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Langgraph's Question on Function_call in OpenAI #77

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opened 2026-02-20 17:24:51 -05:00 by yindo · 1 comment
yindo commented 2026-02-20 17:24:51 -05:00
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Originally created by @coverdust on GitHub (May 4, 2024).

Checked other resources

  • I added a very descriptive title to this issue.
  • I searched the LangChain documentation with the integrated search.
  • I used the GitHub search to find a similar question and didn't find it.
  • I am sure that this is a bug in LangChain rather than my code.

Example Code

prompt = hub.pull("hwchase17/openai-functions-agent")
# Choose the LLM that will drive the agent
llm = ChatOpenAI(model="gpt-4-turbo-preview", openai_api_key=openai_api_key, openai_api_base=openai_api_base)
tools = search_tools
# 可以执行Python代码
python_repl_tool = PythonREPLTool()


def create_agent(llm, tools, system_prompt):
    # 每个工作的节点agent都有一个名字和一些工具
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                system_prompt
            ),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ]
    )
    agent = create_openai_tools_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)
    return executor


def agent_node(state, agent, name):
    result = agent.invoke(state)
    return {"messages": [HumanMessage(content=result["output"], name=name)]}


# 定义agent supervisor
members = ["Researcher", "Coder"]
system_prompt = (
    "You are a supervisor tasked with managing a conversation between the"
    " following workers:  {members}. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. When finished,"
    " respond with FINISH."
)
options = ["FINISH"] + members
# 使用openAI的function_call
function_def = {
    "name": "route",
    "description": "Select the next role.",
    "parameters": {
        "title": "routeSchema",
        "type": "object",
        "properties": {
            "next": {
                "title": "Next",
                "anyOf": [
                    {"enum": options},
                ],
            }
        },
        "required": ["next"],
    },
}
supervisor_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        MessagesPlaceholder(variable_name="messages"),
        (
            "system",
            "Given the conversation above, who should act next?"
            " Or should we FINISH? Select one of: {options}",
         ),
    ]
).partial(options=str(options),members=",".join(members))
supervisor_chain=supervisor_prompt \
                 | llm.bind_functions(functions=[function_def],function_call="route") \
                 | JsonOutputFunctionsParser()
# 构建图
class AgentState(TypedDict):
    # The annotation tells the graph that new messages will always
    # be added to the current states
    messages: Annotated[Sequence[BaseMessage], operator.add]
    # The 'next' field indicates where to route to next
    next: str
researche_agent=create_agent(llm,search_tools,"You are a web researcher.")
# from functools import partial
# def power(base, exponent):
#     return base ** exponent
# # 创建一个偏函数，固定base为2
# square = partial(power, base=2)
# print(square(3))  # 输出8，相当于调用了power(2, 3)
research_node=functools.partial(agent_node,agent=researche_agent,name="Researcher")
code_agent = create_agent(
    llm,
    [python_repl_tool],
    "You may generate safe python code to analyze data and generate charts using matplotlib.",
)
code_node = functools.partial(agent_node, agent=code_agent, name="Coder")
workflow = StateGraph(AgentState)
workflow.add_node("Researcher", research_node)
workflow.add_node("Coder", code_node)
workflow.add_node("supervisor", supervisor_chain)
for member in members:
    # worker执行完之后返回supervisor节点
    workflow.add_edge(member, "supervisor")
conditional_map = {k: k for k in members}
conditional_map["FINISH"] = END
workflow.add_conditional_edges("supervisor",lambda x: x["next"],conditional_map)
workflow.set_entry_point("supervisor")
graph = workflow.compile()
for s in graph.stream(
    {
        "messages": [
            HumanMessage(content="Code hello world and print it to the terminal")
        ]
    }
):
    if "__end__" not in s:
        print(s)
        print("----")

Error Message and Stack Trace (if applicable)

raise OutputParserException(f"Could not parse function call: {exc}")
langchain_core.exceptions.OutputParserException: Could not parse function call: 'function_call'

Description

This is a problem that I encountered while studying Langgraph's multi-agent blog. This problem not only occurs here, but also when I was studying Langgraph's Planning Agent Examples module

System Info

Latest version

Originally created by @coverdust on GitHub (May 4, 2024). ### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. - [X] I am sure that this is a bug in LangChain rather than my code. ### Example Code ```python prompt = hub.pull("hwchase17/openai-functions-agent") # Choose the LLM that will drive the agent llm = ChatOpenAI(model="gpt-4-turbo-preview", openai_api_key=openai_api_key, openai_api_base=openai_api_base) tools = search_tools # 可以执行Python代码 python_repl_tool = PythonREPLTool() def create_agent(llm, tools, system_prompt): # 每个工作的节点agent都有一个名字和一些工具 prompt = ChatPromptTemplate.from_messages( [ ( "system", system_prompt ), MessagesPlaceholder(variable_name="messages"), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) agent = create_openai_tools_agent(llm, tools, prompt) executor = AgentExecutor(agent=agent, tools=tools) return executor def agent_node(state, agent, name): result = agent.invoke(state) return {"messages": [HumanMessage(content=result["output"], name=name)]} # 定义agent supervisor members = ["Researcher", "Coder"] system_prompt = ( "You are a supervisor tasked with managing a conversation between the" " following workers: {members}. Given the following user request," " respond with the worker to act next. Each worker will perform a" " task and respond with their results and status. When finished," " respond with FINISH." ) options = ["FINISH"] + members # 使用openAI的function_call function_def = { "name": "route", "description": "Select the next role.", "parameters": { "title": "routeSchema", "type": "object", "properties": { "next": { "title": "Next", "anyOf": [ {"enum": options}, ], } }, "required": ["next"], }, } supervisor_prompt = ChatPromptTemplate.from_messages( [ ("system", system_prompt), MessagesPlaceholder(variable_name="messages"), ( "system", "Given the conversation above, who should act next?" " Or should we FINISH? Select one of: {options}", ), ] ).partial(options=str(options),members=",".join(members)) supervisor_chain=supervisor_prompt \ | llm.bind_functions(functions=[function_def],function_call="route") \ | JsonOutputFunctionsParser() # 构建图 class AgentState(TypedDict): # The annotation tells the graph that new messages will always # be added to the current states messages: Annotated[Sequence[BaseMessage], operator.add] # The 'next' field indicates where to route to next next: str researche_agent=create_agent(llm,search_tools,"You are a web researcher.") # from functools import partial # def power(base, exponent): # return base ** exponent # # 创建一个偏函数，固定base为2 # square = partial(power, base=2) # print(square(3)) # 输出8，相当于调用了power(2, 3) research_node=functools.partial(agent_node,agent=researche_agent,name="Researcher") code_agent = create_agent( llm, [python_repl_tool], "You may generate safe python code to analyze data and generate charts using matplotlib.", ) code_node = functools.partial(agent_node, agent=code_agent, name="Coder") workflow = StateGraph(AgentState) workflow.add_node("Researcher", research_node) workflow.add_node("Coder", code_node) workflow.add_node("supervisor", supervisor_chain) for member in members: # worker执行完之后返回supervisor节点 workflow.add_edge(member, "supervisor") conditional_map = {k: k for k in members} conditional_map["FINISH"] = END workflow.add_conditional_edges("supervisor",lambda x: x["next"],conditional_map) workflow.set_entry_point("supervisor") graph = workflow.compile() for s in graph.stream( { "messages": [ HumanMessage(content="Code hello world and print it to the terminal") ] } ): if "__end__" not in s: print(s) print("----") ``` ### Error Message and Stack Trace (if applicable) raise OutputParserException(f"Could not parse function call: {exc}") langchain_core.exceptions.OutputParserException: Could not parse function call: 'function_call' ### Description This is a problem that I encountered while studying Langgraph's multi-agent blog. This problem not only occurs here, but also when I was studying Langgraph's Planning Agent Examples module ### System Info Latest version
yindo closed this issue 2026-02-20 17:24:52 -05:00
yindo commented 2026-02-20 17:24:54 -05:00
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@hwchase17 commented on GitHub (Jun 3, 2024):

seems to be a duplicate of https://github.com/langchain-ai/langgraph/issues/486

@hwchase17 commented on GitHub (Jun 3, 2024): seems to be a duplicate of https://github.com/langchain-ai/langgraph/issues/486
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Reference: langchain-ai/langgraph#77
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