docs: add LLM-to-LLM conversation eval example#4041
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This PR contains:
What is the current behavior? (You can also link to an open issue here)
Fixes #2803.
The multi-agent docs describe handoffs, tools, and explicit agent workflows, but they do not include a compact example for fixed-turn LLM-to-LLM evaluations where two agents converse and a third model judges the transcript. Users trying to build client-agent/customer-service-agent style evals have to infer the pattern from lower-level agent APIs.
What is the new behavior?
The multi-agent docs now include an LLM-to-LLM conversation example that:
AgentStateandrun()so the two agents share and extend the same conversation history.submit=Falseis useful when the outer workflow controls turn-taking, and points users to message/token/time limits for tool-using agents that may run too long.Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)
No. This is a docs-only addition for existing agent and scorer APIs.
Other information:
Validation:
uv run pytest tests/agent/test_agent_execute.py -vrun()execution path used by the example.react,run,AgentState,solver, andscorerAPIs after rebasing onto currentorigin/main.uv run make checkuv run make testCI/CD coverage expected:
Closes #2803.