langsmith-fetch

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:

1. ✅ Number of traces found
2. ⚠️ Any errors or failures
3. 🛠️ Tools that were called
4. ⏱️ Execution times
5. 💰 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:

1. 🎯 What the agent was trying to do
2. 🛠️ Which tools were called (in order)
3. ✅ Tool results (success/failure)
4. ❌ Error messages (if any)
5. 💡 Root cause analysis
6. 🔧 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:

1. 📊 Total errors found
2. ❌ Error types and frequency
3. 🕐 When errors occurred
4. 🎯 Which agents/tools failed
5. 💡 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:

1. Check if traces exist:

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

2. 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

3. 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:

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

---

Use Case 3: "Memory Not Working"

User says: "Agent doesn't remember things"

Steps:

1. Search for memory operations:

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

2. 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:

1. Export with metadata:

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

2. Analyze:

   • Execution time per trace
   • Tool call latencies
   • Token usage (context size)
   • Number of iterations
   • Slowest operations

3. 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:

1. No agent activity in the timeframe
2. Tracing is disabled
3. Wrong project name
4. 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:

1. Export the problematic trace
2. Save to error-cases/ folder
3. Note what went wrong in a README
4. 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

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