swarm handoff not able to handle multiple questions #105
Description
Hi,
I want to create a swarm that transfers among agents to create the complete answer to the user query.
Below is the code and the error i am getting, if someone can please help me resolving the issue :
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import create_react_agent
from langgraph_swarm import create_handoff_tool, create_swarm
from langchain_openai import AzureChatOpenAI
from langchain_core.runnables import ConfigurableField
from dotenv import load_dotenv
import os
load_dotenv()
model = AzureChatOpenAI(
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
temperature=0.00,
verbose=True,
max_tokens=None
).configurable_fields(
temperature=ConfigurableField(
id="llm_temperature",
name="LLM Temperature",
description="The temperature of the LLM",
)
)
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
def subtract(a: int, b: int) -> int:
"""Subtract two numbers"""
return a - b
alice = create_react_agent(
model,
[add,
create_handoff_tool(agent_name="Bob", description="Transfer to Bob, she can help answering in pirate style."),
create_handoff_tool(agent_name="Hansel", description="Transfer to Hansel, she can help with Substraction.")],
prompt="You are Alice, an addition expert. Check the last user query, acknowledge the already answered portion and answer only for addition and handoff to the other for the rest of the query.Do not call more than one tool at a time.",
name="Alice",
)
hansel = create_react_agent(
model,
[subtract,
create_handoff_tool(agent_name="Alice",description="Transfer to Alice, she can help with addition."),
create_handoff_tool(agent_name="Bob", description="Transfer to Bob, she can help answering in pirate style.")],
prompt="You are Hansel, an subtraction expert. Check the last user query, acknowledge the already answered portion and answer only for substraction and handoff to the other for the rest of the query.Do not call more than one tool at a time.",
name="Hansel",
)
bob = create_react_agent(
model,
[create_handoff_tool(agent_name="Alice", description="Transfer to Alice, she can help with addition."),
create_handoff_tool(agent_name="Hansel", description="Transfer to Hansel, she can help with Substraction.")],
prompt="You are Bob, you speak like a pirate. Check the last user query, acknowledge the already answered portion and answer only non-addition and substraction question and handoff to the other agent for the rest of the query.Do not call more than one tool at a time.",
name="Bob",
)
checkpointer = InMemorySaver()
workflow = create_swarm(
[alice, bob, hansel],
default_active_agent="Bob"
)
app = workflow.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
turn_2 = app.invoke(
{"messages": [{"role": "user", "content": "can you calculate 2+6-4?"}]},
config,
)
print(turn_2["messages"][-1].pretty_print())Error :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 1
----> 1 turn_2 = app.invoke(
2 {"messages": [{"role": "user", "content": "can you calculate 2+6-4?"}]},
3 config,
4 )
5 print(turn_2["messages"][-1].pretty_print())
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\main.py:3085, in Pregel.invoke(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)
3082 chunks: list[dict[str, Any] | Any] = []
3083 interrupts: list[Interrupt] = []
-> 3085 for chunk in self.stream(
3086 input,
3087 config,
3088 context=context,
3089 stream_mode=["updates", "values"]
3090 if stream_mode == "values"
3091 else stream_mode,
3092 print_mode=print_mode,
3093 output_keys=output_keys,
3094 interrupt_before=interrupt_before,
3095 interrupt_after=interrupt_after,
3096 durability=durability,
3097 **kwargs,
3098 ):
3099 if stream_mode == "values":
3100 if len(chunk) == 2:
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\main.py:2674, in Pregel.stream(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)
2672 for task in loop.match_cached_writes():
2673 loop.output_writes(task.id, task.writes, cached=True)
-> 2674 for _ in runner.tick(
2675 [t for t in loop.tasks.values() if not t.writes],
2676 timeout=self.step_timeout,
2677 get_waiter=get_waiter,
2678 schedule_task=loop.accept_push,
2679 ):
2680 # emit output
2681 yield from _output(
2682 stream_mode, print_mode, subgraphs, stream.get, queue.Empty
2683 )
2684 loop.after_tick()
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\_runner.py:162, in PregelRunner.tick(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)
160 t = tasks[0]
161 try:
--> 162 run_with_retry(
163 t,
164 retry_policy,
165 configurable={
166 CONFIG_KEY_CALL: partial(
167 _call,
168 weakref.ref(t),
169 retry_policy=retry_policy,
170 futures=weakref.ref(futures),
171 schedule_task=schedule_task,
172 submit=self.submit,
173 ),
174 },
175 )
176 self.commit(t, None)
177 except Exception as exc:
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\_retry.py:42, in run_with_retry(task, retry_policy, configurable)
40 task.writes.clear()
41 # run the task
---> 42 return task.proc.invoke(task.input, config)
43 except ParentCommand as exc:
44 ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\_internal\_runnable.py:657, in RunnableSeq.invoke(self, input, config, **kwargs)
655 # run in context
656 with set_config_context(config, run) as context:
--> 657 input = context.run(step.invoke, input, config, **kwargs)
658 else:
659 input = step.invoke(input, config)
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\main.py:3085, in Pregel.invoke(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)
3082 chunks: list[dict[str, Any] | Any] = []
3083 interrupts: list[Interrupt] = []
-> 3085 for chunk in self.stream(
3086 input,
3087 config,
3088 context=context,
3089 stream_mode=["updates", "values"]
3090 if stream_mode == "values"
3091 else stream_mode,
3092 print_mode=print_mode,
3093 output_keys=output_keys,
3094 interrupt_before=interrupt_before,
3095 interrupt_after=interrupt_after,
3096 durability=durability,
3097 **kwargs,
3098 ):
3099 if stream_mode == "values":
3100 if len(chunk) == 2:
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\main.py:2674, in Pregel.stream(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)
2672 for task in loop.match_cached_writes():
2673 loop.output_writes(task.id, task.writes, cached=True)
-> 2674 for _ in runner.tick(
2675 [t for t in loop.tasks.values() if not t.writes],
2676 timeout=self.step_timeout,
2677 get_waiter=get_waiter,
2678 schedule_task=loop.accept_push,
2679 ):
2680 # emit output
2681 yield from _output(
2682 stream_mode, print_mode, subgraphs, stream.get, queue.Empty
2683 )
2684 loop.after_tick()
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\_runner.py:162, in PregelRunner.tick(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)
160 t = tasks[0]
161 try:
--> 162 run_with_retry(
163 t,
164 retry_policy,
165 configurable={
166 CONFIG_KEY_CALL: partial(
167 _call,
168 weakref.ref(t),
169 retry_policy=retry_policy,
170 futures=weakref.ref(futures),
171 schedule_task=schedule_task,
172 submit=self.submit,
173 ),
174 },
175 )
176 self.commit(t, None)
177 except Exception as exc:
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\pregel\_retry.py:42, in run_with_retry(task, retry_policy, configurable)
40 task.writes.clear()
41 # run the task
---> 42 return task.proc.invoke(task.input, config)
43 except ParentCommand as exc:
44 ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\_internal\_runnable.py:657, in RunnableSeq.invoke(self, input, config, **kwargs)
655 # run in context
656 with set_config_context(config, run) as context:
--> 657 input = context.run(step.invoke, input, config, **kwargs)
658 else:
659 input = step.invoke(input, config)
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\_internal\_runnable.py:394, in RunnableCallable.invoke(self, input, config, **kwargs)
392 # run in context
393 with set_config_context(child_config, run) as context:
--> 394 ret = context.run(self.func, *args, **kwargs)
395 except BaseException as e:
396 run_manager.on_chain_error(e)
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\prebuilt\chat_agent_executor.py:620, in create_react_agent.<locals>.call_model(state, runtime, config)
613 msg = (
614 "Async model callable provided but agent invoked synchronously. "
615 "Use agent.ainvoke() or agent.astream(), or "
616 "provide a sync model callable."
617 )
618 raise RuntimeError(msg)
--> 620 model_input = _get_model_input_state(state)
622 if is_dynamic_model:
623 # Resolve dynamic model at runtime and apply prompt
624 dynamic_model = _resolve_model(state, runtime)
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\prebuilt\chat_agent_executor.py:599, in create_react_agent.<locals>._get_model_input_state(state)
596 if messages is None:
597 raise ValueError(error_msg)
--> 599 _validate_chat_history(messages)
600 # we're passing messages under `messages` key, as this is expected by the prompt
601 if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):
File c:\01MyFolder\python-31210\py-repo\dev\agent_framework\Lib\site-packages\langgraph\prebuilt\chat_agent_executor.py:245, in _validate_chat_history(messages)
236 return
238 error_message = create_error_message(
239 message="Found AIMessages with tool_calls that do not have a corresponding ToolMessage. "
240 f"Here are the first few of those tool calls: {tool_calls_without_results[:3]}.\n\n"
(...) 243 error_code=ErrorCode.INVALID_CHAT_HISTORY,
244 )
--> 245 raise ValueError(error_message)
ValueError: Found AIMessages with tool_calls that do not have a corresponding ToolMessage. Here are the first few of those tool calls: [{'name': 'transfer_to_hansel', 'args': {}, 'id': 'call_6V2Ijs8KRkgPZjyflWtViuA4', 'type': 'tool_call'}].
Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage (result of a tool invocation to return to the LLM) - this is required by most LLM providers.
For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/INVALID_CHAT_HISTORY
During task with name 'agent' and id '58ef3fc6-0250-910f-03d0-c89147ed0647'
During task with name 'Alice' and id '48bdb416-82a7-1945-9132-a82a11be748a'