LangGraph `response_format` Troubleshooting Guide

by Viktoria Ivanova 50 views

Hey guys,

It looks like there's an issue with how response_format is being handled in your LangGraph workflow. You're setting it up with QueryResponse, but the llm_call_result doesn't seem to include the expected structured_response key. Let's dive into this and figure out what's going on. This article aims to clarify the correct usage of response_format within LangGraph, address the encountered issue, and provide a comprehensive guide for debugging and resolving similar problems.

Problem Overview

You've correctly configured a LangGraph workflow using a supervisor and several agents (research, JSON, math, file saving, and market news). The goal is to have the final output from the Language Model (LLM) formatted according to the QueryResponse schema. However, the actual result, llm_call_result, only contains the messages key, missing the crucial structured_response.

Specifically, the problem arises in this part of your code:

return str(llm_call_result['structured_response'])

This line attempts to access a key that isn't present, leading to potential errors and unexpected behavior. Based on your LangSmith traces, the OpenAI model is indeed formatting the final message in the QueryResponse format, suggesting the issue lies within LangGraph's processing of the response.

Diving Deep into the Issue

To effectively address this, let's break down the components and potential areas of concern:

1. LangGraph Workflow Setup

Your workflow setup looks solid, creating a supervisor and integrating various specialized agents. You've defined clear roles for each agent, which is excellent for orchestrating complex tasks. The response_format parameter is correctly set when creating the supervisor:

workflow = create_supervisor(
    [agents.research_agent, agents.json_agent,
     agents.math_agent,
     agents.save_to_file_agent,
     agents.save_to_contabo_and_get_presigned_url_agent,
     agents.market_news_agent],
    model=ChatOpenAI(model=llm_settings.LLM_MODEL),
    prompt=(
        "You are a team supervisor managing a research expert json expert save_to_file expert and a math expert. "
        "For current events, use research_agent. "
        "For math problems, use math_agent."
        "For json problems, use json_agent."
        "For file saving problems, use save_to_file_agent."
        "For save file to contabo and return presigned url, use save_to_contabo_and_get_presigned_url_agent."
        "For market news, use market_news_agent."
    ),
    response_format=QueryResponse

2. The Role of response_format

The response_format parameter is designed to instruct the LLM to structure its output in a specific way. By setting it to QueryResponse, you're telling the LLM to format its response according to this schema. It's crucial to ensure that the LLM correctly interprets and adheres to this format. LangGraph should then parse this structured response and make it accessible.

3. Examining llm_call_result

The key issue is that llm_call_result only contains messages. This indicates that LangGraph isn't properly extracting or parsing the structured_response from the LLM's output. This can happen due to several reasons, which we'll explore in the troubleshooting section.

4. LangSmith Traces

The LangSmith traces are invaluable here. They confirm that OpenAI is indeed formatting the final message as QueryResponse. This isolates the problem to LangGraph's handling of the response, rather than the LLM's output generation.

Troubleshooting Steps and Solutions

Okay, let’s get our hands dirty and troubleshoot this. Here are some steps you can take to identify and resolve the issue:

1. Verify QueryResponse Definition:

First, make sure your QueryResponse class is correctly defined. It should inherit from Pydantic's BaseModel and accurately represent the expected structure of the LLM's output. Here’s an example of what it might look like:

from pydantic import BaseModel, Field
from typing import Dict, Any

class QueryResponse(BaseModel):
    structured_response: Dict[str, Any] = Field(..., description="The structured response from the agent")
    # Add other fields as necessary

Ensure that the structured_response field is defined with the correct type and description. A clear, well-defined schema helps LangGraph (and the LLM) understand the expected format.

2. Check LangGraph Version:

Sometimes, issues like this can arise from bugs in specific versions of LangGraph. Make sure you're using the latest version or a stable version that is known to correctly handle response_format. You can upgrade LangGraph using pip:

pip install --upgrade langgraph

3. Inspect Raw LLM Output:

To understand what’s coming directly from the LLM, you might want to temporarily log the raw output before LangGraph processes it. You can do this by accessing the raw response within the LangGraph workflow. This will help confirm whether the LLM is truly formatting the output correctly.

4. Review Agent Logic:

Double-check the logic within your agents, especially the one responsible for generating the final response. Ensure that the agent is correctly producing the output in the QueryResponse format. It’s possible that an agent is inadvertently altering the structure of the response.

5. Debugging with LangSmith:

LangSmith is your best friend here. Use it to trace the execution of your workflow step-by-step. Inspect the inputs and outputs of each node in the graph, paying close attention to the final node that produces the result. This will help you pinpoint exactly where the structured_response is being lost.

6. Simplified Test Case:

To isolate the issue, try creating a simplified test case with a minimal workflow. This can help rule out complexities from other parts of your application. For example, create a simple graph with just one agent that’s designed to return a QueryResponse. If this works, you can gradually add complexity until you identify the point of failure.

7. Examine LangGraph Internals (If Necessary):

If you’re feeling adventurous, you can dive into LangGraph’s source code to understand how it processes response_format. This might give you clues about where things are going wrong. However, this is generally a last resort, as it requires a good understanding of LangGraph’s internals.

8. Consider json_schema for OpenAI Functions:

If you're using OpenAI models, you might want to consider using the json_schema argument in the ChatOpenAI constructor, which leverages OpenAI Functions. This can sometimes provide more reliable structured output:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model=llm_settings.LLM_MODEL).bind(
    function_call={
        "name": "QueryResponse"
    },
    functions=[
        {
            "name": "QueryResponse",
            "description": "The structured response from the agent",
            "parameters": QueryResponse.schema()
        }
    ]
)

workflow = create_supervisor(
    [agents.research_agent, agents.json_agent,
     agents.math_agent,
     agents.save_to_file_agent,
     agents.save_to_contabo_and_get_presigned_url_agent,
     agents.market_news_agent],
    model=llm,
    prompt=(
        "You are a team supervisor managing a research expert json expert save_to_file expert and a math expert. "
        "For current events, use research_agent. "
        "For math problems, use math_agent."
        "For json problems, use json_agent."
        "For file saving problems, use save_to_file_agent."
        "For save file to contabo and return presigned url, use save_to_contabo_and_get_presigned_url_agent."
        "For market news, use market_news_agent."
    ),
    response_format=QueryResponse
)

This approach explicitly tells OpenAI to use the QueryResponse schema, which can improve the consistency of the output format.

Potential Causes and Resolutions

Based on the problem description and troubleshooting steps, here are some of the most likely causes and how to resolve them:

1. Incorrect Parsing of LLM Output:

LangGraph might not be correctly parsing the JSON response from the LLM. This can happen if the response format isn’t exactly as expected, or if there’s a bug in LangGraph’s parsing logic.

Resolution:

  • Ensure that the LLM's output strictly adheres to the QueryResponse schema.
  • Check LangGraph’s documentation and examples for best practices on handling structured outputs.
  • Consider using the json_schema approach with OpenAI Functions for more reliable JSON formatting.

2. Data Transformation Issues:

Somewhere in your workflow, the structured_response might be getting lost or transformed. This could be due to an agent’s logic or a misconfiguration in the graph.

Resolution:

  • Use LangSmith to trace the data flow through your graph.
  • Inspect the inputs and outputs of each node to identify where the structured_response is being modified or discarded.
  • Ensure that each agent is correctly handling and passing along the structured_response.

3. Version Mismatch or Bugs:

There might be a bug in the specific version of LangGraph you’re using, or there could be compatibility issues with other libraries.

Resolution:

  • Upgrade to the latest version of LangGraph.
  • Check the LangGraph issue tracker on GitHub for known bugs related to response_format.
  • If necessary, try downgrading to a previous stable version to see if the issue is resolved.

4. Incorrect Usage of response_format:

While your code snippet seems correct, there might be a subtle misunderstanding of how response_format is intended to be used within LangGraph.

Resolution:

  • Review LangGraph’s documentation and examples on using response_format.
  • Make sure you’re setting it at the correct level in your workflow (e.g., when creating the supervisor).
  • Ensure that the LLM you’re using (e.g., OpenAI) supports the specified response_format.

Putting It All Together: A Practical Example

Let's illustrate a practical example of how to set up a LangGraph workflow with response_format correctly:

from typing import Dict, Any
from pydantic import BaseModel, Field
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain_core.messages import BaseMessage
from langgraph.graph import StateGraph, END

# 1. Define the QueryResponse schema
class QueryResponse(BaseModel):
    structured_response: Dict[str, Any] = Field(..., description="The structured response from the agent")

# 2. Define the state for the graph
class GraphState(BaseModel):
    messages: list[BaseMessage]
    query: str = Field(..., description="The user query")
    structured_response: QueryResponse | None = None

# 3. Define a simple agent
def create_agent():
    prompt_template = ChatPromptTemplate.from_messages([
        ("system", "You are an agent designed to provide structured responses."),
        MessagesPlaceholder(variable_name="messages"),
        ("user", "Please provide a structured response in the format of QueryResponse."),
    ])

    llm = ChatOpenAI(model="gpt-3.5-turbo").bind(
        function_call={
            "name": "QueryResponse"
        },
        functions=[
            {
                "name": "QueryResponse",
                "description": "The structured response from the agent",
                "parameters": QueryResponse.schema()
            }
        ]
    )

    agent = (prompt_template | llm).map({
        "query": (lambda state: state.query),
        "messages": (lambda state: state.messages)
    })
    return agent

# 4. Define a function to process the agent's output
def process_response(state: GraphState, agent_output: QueryResponse) -> dict:
    print("Agent Output:", agent_output)
    return {"messages": state.messages + [agent_output], "structured_response": agent_output}

# 5. Define the graph
def create_graph():
    agent = create_agent()

    graph = StateGraph(GraphState)
    graph.add_node("agent", agent)
    graph.add_edge("agent", END)

    graph.set_entry_point("agent")
    return graph

# 6. Compile and run the graph
if __name__ == "__main__":
    app = create_graph().compile()
    query = "What is the capital of France?"
    result = app.invoke({"messages": [], "query": query})
    print("Final Result:", result)
    if 'structured_response' in result:
        print("Structured Response:", result['structured_response'])
    else:
        print("Structured response not found in the result.")

This example demonstrates a simplified workflow with a single agent. It defines the QueryResponse schema, creates an agent that uses OpenAI Functions to format its output, and then processes the agent’s response. By running this example, you can verify that response_format is being handled correctly.

Conclusion

Troubleshooting response_format in LangGraph can be tricky, but by systematically examining your workflow, leveraging tools like LangSmith, and understanding potential pitfalls, you can resolve these issues. Remember to verify your schema, inspect LLM outputs, and trace the data flow through your graph. By following these steps, you’ll be well on your way to building robust and structured LLM applications.

I hope this comprehensive guide helps you nail down the issue and get your LangGraph workflow running smoothly! Let me know if you have any more questions or run into further snags. Happy coding, guys!