ChatClaudeAgent is a plain Runnable rather than a BaseChatModel, so
LangChain's callback system never fired on_chat_model_start / on_llm_end
for it — leaving the CLI TUI stuck on "LLM: 0" and "Tokens: --" during
runs. Pop callbacks out of the LLM kwargs, invoke them manually around
each SDK call, and attach usage_metadata extracted from the SDK's
ResultMessage (input, output, total — including cached input) to the
returned AIMessage so downstream handlers pick it up.
Tool callbacks now also fire through the MCP wrapper: forward the
callback list into each wrapped LangChain tool's invocation config so
StatsCallbackHandler sees on_tool_start/on_tool_end when the SDK loop
calls a tool.
Verified via direct StatsCallbackHandler round-trip on both Shape A
(ChatClaudeAgent.invoke) and Shape B (run_sdk_analyst): llm_calls,
tool_calls, tokens_in, and tokens_out all increment as expected.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The 4 analysts (market, news, social, fundamentals) now detect a
ChatClaudeAgent LLM and dispatch to an SDK-native runner: LangChain @tool
functions are wrapped as in-process MCP tools via create_sdk_mcp_server, and
the SDK owns the iterative tool-calling loop. Claude returns the final report
in one call, so the analyst node outputs an AIMessage with no tool_calls and
the existing conditional edges route straight to the message-clear step.
Together with the Shape A provider this lets a Claude Max subscription drive
the full TradingAgents graph without an Anthropic API key. Other providers
continue to take the original bind_tools + LangGraph ToolNode path unchanged.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Michael Yang