4")
   openaiReadyMsg["messages"] = messages
   openaiReadyMsg["temperature"] = 0.7
 
@@ -278,7 +274,7 @@ end
 function beforeToolCall(context::beforeToolCallContext, signal::abortSignal, eventSink
   )::beforeToolCallResult
   
-  eventSink("beforeToolCall: $(context.toolCall.name) args=$(context.args)")
+  # eventSink("beforeToolCall: $(context.toolCall.name) args=$(context.args)")
   
   return beforeToolCallResult(false, "N/A")
 end
@@ -306,11 +302,11 @@ Common use cases:
 - `agentToolResult(...)` to return a modified result (content, details, usage,
   terminate flag can all be overridden).
 
-"""   #WORKING arrange result from searchWine tool
+"""   # arrange result from searchWine tool
 function afterToolCall(context::afterToolCallContext, signal::abortSignal, eventSink
   )::Union{agentToolResult, Nothing}
 
-  eventSink("afterToolCall: $(context.toolCall.name) result=$(context.result) error=$(context.isError)")
+  # eventSink("afterToolCall: $(context.toolCall.name) result=$(context.result) error=$(context.isError)")
   
   return nothing
 end
@@ -428,8 +424,7 @@ _toolsToOpenAI(tools)    # => [Dict("type" => "function", "function" => Dict("na
 """
 function _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}, eventSink)::Vector{Dict{String, Any}}
   tools_array = Vector{Dict{String, Any}}()
-  eventSink("_toolsToOpenAI 1")
-  eventSink(string(typeof(tools)))
+
   if tools !== nothing
     for (_, tool) in tools
       push!(tools_array, Dict(
@@ -442,7 +437,7 @@ function _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}, e
       ))
     end
   end
-  eventSink("_toolsToOpenAI 2")
+
   return tools_array
 end
 

user_code.jl

@@ -1,113 +0,0 @@
-using Revise, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
-      NATS, Base.Threads
-using YiemAgent, GeneralUtils, msghandler
-
-
-""" Debug 
-
-using JSON, NATS, msghandler
-using NATS
-
-conn = NATS.connect("nats.yiem.cc")
-
-sub = NATS.subscribe(conn, "sommanion.debug") do msg
-  payload = NATS.payload(msg)
-  @info "debug" payload
-
-  open("./log/error.log", "a") do io
-    println(io, payload)
-  end
-end
-
-NATS.publish(conn, "sommanion.debug", "order-123")
-
-
-# ---------------------------- inject this code into codebase to debug --------------------------- #
-try
-  batch = someFunction(x, y, z)
-catch e
-  bt = catch_backtrace()
-  err_msg = sprint() do io
-      showerror(io, e, bt)
-      println(io)
-  end
-
-  eventSink(err_msg)
-end
-
-"""
-
-struct text2textInstructLLM
-  natsConn::NATS.Connection
-  topic::String
-  senderID::String
-  fileserver_url::String
-end
-
-function (t::text2textInstructLLM)(openai_msg::Dict{String, Any})
-
-  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
-  _, msg_envelope_json_str = msghandler.smartpack(
-        t.topic, 
-        payloads;
-        sender_id=t.senderID,
-        msg_purpose="text2text",  
-        fileserver_url=t.fileserver_url)
-
-  reply = NATS.request(t.natsConn, t.topic, msg_envelope_json_str, timeout=180)
-
-  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]
-
-  return llm_response
-end
-
-
-
-struct eventSink
-  natsConn::NATS.Connection
-  topic::String
-  senderID::String
-end
-
-function (aes::eventSink)(msg::String)
-  NATS.publish(aes.natsConn, aes.topic, msg)
-end
-
-
-
-config = JSON.parsefile("./appconfig.json")
-agent_conn  = NATS.connect(config["nats_server_info"]["url"])
-
-
-#WORKING load tools
-text2text_llm = text2textInstructLLM(agent_conn, 
-                                    config["externalservice"]["servicesloadbalancer"]["nats"], 
-                                    "sender", 
-                                    config["externalservice"]["fileserver"]["url"])
-
-debugNats = eventSink(agent_conn, "sommanion.debug", "sender")
-
-agent = YiemAgent.yiemAgent(
-  text2text_llm;
-  eventSink=debugNats
-)
-
-msg = Dict(
-        "role" => "user",
-        "content" => [
-          Dict("type" => "text", "text" => "What's the weather in Bangkok?"),
-          # Dict(
-          #   "type" => "image_url",
-          #   "image_url" => Dict("url" => "data:mime_type;base64,image2_base64_string")
-          # ),
-        ]
-      )
-      
-push!(agent.inputChannel, msg)
-
-
-
-