/Ui ddlmZmZmZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlmZGddeZe deZe eZd ed eed efd Zd edeeded efdZedddeeded ede edzd ef dZy))START MessagesState StateGraph)Pregel)AnyLiteralOptionalTypeTypeVarUnionget_args get_origin)get_handoff_destinationsc"eZdZUdZeeed<y) SwarmStatez'State schema for the multi-agent swarm. active_agentN)__name__ __module__ __qualname____doc__r str__annotations__q/home/jay/workspace/tools/ai-image-gen/jaaz-app/server/venv/lib/python3.12/site-packages/langgraph_swarm/swarm.pyrrs1 3-rr StateSchema)bound state_schema agent_namesreturncj|jd}|tu}t|tuxrt |dtu}|s|s|St |j |fdi|ji}tjt|}|rt||jd<|S||jd<|S)zKUpdate the state schema to use Literal with agent names for 'active_agent'.rrr) rrrr r typerr __getitem__tupler )rractive_agent_annotation is_str_typeis_optional_strupdated_schema literal_types r _update_state_schema_agent_namesr*s +::>J*S0K*+u4dBY9Z[\9]ad9d ?  !  <|;;<=N&&u['9:L9A,9O&&~6 :F&&~6 rbuilderroute_todefault_active_agentc|j|j}d|vr td|vrtdd|dtffd }|j t |||S)a_Add a router to the currently active agent to the StateGraph. Args: builder: The graph builder (StateGraph) to add the router to. route_to: A list of agent (node) names to route to. default_active_agent: Name of the agent to route to by default (if no agents are currently active). Returns: StateGraph with the router added. Example: ```python from langgraph.checkpoint.memory import InMemorySaver from langgraph.prebuilt import create_react_agent from langgraph.graph import StateGraph from langgraph_swarm import SwarmState, create_handoff_tool, add_active_agent_router def add(a: int, b: int) -> int: '''Add two numbers''' return a + b alice = create_react_agent( "openai:gpt-4o", [add, create_handoff_tool(agent_name="Bob")], prompt="You are Alice, an addition expert.", name="Alice", ) bob = create_react_agent( "openai:gpt-4o", [create_handoff_tool(agent_name="Alice", description="Transfer to Alice, she can help with math")], prompt="You are Bob, you speak like a pirate.", name="Bob", ) checkpointer = InMemorySaver() workflow = ( StateGraph(SwarmState) .add_node(alice, destinations=("Bob",)) .add_node(bob, destinations=("Alice",)) ) # this is the router that enables us to keep track of the last active agent workflow = add_active_agent_router( builder=workflow, route_to=["Alice", "Bob"], default_active_agent="Alice", ) # compile the workflow app = workflow.compile(checkpointer=checkpointer) config = {"configurable": {"thread_id": "1"}} turn_1 = app.invoke( {"messages": [{"role": "user", "content": "i'd like to speak to Bob"}]}, config, ) turn_2 = app.invoke( {"messages": [{"role": "user", "content": "what's 5 + 7?"}]}, config, ) ``` rz@Missing required key 'active_agent' in in builder's state_schemazDefault active agent 'z' not found in routes statec(|jdS)Nr)get)r/r-s rroute_to_active_agentz6add_active_agent_router..route_to_active_agentsyy)=>>r)path_map)schemasschema ValueErrordictadd_conditional_edgesr)r+r,r-channelsr2s ` radd_active_agent_routerr:8sHw~~.HX%[\\8+$%9$::PQYPZ [  ?T? !!%)>!R NrN)r config_schemaagentsr;c L|jjd}| td|Dcgc]}|j}}t ||}t ||}t ||||D]2}|j|j|tt|4|Scc}w)aCreate a multi-agent swarm. Args: agents: List of agents to add to the swarm An agent can be a LangGraph [CompiledStateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.CompiledStateGraph), a functional API [workflow](https://langchain-ai.github.io/langgraph/reference/func/#langgraph.func.entrypoint), or any other [Pregel](https://langchain-ai.github.io/langgraph/reference/pregel/#langgraph.pregel.Pregel) object. default_active_agent: Name of the agent to route to by default (if no agents are currently active). state_schema: State schema to use for the multi-agent graph. config_schema: An optional schema for configuration. Use this to expose configurable parameters via `swarm.config_specs`. Returns: A multi-agent swarm StateGraph. Example: ```python from langgraph.checkpoint.memory import InMemorySaver from langgraph.prebuilt import create_react_agent from langgraph_swarm import create_handoff_tool, create_swarm def add(a: int, b: int) -> int: '''Add two numbers''' return a + b alice = create_react_agent( "openai:gpt-4o", [add, create_handoff_tool(agent_name="Bob")], prompt="You are Alice, an addition expert.", name="Alice", ) bob = create_react_agent( "openai:gpt-4o", [create_handoff_tool(agent_name="Alice", description="Transfer to Alice, she can help with math")], prompt="You are Bob, you speak like a pirate.", name="Bob", ) checkpointer = InMemorySaver() workflow = create_swarm( [alice, bob], default_active_agent="Alice" ) app = workflow.compile(checkpointer=checkpointer) config = {"configurable": {"thread_id": "1"}} turn_1 = app.invoke( {"messages": [{"role": "user", "content": "i'd like to speak to Bob"}]}, config, ) turn_2 = app.invoke( {"messages": [{"role": "user", "content": "what's 5 + 7?"}]}, config, ) ``` rz3Missing required key 'active_agent' in state_schema)r,r-) destinations) rr1r6namer*rr:add_noder$r)r<r-rr;r%agentrr+s r create_swarmrBs@+::>>~N&NOO+12%5::2K23L+NL}5G1   JJ 7>?    N3sB!)langgraph.graphrrrlanggraph.pregelrtyping_extensionsrrr r r r r rlanggraph_swarm.handoffrrrStateSchemaTypelistrr*r:rBrrrrIs<<#```<  m:6 {# ! 04S   FQ Q3iQ Q  Qp%/&* S LSS" S 9t# S  Sr