How to Debug a Stuck LangChain Agent in 30 Seconds

If you've built production LangChain applications, you've probably encountered this scenario: your agent starts running, makes a few tool calls, then... nothing. It's stuck. But where? And why?

In this guide, we'll show you how to debug stuck agents using Orchid's interactive timeline and payload inspection.

The Problem: Invisible Agent Loops

LangChain agents can get stuck for many reasons:

• Infinite loops (agent keeps calling the same tool)
• Malformed tool outputs that confuse the LLM
• Rate limiting or API failures
• Prompt issues that lead to circular reasoning

Traditional logging makes these hard to diagnose because you're looking at a linear stream of text, not the actual decision tree of your agent.

The Solution: Interactive Debugging

With Orchid, you can:

1. See the timeline - Every tool call, LLM invocation, and decision visualized
2. Click through steps - Inspect exact inputs/outputs at each stage
3. Spot patterns - Instantly see if the agent is looping

Example: Debugging a Research Agent

Let's say you have a research agent that should:

1. Search for information
2. Summarize findings
3. Return results

But it gets stuck after the search step. Here's how to debug it with Orchid:

Step 1: View the Pipeline Timeline

from orchid import track_pipeline
from langchain.agents import initialize_agent

@track_pipeline(job_id="research-001")
def run_research_agent(query):
    agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
    return agent.run(query)

When this runs, Orchid automatically tracks every step. Open the Orchid UI and you'll see:

• ✅ Step 1: search_tool → completed (2.3s)
• ✅ Step 2: search_tool → completed (1.8s)
• ⏳ Step 3: search_tool → started (running 45s...)

Aha! The agent is calling search_tool three times. It's stuck in a loop.

Step 2: Inspect the Payloads

Click on Step 2 to see what the tool returned:

{
  "tool": "search_tool",
  "output": "Found 0 results for 'AI trends 2025'"
}

Click on Step 3 to see the LLM's reasoning:

{
  "thought": "I didn't find results, let me try searching again",
  "action": "search_tool",
  "action_input": "AI trends 2025"
}

Root Cause Found: The search returns empty results, but the agent's prompt doesn't handle this case. It just keeps retrying the same query indefinitely.

Step 3: Fix the Issue

Now that you know the problem, the fix is simple:

# Before: Agent has no fallback for empty search results
# After: Add a fallback tool or modify the prompt

system_message = """
If a search returns no results, try rephrasing the query or use a different search term.
If you've tried 2 times with no results, return "Unable to find information" and stop.
"""

Step 4: Verify the Fix

Re-run with Orchid tracking:

• ✅ Step 1: search_tool → completed (2.1s, 0 results)
• ✅ Step 2: search_tool (rephrased) → completed (1.9s, 5 results)
• ✅ Step 3: summarize_tool → completed (3.2s)
• ✅ Step 4: final_answer → completed (0.1s)

Fixed in 30 seconds. No more grep. No more guesswork.

Pro Tips for LangChain Debugging

1. Track costs - Stuck agents burn API credits fast. Orchid shows you the cost of every LLM call.
2. Set timeouts - Use Orchid alerts to notify you if an agent runs longer than expected.
3. Compare runs - Use Orchid's comparison view to see how prompt changes affect agent behavior.

Try It Yourself

Want to see this in action? Check out our interactive demo with a pre-loaded stuck agent example.

Or install Orchid in your own LangChain project:

pip install orchid-sdk-python

from orchid.integrations import setup_langchain

setup_langchain(api_key="your-orchid-api-key")

# That's it! All your chains and agents are now tracked

Conclusion

Debugging stuck agents doesn't have to take hours. With interactive tools like Orchid, you can:

• See the exact execution flow
• Inspect payloads at each step
• Identify loops and failures instantly

Try Orchid today and turn your agent debugging time from hours to seconds.

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Have questions about debugging your LangChain agents? Reach out to us - we'd love to help!