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