update

Reviewed-on: #44

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "0db36d4fb31037ba05065476e6aebaf4cd0e1e8c"
+project_hash = "3ff1783eadf40ccb51801954aa0a8df935689752"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -44,6 +44,12 @@ git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
 uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
 version = "0.4.0"
 
+[[deps.Arrow]]
+deps = ["ArrowTypes", "BitIntegers", "CodecLz4", "CodecZstd", "ConcurrentUtilities", "DataAPI", "Dates", "EnumX", "Mmap", "PooledArrays", "SentinelArrays", "StringViews", "Tables", "TimeZones", "TranscodingStreams", "UUIDs"]
+git-tree-sha1 = "4a69a3eadc1f7da78d950d1ef270c3a62c1f7e01"
+uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
+version = "2.8.1"
+
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -58,6 +64,12 @@ version = "1.11.0"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 
+[[deps.BitIntegers]]
+deps = ["Random"]
+git-tree-sha1 = "091d591a060e43df1dd35faab3ca284925c48e46"
+uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1"
+version = "0.3.7"
+
 [[deps.BufferedStreams]]
 git-tree-sha1 = "6863c5b7fc997eadcabdbaf6c5f201dc30032643"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -90,12 +102,24 @@ git-tree-sha1 = "40956acdbef3d8c7cc38cba42b56034af8f8581a"
 uuid = "6c391c72-fb7b-5838-ba82-7cfb1bcfecbf"
 version = "0.3.4"
 
+[[deps.CodecLz4]]
+deps = ["Lz4_jll", "TranscodingStreams"]
+git-tree-sha1 = "d58afcd2833601636b48ee8cbeb2edcb086522c2"
+uuid = "5ba52731-8f18-5e0d-9241-30f10d1ec561"
+version = "0.4.6"
+
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
 git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
 version = "0.7.8"
 
+[[deps.CodecZstd]]
+deps = ["TranscodingStreams", "Zstd_jll"]
+git-tree-sha1 = "da54a6cd93c54950c15adf1d336cfd7d71f51a56"
+uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
+version = "0.8.7"
+
 [[deps.CommonSolve]]
 git-tree-sha1 = "cf963add2340ad9960e5eb22844e61ad8f931fe1"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
@@ -130,6 +154,12 @@ weakdeps = ["InverseFunctions"]
     [deps.CompositionsBase.extensions]
     CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 
+[[deps.ConcurrentUtilities]]
+deps = ["Serialization", "Sockets"]
+git-tree-sha1 = "3c9be947934c38475bafe822c6d61aaed17f0738"
+uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
+version = "2.6.0"
+
 [[deps.ConstructionBase]]
 git-tree-sha1 = "b4b092499347b18a015186eae3042f72267106cb"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
@@ -531,6 +561,12 @@ git-tree-sha1 = "1d4c737ab26f51ceed52ab2019c09b7660eb7440"
 uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
 version = "3.8.0"
 
+[[deps.Lz4_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "191686b1ac1ea9c89fc52e996ad15d1d241d1e33"
+uuid = "5ced341a-0733-55b8-9ab6-a4889d929147"
+version = "1.10.1+0"
+
 [[deps.MacroTools]]
 git-tree-sha1 = "1e0228a030642014fe5cfe68c2c0a818f9e3f522"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -938,6 +974,11 @@ git-tree-sha1 = "8a90c1d77c3277a5d43b83927b3cbe2c70a37484"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
 version = "0.4.7"
 
+[[deps.StringViews]]
+git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
+uuid = "354b36f9-a18e-4713-926e-db85100087ba"
+version = "1.3.7"
+
 [[deps.StructTypes]]
 deps = ["Dates", "UUIDs"]
 git-tree-sha1 = "159331b30e94d7b11379037feeb9b690950cace8"
@@ -1080,6 +1121,14 @@ git-tree-sha1 = "011b0a7331b41c25524b64dc42afc9683ee89026"
 uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 
+[[deps.msghandler]]
+deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "Revise", "UUIDs"]
+git-tree-sha1 = "e82a79cf6602541ea25409aded57b2ace4a7c29f"
+repo-rev = "main"
+repo-url = "https://git.yiem.cc/ton/msghandler"
+uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
+version = "1.2.1"
+
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

Project.toml

@@ -12,16 +12,15 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
 HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
 NATS = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
 PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
-SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 
 [compat]
 Base64 = "1.11.0"
@@ -30,6 +29,5 @@ DataFrames = "1.7.0"
 GeneralUtils = "0.5.10"
 HTTP = "2.4.0"
 JSON = "1.6.1"
-LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.8"
+msghandler = "1.2.1"

README.md

@@ -1,12 +1,16 @@
 # YiemAgent
 
-Julia framework for building agents with tool use.
+Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
 
 ## Getting Started
 
-1. Install dependencies: `]add JSON, DataStructures, UUIDs, Dates, ...`
+1. Install dependencies: `]add JSON, DataStructures, UUIDs, Dates, NATS, DataFrames`
-2. Create a `yiemAgent` with `loadTools("src/tools")`
+2. Create a callable `mcpServer` struct that communicates with an MCP server via NATS
-3. Call `run_agent(agent, "message")` then `take_response(agent)`
+3. Create a `yiemAgent` with an LLM callable and MCP server:
+   ```julia
+   agent = YiemAgent.yiemAgent(llmCall; mcpServer=mcpServer, eventSink=yourSink)
+   ```
+4. Call `promptAgent(agent, message)` then `takeResponse(agent)`
 
 ## Architecture
 
@@ -14,21 +18,122 @@ Julia framework for building agents with tool use.
 src/
 ├── YiemAgent.jl          # Module entry point
 ├── type.jl               # Core types (messages, tools, agent state)
-├── utils.jl              # Message formatting, validation
+├── utils.jl              # Message formatting, context preparation, validation
 ├── agentCore.jl          # Agent loop, tool execution pipeline
-├── api.jl                # Public API (run_agent, take_response, etc.)
+├── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
-└── tools/
+└── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
-    ├── registry.jl       # Tool registry (loadTools, registerTool, listTools)
-    ├── getWeather.jl     # Weather lookup tool
-    ├── getTime.jl        # Time lookup tool
-    ├── writeTool.jl      # Create new tool files (self-modifying)
-    └── README.md         # Tool development guide
 ```
 
-## Tool Development
+## MCP Protocol
 
-See `src/tools/README.md` for:
+YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement:
-- Tool anatomy (schema, execute, getTool)
+
-- Validation hooks
+```julia
-- Agent loop lifecycle
+# tools/list — returns tool definitions
-- Self-modifying tools (`writeTool`)
+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"`:
+```julia
+Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
+```
+
+### Implementing the MCP Server Client
+
+You must provide a callable struct that communicates with your MCP server. Example using NATS:
+
+```julia
+struct mcpServer
+  natsConn::NATS.Connection
+  topic::String
+  senderID::String
+  fileserver_url::String
+end
+
+# tools/list implementation
+function (c::mcpServer)(method::String)
+  if method != "tools/list"
+    error("mcpServer: unexpected method '$method' (expected 'tools/list')")
+  end
+
+  payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
+  payloads = [("payload", payload, "dictionary")]
+  _, msg_envelope_json_str = msghandler.smartpack(
+        c.topic, payloads;
+        sender_id=c.senderID,
+        msg_purpose="mcp_tools_list",
+        fileserver_url=c.fileserver_url)
+
+  reply = NATS.request(c.natsConn, c.topic, msg_envelope_json_str, timeout=180)
+  incoming_env = msghandler.smartunpack(String(reply.payload))
+  return incoming_env["payloads"][1][2]
+end
+
+# tools/call implementation
+function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
+  if method != "tools/call"
+    error("mcpServer: unexpected method '$method' (expected 'tools/call')")
+  end
+
+  payload = Dict(
+    "jsonrpc" => "2.0",
+    "id" => 2,
+    "method" => method,
+    "params" => Dict(
+      "name" => toolName,
+      "arguments" => arguments
+    )
+  )
+  payloads = [("payload", payload, "dictionary")]
+  _, msg_envelope_json_str = 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_json_str, timeout=180)
+  incoming_env = msghandler.smartunpack(String(reply.payload))
+  return incoming_env["payloads"][1][2]
+end
+```
+
+See `test/runtest.jl` for the complete working example.
+
+## Tool Discovery
+
+Tools are discovered dynamically via MCP server:
+
+1. `listTool` is the only pre-registered tool
+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
+
+## Agent API
+
+| Function | Description |
+|----------|-------------|
+| `promptAgent(agent, msg)` | Send a message to the agent's input channel |
+| `takeResponse(agent)` | Block and take the agent's response from output channel |
+| `followUp(agent, msg)` | Send a follow-up message while agent is still processing |
+| `stopAgent(agent)` | Gracefully stop the agent and close channels |
+
+## Agent Lifecycle
+
+1. `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
+2. User messages enter via `promptAgent()` or `followUp()`
+3. `_processMessage()` handles LLM calls, tool execution, and conversation history
+4. Tool execution follows three phases: `prepareToolCall` → `executePreparedToolCall` → `finalizeExecutedToolCall`
+5. `beforeToolCall`/`afterToolCall` hooks allow pre/post-processing of tool calls
+6. Tool `execute` functions can set `terminate=true` to stop the agent loop
+
+## Hooks
+
+| Hook | Signature | Purpose |
+|------|-----------|---------|
+| `prepareContext` | `(state, sink, llmCall) -> ctx` | Transform messages/context before LLM call |
+| `formatMsgForLLM` | `(ctx, sink) -> dict` | Convert agent context to LLM API format |
+| `beforeToolCall` | `(context, signal) -> result` | Block/allow tool execution |
+| `afterToolCall` | `(context, signal) -> result` | Post-process tool results |
+| `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages |

etc.jl

@@ -1,2 +0,0 @@
-# ── executeToolCalls() Julia pseudo code ──────────────────────────
-# Full call stack from runLoop → executeToolCalls → prepare → execute → finalize → emit

etc.md

@@ -0,0 +1,11 @@
+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?

src/YiemAgent.jl

@@ -1,53 +1,33 @@
-module YiemAgent
+module YiemAgent
-
+
-  # export agent
+  """Order by dependencies of each file. The 1st included file must not depend on any other
-
+  files and each file can only depend on the file included before it."""
-
+
-  """ Order by dependencies of each file. The 1st included file must not depend on any other
+  include("type.jl")
-  files and each file can only depend on the file included before it. 
+  using .type
-  """
+
-
+  include("utils.jl")
-  include("type.jl")
+  using .utils
-  using .type
+
-
+  include("toolRegistry.jl")
-  include("utils.jl")
+  using .toolRegistry
-  using .utils
+
-
+  # include("llmfunction.jl")
-  include("tools/registry.jl")
+  # using .llmfunction
-  using .toolRegistry
+
-
+  include("agentCore.jl")
-  # include("llmfunction.jl")
+  using .agentCore
-  # using .llmfunction
+
-
+  include("api.jl")
-  include("agentCore.jl")
+  using .api
-  using .agentCore
+
-
+  export promptAgent, takeResponse, followUp, stopAgent
-  include("api.jl")
+  
-  using .api
+  
-  
+
-  
+# ---------------------------------------------- 100 --------------------------------------------- #
-# ---------------------------------------------- 100 --------------------------------------------- #
+
-
+
-
+
-
+
-
+end # module YiemAgent_v1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-end # module YiemAgent_v1

src/api.jl

@@ -1,16 +1,15 @@
 module api
 
-export prompt
+export promptAgent, takeResponse, followUp, stopAgent
 
 using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
       DataFrames
 using GeneralUtils
-using ..type, ..utils
+using ..type, ..utils, ..agentCore, ..toolRegistry
 
 # ---------------------------------------------- 100 --------------------------------------------- #
 
 
-
 """
 Send a message to the agent's input channel.
 
@@ -19,22 +18,22 @@ The agent processes messages from `inputChannel` in the background task.
 
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a message to
-- `msg`: The message to send (any type accepted by the agent's processing pipeline)
+- `msg`: The message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Hello!")`)
 
 # Returns
 - The same `agent` instance for chaining
 
 # Notes
-- Use `take_response(agent)` to receive the agent's response after sending a message.
+- Use `takeResponse(agent)` to receive the agent's response after sending a message.
-- Use `follow_up(agent, msg)` to send messages while the agent is still processing.
+- Use `followUp(agent, msg)` to send messages while the agent is still processing.
 
 # Examples
 ```jldoctest
-julia> run_agent(agent, "Hello!")
+julia> promptAgent(agent, Dict("role" => "user", "content" => "Hello!"))
 yiemAgent(...)
 ```
 """
-function run_agent(agent::yiemAgent, msg)
+function promptAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
   put!(agent.inputChannel, msg)
   return agent
 end
@@ -51,15 +50,15 @@ Blocks until the agent sends a response.
 - An `assistantMessage` instance representing the agent's response
 
 # Notes
-- Use `run_agent(agent, msg)` to send a message before calling this function.
+- Use `promptAgent(agent, msg)` to send a message before calling this function.
 
 # Examples
 ```jldoctest
-julia> response = take_response(agent)
+julia> response = takeResponse(agent)
 assistantMessage(...)
 ```
 """
-function take_response(agent::yiemAgent)
+function takeResponse(agent::yiemAgent)
   return take!(agent.outputChannel)
 end
 
@@ -71,23 +70,23 @@ and before any tool call results are sent.
 
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a follow-up message to
-- `msg`: The follow-up message to send
+- `msg`: The follow-up message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Also consider red wines")`)
 
 # Returns
 - The same `agent` instance for chaining
 
 # Notes
-- Use `run_agent(agent, msg)` for the primary message and `follow_up(agent, msg)` for additional
+- Use `promptAgent(agent, msg)` for the primary message and `followUp(agent, msg)` for additional
   messages while the agent is processing.
 - Follow-up messages are buffered in a separate channel (capacity 32 by default).
 
 # Examples
 ```jldoctest
-julia> follow_up(agent, "Also consider red wines")
+julia> followUp(agent, Dict("role" => "user", "content" => "Also consider red wines"))
 yiemAgent(...)
 ```
 """
-function follow_up(agent::yiemAgent, msg)
+function followUp(agent::yiemAgent, msg::AbstractDict{String, Any})
   put!(agent.followUpChannel, msg)
   return agent
 end
@@ -105,19 +104,19 @@ then closes all channels (`inputChannel`, `outputChannel`, `followUpChannel`).
 - `nothing`
 
 # Notes
-- After calling `stop_agent`, the agent is no longer usable. A new agent must be created
+- After calling `stopAgent`, the agent is no longer usable. A new agent must be created
   for further interaction.
 - If the background task throws a `TaskFailedException`, it is rethrown.
 
 # Examples
 ```jldoctest
-julia> stop_agent(agent)
+julia> stopAgent(agent)
 ```
 """
-function stop_agent(agent::yiemAgent)
+function stopAgent(agent::yiemAgent)
   put!(agent.inputChannel, :shutdown)
   try
-    fetch(agent._agent_loop)
+    fetch(agent._agentLoop)
   catch e
     if e isa TaskFailedException
       rethrow(e)

src/toolRegistry.jl

@@ -0,0 +1,293 @@
+module toolRegistry
+
+export registerTool, registerAllTools, clearTools, listTool
+
+using Dates
+using JSON, DataStructures
+using ..type
+
+
+
+# ── MCP helper functions ────────────────────────────────────────────
+
+"""
+Extract text from MCP tool result content array.
+
+Handles JSON-RPC 2.0 result content format:
+    {"content": [{"type": "text", "text": "..."}], "isError": false}
+"""
+function _extract_text_content(result::AbstractDict)::String
+    content = get(result, "content", Any[])
+    if content isa Vector && !isempty(content)
+        lines = String[]
+        for block in content
+            if block isa Dict && get(block, "type", "") == "text"
+                push!(lines, string(get(block, "text", "")))
+            end
+        end
+        if !isempty(lines)
+            return join(lines, "\n")
+        end
+    end
+    return JSON.json(result)
+end
+
+"""
+Wrap an MCP tool definition as an `agentTool`.
+
+The returned tool's `execute` function calls the MCP server's "tools/call"
+method with the validated arguments.
+"""
+function _wrap_mcp_tool(mcpserver, tool_def::AbstractDict{String, Any}; eventSink=nothing)::agentTool
+    name = tool_def["name"]
+    title = get(tool_def, "title", get(tool_def, "label", name))
+    desc = get(tool_def, "description", "")
+    input_schema = get(tool_def, "inputSchema", Dict{String,Any}())
+
+    # Normalize inputSchema to OpenAI function format
+    if haskey(input_schema, "properties") && input_schema["type"] == "object"
+        params = Dict(
+            "type" => "object",
+            "properties" => input_schema["properties"],
+            "required" => get(input_schema, "required", Any[]),
+        )
+    else
+        params = Dict(
+            "type" => "object",
+            "properties" => Dict{String,Any}(),
+            "required" => Any[],
+        )
+    end
+
+    return agentTool(
+        name=name,
+        label=title,
+        description=desc,
+        inputSchema=params,
+        execute=(toolCallId::String, args::AbstractDict{String, Any},
+                 signal::Union{Nothing,abortSignal},
+                 eventSink) -> begin
+            try
+                response = mcpserver("tools/call", name, args)
+
+                # Parse JSON-RPC 2.0 response envelope
+                if haskey(response, "error")
+                    rpc_error = response["error"]
+                    err_msg = get(rpc_error, "message", "Unknown MCP error")
+                    return agentToolResult(
+                        [textContent("MCP error: $err_msg")],
+                        Dict{Any,Any}("error" => err_msg),
+                        nothing, false
+                    )
+                end
+
+                result_data = get(response, "result", response)
+                content_text = _extract_text_content(result_data)
+                is_error = get(result_data, "isError", false)
+
+                return agentToolResult(
+                    [textContent(content_text)],
+                    Dict{Any,Any}("isError" => is_error),
+                    nothing, false
+                )
+            catch e
+                errMsg = sprint(showerror, e)
+                return agentToolResult(
+                    [textContent("MCP call error: $errMsg")],
+                    Dict{Any,Any}("error" => errMsg),
+                    nothing, false
+                )
+            end
+        end,
+        prepareArguments=nothing,
+        validateRequiredArgs=nothing,
+        parallelToolExecute=false,
+    )
+end
+
+"""
+Discover and register MCP tools into `store.tools`.
+
+Queries the MCP server via `mcpserver("tools/list")` (JSON-RPC 2.0 format),
+parses the response envelope, and registers each discovered tool.
+Handles pagination via `nextCursor`. Skips tools already registered.
+
+# Returns
+- `Int`: number of new tools registered
+"""
+function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing)::Int
+    if mcpserver === nothing
+        return 0
+    end
+
+    new_count = 0
+
+    try
+        eventSink("register_mcp_tools 1")
+        response = mcpserver("tools/list")
+        eventSink("register_mcp_tools 2")
+        # Parse JSON-RPC 2.0 response envelope
+        if haskey(response, "error")
+            rpc_error = response["error"]
+            err_msg = get(rpc_error, "message", "Unknown MCP error")
+            println("[toolRegistry] MCP tools/list failed: $err_msg")
+            return 0
+        end
+        if haskey(response, "result")
+            response = response["result"]
+        end
+
+        tools_array = response["tools"]
+        cursor = get(response, "nextCursor", nothing)
+
+        for tool_def in tools_array
+            name = tool_def["name"]
+            if haskey(tools, name)
+                continue
+            end
+
+            wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink)
+            tools[name] = wrapped
+            new_count += 1
+        end
+
+        # Paginate: fetch remaining tools if nextCursor is present
+        while cursor !== nothing && cursor !== ""
+            response = mcpserver("tools/list")
+            if haskey(response, "result")
+                response = response["result"]
+            end
+            tools_array = get(response, "tools", Any[])
+            cursor = get(response, "nextCursor", nothing)
+            for tool_def in tools_array
+                name = tool_def["name"]
+                if haskey(tools, name)
+                    continue
+                end
+                wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink)
+                tools[name] = wrapped
+                new_count += 1
+            end
+        end
+
+        println("[toolRegistry] Discovered $new_count MCP tools. Total: $(length(tools))")
+    catch e
+        bt = catch_backtrace()
+        err_msg = sprint() do io
+            showerror(io, e, bt)
+            println(io)
+        end
+
+        eventSink(err_msg)
+
+        errMsg = sprint(showerror, e)
+        println("[toolRegistry] MCP tools/list failed: $errMsg")
+    end
+
+    return new_count
+end
+
+"""
+    registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool) -> OrderedDict{String, agentTool}
+
+Add `tool` to `tools`, overwriting any existing tool with the same name.
+
+# Arguments
+- `tools`: Tool dict to modify
+- `tool`: The `agentTool` to register
+
+# Returns
+- The same `tools` dict (modified in place)
+"""
+function registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool; eventSink=nothing
+  )::OrderedDict{String, agentTool}
+    tools[tool.name] = tool
+    println("[toolRegistry] Registered tool: $(tool.name)")
+    return tools
+end
+
+"""
+Remove all tools from the dict.
+
+# Arguments
+- `tools`: Tool dict to clear
+
+# Returns
+- `nothing`
+"""
+function clearTools(tools::OrderedDict{String, agentTool})::Nothing
+    empty!(tools)
+    println("[toolRegistry] Registry cleared")
+    return nothing
+end
+
+# ── listTools tool (auto-discover new MCP tools at runtime) ─────────
+
+"""
+    listTool(tools::OrderedDict{String, agentTool}, mcpserver) -> agentTool
+
+MCP-aware listTools tool (JSON-RPC 2.0 protocol).
+
+First call: queries the MCP server via `mcpserver("tools/list")`, registers
+all discovered tools into `tools` (in-place mutation, with
+pagination via nextCursor), then returns the full tool list.
+
+Subsequent calls: returns the current list (tools remain registered).
+"""
+function listTool(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing)::agentTool
+    return agentTool(
+        name="listTools",
+        label="List Tools",
+        description="List all available tools. First call discovers and registers all tools from the MCP server. After discovery, new tools become immediately available for use.",
+        inputSchema=Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict{String,Any}(),
+            "required" => Any[]
+        ),
+        execute=(toolCallId::String, args::AbstractDict{String, Any},
+                 signal::Union{Nothing,abortSignal},
+                 eventSink) -> begin
+            # Discover and register MCP tools (idempotent — skips already registered)
+            new_count = register_mcp_tools(tools, mcpserver; eventSink=eventSink)
+            eventSink("tools dump: " * sprint(show, tools))
+
+            # Always include listTools itself in the count
+            total = length(tools)
+
+            lines = String[
+                "- $(t.name): $(t.label) — $(t.description)"
+                for (k, t) in tools
+            ]
+            result_text = "Available tools ($total):\n" * join(lines, "\n")
+
+            return agentToolResult(
+                [textContent(result_text)],
+                Dict{Any,Any}("count" => total),
+                nothing, false
+            )
+        end,
+        prepareArguments=nothing,
+        validateRequiredArgs=nothing,
+        parallelToolExecute=false,
+    )
+end
+
+# ── High-level API ──────────────────────────────────────────────────
+
+"""
+    registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver)
+
+Register all tools for an agent:
+  1. Auto-discover existing tools from the MCP server
+  2. Register the `listTools` tool so the agent can discover new tools at runtime
+
+# Arguments
+- `tools`: The tool dict to populate
+- `mcpserver`: A callable struct that communicates with the MCP server
+"""
+function registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver=nothing; eventSink=nothing)
+    list_t = listTool(tools, mcpserver; eventSink=eventSink)
+    registerTool(tools, list_t; eventSink=eventSink)
+end
+
+end # module

src/tools/README.md

@@ -1,525 +0,0 @@
-# Tools
-
-Tools allow the agent to perform actions and fetch data. Each tool defines a **schema** (what arguments it accepts) and an **execution function** (what it does).
-
-## Tool Anatomy
-
-Each tool has 3 main parts:
-
-### 1. Schema (`inputSchema`)
-
-JSON Schema (MCP format) describing the tool's arguments. The `"required"` array lists mandatory fields:
-
-```julia
-inputSchema = Dict{String,Any}(
-    "type" => "object",
-    "properties" => Dict(
-        "city" => Dict("type" => "string", "description" => "City name"),
-        "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
-    ),
-    "required" => ["city"]
-)
-```
-
-### 2. Execution Function (`execute`)
-
-A function with the signature:
-
-```julia
-execute(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
-```
-
-- **`toolCallId`** — unique ID for this invocation (from the LLM's tool call)
-- **`args`** — validated arguments provided by the LLM
-- **`signal`** — abort signal for cancellable operations
-- **`onPartialResult`** — callback for streaming progress updates
-- **Returns** — `agentToolResult` with content, details, usage, and termination flag
-
-```julia
-function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
-    # Optional: stream progress updates
-    onPartialResult(Dict("status" => "Fetching data..."))
-    
-    # Do work
-    result = "Weather in $(args["city"]): Sunny, 22°C"
-    
-    # Return result
-    return agentToolResult(
-        [textContent(result)],
-        Dict{Any,Any}(),  # details
-        nothing,           # usage
-        false              # terminate (true to stop agent loop)
-    )
-end
-```
-
-### 3. Tool Definition (`getTool()`)
-
-Returns an `agentTool` struct:
-
-| Field | Type | Description |
-|---|---|---|
-| `name` | `String` | Unique identifier (e.g. `"getWeather"`) |
-| `label` | `String` | Human-readable name (e.g. `"Weather Lookup"`) |
-| `description` | `String` | What the tool does (shown to the LLM) |
-| `inputSchema` | `Any` | JSON Schema (MCP format) |
-| `execute` | `Function` | The execution function |
-| `prepareArguments` | `Union{Function,Nothing}` | Optional argument transform before validation |
-| `validateRequiredArgs` | `Union{Function,Nothing}` | Optional custom validation |
-| `parallelToolExecute` | `Bool` | Run this tool in parallel with others |
-
-## Argument Validation
-
-Validation happens **before** tool execution, in the `prepareToolCall` phase. Invalid calls return an error immediately without invoking `execute`, `beforeToolCall`, or logging `toolExecutionStart`.
-
-### Default: JSON Schema Required Fields
-
-Set `validateRequiredArgs = nothing` to use the default validator, which checks that all fields in `inputSchema["required"]` are present:
-
-```julia
-# src/tools/getWeather.jl — uses default validation
-function getTool()::agentTool
-    return agentTool(
-        name = "getWeather",
-        # ...
-        validateRequiredArgs = nothing,  # uses default
-    )
-end
-```
-
-### Custom Validation Hook
-
-Override `validateRequiredArgs` when you need:
-- **Cross-field constraints** (e.g. "at least one of X or Y")
-- **Format validation** (e.g. regex patterns, date parsing)
-- **Domain rules** (e.g. value ranges, business logic)
-
-The hook signature takes only `args`:
-
-```julia
-function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
-    tz = get(args, "timezone", nothing)
-    city = get(args, "city", "")
-    
-    if !haskey(args, "timezone") && isempty(city)
-        return "Missing required argument: provide at least one of 'timezone' or 'city'"
-    end
-    
-    if tz !== nothing
-        tz_str = string(tz)
-        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
-            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York'"
-        end
-    end
-    
-    return nothing
-end
-```
-
-Return `nothing` to pass, or an error `String` to fail. The error is fed back to the LLM so it can retry with corrected arguments.
-
-## Tool Discovery and Lifecycle
-
-The agent iterates through tools via a **discover → execute → loop** cycle. Here is the complete flow from the framework author's perspective:
-
-### The Agent Loop
-
-```julia
-# agentCore.jl:175 - _process_message()
-while true
-    # 1. Drain messages from inputChannel
-    while isready(agent.inputChannel)
-        raw_msg = take!(agent.inputChannel)
-        user_msg = OpenAiToUserMessage(raw_msg)
-        push!(agent._state.messages, user_msg)
-    end
-
-    # 2. Format messages for LLM
-    ctx = agent.prepareContext(agent._state)
-    formatted = agent.formatMsgForLLM(ctx)
-
-    # 3. Call LLM
-    response = agent.llmCall(formatted)
-
-    # 4. Check if LLM used tool calls
-    if has_tool_calls(response.content)
-        # 5. Execute tools, feed results back to LLM, loop
-    else
-        # 6. No tool calls — return final response
-        break
-    end
-end
-```
-
-### Step 1: Tool Discovery
-
-Tools are discovered from `agent._state.tools`, which is a `Vector{agentTool}` populated during agent creation:
-
-```julia
-# Loading tools
-tools = loadTools("src/tools")  # returns Vector{agentTool}
-
-# Passing to agent
-agent = yiemAgent(
-    systemPrompt = "...",
-    tools = tools,  # ← tools stored in agent._state.tools
-    llmCall = my_llm_call,
-    agentEventSink = my_event_sink,
-)
-```
-
-When the LLM response contains tool calls, the agent builds an `agentContext` with those tools:
-
-```julia
-context = agentContext(
-    agent._state.systemPrompt,
-    agent._state.messages,
-    agent._state.tools,  # ← tools available for discovery
-)
-```
-
-### Step 2: Extract Tool Calls from LLM Response
-
-The agent inspects the `response.content` blocks for `tool_calls`:
-
-```julia
-# agentCore.jl:217-245
-tool_call_list = agentToolCall[]
-
-for content_block in response.content
-    if content_block isa Dict
-        if get(content_block, :type, "") == "tool_calls"
-            # OpenAI format: {"type": "tool_calls", "tool_calls": [...]}
-            for tc_data in get(content_block, :tool_calls, [])
-                tc = agentToolCall(
-                    type = "function",
-                    id = get(tc_data, :id, string(uuid4())),
-                    name = get(tc_data, :function, Dict())[:name],
-                    arguments = get(tc_data, :function, Dict())[:arguments],
-                )
-                push!(tool_call_list, tc)
-            end
-        elseif get(content_block, :type, "") == "tool_call"
-            # Alternative format: single tool_call block
-            tc = agentToolCall(
-                type = "function",
-                id = get(content_block, :id, string(uuid4())),
-                name = get(content_block, :name, ""),
-                arguments = get(content_block, :arguments, Dict()),
-            )
-            push!(tool_call_list, tc)
-        end
-    end
-end
-```
-
-### Step 3: Execute Each Tool Call
-
-For each tool call, the agent runs through the **prepare → execute → finalize** pipeline:
-
-```julia
-# agentCore.jl:247-302
-if has_tool_calls && length(tool_call_list) > 0
-    context = agentContext(agent._state.systemPrompt, agent._state.messages, agent._state.tools)
-    config = agentLoopConfig(agent._state.tools, agent.beforeToolCall, agent.afterToolCall, execution_mode)
-    signal = nothing
-    emit = agent.agentEventSink
-
-    # Execute all tool calls (sequential or parallel)
-    batch = executeToolCalls(context, response, tool_call_list, config, signal, emit)
-
-    # Save results to conversation history
-    for tool_result in batch.messages
-        push!(agent._state.messages, tool_result)
-    end
-
-    # If any tool requested termination, break the loop
-    if batch.terminate
-        final_response = build_final_response(batch)
-        break
-    end
-    # Otherwise, loop back to step 2 (format + call LLM again)
-end
-```
-
-### Step 4: The Per-Call Pipeline
-
-Each tool call goes through three phases:
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│ PREPARE  →  prepareToolCall()                                  │
-│                                                                 │
-│   1. Find tool by name in context.tools                         │
-│   2. Transform args via tool.prepareArguments (if defined)      │
-│   3. Validate via tool.validateRequiredArgs (or default)        │
-│   4. Run beforeToolCall hook (if defined)                       │
-│   └── on any failure → return immediateOutcome (skip execution) │
-│   └── success → return preparedToolCall                         │
-├─────────────────────────────────────────────────────────────────┤
-│ EXECUTE  →  executePreparedToolCall()                           │
-│                                                                 │
-│   1. emit toolExecutionStart event                              │
-│   2. call tool.execute(toolCallId, args, signal, onPartialResult)│
-│   3. wait for all pending update events                         │
-│   └── on error → return executedOutcome(isError=true)           │
-│   └── success → return executedOutcome(isError=false)           │
-├─────────────────────────────────────────────────────────────────┤
-│ FINALIZE  →  finalizeExecutedToolCall()                         │
-│                                                                 │
-│   1. Run afterToolCall hook (if defined)                        │
-│      - can mutate content, details, usage, terminate, isError   │
-│   2. emit toolExecutionEnd event                                │
-│   3. createToolResultMessage → adds to conversation history     │
-│   └── return finalizedOutcome                                   │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-### Step 5: Feed Results Back to LLM
-
-Tool results are added to `agent._state.messages` as `toolResultMessage` objects. On the next loop iteration, `formatMsgForLLM()` converts them to OpenAI format and the LLM receives the results:
-
-```
-Conversation history after tool execution:
-  [system] "You are a helpful assistant."
-  [user] "What's the weather in Tokyo?"
-  [assistant] (tool_calls: getWeather(city="Tokyo"))
-  [tool] tool_call_id="call_1", tool_name="getWeather", content="Weather in Tokyo: Sunny, 22°C"
-```
-
-The LLM then decides: call another tool, or return a final text answer.
-
-## Execution Modes
-
-### Sequential
-
-Tools execute one at a time in order. Required when:
-- Tools have implicit dependencies
-- Tools share state (e.g. writing to the same file)
-- Tools have `parallelToolExecute = false`
-
-Set globally via `agentLoopConfig.toolExecution = "sequential"`, or per-tool via `parallelToolExecute = false`.
-
-### Parallel
-
-Tools execute concurrently when all are independent. Reduces wall-clock time. Set `parallelToolExecute = true` on individual tools, or set `agentLoopConfig.toolExecution = "parallel"`.
-
-## Streaming Partial Results
-
-For long-running tools (API calls, file uploads, training), use `onPartialResult` to stream progress:
-
-```julia
-function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
-    onPartialResult(Dict("status" => "Step 1: Fetching data..."))
-    sleep(1)
-    
-    onPartialResult(Dict("status" => "Step 2: Processing..."))
-    sleep(1)
-    
-    return agentToolResult(
-        [textContent("Done!")],
-        Dict{Any,Any}(), nothing, false
-    )
-end
-```
-
-UI listeners and the TUI consume these events in real time via `toolExecutionUpdate`.
-
-## Loading Tools
-
-### Auto-load from Directory
-
-```julia
-using .toolRegistry
-
-tools = loadTools("src/tools")  # scans for *.jl files with getTool()
-```
-
-Files are loaded alphabetically for deterministic registration order.
-
-### Manual Registration
-
-```julia
-tool = getTool()  # from your tool module
-registerTool(tool)
-```
-
-## Complete Lifecycle Example
-
-```julia
-# ─── USER SENDS MESSAGE ───────────────────────────────────────────
-run_agent(agent, "What's the weather in Tokyo?")
-
-# ─── LOOP ITERATION 1 ─────────────────────────────────────────────
-# Agent formats messages and calls LLM
-formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
-response = agent.llmCall(formatted)
-# LLM returns: {"content": [{"type": "tool_calls", "tool_calls": [{"name": "getWeather", "arguments": {"city": "Tokyo"}}]}]}
-
-# Agent extracts tool call, builds context
-context = agentContext(systemPrompt, messages, agent._state.tools)
-tool_call_list = [agentToolCall("call_1", "getWeather", Dict("city" => "Tokyo"))]
-
-# PREPARE: find tool, validate args
-tool = find(t -> t.name == "getWeather", context.tools)  # found!
-validateRequiredArgs(Dict("city" => "Tokyo"), tool.inputSchema)  # passes
-beforeToolCall_hook(agentMsgCtx, nothing)  # nil, skipped
-
-# EXECUTE: call tool.execute()
-result = tool.execute("call_1", Dict("city" => "Tokyo"), nothing, onPartialResult)
-# Returns: agentToolResult([textContent("Weather in Tokyo: Sunny, 22°C")], Dict(), nothing, false)
-
-# FINALIZE: afterToolCall hook, emit events
-finalized = finalizedOutcome(tc, result, false)
-emit(toolExecEndEvent("call_1", "getWeather", result, false))
-msg = createToolResultMessage(finalized)  # toolResultMessage for conversation history
-
-# Add result to conversation
-push!(agent._state.messages, msg)
-# Messages now: [user: "What's the weather?", assistant: {tool_calls: getWeather}, tool: "Sunny, 22°C"]
-
-# ─── LOOP ITERATION 2 ─────────────────────────────────────────────
-# LLM called again with tool result included
-formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
-response = agent.llmCall(formatted)
-# LLM returns: {"content": [{"type": "text", "text": "The weather in Tokyo is sunny, 22°C."}]}
-
-# No tool calls detected → break loop, return final response
-return assistantMessage(content=[textContent("The weather in Tokyo is sunny, 22°C.")], ...)
-
-# ─── USER RECEIVES RESPONSE ───────────────────────────────────────
-response = take_response(agent)
-println(response.content)
-# => "[textContent(\"The weather in Tokyo is sunny, 22°C.\")]"
-```
-
-## Self-Modifying Tools
-
-The framework includes tools that allow the agent to create new tools at runtime.
-
-### `writeTool` — Create New Tool Files
-
-`writeTool` is a **file writer**, not a code generator. The LLM provides the tool logic as `executeCode` (the actual Julia code), and `writeTool` wraps it in the required boilerplate.
-
-**How it works:**
-
-The LLM constructs `writeTool` with:
-- **`executeCode`** — the actual tool logic (Julia code body, NOT wrapped in a function)
-- **`name`, `label`, `description`** — tool metadata
-- **`inputSchema`** — parameter schema in MCP format
-- **`validateCode`, `prepareCode`** (optional) — custom validation/preparation logic
-
-`writeTool` produces `src/tools/<name>.jl` by:
-1. Converting the `inputSchema` Dict into a Julia `Dict{String,Any}(...)` string literal
-2. Indenting `executeCode` with 4 spaces
-3. Wrapping it inside a `function executeTool(...)::agentToolResult ... end` template
-4. Appending the `getTool()` definition that returns an `agentTool` struct
-5. Writing the combined string to disk
-
-**Workflow:**
-
-```
-LLM decides: "Need a searchWine tool. I'll provide the logic."
-
-LLM calls writeTool:
-  name: "searchWine"
-  executeCode: "query = args[\"query\"]\nresult = search(query)\nreturn ..."
-
-writeTool wraps it → src/tools/searchWine.jl:
-  function executeTool(...)::agentToolResult
-      query = args["query"]          ← LLM code (indented 4 spaces)
-      result = search(query)
-      return agentToolResult(...)
-  end
-
-  function getTool()::agentTool
-      return agentTool(name="searchWine", ...)
-  end
-
-Restart → loadTools("src/tools") loads searchWine.jl
-```
-
-**Example specification:**
-
-```julia
-Dict(
-    "name" => "searchWine",
-    "label" => "Wine Search",
-    "description" => "Search a wine database by name, region, or variety",
-    "inputSchema" => Dict(
-        "type" => "object",
-        "properties" => Dict(
-            "query" => Dict("type" => "string", "description" => "Search query"),
-            "maxResults" => Dict("type" => "integer", "default" => 10)
-        ),
-        "required" => ["query"]
-    ),
-    "executeCode" => """
-        query = args["query"]
-        max_results = get(args, "maxResults", 10)
-        # Perform search logic here
-        result = "Found 3 wines matching: $query"
-        return agentToolResult([textContent(result)], Dict{Any,Any}(), nothing, false)
-    """,
-    "parallel" => false
-)
-```
-
-**Optional hooks:**
-
-| Field | Description |
-|---|---|
-| `validateCode` | Custom validation Julia code (runs before execute). Return `nothing` to pass, or an error `String` to fail. |
-| `prepareCode` | Argument preparation code (runs before validation). Return modified args dict. |
-
-### `listTools` — Discover Available Tools
-
-Returns all registered tools. Primarily useful for **collision detection** before creating a new tool via `writeTool` — the LLM checks existing names before picking a unique one.
-
-```julia
-# Result from listTools:
-# Available tools:
-# - getWeather: Weather Lookup — Fetch current weather and forecast for a given city.
-# - getTime: Time Lookup — Get current local time for a timezone or city.
-# - writeTool: Create Tool — Generate new tool files...
-# - listTools: List Tools — List all available tools with their names and labels...
-```
-
-### Complete Self-Tooling Example
-
-```
-User: "I need to search for wines. Do you have a tool for that?"
-
-# ─── LOOP: Agent realizes no wine search tool exists ─────────────────
-
-# LLM generates the tool logic and calls writeTool to write it to disk
-
-[Tool Call] writeTool(name="searchWine", label="Wine Search",
-    description="Search a wine database by name, region, or variety",
-    inputSchema={...},
-    executeCode="query = args[\"query\"]\nresult = \"Found wines...\"\nreturn agentToolResult([textContent(result)], ...)")
-
-# writeTool generates src/tools/searchWine.jl
-
-# ─── SYSTEM RESTARTS ─────────────────────────────────────────────────
-
-# loadTools("src/tools") loads searchWine.jl alongside all other tools
-
-# ─── Agent calls the new tool ─────────────────────────────────────────
-
-[Tool Call] searchWine(query="cabernet", maxResults=5)
-# Result: "Found 5 cabernet wines..."
-
-# ─── Final response ──────────────────────────────────────────────────
-
-"The search found 5 cabernet wines: ..."
-```
-
-## Available Tools
-
-| Tool | Description | Validation |
-|---|---|---|
-| `getWeather` | Fetch weather for a city | Default (JSON Schema required) |
-| `getTime` | Get current time for a timezone or city | Custom (cross-field + format) |
-| `writeTool` | Create a new Julia tool module at runtime | Built-in (name + schema validation) |
-| `listTools` | List all available tools with descriptions | None (no arguments) |

src/tools/getTime.jl

@@ -1,72 +0,0 @@
-"""
-Validate required arguments for the getTime tool.
-
-Demonstrates custom validation beyond simple required-field checking:
-- Ensures at least one time source (timezone or city) is provided
-- Validates timezone is in IANA format if specified
-- Validates city name is not empty if specified
-
-# Arguments
-- `args::Dict{String,Any}`: Arguments from the LLM
-
-# Returns
-- `nothing` if validation passes
-- `String` error message if validation fails
-"""
-function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
-    tz = get(args, "timezone", nothing)
-    city = get(args, "city", "")
-    
-    hasTz = tz !== nothing && !isempty(tz)
-    hasCity = !isempty(city)
-    
-    # At least one of timezone or city is required
-    if !hasTz && !hasCity
-        return "Missing required argument: provide at least one of 'timezone' or 'city'"
-    end
-    
-    # Validate timezone format (IANA tz database: "Continent/City" or "Continent/City/SubCity")
-    if hasTz
-        tz_str = string(tz)
-        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
-            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York' or 'Asia/Tokyo'"
-        end
-    end
-    
-    return nothing
-end
-
-"""
-Define and return the getTime agentTool.
-"""
-function getTool()::agentTool
-    return agentTool(
-        name = "getTime",
-        label = "Time Lookup",
-        description = "Get current local time for a timezone or city.",
-        inputSchema = Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict(
-                "timezone" => Dict("type" => "string", "description" => "IANA timezone, e.g. 'America/New_York'"),
-                "city" => Dict("type" => "string", "description" => "City name as fallback")
-            ),
-            "required" => []
-        ),
-        execute = (toolCallId, args, signal, onPartialResult) -> begin
-            tz = get(args, "timezone", nothing)
-            city = get(args, "city", "")
-            if tz !== nothing
-                result = "Current time in $(tz): $(now())"
-            else
-                result = "Current time in $(city): $(now())"
-            end
-            return agentToolResult(
-                [textContent(result)],
-                Dict{Any,Any}(), nothing, false
-            )
-        end,
-        prepareArguments = nothing,
-        validateRequiredArgs = validateRequiredArgs,
-        parallelToolExecute = false
-    )
-end

src/tools/getWeather.jl

@@ -1,31 +0,0 @@
-"""
-Define and return the getWeather agentTool.
-"""
-function getTool()::agentTool
-    return agentTool(
-        name = "getWeather",
-        label = "Weather Lookup",
-        description = "Fetch current weather and forecast for a given city.",
-        inputSchema = Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict(
-                "city" => Dict("type" => "string", "description" => "City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'"),
-                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius", "description" => "Temperature scale")
-            ),
-            "required" => ["city"]
-        ),
-        execute = (toolCallId, args, signal, onPartialResult) -> begin
-            city = get(args, "city", "")
-            units = get(args, "units", "celsius")
-            temp = units == "fahrenheit" ? "72" : "22"
-            unit_symbol = units == "celsius" ? "°C" : "°F"
-            return agentToolResult(
-                [textContent("Weather in $(city): Sunny, $(temp)$(unit_symbol)")],
-                Dict{Any,Any}(), nothing, false
-            )
-        end,
-        prepareArguments = nothing,
-        validateRequiredArgs = nothing,
-        parallelToolExecute = false
-    )
-end

src/tools/registry.jl

@@ -1,174 +0,0 @@
-module toolRegistry
-
-export loadTools, registerTool, getTools, clearTools
-
-using Dates
-using JSON
-using ..type
-
-# Global registry — populated at runtime by loadTools() or registerTool()
-const _registry = Vector{agentTool}()
-
-# Auto-register the built-in listTools tool
-function __init__()
-    registerTool(_listTool())
-end
-
-"""
-List tool definition — lets the agent query available tools for collision detection
-when creating new tools via writeTool.
-"""
-function _listTool()::agentTool
-    return agentTool(
-        name = "listTools",
-        label = "List Tools",
-        description = "List all available tools with their names, labels, and descriptions. Use this before creating a new tool to check for name collisions.",
-        inputSchema = Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict{String,Any}(),
-            "required" => Any[]
-        ),
-        execute = (toolCallId, args, signal, onPartialResult) -> begin
-            tools = getTools()
-            if isempty(tools)
-                result_text = "No tools registered."
-            else
-                lines = String["- $(t.name): $(t.label) — $(t.description)" for t in tools]
-                result_text = "Available tools:\n" * join(lines, "\n")
-            end
-            return agentToolResult(
-                [textContent(result_text)],
-                Dict{Any,Any}("count" => length(tools)),
-                nothing, false
-            )
-        end,
-        prepareArguments = nothing,
-        validateRequiredArgs = nothing,
-        parallelToolExecute = false
-    )
-end
-
-"""
-Load all tool modules from a directory.
-
-Scans `dir` for `.jl` files. Each file must define a function named
-`getTool()::agentTool`.  Files are sorted alphabetically so tool
-registration order is deterministic.
-
-# Tool file format
-Each `.jl` file defines one function `getTool()` that returns an `agentTool`:
-
-```julia
-# src/tools/getWeather.jl
-function getTool()::agentTool
-    return agentTool(
-        name = "getWeather",
-        label = "Weather Lookup",
-        description = "Fetch current weather and forecast for a given city.",
-        inputSchema = Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict(
-                "city" => Dict("type" => "string", "description" => "City and country"),
-                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
-            ),
-            "required" => ["city"]
-        ),
-        execute = (toolCallId, args, signal, onPartialResult) -> begin
-            city = args["city"]
-            return agentToolResult(
-                [textContent("Sunny, 22C in Bangkok")],
-                Dict{Any,Any}(), nothing, false
-            )
-        end,
-        prepareArguments = nothing,
-        parallelToolExecute = false
-    )
-end
-```
-
-# Arguments
-- `dir::String`: Directory path to scan for `.jl` tool files
-
-# Returns
-- `Vector{agentTool}`: All loaded tools
-
-# Errors
-- Throws `ArgumentError` if a tool file does not define a `getTool` function
-"""
-function loadTools(dir::String)::Vector{agentTool}
-    if !isdir(dir)
-        throw(ArgumentError("Tool directory does not exist: $dir"))
-    end
-
-    tools = agentTool[]
-    jl_files = filter(f -> endswith(f, ".jl") && !occursin(r"(?i)registry", f), readdir(dir))
-    sort!(jl_files)
-
-    for filename in jl_files
-        filepath = joinpath(dir, filename)
-        println("[toolRegistry] Loading tool from: $filepath")
-
-        # Include the file in the current module scope so all types resolve
-        # (agentTool, textContent, agentToolResult, etc. are all available)
-        include(filepath)
-
-        # Validate that getTool was defined (include() places it in current module scope)
-        if !isdefined(@__MODULE__, :getTool)
-            throw(ArgumentError(
-                "Tool file $(filepath) does not define a `getTool()` function. " *
-                "Each tool file must define: function getTool()::agentTool ... end"
-            ))
-        end
-
-        # Call getTool() — it runs in current scope where types are visible
-        # Use invokelatest to handle world-age semantics after include()
-        tool = invokelatest(getTool)
-        if !(tool isa agentTool)
-            throw(ArgumentError(
-                "getTool() in $(filepath) did not return an agentTool instance, got: $(typeof(tool))"
-            ))
-        end
-
-        push!(_registry, tool)
-        push!(tools, tool)
-        println("[toolRegistry] Loaded tool: $(tool.name) — $(tool.label)")
-    end
-
-    return tools
-end
-
-"""
-Register a single agentTool into the global registry.
-
-# Arguments
-- `tool::agentTool`: The tool to register
-
-# Returns
-- `Vector{agentTool}`: Updated registry
-"""
-function registerTool(tool::agentTool)::Vector{agentTool}
-    push!(_registry, tool)
-    println("[toolRegistry] Registered tool: $(tool.name)")
-    return _registry
-end
-
-"""
-Get all registered tools.
-
-# Returns
-- `Vector{agentTool}`: Copy of the registry
-"""
-function getTools()::Vector{agentTool}
-    return deepcopy(_registry)
-end
-
-"""
-Clear all registered tools from the global registry.
-"""
-function clearTools()::Nothing
-    empty!(_registry)
-    println("[toolRegistry] Registry cleared")
-    return nothing
-end
-
-end # module

src/tools/writeTool.jl

@@ -1,271 +0,0 @@
-"""
-Tool that writes new Julia tool module files to disk.
-
-The agent can use this tool when it encounters a task that no existing tool
-can handle. Provide the tool's name, label, description, inputSchema, and
-execute logic as Julia code. The tool is written to `src/tools/<name>.jl`.
-
-After calling this tool, restart the agent so `loadTools("src/tools")` picks
-up the new file. The new tool is immediately available.
-
-# Example
-
-1. Agent calls writeTool with a spec for a "searchWine" tool
-2. writeTool generates src/tools/searchWine.jl
-3. Restart agent — loadTools() picks up the new file
-4. Agent calls searchWine with args
-
-# How It Works
-
-writeTool is a **file writer**, not a code generator. The LLM provides the
-tool logic as `executeCode`, and writeTool wraps it in Julia boilerplate:
-  - Converts `inputSchema` Dict into Julia `Dict{String,Any}(...)` string
-  - Indents `executeCode` with 4 spaces
-  - Wraps it inside `function executeTool(...)::agentToolResult ... end`
-  - Appends `getTool()` returning an `agentTool` struct
-  - Writes the combined string to `src/tools/<name>.jl`
-
-# Important Notes
-
-- The `executeCode` string is embedded literally into the generated tool.
-  Use `args["param_name"]` to access input parameters.
-- The code string should be the function body (NOT wrapped in a function).
-  Lines will be indented with 4 spaces inside the execute function.
-- Tool names must be valid Julia identifiers (lowercase letters, digits, underscores,
-  no leading digits or special characters).
-"""
-
-"""
-Validate that a tool name is a valid Julia identifier.
-"""
-function validateToolName(name::String)::Union{Nothing,String}
-    if !occursin(r"^[a-zA-Z_][a-zA-Z0-9_!]*$", name)
-        return "Invalid tool name: '$name'. Tool names must be valid Julia identifiers (letters, digits, underscores, starting with a letter or underscore)."
-    end
-    return nothing
-end
-
-"""
-Indent a multi-line code string by the specified number of spaces.
-"""
-function indent_code(code::String, n::Int)::String
-    prefix = " "^n
-    lines = split(code, '\n')
-    result_lines = String[prefix * line for line in lines]
-    return join(result_lines, "\n")
-end
-
-"""
-Convert a Julia Dict to a valid Julia Dict{String,Any}(...) literal string.
-"""
-function dict_to_julia_literal(d)::String
-    if d isa Dict
-        items = String[]
-        for (k, v) in d
-            key_str = json_string(k)
-            val_str = value_to_julia(v)
-            push!(items, "$key_str => $val_str")
-        end
-        return "Dict{String,Any}(" * join(items, ", ") * ")"
-    else
-        return value_to_julia(d)
-    end
-end
-
-function value_to_julia(v)::String
-    if v isa Dict
-        return dict_to_julia_literal(v)
-    elseif v isa Vector
-        items = [value_to_julia(x) for x in v]
-        return "[" * join(items, ", ") * "]"
-    elseif v isa String
-        escaped = replace(v, "\\" => "\\\\")
-        escaped = replace(escaped, "\"" => "\\\"")
-        return "\"$escaped\""
-    elseif v isa Number
-        return string(v)
-    elseif v isa Bool
-        return string(v)
-    elseif v === nothing
-        return "nothing"
-    else
-        return "\"$(v)\""
-    end
-end
-
-"""
-Convert any Julia value to a JSON string.
-"""
-function json_string(v)::String
-    return JSON.json(v)
-end
-
-"""
-Define and return the writeTool agentTool.
-"""
-function getTool()::agentTool
-    return agentTool(
-        name = "writeTool",
-        label = "Create Tool",
-        description = "Write a new Julia tool module file to src/tools/<name>.jl. The LLM provides the tool logic as executeCode; writeTool wraps it in Julia boilerplate and writes the file. Restart the agent to load the new tool.",
-        inputSchema = Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict(
-                "name" => Dict("type" => "string", "description" => "Unique tool name (valid Julia identifier, no spaces or special chars)"),
-                "label" => Dict("type" => "string", "description" => "Human-readable tool name shown in tool descriptions"),
-                "description" => Dict("type" => "string", "description" => "What the tool does (shown to LLM for tool selection decisions)"),
-                "inputSchema" => Dict(
-                    "type" => "object",
-                    "description" => "JSON Schema describing tool parameters in MCP format"
-                ),
-                "executeCode" => Dict("type" => "string", "description" => "Julia code for the execute function body. Use args[\"key\"] to access parameters. Do NOT wrap in a function definition."),
-                "validateCode" => Dict("type" => "string", "optional" => true, "description" => "Optional custom validation Julia code (runs before execute). Use args[\"key\"] to access parameters. Return nothing to pass, or a string error message to fail."),
-                "prepareCode" => Dict("type" => "string", "optional" => true, "description" => "Optional argument preparation code (runs before validation). Return modified args dict."),
-                "parallel" => Dict("type" => "boolean", "default" => false, "description" => "Whether this tool can run in parallel with other tools")
-            ),
-            "required" => ["name", "label", "description", "inputSchema", "executeCode"]
-        ),
-        execute = (toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function) -> begin
-            tool_name = get(args, "name", "")::String
-            tool_label = get(args, "label", tool_name)::String
-            tool_description = get(args, "description", "")::String
-            tool_schema = get(args, "inputSchema", Dict{String,Any}())::Dict{String,Any}
-            execute_code = get(args, "executeCode", "")::String
-            validate_code = get(args, "validateCode", nothing)::Union{String,Nothing}
-            prepare_code = get(args, "prepareCode", nothing)::Union{String,Nothing}
-            parallel = get(args, "parallel", false)::Bool
-
-            # Validate tool name
-            name_err = validateToolName(tool_name)
-            if name_err !== nothing
-                return agentToolResult(
-                    [textContent(name_err)],
-                    Dict{Any,Any}(), nothing, false
-                )
-            end
-
-            # Validate required fields
-            if isempty(tool_name)
-                return agentToolResult(
-                    [textContent("Missing required field: 'name'")],
-                    Dict{Any,Any}(), nothing, false
-                )
-            end
-            if isempty(tool_description)
-                return agentToolResult(
-                    [textContent("Missing required field: 'description'")],
-                    Dict{Any,Any}(), nothing, false
-                )
-            end
-            if isempty(execute_code)
-                return agentToolResult(
-                    [textContent("Missing required field: 'executeCode'")],
-                    Dict{Any,Any}(), nothing, false
-                )
-            end
-
-            onPartialResult(Dict("status" => "Generating tool: $tool_name"))
-
-            # Build the tool file path
-            script_dir = dirname(@__FILE__)
-            tools_dir = dirname(script_dir)
-            filepath = joinpath(tools_dir, "$(tool_name).jl")
-
-            # Check for naming conflicts
-            if isfile(filepath)
-                return agentToolResult(
-                    [textContent("Tool file already exists: $filepath. Rename the tool or delete the existing file first.")],
-                    Dict{Any,Any}(), nothing, false
-                )
-            end
-
-            onPartialResult(Dict("status" => "Writing file: $(basename(filepath))"))
-
-            # Convert schema Dict to a Julia Dict literal string
-            schema_literal = dict_to_julia_literal(tool_schema)
-
-            # Build optional validation function
-            validate_section = if validate_code !== nothing && !isempty(validate_code)
-                indented = indent_code(validate_code, 4)
-                "function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}\n$indented\n    return nothing\nend\n"
-            else
-                ""
-            end
-
-            # Build optional prepare function
-            prepare_section = if prepare_code !== nothing && !isempty(prepare_code)
-                indented = indent_code(prepare_code, 4)
-                "function prepareArguments(args::Dict{String,Any})::Dict{String,Any}\n$indented\n    return args\nend\n"
-            else
-                ""
-            end
-
-            # Indent user's execute code for embedding inside execute function body
-            indented_exec = indent_code(execute_code, 4)
-
-            # Escape description for Julia string literal
-            escaped_desc = replace(tool_description, "\\" => "\\\\")
-            escaped_desc = replace(escaped_desc, "\"" => "\\\"")
-
-            # Build the complete tool file content
-            parts = String[]
-            push!(parts, "# Auto-generated tool: $tool_name\n")
-            push!(parts, "# Generated by writeTool at $(now())\n\n")
-            if !isempty(validate_section)
-                push!(parts, validate_section)
-                push!(parts, "\n")
-            end
-            if !isempty(prepare_section)
-                push!(parts, prepare_section)
-                push!(parts, "\n")
-            end
-            push!(parts, "\n")
-            push!(parts, "# Execute function\n")
-            push!(parts, "function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult\n")
-            push!(parts, "$indented_exec\n")
-            push!(parts, "end\n\n")
-            push!(parts, "# Tool definition\n")
-            push!(parts, "function getTool()::agentTool\n")
-            push!(parts, "    return agentTool(\n")
-            push!(parts, "        name = \"$(tool_name)\",\n")
-            push!(parts, "        label = \"$(tool_label)\",\n")
-            push!(parts, "        description = \"$(escaped_desc)\",\n")
-            push!(parts, "        inputSchema = $schema_literal,\n")
-            push!(parts, "        execute = executeTool,\n")
-            if validate_code !== nothing && !isempty(validate_code)
-                push!(parts, "        validateRequiredArgs = validateRequiredArgs,\n")
-            else
-                push!(parts, "        validateRequiredArgs = nothing,\n")
-            end
-            if prepare_code !== nothing && !isempty(prepare_code)
-                push!(parts, "        prepareArguments = prepareArguments,\n")
-            else
-                push!(parts, "        prepareArguments = nothing,\n")
-            end
-            push!(parts, "        parallelToolExecute = $parallel\n")
-            push!(parts, "    )\n")
-            push!(parts, "end\n")
-
-            tool_code = join(parts)
-
-            # Write the file — tool is loaded on next agent restart via loadTools()
-            write(filepath, tool_code)
-
-            onPartialResult(Dict("status" => "Done"))
-
-            return agentToolResult(
-                [textContent("Tool '$(tool_name)' written to $filepath. Restart the agent so loadTools() picks it up, then call listTools to verify.")],
-                Dict{Any,Any}(
-                    "file" => filepath,
-                    "name" => tool_name,
-                    "label" => tool_label,
-                    "description" => tool_description,
-                ),
-                nothing, false
-            )
-        end,
-        prepareArguments = nothing,
-        validateRequiredArgs = nothing,
-        parallelToolExecute = false
-    )
-end

src/type.jl

@@ -4,17 +4,17 @@
            messageContent, agentMessage, agent,
            # Model types
            modelCost, llmModel, llmUsage,
-           # Message content types
+            # Message content types
-           textContent, imageContent,
+            textContent, imageContent, reasoningContent,
-           # Message types
+            # Message types
-           userMessage, assistantMessage, toolResultMessage,
+            userMessage, assistantMessageToolCall, assistantMessage, toolResultMessage,
             # Tool types
              agentTool, validateRequiredArgs,
            # Context types
            agentContext, agentState, agentToolCall, prepareNextTurnContext,
            # Loop & execution types
-           agentLoopConfig, abortSignal, agentToolResult,
+           agentLoopConfig, abortSignal, agentToolResult,beforeToolCallContext, 
-           assistantMsgCtx, afterCtx,
+           beforeToolCallResult, afterToolCallContext,
            # Event types
            toolExecStartEvent, toolExecUpdateEvent, toolExecEndEvent,
            # Agent
@@ -23,7 +23,7 @@
            preparedToolCall, immediateOutcome, executedOutcome, finalizedOutcome,
            agentToolCallBatch,
            # Functions (defined elsewhere)
-           run_agent, take_response, follow_up, stop_agent
+           promptAgent, takeResponse, followUp, stopAgent
 
 
 using Dates, UUIDs, DataStructures, JSON, NATS, Base.Threads
@@ -31,6 +31,13 @@ using GeneralUtils
 
 const Timestamp = DateTime
 
+struct agentToolCall               # A tool invocation from the LLM
+  type::String                              # Always "function"
+  id::String                                # Unique tool call identifier
+  name::String                              # Tool name
+  arguments::Dict{String, Any}              # Parsed tool arguments
+end
+
 # ------------------------------------------------------------------------------------------------ #
 #                                          LLM model info                                          #
 # ------------------------------------------------------------------------------------------------ #
@@ -75,6 +82,10 @@ struct imageContent <: messageContent         # Image message content
     mimeType::String                          # MIME type (e.g., "image/png")
 end
 
+struct reasoningContent <: messageContent    # LLM reasoning/thinking content
+    text::String                              # The reasoning text
+end
+
 
 # ------------------------------------------------------------------------------------------------ #
 #                                           Message types                                          #
@@ -108,6 +119,52 @@ function userMessage(; role="user", content=Vector{messageContent}(), timestamp=
     return userMessage(role, content, timestamp)
 end
 
+struct assistantMessageToolCall <: agentMessage   # Assistant message containing tool calls
+    role::String                            # Always "assistant"
+    toolCalls::Vector{agentToolCall}        # Tool calls to execute
+    content::Vector{messageContent}         # Reasoning/thinking content blocks
+    api::String                             # API name used (e.g., "openai")
+    provider::String                        # Provider name (e.g., "anthropic")
+    model::String                           # Model identifier
+    usage::llmUsage                         # Token usage for this message
+    stopReason::String                      # Why generation stopped (e.g., "tool_calls")
+    errorMessage::Union{String, Nothing}    # Error if generation failed
+    timestamp::Timestamp                    # When the message was received
+end
+
+"""
+Create a new assistant message containing tool calls.
+
+# Arguments
+- `role::String`: Always "assistant"
+- `toolCalls::Vector{agentToolCall}`: Tool calls to execute
+- `content::Vector{messageContent}`: Reasoning/thinking content blocks
+- `api::String`: API name used
+- `provider::String`: Provider name
+- `model::String`: Model identifier
+- `usage::llmUsage`: Token usage
+- `stopReason::String`: Why generation stopped
+- `errorMessage::Union{String, Nothing}`: Error if generation failed
+- `timestamp::Timestamp`: When the message was received
+
+# Returns
+- A new `assistantMessageToolCall` instance
+
+# Examples
+```julia
+julia> tc = agentToolCall("function", "call_1", "getWeather", Dict("city" => "Tokyo"))
+julia> msg = assistantMessageToolCall(toolCalls=[tc], stopReason="tool_calls")
+assistantMessageToolCall("assistant", [agentToolCall(...)], messageContent[], "", "", "", llmUsage(0, 0), "tool_calls", nothing, DateTime(...))
+```
+"""
+function assistantMessageToolCall(; role="assistant", toolCalls=agentToolCall[],
+    content=Vector{messageContent}(), api="", provider="", model=nothing, usage=llmUsage(0, 0),
+    stopReason="tool_calls", errorMessage=nothing, timestamp=now())
+    model_str = model isa AbstractString ? String(model) : ""
+    return assistantMessageToolCall(role, toolCalls, content, api, provider, model_str,
+                                     usage, stopReason, errorMessage, timestamp)
+end
+
 struct assistantMessage <: agentMessage     # Message from the AI assistant
     role::String                            # Always "assistant"
     content::Vector{messageContent}         # Text and/or image content
@@ -144,9 +201,10 @@ assistantMessage("assistant", [textContent("Hello!")], "", "", "gpt-4", ..., "en
 ```
 """
 function assistantMessage(; role="assistant", content=Vector{messageContent}(),
-    api="", provider="", model="", usage=llmUsage(0, 0), stopReason="end_turn",
+    api="", provider="", model=nothing, usage=llmUsage(0, 0), stopReason="end_turn",
     errorMessage=nothing, timestamp=now())
-    return assistantMessage(role, content, api, provider, model, usage, stopReason, errorMessage, timestamp)
+    model_str = model isa AbstractString ? String(model) : ""
+    return assistantMessage(role, content, api, provider, model_str, usage, stopReason, errorMessage, timestamp)
 end
 
 struct toolResultMessage <: agentMessage    # Result returned from a tool execution
@@ -263,7 +321,7 @@ struct agentTool                              # A tool available to the agent
     label::String                             # Human-readable tool name
     description::String                       # What the tool does
     inputSchema::Any                          # Tool parameters schema (JSON schema, MCP inputSchema format)
-    execute::Function                         # Tool execution function
+    execute                         # Tool execution function
     prepareArguments::Union{Function, Nothing}  # Optional argument preparation callback
     validateRequiredArgs::Union{Function, Nothing}  # Optional validation hook for required args
     parallelToolExecute::Bool  # Override: run tool calls sequentially or in parallel
@@ -273,7 +331,7 @@ end
 Keyword constructor for agentTool — allows `agentTool(name=..., label=..., ...)`.
 """
 function agentTool(; name::String, label::String, description::String, inputSchema::Any,
-                     execute::Function, prepareArguments::Union{Function, Nothing}=nothing,
+                     execute, prepareArguments::Union{Function, Nothing}=nothing,
                      validateRequiredArgs::Union{Function, Nothing}=nothing,
                      parallelToolExecute::Bool=false)
     return agentTool(name, label, description, inputSchema, execute,
@@ -291,7 +349,7 @@ Snapshot of the agent's conversation context.
 # Arguments
 - `systemPrompt::String`: System prompt for the agent
 - `messages::Vector{agentMessage}`: Conversation messages
-- `tools::Union{Vector{agentTool}, Nothing}`: Available tools
+- `tools::Union{OrderedDict{String, agentTool}, Nothing}`: Available tools keyed by name for O(1) lookup
 
 # Returns
 - A new `agentContext` instance
@@ -299,7 +357,7 @@ Snapshot of the agent's conversation context.
 struct agentContext                           # Snapshot of the agent's conversation context
     systemPrompt::String                      # System prompt for the agent
     messages::Vector{agentMessage}            # Conversation messages
-    tools::Union{Vector{agentTool}, Nothing}  # Available tools
+    tools::Union{OrderedDict{String, agentTool}, Nothing}  # Available tools keyed by name
 end
 
 
@@ -308,15 +366,14 @@ end
 # ------------------------------------------------------------------------------------------------ #
 
 mutable struct agentState                     # Mutable runtime state of an agent
-    systemPrompt::String                      # System prompt text
+    systemPrompt::String                      # System prompt for the agent
-    model::llmModel                           # LLM model to use
+    model::Union{llmModel, Nothing}                           # LLM model to use
-    tools::Vector{agentTool}                  # Available tools
+    tools::OrderedDict{String, agentTool}     # Available tools keyed by name, insertion-ordered
 
     # messages history includes userMessage, assistantMessage, toolResultMessage. NO system prompt
     messages::Vector{agentMessage}            
     
     pendingToolCalls::Vector{String}          # Tool call IDs waiting for results
-    activeRun::Bool                             # is agent processing user message?
     errorMessage::Union{String, Nothing}      # Last error message
 end
 
@@ -329,7 +386,7 @@ new state from external references.
 # Arguments
 - `systemPrompt::String`: System prompt text
 - `model::llmModel`: LLM model to use (defaults to an unknown model)
-- `tools::Vector{agentTool}`: Available tools (deep copied)
+- `tools::OrderedDict{String, agentTool}`: Available tools keyed by name (deep copied)
 - `messages::Vector{agentMessage}`: Conversation messages (deep copied)
 
 # Returns
@@ -338,32 +395,23 @@ new state from external references.
 # Examples
 ```julia
 julia> state = agentState(systemPrompt="You are a helpful assistant")
-agentState("You are a helpful assistant", ..., agentTool[], agentMessage[], String[], nothing)
+agentState("You are a helpful assistant", OrderedDict{String, agentTool}(), agentMessage[], String[], nothing)
-```
 """
 function agentState(
-    systemPrompt::String="",
+  systemPrompt::String="",
-    model::llmModel=llmModel{String}("", "", "unknown", "unknown", "", false, String[], modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
+  model=llmModel("model_1", "unknown", "unknown", "", false, String[], 
-    tools::Vector{agentTool}=agentTool[],
+                  modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
-    messages::Vector{agentMessage}=agentMessage[],
+  tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
+  messages::Vector{agentMessage}=agentMessage[],
 )
-    agentState(
+  agentState(
-      systemPrompt,
+    systemPrompt,
-      model,
+    model,
-      deepcopy(tools),
+    tools,
-      deepcopy(messages),
+    deepcopy(messages),
-      Vector{String}(),
+    Vector{String}(),
-      false,
+    nothing,
-      nothing,
+  )
-    )
-end
-
-
-struct agentToolCall               # A tool invocation from the LLM
-    type::String                              # Always "function"
-    id::String                                # Unique tool call identifier
-    name::String                              # Tool name
-    arguments::Dict{String, Any}              # Parsed tool arguments
 end
 
 
@@ -394,14 +442,12 @@ end
 """
 Configuration for the agent tool execution loop.
 
-# Arguments
+# Fields
-- `tools::Vector{agentTool}`: Available tools
 - `beforeToolCall::Union{Function, Nothing}`: Callback before tool execution
 - `afterToolCall::Union{Function, Nothing}`: Callback after tool execution
 - `toolExecution::String`: Execution mode — "sequential" or "parallel"
 """
 struct agentLoopConfig
-    tools::Vector{agentTool}
     beforeToolCall::Union{Function, Nothing}
     afterToolCall::Union{Function, Nothing}
     toolExecution::String
@@ -437,33 +483,38 @@ end
 Context passed to the `beforeToolCall` hook.
 
 # Arguments
-- `message::assistantMessage`: The assistant message containing the tool call
+- `message::assistantMessageToolCall`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call being prepared
-- `args::Dict{String,Any}`: Validated tool arguments
+- `args::AbstractDict{String, Any}`: Validated tool arguments
 - `context::agentContext`: Current conversation context
 """
-struct assistantMsgCtx
+struct beforeToolCallContext
-    message::assistantMessage
+    message::assistantMessageToolCall
     toolCall::agentToolCall
-    args::Dict{String,Any}
+    args::AbstractDict{String, Any}
     context::agentContext
 end
 
+struct beforeToolCallResult
+  block::Bool
+  reason::String
+end
+
 """
 Context passed to the `afterToolCall` hook.
 
 # Arguments
-- `message::assistantMessage`: The assistant message containing the tool call
+- `message::assistantMessageToolCall`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call that was executed
-- `args::Dict{String,Any}`: Tool arguments
+- `args::AbstractDict{String, Any}`: Tool arguments
 - `result::agentToolResult`: The raw tool result
 - `isError::Bool`: Whether execution resulted in an error
 - `context::agentContext`: Current conversation context
 """
-struct afterCtx
+struct afterToolCallContext
-    message::assistantMessage
+    message::assistantMessageToolCall
     toolCall::agentToolCall
-    args::Dict{String,Any}
+    args::AbstractDict{String, Any}
     result::agentToolResult
     isError::Bool
     context::agentContext
@@ -475,12 +526,12 @@ Event emitted when a tool call execution starts.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
-- `arguments::Dict{String,Any}`: Tool arguments
+- `arguments::AbstractDict{String, Any}`: Tool arguments
 """
 struct toolExecStartEvent
     toolCallId::String
     toolName::String
-    arguments::Dict{String,Any}
+    arguments::AbstractDict{String, Any}
 end
 
 """
@@ -489,13 +540,13 @@ Event emitted with partial results during tool execution.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
-- `arguments::Dict{String,Any}`: Tool arguments
+- `arguments::AbstractDict{String, Any}`: Tool arguments
 - `partialResult::Any`: The partial result data
 """
 struct toolExecUpdateEvent
     toolCallId::String
     toolName::String
-    arguments::Dict{String,Any}
+    arguments::AbstractDict{String, Any}
     partialResult::Any
 end
 
@@ -521,138 +572,6 @@ end
 
 abstract type agent end 
 
-"""
-docstring
-"""
-mutable struct yiemAgent <: agent             # High-level agent wrapper
-  _state::agentState                          # Current state (prompt, model, messages, tools, etc.)
-  
-  # user sends prompt message to agent. if agent is idle, it process user message right away. 
-  # if agent is running, it process user message after  the current tool call finished.
-  inputChannel::Channel                        
-                                              
-  # Buffers messages the user sends while the agent is busy. Processed after all inputChannel 
-  # messages are handled and the agent is idle (not using a tool call).
-  followUpChannel::Channel                    
-
-  # agent sends response message to user after processing all user messages in inputChannel 
-  # and all followUp messages.
-  outputChannel::Channel                     
-                                              
-  _agent_loop::Union{Task, Nothing}                 # agent loop running in the background
-
-  # Preprocess/transform messages and context (modify, filter, prune, inject context from memory,  
-  # reorder, ...) for a single LLM call in _process_message()'s loop.
-  # returns new Vector{agentMessage}
-  prepareContext ::Union{Function, Nothing}   
-
-  # Convert prepareContext()'s new Vector{agentMessage} to LLM message format
-  formatMsgForLLM::Function    
-
-  # Actually invoke the LLM to get a completion response. The LLM response comes back as an 
-  # assistantMessage whose content is an array of content blocks. 
-  # Each block has a type — "text", "thinking", or "toolCall". 
-  # The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
-  llmCall::Function                           
-                                              
-  # Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
-  beforeToolCall::Union{Function, Nothing}    
-
-  executeToolCalls::Function                  # execute tool calls ()
-  
-  # Callback invoked after executing a tool call to sanitize tools output so the output is ready 
-  # to be converted into toolResults message
-  afterToolCall::Union{Function, Nothing}     
-  # prepareNextTurn::Union{Function, Nothing}   # Callback to prepare the next conversation turn
-  # prepareNextTurnWithContext::Union{Function, Nothing}  # Same but receives context
-  sessionId::Union{String, Nothing}           # Optional session identifier
-  maxRetryDelayMs::Union{Int64, Nothing}      # Maximum delay between retries (ms)
-  parallelToolExecute::Bool                   # Default: false
-  agentEventSink::Function                    # agent emits its status via this function
-end
-
-"""
-Create a new yiemAgent instance with a background loop task.
-
-Spawns a background `@spawn` task that runs the agent loop, listening
-on `inputChannel` and `followUpChannel` channels concurrently.
-
-# Keyword Arguments
-- `systemPrompt::String`: System prompt for the agent
-- `model`: LLM model to use
-- `tools::Vector{agentTool}`: Available tools (default: empty)
-- `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
-- `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
-- `llmCall::Function`: Function to invoke the LLM (required)
-- `prepareContext::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
-- `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
-- `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
-- `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
-- `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
-- `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
-- `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
-- `parallelToolExecute::Bool`: Run tool calls in parallel (default: `false`)
-- `agentEventSink::Function`: Callback to receive agent events
-
-# Returns
-- A new `yiemAgent` instance with an active background task
-
-# Examples
-```julia
-julia> agent = yiemAgent(systemPrompt="You are a helpful assistant", model=my_model)
-yiemAgent(agentState(...), Channel(...), Channel(...), Channel(...), ..., ...)
-```
-"""
-function yiemAgent(
-  ; systemPrompt::String="You are helpful assistant.",
-    model=nothing,
-    tools::Vector{agentTool}=agentTool[],
-    messages::Vector{agentMessage}=agentMessage[],
-    prepareContext::Union{Function, Nothing}=nothing,
-    formatMsgForLLM::Function=defaultformatMsgForLLM,
-    llmCall::Function,
-    beforeToolCall::Union{Function, Nothing}=nothing,
-    afterToolCall::Union{Function, Nothing}=nothing,
-    # prepareNextTurn::Union{Function, Nothing}=nothing,
-    # prepareNextTurnWithContext::Union{Function, Nothing}=nothing,
-    sessionId::Union{String, Nothing}=nothing,
-    maxRetryDelayMs::Union{Int64, Nothing}=nothing,
-    parallelToolExecute::Bool=false,
-    agentEventSink::Function,
-  )
-  # Create channels: input (user -> agent), followUp (async queue), output (agent -> user)
-  inputChannel    = Channel(16)
-  followUp    = Channel(32)
-  outputChannel   = Channel(16)
-
-  # Create struct with a placeholder task, then spawn and replace it
-  agent = yiemAgent(
-    agentState(systemPrompt, model, tools, messages),
-    inputChannel,
-    followUp,
-    outputChannel,
-    nothing,  # placeholder — replaced below
-    prepareContext,
-    formatMsgForLLM,
-    llmCall,
-    beforeToolCall,
-    afterToolCall,
-    # prepareNextTurn,
-    # prepareNextTurnWithContext,
-    sessionId,
-    maxRetryDelayMs,
-    parallelToolExecute,
-    agentEventSink,
-  )
-
-  # Spawn the background loop and attach it
-  agent._agent_loop = @spawn _agent_loop(agent)
-
-  return agent
-end
-
-
-
 """
     preparedToolCall(tool, toolCall, args)
 

src/utils.jl

@@ -1,9 +1,11 @@
 module utils
 
-export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, validateToolArguments, _userMessageToOpenAI, 
+export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, 
-        _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks
+      validateToolArguments, _userMessageToOpenAI, 
+      _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks, _toolsToOpenAI,
+      beforeToolCall, afterToolCall, eventSink
 
-using UUIDs, Dates, DataStructures, HTTP, JSON
+using UUIDs, Dates, DataStructures, HTTP, JSON, NATS
 using GeneralUtils
 using ..type
 
@@ -73,7 +75,6 @@ function availableWineToText(vecd::Vector)::String
 end
 
 
-
 """
     prepareContext(state::agentState) -> agentContext
 
@@ -108,14 +109,14 @@ prepareContext(state).messages == deepcopy(state.messages)
 # end
 ```
 """
-function prepareContext(state::agentState)::agentContext
+function prepareContext(state::agentState, eventSink)::agentContext
-  
+
   #TODO filter tools from state.tools based on user intend in user message and tool description
   filteredTools = state.tools
 
   #TODO add filtered tools to the current system prompt / modify systemPrompt here
   preparedSystemPrompt = state.systemPrompt
-  
+
   #TODO add system prompt, adjust/modify and inject additional context into messages
   preparedMessages = deepcopy(state.messages) # messages that will be send to LLM
 
@@ -155,7 +156,7 @@ formatMsgForLLm(ctx) == Dict("messages" => [
 ])
 ```
 """
-function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
+function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
 
   """ openai message format example
   msg = Dict(
@@ -183,19 +184,31 @@ function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
           Dict("type" => "text", "text" => "let me check."),
         ]
       ),
+    ],
+    "tools"=> [
       Dict(
-        "role" => "toolResult",
+        "type" => "function",
-        "content" => [
+        "function" => Dict(
-          Dict("type" => "text", "text" => "name: Chateau Montelena ..."),
+          "name" => "getWeather",
-        ]
+          "description" => "Get current weather",
-      ),
+          "parameters" => Dict(
+            "type" => "object",
+            "properties" => Dict(
+              "city" => Dict("type" => "string")
+            ),
+            "required" => ["city"]
+          )
+        )
+      )
     ],
     "temperature" => 0.7
   )
   """
 
+  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(
@@ -203,19 +216,113 @@ function formatMsgForLLM(ctx::agentContext)::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
       push!(messages, _userMessageToOpenAI(msg))
+    elseif msg isa assistantMessageToolCall
+      push!(messages, _assistantMessageToolCallToOpenAI(msg))
     elseif msg isa assistantMessage
       push!(messages, _assistantMessageToOpenAI(msg))
     elseif msg isa toolResultMessage
       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
 
-  return Dict("messages" => messages)
+  if !isempty(tools_array)
+    openaiReadyMsg["tools"] = tools_array
+  end
+
+  return openaiReadyMsg
+end
+
+"""
+    beforeToolCall(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult
+
+Callback invoked before executing a tool call. Use this hook to inspect
+the tool call and decide whether to allow, block, or modify it.
+
+Common use cases:
+  - Request user approval via UI before running destructive tools.
+  - Validate business rules that cannot be expressed in the JSON schema.
+  - Check final context (e.g. session state, rate limits, permissions).
+
+# Arguments
+- `context::beforeToolCallContext`: Contains the assistant message, tool call,
+  validated arguments, and current conversation context.
+- `signal::abortSignal`: Signal that may be set to abort the operation.
+
+# Returns
+- `beforeToolCallResult(false, "N/A")` to allow the call to proceed.
+- `beforeToolCallResult(true, "Reason")` to block the call with a reason.
+- `nothing` is treated as allow (equivalent to `beforeToolCallResult(false, "N/A")`).
+
+# Example
+```julia
+function beforeToolCall(context::beforeToolCallContext, signal::abortSignal)
+    if context.toolCall.name == "deleteFile"
+        # Block file deletion unless explicitly approved
+        return beforeToolCallResult(true, "User must approve file deletion")
+    end
+    return beforeToolCallResult(false, "N/A")
+end
+```
+"""
+function beforeToolCall(context::beforeToolCallContext, signal::abortSignal
+  )::beforeToolCallResult
+  
+  # final context check
+
+  # seek user approval via UI
+
+  # other check
+  
+  return beforeToolCallResult(false, "N/A")
+end
+
+"""
+    afterToolCall(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}
+
+Callback invoked after a tool call finishes executing (before and after errors).
+Use this hook to post-process the tool result before it is fed back to the LLM.
+
+Common use cases:
+  - Mask sensitive data (API keys, tokens) from result content.
+  - Normalize usage tracking data into a consistent format.
+  - Inspect the result and set `terminate: true` based on business logic
+    (e.g. "if deployment failed, stop the agent rather than retrying").
+  - Wrap error results in friendlier messages for the LLM to understand.
+
+# Arguments
+- `context::afterToolCallContext`: Contains the assistant message, tool call,
+  arguments, raw result, error status, and current conversation context.
+- `signal::abortSignal`: Signal that may be set to abort the operation.
+
+# Returns
+- `nothing` to pass the result through unchanged.
+- `agentToolResult(...)` to return a modified result (content, details, usage,
+  terminate flag can all be overridden).
+
+"""
+function afterToolCall(context::afterToolCallContext, signal::abortSignal
+  )::Union{agentToolResult, Nothing}
+
+  # modify context.result if needed and return agentToolResult
+  
+  return nothing
+end
+
+
+#TODO
+function eventSink(x)
+  
 end
 
 
@@ -230,6 +337,44 @@ function _userMessageToOpenAI(msg::userMessage)::Dict{String, Any}
 end
 
 
+"""
+Convert an assistantMessageToolCall to OpenAI message format.
+
+Produces a message with role="assistant", content=null, and a tool_calls array:
+{
+  "role": "assistant",
+  "content": null,
+  "tool_calls": [
+    {
+      "id": "call_1",
+      "type": "function",
+      "function": {
+        "name": "get_weather",
+        "arguments": "{\"location\": \"San Francisco, CA\"}"
+      }
+    }
+  ]
+}
+"""
+function _assistantMessageToolCallToOpenAI(msg::assistantMessageToolCall)::Dict{String, Any}
+  tool_calls = Dict{String, Any}[]
+  for tc in msg.toolCalls
+    push!(tool_calls, Dict(
+      "id" => tc.id,
+      "type" => tc.type,
+      "function" => Dict(
+        "name" => tc.name,
+        "arguments" => JSON.json(tc.arguments)
+      )
+    ))
+  end
+  return Dict(
+    "role" => "assistant",
+    "content" => nothing,
+    "tool_calls" => tool_calls
+  )
+end
+
 """
 Convert an assistantMessage to OpenAI message format.
 """
@@ -280,7 +425,41 @@ end
 
 
 """
-    validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any}) -> Union{Nothing,String}
+    _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}) -> Vector{Dict{String, Any}}
+
+Convert an OrderedDict of agentTool definitions into OpenAI function tool format.
+
+Returns an empty vector when `tools` is `nothing` or empty.
+
+# Examples
+```julia
+_toolsToOpenAI(nothing)  # => Dict{String, Any}[]
+_toolsToOpenAI(tools)    # => [Dict("type" => "function", "function" => Dict("name" => "getWeather", ...))]
+```
+"""
+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(
+        "type" => "function",
+        "function" => Dict(
+          "name" => tool.name,
+          "description" => tool.description,
+          "parameters" => tool.inputSchema
+        )
+      ))
+    end
+  end
+  eventSink("_toolsToOpenAI 2")
+  return tools_array
+end
+
+
+"""
+    validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any}) -> Union{Nothing,String}
 
 Validates that all required fields listed in the tool's JSON Schema are present
 in `args`. Returns `nothing` if validation passes, or a descriptive error string
@@ -291,8 +470,8 @@ with a custom validation function that performs additional checks (e.g. type
 coercion, format validation, cross-field constraints).
 
 # Arguments
-- `args::Dict{String,Any}`: The arguments provided by the LLM
+- `args::AbstractDict{String, Any}`: The arguments provided by the LLM
-- `inputSchema::Dict{String,Any}`: The tool's `inputSchema` (JSON Schema format)
+- `inputSchema::AbstractDict{String, Any}`: The tool's `inputSchema` (JSON Schema format)
 
 # Returns
 - `nothing` if all required args are present
@@ -308,7 +487,7 @@ args2 = Dict("city" => "Tokyo")
 validateRequiredArgs(args2, schema)  # => nothing
 ```
 """
-function validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any})::Union{Nothing,String}
+function validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any})::Union{Nothing,String}
     required = get(inputSchema, "required", Any[])
     if isempty(required)
         return nothing
@@ -362,7 +541,7 @@ validateToolArguments(toolWithHook, tc)  # => validated args or throws
 validateToolArguments(toolDefault, tc)  # => args or throws
 ```
 """
-function validateToolArguments(tool::agentTool, prepared::agentToolCall)::Dict{String,Any}
+function validateToolArguments(tool::agentTool, prepared::agentToolCall)::AbstractDict{String, Any}
     # Use default (2-arg: args + schema) or tool-specific hook (1-arg: args only)
     if isnothing(tool.validateRequiredArgs)
         result = validateRequiredArgs(prepared.arguments, tool.inputSchema)

src_OLD/OLD_type.jl

@@ -1,375 +0,0 @@
-module type
-  
-export agent, sommelier, companion, virtualcustomer, agentcontext
-
-using Dates, UUIDs, DataStructures, JSON, NATS
-using GeneralUtils
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-mutable struct agentcontext
-  text2textInstructLLM::Function
-  getTextEmbedding::Function
-  executeSQL::Function
-  similarSQLVectorDB::Function
-  insertSQLVectorDB::Function
-  similarSommelierDecision::Function
-  insertSommelierDecision::Function
-  find_related_tables_for_user_question::Function
-  pg_conn_str::String
-  agentconfig::AbstractDict
-end
-
-abstract type agent end
-
-mutable struct sommelier <: agent
-  name::String  # agent name
-  id::String    # agent id
-  retailername::String
-  retailerid::String
-  tools::Dict   
-  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
-  chathistory::Vector{Dict{String, Any}}
-  memory::Dict{String, Any}
-  context::agentcontext 
-  llmFormatName::String
-end
- 
-""" A sommelier agent.
-
-# Arguments
-  - `context::agentcontext`
-    Application context containing shared functions for LLM, SQL, and vector database operations.
-
-# Keyword Arguments
-  - `name::String`
-    Agent's name. Default: `"Assistant"`
-  - `id::String`
-    Agent's ID. Default: generated UUID string.
-  - `retailername::String`
-    Retailer name associated with the sommelier. Default: `"retailer_name"`
-  - `maxHistoryMsg::Integer`
-    Maximum history messages. Default: `20`
-  - `chathistory::Vector{Dict{String, String}}`
-    Chat history. Default: empty vector.
-  - `llmFormatName::String`
-    LLM format name. Default: `"granite3"`
-
-# Return
-  - `sommelier`: An instantiated sommelier agent.
-
-# Example
-```julia
-julia> using YiemAgent
-julia> context = agentcontext(
-           text2textInstructLLM,
-           getTextEmbedding,
-           executeSQL,
-           similarSQLVectorDB,
-           insertSQLVectorDB,
-           similarSommelierDecision,
-           insertSommelierDecision
-       )
-julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyWineShop")
-```
-"""
-function sommelier(
-  context::agentcontext,  # agent functions, db connect and other context
-  ;
-  name::String= "Assistant",
-  id::String= string(uuid4()),
-  retailername::String= "not specified",
-  retailerid::String= "not specified",
-  maxHistoryMsg::Integer= 20,
-  chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
-  llmFormatName::String= "granite3"
-  )
-
-  tools = Dict( # update input format
-      "chatbox"=> Dict(
-        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
-        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
-        "output" => "" ,
-      ),
-      "winestock"=> Dict(
-        "description" => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
-        "input" => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
-        "output" => """<output>Output are wines that match the search query in JSON format.""",
-      ),
-    )
-
-  """ Memory
-
-    Chat history use openai format as follow:
-
-    image1_path = "test/large_image.png"                           ---
-    image1_bytes = read(image1_path)                                 | this part must be done
-    image1_base64_string = base64encode(image1_bytes)                | in frontend 
-    mime_type = "image/png"                                          | not in agent code
-    data1_uri = "data:<mime_type>;base64,<image1_base64_string>"   ---
-
-    chathistory= [
-      Dict(
-        "role" => "system",
-        "content" => [
-          Dict("type" => "text", "text" => "You are a helpful assistant"),
-        ]
-      ),
-      Dict(
-        "role" => "user",
-        "content" => [
-          Dict("type" => "text", "text" => "<internal_context_for_assistant> 
-                                            LLM context here...
-                                            </internal_context_for_assistant>
-                                            Do you know this wine? Just give me brief intro."
-          ),
-          Dict(
-            "type" => "image_url",
-            "image_url" => Dict("url" => data1_uri)
-          ),
-        ]
-      ),
-    ]
-  
-  shortmem = Dict(
-    "1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
-    "2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
-    ...
-    )
-  """
-  memory = Dict{String, Any}(
-            "shortmem"=> OrderedDict{String, Any}(),
-            "scratchpad"=> "",
-            "recap"=> OrderedDict{String, Any}(),
-          )
-
-  newAgent = sommelier(
-    name,
-    id,
-    retailername,
-    retailerid,
-    tools,
-    maxHistoryMsg,
-    chathistory,
-    memory,
-    context,
-    llmFormatName
-    )
-  systemmsg = 
-    """
-    # store_policy
-      - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
-      - If you found wines in the store's database, they are in stock.
-      - You can only recommend wines that are currently in our inventory
-      - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
-      - Ask the user one question at a time.
-      - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
-      - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
-      - Spicy foods should be paired only with light red wines.
-      - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
-      - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
-
-    # store_guidelines
-      - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
-      - Customer may provide images for you to look up.
-      - Encourage the customer to explore different options and try new things.
-      - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
-      - Your store carries only wine.
-      - Vintage 0 means non-vintage.
-      - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
-      - User usually ask for something similar. This means you should use the search term based on the profile they like.
-
-    # situation
-      You are having conversation with a customer.
-
-    # your role
-      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
-
-    # objective
-      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-
-    # your responsibility includes
-      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
-      - Keep the conversation with the customer going smoothly
-
-    # your responsibility does NOT includes
-      - Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
-      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
-      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
-
-    # you should then respond to the user with interleaving plan, action_name, action_input in JSON format
-      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-      2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
-      3) "action_input", The input to the action you are about to perform according to your plan.
-      After the action is executed you gets "action_result". It is the output from the action you selected.
-
-    # available actions
-      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
-      "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
-        Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
-        Example query 2: "Red or white wine, medium tannin, price under 700 USD"
-        Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
-      "WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-      "END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-    """
-    
-  system_msg = Dict(
-      "role" => "system",
-      "content" => [
-        Dict("type" => "text", "text" => systemmsg),
-      ]
-    )
-
-  push!(newAgent.chathistory, system_msg)
-  
-  return newAgent
-end
- 
-
-mutable struct virtualcustomer <: agent
-  name::String  # agent name
-  id::String    # agent id
-  systemmsg::String # system message
-  tools::Dict   
-  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
-  chathistory::Vector{Dict{String, Any}}
-  memory::Dict{String, Any}
-  context  # NamedTuple of functions 
-  llmFormatName::String
-end
-
-function virtualcustomer(
-  context,  # NamedTuple of functions
-  ;
-  name::String= "Assistant",
-  id::String= string(uuid4()),
-  maxHistoryMsg::Integer= 20,
-  chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
-  llmFormatName::String= "granite3",
-  systemmsg::String=
-    """
-      Your name: $name
-      Your sex: Female
-      Your role: You are a helpful assistant.
-      You should follow the following guidelines:
-      - Focus on the latest conversation.
-      - Your like to be short and concise.
-
-      Let's begin!
-      """,
-  )
-
-  tools = Dict( # update input format
-      "chatbox"=> Dict(
-        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
-        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
-        "output" => "" ,
-      ),
-    )
-
-  """ Memory
-    Ref: Chat prompt format is openai
-    chathistory = [
-      Dict(
-        "role" => "system",
-        "content" => [
-          Dict("type" => "text", "text" => system_msg),
-        ]
-      ),
-      Dict(
-        "role" => "user",
-        "content" => [
-          Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
-          Dict(
-            "type" => "image_url",
-            "image_url" => Dict("url" => data1_uri)
-          )
-        ]
-      )
-    ]
-  """
-  memory = Dict{String, Any}(
-            "shortmem"=> OrderedDict{String, Any}(
-            ),
-            "scratchpad"=> "",
-            "events"=> Vector{Dict{String, Any}}(),
-            "state"=> Dict{String, Any}(
-            ),
-            "recap"=> OrderedDict{String, Any}(),
-          )
-
-  newAgent = virtualcustomer(
-    name,
-    id,
-    systemmsg,
-    tools,
-    maxHistoryMsg,
-    chathistory,
-    memory,
-    context,
-    llmFormatName
-    )
-  
-  return newAgent
-end
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-end # module type

test/_extractToolCalls.jl

@@ -0,0 +1,642 @@
+using Test
+using YiemAgent
+using YiemAgent.agentCore
+using YiemAgent.type
+using JSON
+
+# Import the function from the private module scope
+import YiemAgent.agentCore: _extractToolCalls
+
+@testset "_extractToolCalls" begin
+
+    # ------------------------------------------------------------------ #
+    # Format 1: response["message"]["tool_calls"] (LMStudio.jl style)     #
+    # ------------------------------------------------------------------ #
+
+    @testset "single tool call via message format" begin
+        response = Dict{String,Any}(
+            "finish_reason" => "tool_calls",
+            "index" => 0,
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "content" => "",
+                "reasoning_content" => "Let me check the weather.",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getWeather",
+                            "arguments" => "{\"city\":\"Bangkok, Thailand\"}",
+                        ),
+                        "id" => "tc_001",
+                    )
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getWeather"
+        @test tc_list[1].id == "tc_001"
+        @test tc_list[1].type == "function"
+        @test tc_list[1].arguments["city"] == "Bangkok, Thailand"
+        @test assistant_msg isa assistantMessage
+        @test assistant_msg.role == "assistant"
+        @test assistant_msg.stopReason == "tool_calls"
+        @test length(assistant_msg.content) == 1
+        @test assistant_msg.content[1] isa reasoningContent
+        @test assistant_msg.content[1].text == "Let me check the weather."
+    end
+
+    @testset "multiple tool calls via message format" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "content" => "",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getWeather",
+                            "arguments" => "{\"city\":\"Tokyo, Japan\"}",
+                        ),
+                        "id" => "tc_001",
+                    ),
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getTime",
+                            "arguments" => "{\"timezone\":\"Asia/Tokyo\"}",
+                        ),
+                        "id" => "tc_002",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 2
+        @test tc_list[1].name == "getWeather"
+        @test tc_list[1].arguments["city"] == "Tokyo, Japan"
+        @test tc_list[2].name == "getTime"
+        @test tc_list[2].arguments["timezone"] == "Asia/Tokyo"
+        @test assistant_msg.role == "assistant"
+    end
+
+    @testset "tool call with empty arguments string" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "listTools",
+                            "arguments" => "{}",
+                        ),
+                        "id" => "tc_empty",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "listTools"
+        @test tc_list[1].arguments == Dict{String,Any}()
+        @test assistant_msg.stopReason == "end_turn"
+    end
+
+    @testset "tool call with missing id falls back to uuid" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getTime",
+                            "arguments" => "{\"city\":\"NYC\"}",
+                        ),
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test !isempty(tc_list[1].id)
+        @test tc_list[1].name == "getTime"
+    end
+
+    @testset "tool call with non-string arguments (pre-parsed dict)" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getWeather",
+                            "arguments" => Dict{String,Any}("city" => "London", "units" => "fahrenheit"),
+                        ),
+                        "id" => "tc_parsed",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].arguments["city"] == "London"
+        @test tc_list[1].arguments["units"] == "fahrenheit"
+    end
+
+    @testset "tool call with api/provider/model/usage metadata" begin
+        response = Dict{String,Any}(
+            "api" => "openai",
+            "provider" => "anthropic",
+            "model" => "claude-3-opus",
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getTime",
+                            "arguments" => "{}",
+                        ),
+                        "id" => "tc_meta",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test assistant_msg.api == "openai"
+        @test assistant_msg.provider == "anthropic"
+        @test assistant_msg.model == "claude-3-opus"
+    end
+
+    # --------------------------------------------------------------- #
+    # Format 2: response.content blocks (OpenAI API style)            #
+    # --------------------------------------------------------------- #
+
+    @testset "content blocks with tool_calls" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}("type" => "text", "text" => "Let me check."),
+                Dict{String,Any}(
+                    "type" => "tool_calls",
+                    "tool_calls" => Any[
+                        Dict{String,Any}(
+                            "type" => "function",
+                            "function" => Dict{String,Any}(
+                                "name" => "getWeather",
+                                "arguments" => "{\"city\":\"Paris\"}",
+                            ),
+                            "id" => "tc_block_1",
+                        ),
+                    ],
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getWeather"
+        @test tc_list[1].arguments["city"] == "Paris"
+        # text block before tool_calls should be included in content
+        @test length(assistant_msg.content) == 1
+        @test assistant_msg.content[1].text == "Let me check."
+    end
+
+    @testset "content blocks with tool_call (single-call format)" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_call",
+                    "id" => "tc_single",
+                    "name" => "getTime",
+                    "arguments" => Dict{String,Any}("timezone" => "Europe/London"),
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getTime"
+        @test tc_list[1].id == "tc_single"
+        @test tc_list[1].arguments["timezone"] == "Europe/London"
+    end
+
+    @testset "content blocks with reasoning and text" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}("type" => "reasoning", "text" => "Thinking..."),
+                Dict{String,Any}("type" => "text", "text" => "Here's the answer."),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test length(assistant_msg.content) == 2
+        @test assistant_msg.content[1] isa reasoningContent
+        @test assistant_msg.content[1].text == "Thinking..."
+        @test assistant_msg.content[2] isa textContent
+        @test assistant_msg.content[2].text == "Here's the answer."
+    end
+
+    @testset "content blocks with text and tool_call (tool_call not in content)" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}("type" => "text", "text" => "Sure, I'll check."),
+                Dict{String,Any}(
+                    "type" => "tool_call",
+                    "id" => "tc_mix",
+                    "name" => "getWeather",
+                    "arguments" => Dict{String,Any}("city" => "London"),
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getWeather"
+        # text block included, tool_call block excluded from content
+        @test length(assistant_msg.content) == 1
+        @test assistant_msg.content[1].text == "Sure, I'll check."
+    end
+
+    # ------------------------------------------------------------------ #
+    # assistantMessage construction                                       #
+    # ------------------------------------------------------------------ #
+
+    @testset "assistantMessage with error_message and errorMessage fallback" begin
+        response = Dict{String,Any}(
+            "error_message" => "rate limit",
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fail")],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test assistant_msg.errorMessage == "rate limit"
+    end
+
+    @testset "assistantMessage with usage tracking" begin
+        response = Dict{String,Any}(
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
+            "usage" => llmUsage(100, 50),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test assistant_msg.usage.inputTokens == 100
+        @test assistant_msg.usage.outputTokens == 50
+    end
+
+    @testset "assistantMessage with invalid usage defaults to zero" begin
+        response = Dict{String,Any}(
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
+            "usage" => "invalid",
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test assistant_msg.usage.inputTokens == 0
+        @test assistant_msg.usage.outputTokens == 0
+    end
+
+    @testset "reasoning_content as textContent" begin
+        response = Dict{String,Any}(
+            "reasoning_content" => textContent("internal thought"),
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "output")],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test length(assistant_msg.content) == 2
+        @test assistant_msg.content[1] isa reasoningContent
+        @test assistant_msg.content[1].text == "internal thought"
+        @test assistant_msg.content[2] isa textContent
+        @test assistant_msg.content[2].text == "output"
+    end
+
+    # ------------------------------------------------------------------ #
+    # No tool call cases                                                  #
+    # ------------------------------------------------------------------ #
+
+    @testset "no tool calls found" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}("type" => "text", "text" => "Hello world."),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test assistant_msg.stopReason == "end_turn"
+    end
+
+    @testset "empty message" begin
+        response = Dict{String,Any}()
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test assistant_msg.role == "assistant"
+        @test assistant_msg.stopReason == "end_turn"
+        @test length(assistant_msg.content) == 0
+    end
+
+    @testset "message with empty tool_calls array" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test assistant_msg.role == "assistant"
+    end
+
+    @testset "message field is not a Dict" begin
+        response = Dict{String,Any}(
+            "message" => "not a dict",
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fallback")],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test length(assistant_msg.content) == 1
+    end
+
+    @testset "Format 1 takes priority over Format 2" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getWeather",
+                            "arguments" => "{\"city\":\"Format1\"}",
+                        ),
+                        "id" => "tc_fmt1",
+                    ),
+                ],
+            ),
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_call",
+                    "id" => "tc_fmt2",
+                    "name" => "getTime",
+                    "arguments" => Dict{String,Any}("city" => "Format2"),
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getWeather"
+    end
+
+    # ------------------------------------------------------------------ #
+    # Edge cases                                                          #
+    # ------------------------------------------------------------------ #
+
+    @testset "tool call with null arguments" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getTime",
+                            "arguments" => nothing,
+                        ),
+                        "id" => "tc_null",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getTime"
+    end
+
+    @testset "tool call with missing function key" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "id" => "tc_nofunc",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == ""
+    end
+
+    @testset "tool call with missing name in function block" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "assistant",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}("arguments" => "{}"),
+                        "id" => "tc_noname",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == ""
+    end
+
+    @testset "message format with JSON.Object (JSON.parse result)" begin
+        json_str = JSON.json(Dict(
+            "message" => Dict(
+                "role" => "assistant",
+                "tool_calls" => [
+                    Dict(
+                        "type" => "function",
+                        "function" => Dict("name" => "getWeather", "arguments" => "{\"city\":\"Test\"}"),
+                        "id" => "tc_jsonobj",
+                    ),
+                ],
+            ),
+        ))
+        parsed = JSON.parse(json_str)
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(parsed)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getWeather"
+        @test tc_list[1].arguments["city"] == "Test"
+    end
+
+    @testset "tool_calls block with mixed content types (text + tool_calls)" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}("type" => "text", "text" => "I'll check both."),
+                Dict{String,Any}(
+                    "type" => "tool_calls",
+                    "tool_calls" => Any[
+                        Dict{String,Any}(
+                            "type" => "function",
+                            "function" => Dict{String,Any}(
+                                "name" => "getWeather",
+                                "arguments" => "{\"city\":\"London\"}",
+                            ),
+                            "id" => "tc_mix1",
+                        ),
+                        Dict{String,Any}(
+                            "type" => "function",
+                            "function" => Dict{String,Any}(
+                                "name" => "getTime",
+                                "arguments" => "{\"timezone\":\"UTC\"}",
+                            ),
+                            "id" => "tc_mix2",
+                        ),
+                    ],
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 2
+        @test tc_list[1].name == "getWeather"
+        @test tc_list[2].name == "getTime"
+        @test length(assistant_msg.content) == 1
+        @test assistant_msg.content[1].text == "I'll check both."
+    end
+
+    @testset "tool_call block without arguments field" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_call",
+                    "id" => "tc_noargs",
+                    "name" => "getTime",
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].arguments == Dict{String,Any}()
+    end
+
+    @testset "tool_calls block with empty tool_calls array" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_calls",
+                    "tool_calls" => Any[],
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+    end
+
+    @testset "tool_calls block with non-AbstractDict elements" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_calls",
+                    "tool_calls" => Any["not a dict", 42, nothing],
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+    end
+
+    @testset "content field is not a Vector" begin
+        response = Dict{String,Any}(
+            "content" => "not a vector",
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(tc_list) == 0
+        @test length(assistant_msg.content) == 0
+    end
+
+    @testset "Dict-based response with all metadata fields" begin
+        response = Dict{String,Any}(
+            "api" => "openai",
+            "provider" => "anthropic",
+            "model" => "claude-3-sonnet",
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "tool_call",
+                    "id" => "tc_meta",
+                    "name" => "getTime",
+                    "arguments" => Dict{String,Any}("city" => "Seoul"),
+                ),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == true
+        @test length(tc_list) == 1
+        @test tc_list[1].name == "getTime"
+        @test tc_list[1].arguments["city"] == "Seoul"
+        @test assistant_msg.api == "openai"
+        @test assistant_msg.provider == "anthropic"
+        @test assistant_msg.model == "claude-3-sonnet"
+    end
+
+    @testset "default role is assistant" begin
+        response = Dict{String,Any}(
+            "content" => Any[Dict{String,Any}("type" => "text", "text" => "no role specified")],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test assistant_msg.role == "assistant"
+    end
+
+    @testset "tool call with custom role in message format" begin
+        response = Dict{String,Any}(
+            "message" => Dict{String,Any}(
+                "role" => "custom_role",
+                "tool_calls" => Any[
+                    Dict{String,Any}(
+                        "type" => "function",
+                        "function" => Dict{String,Any}(
+                            "name" => "getWeather",
+                            "arguments" => "{}",
+                        ),
+                        "id" => "tc_role",
+                    ),
+                ],
+            ),
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test assistant_msg.role == "custom_role"
+    end
+
+    @testset "image content block handling" begin
+        response = Dict{String,Any}(
+            "content" => Any[
+                Dict{String,Any}(
+                    "type" => "image_url",
+                    "image_url" => Dict("url" => "data:image/png;base64,abc123"),
+                ),
+                Dict{String,Any}("type" => "text", "text" => "What is this?"),
+            ],
+        )
+        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
+        @test has_toolcalls == false
+        @test length(assistant_msg.content) == 2
+        @test assistant_msg.content[1] isa textContent
+        @test assistant_msg.content[1].text == ""
+        @test assistant_msg.content[2].text == "What is this?"
+    end
+
+end

test/loadToolTest.jl

@@ -1,136 +0,0 @@
-using Test
-using YiemAgent
-using YiemAgent.toolRegistry
-using YiemAgent.type
-
-# Path to the real tools directory
-TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
-
-@testset "loadTools" begin
-
-    # ------------------------------------------------------------------ #
-    # 1. loadTools throws on non-existent directory                      #
-    # ------------------------------------------------------------------ #
-    @test_throws ArgumentError loadTools("/nonexistent/dir/that/does/not/exist")
-
-    # ------------------------------------------------------------------ #
-    # 2. loadTools throws if a .jl file does not define getTool()        #
-    #    Must run BEFORE any other loadTools call (getTool binding       #
-    #    persists in module scope after include()).                      #
-    # ------------------------------------------------------------------ #
-    bad_dir = mktempdir()
-    write(joinpath(bad_dir, "noTool.jl"), "x = 42\n")
-    @test_throws ArgumentError loadTools(bad_dir)
-
-    # ------------------------------------------------------------------ #
-    # 3. loadTools loads actual tool files from src/tools/               #
-    # ------------------------------------------------------------------ #
-    loaded = loadTools(TOOLS_DIR)
-    @test !isempty(loaded)
-    @test length(loaded) == 3
-
-    names = [t.name for t in loaded]
-    @test "getTime" in names
-    @test "getWeather" in names
-    @test "writeTool" in names
-
-    # ------------------------------------------------------------------ #
-    # 4. loadTools returns tools sorted alphabetically by filename       #
-    #    (getTime.jl < getWeather.jl < writeTool.jl)                      #
-    #    because 'T' < 'W' in ASCII                                       #
-    # ------------------------------------------------------------------ #
-    @test loaded[1].name == "getTime"
-    @test loaded[2].name == "getWeather"
-    @test loaded[3].name == "writeTool"
-
-    # ------------------------------------------------------------------ #
-    # 5. Verify loaded tool fields are correct                           #
-    # ------------------------------------------------------------------ #
-    # getTime
-    time_tool = loaded[1]
-    @test time_tool.name == "getTime"
-    @test time_tool.label == "Time Lookup"
-    @test time_tool.validateRequiredArgs !== nothing
-    @test time_tool.parallelToolExecute == false
-    @test time_tool.inputSchema["required"] == Any[]
-
-    # getWeather
-    weather = loaded[2]
-    @test weather.name == "getWeather"
-    @test weather.label == "Weather Lookup"
-    @test weather.execute !== nothing
-    @test weather.parallelToolExecute == false
-    @test weather.inputSchema["required"] == ["city"]
-
-    # writeTool
-    wt = loaded[3]
-    @test wt.name == "writeTool"
-    @test wt.label == "Create Tool"
-    @test wt.execute !== nothing
-    @test "name" in wt.inputSchema["required"]
-    @test "executeCode" in wt.inputSchema["required"]
-
-    # ------------------------------------------------------------------ #
-    # 6. Tool execution returns valid results                            #
-    # ------------------------------------------------------------------ #
-    sig = nothing
-    op = x -> x  # no-op partial result callback
-
-    # execute getTime
-    result_t = time_tool.execute("call-1", Dict{String,Any}("city" => "Tokyo"), sig, op)
-    @test result_t isa agentToolResult
-    @test result_t.content[1] isa textContent
-    @test occursin("Tokyo", result_t.content[1].text)
-
-    # execute getTime with timezone
-    result_tz = time_tool.execute("call-2", Dict{String,Any}("timezone" => "America/New_York"), sig, op)
-    @test result_tz isa agentToolResult
-    @test occursin("America/New_York", result_tz.content[1].text)
-
-    # execute getWeather
-    result_w = weather.execute("call-3", Dict{String,Any}("city" => "Bangkok"), sig, op)
-    @test result_w isa agentToolResult
-    @test result_w.content[1] isa textContent
-    @test occursin("Bangkok", result_w.content[1].text)
-
-    # execute getWeather with units
-    result_w2 = weather.execute("call-4", Dict{String,Any}("city" => "London", "units" => "fahrenheit"), sig, op)
-    @test occursin("72°F", result_w2.content[1].text)
-
-    # ------------------------------------------------------------------ #
-    # 7. getTools / registerTool / clearTools                            #
-    # ------------------------------------------------------------------ #
-    registry_tools = getTools()
-    @test !isempty(registry_tools)
-    @test any(t -> t.name == "getTime", registry_tools)
-    @test any(t -> t.name == "getWeather", registry_tools)
-
-    clearTools()
-    @test isempty(getTools())
-
-    test_tool = agentTool(
-        name = "manualTool",
-        label = "Manual Tool",
-        description = "Registered manually",
-        inputSchema = Dict{String,Any}("type" => "object", "properties" => Dict{String,Any}(), "required" => Any[]),
-        execute = (toolCallId, args, signal, onPartialResult) ->
-            agentToolResult([textContent("manual")], Dict{Any,Any}(), nothing, false),
-        prepareArguments = nothing,
-        validateRequiredArgs = nothing,
-        parallelToolExecute = true
-    )
-    registerTool(test_tool)
-    reg = getTools()
-    @test any(t -> t.name == "manualTool", reg)
-    @test count(t -> t.name == "manualTool", reg) == 1
-    @test reg[1].parallelToolExecute == true
-
-    # ------------------------------------------------------------------ #
-    # 8. getTools returns deep copy (mutations don't affect registry)    #
-    # ------------------------------------------------------------------ #
-    copy1 = getTools()
-    copy2 = getTools()
-    @test copy1 !== copy2
-    empty!(copy1)
-    @test !isempty(getTools())
-end

test/runtest.jl

@@ -1,401 +1,5 @@
-using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
+using Test
-      NATS, Base.Threads
+using YiemAgent
-using YiemAgent, GeneralUtils, msghandler
-
-  function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
-    payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
-    _, msg_envelope_json_str = msghandler.smartpack(
-          config["externalservice"]["servicesloadbalancer"]["nats"], 
-          payloads;
-          sender_id=sender_id,
-          msg_purpose="text2text",  
-          broker_url=config["nats_server_info"]["url"],
-          fileserver_url=config["externalservice"]["fileserver"]["url"])
-
-    reply = NATS.request(agent_conn, 
-                        config["externalservice"]["servicesloadbalancer"]["nats"], 
-                        msg_envelope_json_str, timeout=120)
-
-    incoming_env_json_str = String(reply.payload)
-    incoming_env = msghandler.smartunpack(incoming_env_json_str)
-    _llm_response = incoming_env["payloads"][1][2]
-    llm_response = _llm_response["choices"][1]["message"]["content"]
-    return llm_response
-  end
-
-  """ get a single text embedding from a LLM service
-  Example
-  text = ["hello"]
-  embedding = get_embedding(text)
-  """
-  function get_embedding(text::AbstractArray{String})
-    documents_dict = Dict("documents" => text)
-    payloads = [("documents", documents_dict, "dictionary")]
-    _, msg_envelope_json_str = msghandler.smartpack(
-      config["externalservice"]["servicesloadbalancer"]["nats"], 
-      payloads;   
-      msg_purpose="embedding",  
-      broker_url=config["nats_server_info"]["url"],
-      fileserver_url=config["externalservice"]["fileserver"]["url"])
-
-    reply = NATS.request(agent_conn, 
-                        config["externalservice"]["servicesloadbalancer"]["nats"], 
-                        msg_envelope_json_str, timeout=120)
-    incoming_env_json_str = String(reply.payload)
-    incoming_env = msghandler.smartunpack(incoming_env_json_str)
-    embedding_response = incoming_env["payloads"][1][2]
-
-    return embedding_response
-  end
-
-  """ sql = "SELECT * FROM wine;"
-  result = execute_sql_winedb(sql)
-  """
-  function execute_sql_winedb(sql::T) where {T<:AbstractString}
-    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-    port = parse(Int, _port)
-    dbname = "winedb"
-    user = config["externalservice"]["sommpanion_db"]["user"]
-    password = config["externalservice"]["sommpanion_db"]["password"]
-    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-    result = nothing
-    try
-      result = LibPQ.execute(db_connection, sql)  
-    catch e
-      LibPQ.close(db_connection)
-    end
-    
-    LibPQ.close(db_connection)
-    return result
-  end
-
-  """ find similar sql from vector database
-  sql = "SELECT * FROM wine;"
-  result, distance = similar_sql_vectordb(sql)
-  """
-  function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
-    tablename = "sqlllm_decision_repository"
-    # get embedding of the query
-    df = find_similar_text_from_vectordb(sql, tablename, 
-          "function_input_embedding", execute_sql_vectordb)
-    # println(df[1, [:id, :function_output]])
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]  
-    if row != 0 && distance < maxdistance
-      # if there is usable SQL, return it.
-      output_b64 = df[1, :function_output_base64]   # pick the closest match
-      output_str = String(base64decode(output_b64))
-      rowid = df[1, :id]
-      println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      pprintln(output_str)
-      return (result=output_str, distance=distance)
-    else
-      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      return (result=nothing, distance=nothing)
-    end
-  end
-
-  """ insert query and sql into vector database
-  query = "get all wines from wine table"
-  sql = "SELECT * FROM wine;"
-  insert_sql_vectordb(query, sql)
-  """
-  function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
-    ) where {T1<:AbstractString, T2<:AbstractString}
-    
-    tablename = "sqlllm_decision_repository"
-    # get embedding of the query
-    # query = state[:thoughtHistory][:question]
-    df = find_similar_text_from_vectordb(query, tablename, 
-                                    "function_input_embedding", execute_sql_vectordb)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-      _query_embedding = get_embedding([query])
-      _query_embedding = GeneralUtils.dictify(_query_embedding)
-      # println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      # println(_query_embedding)
-      # println("---\n")
-      query_embedding = _query_embedding["data"][1]["embedding"]
-      query = replace(query, "'" => "")
-      sql_base64 = base64encode(SQL)
-      sql_ = replace(SQL, "'" => "")
-
-      sql = 
-        """
-        INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-        """
-      # println("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      # println(sql)
-      _ = execute_sql_vectordb(sql)
-    end
-  end
-
-  """ execute sql against vectordb
-  sql = "SELECT * FROM wine;"
-  result = execute_sql_vectordb(sql)
-  """
-  function execute_sql_vectordb(sql::T) where {T<:AbstractString}
-    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
-    port = parse(Int, _port)
-    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
-    user = config["externalservice"]["sommpanion_vectordb"]["user"]
-    password = config["externalservice"]["sommpanion_vectordb"]["password"]
-    DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-    result = LibPQ.execute(DBconnection, sql)
-    close(DBconnection)
-    return result
-  end
-
-  """ search similar decision llm made from vectordb
-  """
-  function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
-    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-
-    tablename = "sommelier_decision_repository"
-    # find similar
-    df = find_similar_text_from_vectordb(recentevents, tablename, 
-                                    "function_input_embedding", execute_sql_vectordb)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row != 0 && distance < maxdistance
-      # if there is usable decision, return it.
-      rowid = df[1, :id]
-      println("\n--| found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-      output_b64 = df[1, :function_output_base64]   # pick the closest match
-      _output_str = String(base64decode(output_b64))
-      output = copy(JSON.read(_output_str))
-      return output
-    else
-      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-      return nothing
-    end
-  end
-
-  """ search similar text from vectordb
-  """
-  function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
-    vectorDB::Function; limit::Integer=1
-    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-    # get embedding from LLM service
-    _embedding = get_embedding([text])
-    _embedding = _embedding["data"][1]["embedding"]
-    _embedding = "$_embedding"
-
-    embedding = _embedding[4:end]
-
-    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    sql = """
-          SELECT *, $embeddingColumnName <-> '$embedding' as distance
-          FROM $tablename
-          ORDER BY distance LIMIT $limit;
-          """
-    response = vectorDB(sql)
-    df = DataFrame(response)
-
-    return df
-  end
-
-  """ insert decision llm made to vectordb
-  """
-  function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
-    ) where {T1<:AbstractString, T2<:AbstractDict}
-    tablename = "sommelier_decision_repository"
-    # find similar
-    df = find_similar_text_from_vectordb(recentevents, tablename, 
-                                    "function_input_embedding", execute_sql_vectordb)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-      _embedding = get_embedding([recentevents])[1]
-      recentevents_embedding = _embedding["data"][1]["embedding"]
-      recentevents = replace(recentevents, "'" => "")
-      decision_json = JSON.json(decision)
-      decision_base64 = base64encode(decision_json)
-      decision = replace(decision_json, "'" => "")
-      
-      sql = 
-      """
-      INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
-      """
-      println("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-      println(sql)
-      _ = execute_sql_vectordb(sql)
-    else
-      println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-    end
-  end
-
-config = JSON.parsefile("./appconfig.json")
-sessionId = "0"
-backend_session_topic = "sommpanion.testsubject"
-agent_ch = Channel(8)
-agent_conn  = NATS.connect(config["nats_server_info"]["url"])
-
-sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
-  put!(agent_ch, msg)
-end
-
-agent_context = YiemAgent.agentcontext(
-    text2text_instruct_llm,
-    get_embedding,
-    execute_sql_winedb,
-    similar_sql_vectordb,
-    insert_sql_vectordb,
-    similar_sommelier_decision,
-    insert_sommelier_decision
-  )
-
-  # can't instantiate
-  agent = YiemAgent.sommelier(
-    agent_context;
-    name="Janie",
-    id=sessionId,  # agent instance id
-    retailername="Yiem Wine Ltd.",
-    llmFormatName=""
-  )
-
-
-image1_path = "test/large_image.png"
-image1_bytes = read(image1_path)
-image1_base64_string = base64encode(image1_bytes)
-mime_type = "image/png" 
-data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
-
-# 1. Read local file and encode to base64 string
-image2_path = "test/small_image.png"
-image2_bytes = read(image2_path)
-image2_base64_string = base64encode(image2_bytes)
-mime_type = "image/png" 
-data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
-
-# 3. Construct payload with the Data URI
-message = Dict(
-    "role" => "user",
-    "content" => [
-      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
-      Dict(
-        "type" => "image_url",
-        "image_url" => Dict("url" => data1_uri)
-      )
-    ]
-  )
-
-result = YiemAgent.conversation(agent; userinput=message)
-println("\n$result")
-
-
-
-
-
-
-
-
-
-# message = Dict(
-#     "role" => "user",
-#     "content" => [
-#       Dict("type" => "text", "text" => 
-#       "
-#       เป็นงานเลี้ยงทั่วไป
-#       "),
-#     ]
-#   )
-
-# result = YiemAgent.conversation(agent; userinput=message)
-# println("\n$result")
-
-
-
-
-
-
-
-
-
-# message = Dict(
-#     "role" => "user",
-#     "content" => [
-#       Dict("type" => "text", "text" => "no thanks. that's all"),
-#     ]
-#   )
-
-# result = YiemAgent.conversation(agent; userinput=message)
-# println("\n$result")
-
-
-
-
-
-
-# message = Dict(
-#     "role" => "user",
-#     "content" => [
-#       Dict("type" => "text", "text" => "What about this wine?"),
-#       Dict(
-#         "type" => "image_url",
-#         "image_url" => Dict("url" => data2_uri)
-#       )
-#     ]
-#   )
-
-# result = YiemAgent.conversation(agent; userinput=message)
-# println("\n$result")
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 
+include("toolTest.jl")
+include("_extractToolCalls.jl")

user_code.jl

@@ -0,0 +1,113 @@
+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)
+
+
+
+