refactor(a2a): use tool calling for delegation instead of structured output#5751
refactor(a2a): use tool calling for delegation instead of structured output#5751greysonlalonde wants to merge 3 commits intomainfrom
Conversation
…output Each remote A2A agent is now exposed to the local LLM as a BaseTool (delegate_to_<card_name>); the local agent's tool-call loop drives multi-turn delegation. The Literal-constrained AgentResponse model and the explicit per-turn re-prompting loop are gone. Closes #3897. The original failure mode — Pydantic literal_error when skill.id != endpoint URL, and Gemini flash-lite hallucinating out-of-enum values — is structurally impossible: provider-side tool-call validation enforces the tool name, and there's no competing identifier.
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| from a2a.types import Message, Role | ||
| from a2a.types import TaskState # noqa: F401 |
1 participant
Why
Closes #3897.
A2A delegation currently relies on a
Literal[endpoint_url, ...]-constrainedAgentResponsemodel to pick a remote agent. The prompt shows the LLM each agent's card (skill IDs, names, URLs), but the only valid value fora2a_idsis the well-known endpoint URL — which is never explicitly labeled as the identifier. Predictable failures:skills[0].id(e.g."Research") instead of the endpoint URL →pydantic ValidationError: literal_error.Literal/enum constraints in JSON Schema emit out-of-set values → same error.A fuzzy-match fallback would paper over the symptom; the structural fix is to make the identifier set itself unambiguous and provider-enforced.
What
Each remote A2A agent is now exposed to the local LLM as a
BaseTool(delegate_to_<sanitized_card_name>); the local agent's tool-call loop drives multi-turn delegation.AgentResponse(a2a_ids, message, is_a2a)and the explicit per-turn re-prompting loop are gone.crewai/a2a/tools.py:A2ADelegationTool+A2ADelegationState(per-task shared state with per-endpoint history, IDs, turn counts).crewai/a2a/wrapper.pycollapsed from 1772 → ~530 LOC. Deleted_delegate_to_a2a/_adelegate_to_a2a,_prepare_delegation_context,_parse_agent_response,_handle_agent_response_and_continue,_handle_max_turns_exceeded,_emit_delegation_failed,_process_response_result,_init_delegation_state,_get_turn_context,_handle_task_completion,DelegationContext,DelegationState. Each of the four entry points (sync/async × execute_task/kickoff) now augments the prompt with agent cards, builds A2A tools, merges them into the call'stoolslist (or temporarily extendsself.toolsfor kickoff), and callsoriginal_fn.PREVIOUS_A2A_CONVERSATION_TEMPLATE,CONVERSATION_TURN_INFO_TEMPLATE,REMOTE_AGENT_*_NOTICE.AVAILABLE_AGENTS_TEMPLATEnow describes the tool-call protocol.response_model.py:create_agent_response_model/get_a2a_agents_and_response_modelreplaced with a singleextract_a2a_client_configs().types.py:AgentResponseProtocolremoved.agent/core.py+lite_agent.pyupdated to drop theAgentResponseProtocolbranch and theagent_response_modelarg.The original failure is now structurally impossible: provider-side tool-call validation (OpenAI / Anthropic / Gemini) enforces the tool name; there's no competing identifier set for the model to confuse.
A2AConfig.max_turnswires through toBaseTool.max_usage_count, so the existing per-agent turn limit is preserved without an explicit Python-side loop.Notes
tools.py. Net ≈ −600 LOC.pip-audit); will retarget tomainonce that lands.mypyclean across 473 files.ruffclean.