Principle:HKUDS AI Trader LangChain Agent Initialization
| Knowledge Sources | |
|---|---|
| Domains | LLM_Agents, Infrastructure |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
An initialization pattern that sets up the LangChain agent runtime by connecting to MCP tool servers, loading available tools, and creating the LLM model instance.
Description
LangChain Agent Initialization establishes the runtime environment for the LLM trading agent. This involves three key steps:
- MCP Client Connection: Creates a MultiServerMCPClient that connects to all MCP tool servers (math, search, trade, price, crypto)
- Tool Discovery: Fetches the list of available tools from connected MCP servers via the client
- Model Creation: Instantiates the LLM model (ChatOpenAI or a DeepSeek variant) with the configured API endpoint and key
The initialization must complete before any trading session can begin. The MCP tools and model instance are reused across all trading days in the session.
Usage
Use this principle after MCP services are running and before the first trading day loop begins. The initialization is performed once per agent lifetime and creates the reusable components needed for all subsequent trading sessions.
Theoretical Basis
# Pseudocode for agent initialization
async def initialize_agent():
# 1. Connect to MCP servers
mcp_client = MultiServerMCPClient(mcp_config)
await mcp_client.connect()
# 2. Discover available tools
tools = await mcp_client.get_tools()
# 3. Create LLM model
model = ChatOpenAI(
model=model_name,
api_key=api_key,
base_url=base_url
)
return mcp_client, tools, model
Key properties:
- Async: Initialization is asynchronous due to MCP network connections
- Reusable components: Client, tools, and model persist across trading sessions
- Model flexibility: Supports any OpenAI-compatible API endpoint
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