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langsmith-fetch

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github.com/ComposioHQ/awesome-claude-skills

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

Review Score

71%

Validation Score

14/16

Implementation Score

42%

Activation Score

100%

LangSmith Fetch - Agent Debugging Skill

Debug LangChain and LangGraph agents by fetching execution traces directly from LangSmith Studio in your terminal.

When to Use This Skill

Automatically activate when user mentions:

🐛 "Debug my agent" or "What went wrong?"
🔍 "Show me recent traces" or "What happened?"
❌ "Check for errors" or "Why did it fail?"
💾 "Analyze memory operations" or "Check LTM"
📊 "Review agent performance" or "Check token usage"
🔧 "What tools were called?" or "Show execution flow"

Prerequisites

1. Install langsmith-fetch

pip install langsmith-fetch

2. Set Environment Variables

export LANGSMITH_API_KEY="your_langsmith_api_key"
export LANGSMITH_PROJECT="your_project_name"

Verify setup:

echo $LANGSMITH_API_KEY
echo $LANGSMITH_PROJECT

Core Workflows

Workflow 1: Quick Debug Recent Activity

When user asks: "What just happened?" or "Debug my agent"

Execute:

langsmith-fetch traces --last-n-minutes 5 --limit 5 --format pretty

Analyze and report:

✅ Number of traces found
⚠️ Any errors or failures
🛠️ Tools that were called
⏱️ Execution times
💰 Token usage

Example response format:

Found 3 traces in the last 5 minutes:

Trace 1: ✅ Success
- Agent: memento
- Tools: recall_memories, create_entities
- Duration: 2.3s
- Tokens: 1,245

Trace 2: ❌ Error
- Agent: cypher
- Error: "Neo4j connection timeout"
- Duration: 15.1s
- Failed at: search_nodes tool

Trace 3: ✅ Success
- Agent: memento
- Tools: store_memory
- Duration: 1.8s
- Tokens: 892

💡 Issue found: Trace 2 failed due to Neo4j timeout. Recommend checking database connection.

Workflow 2: Deep Dive Specific Trace

When user provides: Trace ID or says "investigate that error"

Execute:

langsmith-fetch trace <trace-id> --format json

Analyze JSON and report:

🎯 What the agent was trying to do
🛠️ Which tools were called (in order)
✅ Tool results (success/failure)
❌ Error messages (if any)
💡 Root cause analysis
🔧 Suggested fix

Example response format:

Deep Dive Analysis - Trace abc123

Goal: User asked "Find all projects in Neo4j"

Execution Flow:
1. ✅ search_nodes(query: "projects")
   → Found 24 nodes

2. ❌ get_node_details(node_id: "proj_123")
   → Error: "Node not found"
   → This is the failure point

3. ⏹️ Execution stopped

Root Cause:
The search_nodes tool returned node IDs that no longer exist in the database,
possibly due to recent deletions.

Suggested Fix:
1. Add error handling in get_node_details tool
2. Filter deleted nodes in search results
3. Update cache invalidation strategy

Token Usage: 1,842 tokens ($0.0276)
Execution Time: 8.7 seconds

Workflow 3: Export Debug Session

When user says: "Save this session" or "Export traces"

Execute:

# Create session folder with timestamp
SESSION_DIR="langsmith-debug/session-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$SESSION_DIR"

# Export traces
langsmith-fetch traces "$SESSION_DIR/traces" --last-n-minutes 30 --limit 50 --include-metadata

# Export threads (conversations)
langsmith-fetch threads "$SESSION_DIR/threads" --limit 20

Report:

✅ Session exported successfully!

Location: langsmith-debug/session-20251224-143022/
- Traces: 42 files
- Threads: 8 files

You can now:
1. Review individual trace files
2. Share folder with team
3. Analyze with external tools
4. Archive for future reference

Session size: 2.3 MB

Workflow 4: Error Detection

When user asks: "Show me errors" or "What's failing?"

Execute:

# Fetch recent traces
langsmith-fetch traces --last-n-minutes 30 --limit 50 --format json > recent-traces.json

# Search for errors
grep -i "error\|failed\|exception" recent-traces.json

Analyze and report:

📊 Total errors found
❌ Error types and frequency
🕐 When errors occurred
🎯 Which agents/tools failed
💡 Common patterns

Example response format:

Error Analysis - Last 30 Minutes

Total Traces: 50
Failed Traces: 7 (14% failure rate)

Error Breakdown:
1. Neo4j Connection Timeout (4 occurrences)
   - Agent: cypher
   - Tool: search_nodes
   - First occurred: 14:32
   - Last occurred: 14:45
   - Pattern: Happens during peak load

2. Memory Store Failed (2 occurrences)
   - Agent: memento
   - Tool: store_memory
   - Error: "Pinecone rate limit exceeded"
   - Occurred: 14:38, 14:41

3. Tool Not Found (1 occurrence)
   - Agent: sqlcrm
   - Attempted tool: "export_report" (doesn't exist)
   - Occurred: 14:35

💡 Recommendations:
1. Add retry logic for Neo4j timeouts
2. Implement rate limiting for Pinecone
3. Fix sqlcrm tool configuration

Common Use Cases

Use Case 1: "Agent Not Responding"

User says: "My agent isn't doing anything"

Steps:

Check if traces exist:

langsmith-fetch traces --last-n-minutes 5 --limit 5

If NO traces found:
- Tracing might be disabled
- Check: LANGCHAIN_TRACING_V2=true in environment
- Check: LANGCHAIN_API_KEY is set
- Verify agent actually ran
If traces found:
- Review for errors
- Check execution time (hanging?)
- Verify tool calls completed

Use Case 2: "Wrong Tool Called"

User says: "Why did it use the wrong tool?"

Steps:

Get the specific trace
Review available tools at execution time
Check agent's reasoning for tool selection
Examine tool descriptions/instructions
Suggest prompt or tool config improvements

Use Case 3: "Memory Not Working"

User says: "Agent doesn't remember things"

Steps:

Search for memory operations:

langsmith-fetch traces --last-n-minutes 10 --limit 20 --format raw | grep -i "memory\|recall\|store"

Check:
- Were memory tools called?
- Did recall return results?
- Were memories actually stored?
- Are retrieved memories being used?

Use Case 4: "Performance Issues"

User says: "Agent is too slow"

Steps:

Export with metadata:

langsmith-fetch traces ./perf-analysis --last-n-minutes 30 --limit 50 --include-metadata

Analyze:
- Execution time per trace
- Tool call latencies
- Token usage (context size)
- Number of iterations
- Slowest operations
Identify bottlenecks and suggest optimizations

Output Format Guide

Pretty Format (Default)

langsmith-fetch traces --limit 5 --format pretty

Use for: Quick visual inspection, showing to users

JSON Format

langsmith-fetch traces --limit 5 --format json

Use for: Detailed analysis, syntax-highlighted review

Raw Format

langsmith-fetch traces --limit 5 --format raw

Use for: Piping to other commands, automation

Advanced Features

Time-Based Filtering

# After specific timestamp
langsmith-fetch traces --after "2025-12-24T13:00:00Z" --limit 20

# Last N minutes (most common)
langsmith-fetch traces --last-n-minutes 60 --limit 100

Include Metadata

# Get extra context
langsmith-fetch traces --limit 10 --include-metadata

# Metadata includes: agent type, model, tags, environment

Concurrent Fetching (Faster)

# Speed up large exports
langsmith-fetch traces ./output --limit 100 --concurrent 10

Troubleshooting

"No traces found matching criteria"

Possible causes:

No agent activity in the timeframe
Tracing is disabled
Wrong project name
API key issues

Solutions:

# 1. Try longer timeframe
langsmith-fetch traces --last-n-minutes 1440 --limit 50

# 2. Check environment
echo $LANGSMITH_API_KEY
echo $LANGSMITH_PROJECT

# 3. Try fetching threads instead
langsmith-fetch threads --limit 10

# 4. Verify tracing is enabled in your code
# Check for: LANGCHAIN_TRACING_V2=true

"Project not found"

Solution:

# View current config
langsmith-fetch config show

# Set correct project
export LANGSMITH_PROJECT="correct-project-name"

# Or configure permanently
langsmith-fetch config set project "your-project-name"

Environment variables not persisting

Solution:

# Add to shell config file (~/.bashrc or ~/.zshrc)
echo 'export LANGSMITH_API_KEY="your_key"' >> ~/.bashrc
echo 'export LANGSMITH_PROJECT="your_project"' >> ~/.bashrc

# Reload shell config
source ~/.bashrc

Best Practices

1. Regular Health Checks

# Quick check after making changes
langsmith-fetch traces --last-n-minutes 5 --limit 5

2. Organized Storage

langsmith-debug/
├── sessions/
│   ├── 2025-12-24/
│   └── 2025-12-25/
├── error-cases/
└── performance-tests/

3. Document Findings

When you find bugs:

Export the problematic trace
Save to error-cases/ folder
Note what went wrong in a README
Share trace ID with team

4. Integration with Development

# Before committing code
langsmith-fetch traces --last-n-minutes 10 --limit 5

# If errors found
langsmith-fetch trace <error-id> --format json > pre-commit-error.json

Quick Reference

# Most common commands

# Quick debug
langsmith-fetch traces --last-n-minutes 5 --limit 5 --format pretty

# Specific trace
langsmith-fetch trace <trace-id> --format pretty

# Export session
langsmith-fetch traces ./debug-session --last-n-minutes 30 --limit 50

# Find errors
langsmith-fetch traces --last-n-minutes 30 --limit 50 --format raw | grep -i error

# With metadata
langsmith-fetch traces --limit 10 --include-metadata

Resources

LangSmith Fetch CLI: https://github.com/langchain-ai/langsmith-fetch
LangSmith Studio: https://smith.langchain.com/
LangChain Docs: https://docs.langchain.com/
This Skill Repo: https://github.com/OthmanAdi/langsmith-fetch-skill

Notes for Claude

Always check if langsmith-fetch is installed before running commands
Verify environment variables are set
Use --format pretty for human-readable output
Use --format json when you need to parse and analyze data
When exporting sessions, create organized folder structures
Always provide clear analysis and actionable insights
If commands fail, help troubleshoot configuration issues

Version: 0.1.0 Author: Ahmad Othman Ammar Adi License: MIT Repository: https://github.com/OthmanAdi/langsmith-fetch-skill