agent got "No I need to use a tool? No" response from llmm,which CANNOT be parsed

[JxKim]

System Info

def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
    if f"{self.ai_prefix}:" in text:
        return AgentFinish(
            {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text
        )
    regex = r"Action: (.*?)[\n]*Action Input: (.*)"
    match = re.search(regex, text)
    if not match:
        raise OutputParserException(f"Could not parse LLM output: `{text}`")
    action = match.group(1)
    action_input = match.group(2)
    return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text)

Who can help?

No response

Information

• The official example notebooks/scripts
• My own modified scripts

Related Components

• LLMs/Chat Models
• Embedding Models
• Prompts / Prompt Templates / Prompt Selectors
• Output Parsers
• Document Loaders
• Vector Stores / Retrievers
• Memory
• Agents / Agent Executors
• Tools / Toolkits
• Chains
• Callbacks/Tracing
• Async

Reproduction

1. init an agent
2. ask the agent a simple question which it can solve without using any tools

Expected behavior

DONT RAISE ERROR

[dosubot]

🤖

Hello,

Thank you for bringing this issue to our attention. It seems like the error you're encountering is related to the way LangChain's agent parses the output from the language model. If the language model's response includes an action that is not defined in the agent's tools, the agent will raise an error.

This behavior is demonstrated in the test_agent_invalid_tool function in the LangChain's test suite. Here is the relevant code:

def test_agent_invalid_tool() -> None:
    """Test agent invalid tool and correct suggestions."""
    fake_llm = FakeListLLM(responses=["FooBarBaz\nAction: Foo\nAction Input: Bar"])
    tools = [
        Tool(
            name="Search",
            func=lambda x: x,
            description="Useful for searching",
            return_direct=True,
        ),
    ]
    agent = initialize_agent(
        tools=tools,
        llm=fake_llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        return_intermediate_steps=True,
        max_iterations=1,
    )

    resp = agent("when was langchain made")
    resp["intermediate_steps"][0][1] == "Foo is not a valid tool, try one of [Search]."

In this test, the agent is initialized with a single tool named "Search". When the agent runs with the input "when was langchain made", the language model's response includes an action named "Foo". Since "Foo" is not a valid tool in the agent's toolset, the agent raises an error.

This issue has been encountered before in the LangChain repository. Here are some similar issues that have been resolved:

1. ChatAgent gets "Would you like me to search for more information on it?" instead of Action: or FinalAnswer:
2. Error: raise ValueError(f"Could not parse LLM output: {text}")
3. Vertex ChatVertexAI() doesn't support initialize_agent() as OutputParserException error

Unfortunately, I couldn't find the exact error message that is being raised in your case. Could you please provide more details about the error message you're seeing? This will help us better understand the issue and find a solution.

Best,
Dosu

Sources

• ChatAgent gets "Would you like me to search for more information on it?" instead of Action: or FinalAnswer:
• Error: raise ValueError(f"Could not parse LLM output: {text}")
• Vertex ChatVertexAI() doesn't support initialize_agent() as OutputParserException error
• libs/langchain/tests/unit_tests/agents/test_agent.py

---

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[dosubot]

Hi, @JxKim,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you raised an issue about difficulty parsing a response from a tool, and Dosubot responded with an explanation of the error, requested more details, and referenced similar resolved issues in the LangChain repository.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, kindly let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days. Thank you!