update

README.md

@@ -10,7 +10,7 @@ Julia framework for building agents with tool use and MCP (Model Context Protoco
    ```julia
    agent = YiemAgent.yiemAgent(llmCall; mcpServer=mcpServer, eventSink=yourSink)
    ```
-4. Call `runAgent(agent, message)` then `takeResponse(agent)`
+4. Call `promptAgent(agent, message)` then `takeResponse(agent)`
 
 ## Architecture
 
@@ -20,7 +20,7 @@ src/
 ├── type.jl               # Core types (messages, tools, agent state)
 ├── utils.jl              # Message formatting, context preparation, validation
 ├── agentCore.jl          # Agent loop, tool execution pipeline
-├── api.jl                # Public API (runAgent, takeResponse, followUp, stopAgent)
+├── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
 └── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
 ```
 
@@ -114,7 +114,7 @@ Tools are discovered dynamically via MCP server:
 
 | Function | Description |
 |----------|-------------|
-| `runAgent(agent, msg)` | Send a message to the agent's input channel |
+| `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 |
@@ -122,7 +122,7 @@ Tools are discovered dynamically via MCP server:
 ## Agent Lifecycle
 
 1. `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
-2. User messages enter via `runAgent()` or `followUp()`
+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

README_tools.md

@@ -126,7 +126,7 @@ result = getWeather_tool.execute("call-1", Dict("city" => "Tokyo"), sig, op)
 
 **Via agent loop (production):**
 ```
-user message → runAgent(agent, Dict("role"=>"user", "content"=>...))
+user message → promptAgent(agent, Dict("role"=>"user", "content"=>...))
   → _agentLoop detects message → @spawn _processMessage(agent)
     → prepareContext → formatMsgForLLM → llmCall
       → LLM returns tool_calls
@@ -346,7 +346,7 @@ The `_agentLoop()` function runs as a background `@spawn` task, created when `yi
 
 ```
 yiemAgent struct contains:
-  - inputChannel (Channel, capacity 16)   ← user sends messages here via runAgent()
+  - inputChannel (Channel, capacity 16)   ← user sends messages here via promptAgent()
   - followUpChannel (Channel, capacity 32) ← user sends follow-ups here via followUp()
   - outputChannel (Channel, capacity 16)   ← agent sends responses here via takeResponse()
 ```
@@ -1243,7 +1243,7 @@ Each `toolStore` gets its own `listTool` instance bound to that store via `listT
 
 ```
 USER SENDS MESSAGE
-  └─> runAgent(agent, "What's the weather in Tokyo?")
+  └─> promptAgent(agent, "What's the weather in Tokyo?")
         └─> put!(agent.inputChannel, Dict("role" => "user", "content" => [...]))
 
 

src/YiemAgent.jl

@@ -20,6 +20,8 @@ module YiemAgent
 
   include("api.jl")
   using .api
+
+  export promptAgent, takeResponse, followUp, stopAgent
   
   
 

src/api.jl

@@ -1,6 +1,6 @@
 module api
 
-export prompt
+export promptAgent, takeResponse, followUp, stopAgent
 
 using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
       DataFrames
@@ -29,11 +29,11 @@ The agent processes messages from `inputChannel` in the background task.
 
 # Examples
 ```jldoctest
-julia> runAgent(agent, Dict("role" => "user", "content" => "Hello!"))
+julia> promptAgent(agent, Dict("role" => "user", "content" => "Hello!"))
 yiemAgent(...)
 ```
 """
-function runAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
+function promptAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
   put!(agent.inputChannel, msg)
   return agent
 end
@@ -50,7 +50,7 @@ Blocks until the agent sends a response.
 - An `assistantMessage` instance representing the agent's response
 
 # Notes
-- Use `runAgent(agent, msg)` to send a message before calling this function.
+- Use `promptAgent(agent, msg)` to send a message before calling this function.
 
 # Examples
 ```jldoctest
@@ -76,7 +76,7 @@ and before any tool call results are sent.
 - The same `agent` instance for chaining
 
 # Notes
-- Use `runAgent(agent, msg)` for the primary message and `followUp(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).
 

src/type.jl

@@ -23,7 +23,7 @@
            preparedToolCall, immediateOutcome, executedOutcome, finalizedOutcome,
            agentToolCallBatch,
            # Functions (defined elsewhere)
-           runAgent, takeResponse, followUp, stopAgent
+           promptAgent, takeResponse, followUp, stopAgent
 
 
 using Dates, UUIDs, DataStructures, JSON, NATS, Base.Threads