Skip to main content
Multi-agent is not one architecture. Choose a pattern based on control ownership, concurrency, and termination, then compose each agent as a subgraph or node.

​
Handoff

create_handoff_tool() produces Command(scope=PARENT), allowing a child agent to return control to its immediate parent graph. State crossing the boundary must be explicit in Command.update; LingxiGraph never copies all child state implicitly.

​
Map-reduce fan-out

A conditional edge can return multiple Send(node, arg) values, creating parallel tasks with private inputs. An Annotated reducer merges their results into shared state. Do not let parallel nodes write the same ordinary LastValue field.

​
Production guardrails

  • Give group chat a finite max_turns or deterministic termination rule.
  • Share max_model_calls, max_tool_calls, max_tokens, and max_cost across parent/child graphs.
  • Configure timeouts, retries, and cancellation propagation for remote agents.
  • Put only the next role’s required data in a handoff.
  • Use stable idempotency keys for every side-effecting expert tool.
  • Preserve agent, namespace, and task path in event metadata for tracing.
Runnable examples are available in examples/multi_agent_supervisor.py, examples/map_reduce_send.py, and examples/subgraph_team_review.py.