LangSmith Fetch
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio for error analysis and performance optimization.
Download this file and place it in your project folder to get started.
# LangSmith Fetch
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Investigate agent behavior, errors, tool calls, memory operations, and performance metrics.
## Installation
```bash
pip install langsmith-fetch
```
## Configuration
Required environment variables:
```bash
export LANGSMITH_API_KEY="your_api_key"
export LANGSMITH_PROJECT="your_project_name"
```
## Activation Triggers
This skill activates when users mention:
- "Debug my agent"
- "Show recent traces"
- "Check for errors"
- "Review agent performance"
- "Why isn't my agent working"
- "Trace analysis"
## Four Main Workflows
### 1. Quick Debug Recent Activity
Fetches last 5 minutes of traces and reports:
- Success and failure counts
- Tools used and frequency
- Execution times
- Token consumption
- Error summary
```bash
langsmith-fetch recent --minutes 5 --format pretty
```
### 2. Deep Dive Specific Trace
Analyzes individual traces with detailed breakdown:
- Complete execution flow
- Step-by-step operations
- Root cause analysis
- Suggested fixes
```bash
langsmith-fetch trace <trace_id> --format json
```
### 3. Export Debug Session
Creates timestamped folders with traces and threads:
- Archived for later review
- Shareable with team members
- Includes all metadata
```bash
langsmith-fetch export --output ./debug-session
```
### 4. Error Detection
Identifies and categorizes errors across recent traces:
- Error frequency patterns
- Categorized by type
- Recommendations for fixes
```bash
langsmith-fetch errors --hours 24 --format pretty
```
## Output Formats
### Pretty (Visual Inspection)
Human-readable format with colors and formatting for terminal review.
### JSON (Detailed Analysis)
Structured data for programmatic analysis or integration with other tools.
### Raw (Piping)
Unformatted output for piping to other commands or scripts.
## Common Use Cases
### Unresponsive Agent
```
User: "Debug my agent - it's not responding"
Action:
1. Fetch recent traces
2. Identify last successful execution
3. Find where execution stopped
4. Report blocking issue
```
### Incorrect Tool Selection
```
User: "Why is my agent using the wrong tool?"
Action:
1. Fetch traces with tool calls
2. Analyze tool selection logic
3. Compare expected vs actual
4. Suggest prompt improvements
```
### Memory Operation Failures
```
User: "Agent isn't remembering context"
Action:
1. Fetch traces with memory ops
2. Check read/write operations
3. Identify failed operations
4. Report memory issues
```
### Performance Bottlenecks
```
User: "Agent is too slow"
Action:
1. Fetch recent traces with timing
2. Identify slow operations
3. Calculate average times
4. Recommend optimizations
```
## Debugging Workflow
### Step 1: Quick Overview
Start with recent activity summary:
- How many traces in last N minutes
- Success rate
- Common errors
### Step 2: Identify Problem Traces
Filter for issues:
- Failed executions
- Slow operations
- Specific error types
### Step 3: Deep Dive
Analyze specific problematic traces:
- Full execution flow
- Input/output at each step
- Error context
### Step 4: Root Cause
Determine underlying issue:
- Prompt problems
- Tool configuration
- Memory issues
- External API failures
### Step 5: Fix and Verify
After implementing fix:
- Run new executions
- Fetch fresh traces
- Confirm resolution
## Best Practices
1. **Start broad**: Use quick debug before deep dive
2. **Use trace IDs**: Pin down specific issues
3. **Export regularly**: Keep records of debugging sessions
4. **Track patterns**: Look for recurring error types
5. **Check tokens**: Monitor consumption for cost control
6. **Share with team**: Export sessions for collaboration
## Troubleshooting
### No Traces Found
- Verify LANGSMITH_PROJECT is correct
- Check API key permissions
- Confirm traces are being logged
### Incomplete Data
- Increase time window
- Check if traces finished executing
- Verify LangSmith logging is complete
### Performance Issues
- Reduce time window
- Use filters to narrow results
- Export and analyze offline
What This Does
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Investigate agent behavior, errors, tool calls, memory operations, and performance metrics through terminal commands.
Quick Start
Step 1: Install
pip install langsmith-fetch
Step 2: Configure
export LANGSMITH_API_KEY="your_key"
export LANGSMITH_PROJECT="your_project"
Step 3: Debug
claude
Then say: "Debug my agent's recent activity"
Four Debug Workflows
| Workflow | Purpose |
|---|---|
| Quick Debug | Last 5 minutes of activity |
| Deep Dive | Analyze specific trace ID |
| Export Session | Save traces for sharing |
| Error Detection | Find and categorize errors |
Activation Triggers
Claude activates this skill when you mention:
- "Debug my agent"
- "Show recent traces"
- "Check for errors"
- "Review agent performance"
Quick Debug Report
Get a summary of recent activity:
- Success/failure counts
- Tools used
- Execution times
- Token consumption
- Error summary
Deep Dive Analysis
For specific trace investigation:
- Complete execution flow
- Step-by-step breakdown
- Root cause analysis
- Suggested fixes
Output Formats
| Format | Best For |
|---|---|
| pretty | Visual inspection |
| json | Detailed analysis |
| raw | Piping to other commands |
Example Prompts
- "Why is my agent not responding?"
- "Show traces with errors from the last hour"
- "Analyze trace abc123 for issues"
- "Export today's debug session"
- "What tools is my agent using most?"
Common Issues Detected
- Unresponsive agents
- Incorrect tool selection
- Memory operation failures
- Performance bottlenecks
- Token limit issues
Tips
- Check recent first: Start with quick debug
- Use trace IDs: Deep dive specific issues
- Export for team: Share debug sessions
- Track patterns: Look for recurring errors