micsapp LangChain Debug Bundle
Contents
Overview
Collect comprehensive debug information for LangChain issues including traces, versions, and reproduction steps.
Prerequisites
- LangChain installed
- Reproducible error condition
- Access to logs and environment
Instructions
Step 1: Collect Environment Info
Run pip show on all LangChain packages to gather versions, Python version, and platform info.
Step 2: Enable Full Tracing
Set langchain.debug = True and enable LangSmith tracing. Attach a DebugCallback that logs all LLM start/end/error events with timestamps.
Step 3: Create Minimal Reproduction
Write a standalone script that reproduces the issue with minimal code and redacted API keys.
Step 4: Generate Debug Bundle
Combine environment info, trace logs, and reproduction steps into a debug_bundle.json file.
See detailed implementation for complete debug callback and bundle generator code.
Output
debug_bundle.jsonwith full diagnostic informationminimal_repro.pyfor issue reproduction- Environment and version information
- Trace logs with timestamps
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Callback not capturing | Not attached to LLM | Pass via callbacks= parameter |
| Large trace logs | Long-running operation | Filter by time range |
| API key in logs | Missing redaction | Always redact before sharing |
Examples
Basic usage: Apply langchain debug bundle to a standard project setup with default configuration options.
Advanced scenario: Customize langchain debug bundle for production environments with multiple constraints and team-specific requirements.
Resources
Next Steps
Use langchain-common-errors for quick fixes or escalate with the bundle.