
langsmith-fetch
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/composiohq/awesome-claude-skills --skill langsmith-fetch- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads langsmith-fetch automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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=truein environment - Check:
LANGCHAIN_API_KEYis 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-fetchis installed before running commands - Verify environment variables are set
- Use
--format prettyfor human-readable output - Use
--format jsonwhen 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
FAQ
What does the langsmith-fetch skill do?
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.
How do I install langsmith-fetch?
Run npx skills add https://github.com/composiohq/awesome-claude-skills --skill langsmith-fetch in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/langsmith-fetch/ (personal) or .claude/skills/langsmith-fetch/ (project).
Which AI agents can use langsmith-fetch?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is langsmith-fetch safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk flagged items to review; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains langsmith-fetch?
It is published by composiohq in composiohq/awesome-claude-skills. It has 4,278 recorded installs.
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