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Overview

• Agent execution flow with detailed step-by-step tracing
• Tool usage patterns and performance metrics
• LLM calls with token usage and costs
• Error tracking and debugging information
• Conversation analytics across sessions

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Supported Platforms

• Langfuse - Open-source LLM observability with self-hosting options
• Laminar - Comprehensive AI application monitoring platform
• LangSmith - LangChain’s observability platform

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What Gets Traced

• Agent conversations - Full query/response pairs
• LLM calls - Model usage, tokens, and costs
• Tool executions - Which MCP tools were used and their outputs
• Performance metrics - Execution times and step counts
• Error tracking - Failed operations with full context

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Example Trace View

🔍 mcp_agent_run
├── 💬 LLM Call (gpt-4)
│   ├── Input: "Help me analyze the sales data"
│   └── Output: "I'll help you analyze the sales data..."
├── 🔧 Tool: read_file
│   ├── Input: {"path": "sales_data.csv"}
│   └── Output: "CSV content loaded..."
├── 🔧 Tool: analyze_data
│   ├── Input: {"data": "...", "analysis_type": "summary"}
│   └── Output: "Analysis complete..."
└── 💬 Final Response
    └── "Based on the sales data analysis..."

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Langfuse Integration

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Setup Langfuse

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1. Install Langfuse

pip install langfuse

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2. Get Your Keys

• Cloud: Sign up at cloud.langfuse.com
• Self-hosted: Follow the self-hosting guide

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3. Set Environment Variables

export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_SECRET_KEY="sk-lf-..."

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4. Start Using

# Langfuse automatically initializes when mcp_use is imported
import mcp_use
from mcp_use import MCPAgent

agent = MCPAgent(llm=your_llm, ...)
result = await agent.run("Your query")  # Automatically traced!

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Langfuse Dashboard Features

• Timeline view - Step-by-step execution flow
• Performance metrics - Response times and costs
• Error analysis - Debug failed operations
• Usage analytics - Tool and model usage patterns
• Session grouping - Track conversations over time
• Self-hosting - Full control over your data

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Environment Variables

# Required
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_SECRET_KEY="sk-lf-..."

# Optional - for self-hosted instances
LANGFUSE_HOST="https://your-langfuse-instance.com"

# Optional - disable Langfuse
MCP_USE_LANGFUSE="false"

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Laminar Integration

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Setup Laminar

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1. Install Laminar

pip install lmnr

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2. Get Your API Key

• Sign up at lmnr.ai
• Create a new project
• Copy your project API key

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3. Set Environment Variable

export LAMINAR_PROJECT_API_KEY="your-api-key-here"

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4. Start Using

# Laminar automatically initializes when mcp_use is imported
import mcp_use
from mcp_use import MCPAgent

agent = MCPAgent(llm=your_llm, ...)
result = await agent.run("Your query")  # Automatically traced!

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Laminar Features

• Advanced tracing - Detailed execution flow visualization
• Real-time monitoring - Live performance metrics
• Cost tracking - LLM usage and billing analytics
• Error analysis - Comprehensive error tracking and debugging
• Team collaboration - Shared dashboards and insights
• Production monitoring - Built for scale

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Environment Variables

# Required
LAMINAR_PROJECT_API_KEY="your-api-key-here"

# Optional - disable Laminar
MCP_USE_LAMINAR="false"

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Additional Information

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Privacy & Data Security

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What’s Collected

• Queries and responses (for debugging context)
• Tool inputs/outputs (to understand workflows)
• Model metadata (provider, model name, tokens)
• Performance data (execution times, success rates)

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What’s NOT Collected

• No personal information beyond what you send to your LLM
• No API keys or credentials
• No unauthorized data - you control what gets traced

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Security Features

• HTTPS encryption for all data transmission
• Self-hosting options available (Langfuse)
• Easy to disable with environment variables
• Data ownership - you control your observability data

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Disabling Observability

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Temporarily Disable

# Disable Langfuse
export MCP_USE_LANGFUSE="false"

# Disable Laminar
export MCP_USE_LAMINAR="false"

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Troubleshooting

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Common Issues

# Install the required packages
pip install langfuse  # For Langfuse
pip install lmnr      # For Laminar

# Check your environment variables
echo $LANGFUSE_PUBLIC_KEY
echo $LANGFUSE_SECRET_KEY
echo $LAMINAR_PROJECT_API_KEY

• Verify your API keys are correct
• Check that observability isn’t disabled (MCP_USE_LANGFUSE or MCP_USE_LAMINAR set to “false”)
• Check network connectivity to the platform
• Enable debug logging: logging.basicConfig(level=logging.DEBUG)

export LANGFUSE_HOST="https://your-langfuse-instance.com"

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LangSmith Integration

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Benefits

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For Development

• Faster debugging - See exactly where workflows fail
• Performance optimization - Identify slow operations
• Cost monitoring - Track LLM usage and expenses

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For Production

• Real-time monitoring - Monitor agent performance
• Error tracking - Get alerted to failures
• Usage analytics - Understand user interaction patterns

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For Teams

• Shared visibility - Everyone can see agent behavior
• Knowledge sharing - Learn from successful workflows
• Collaborative debugging - Debug issues together

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Getting Help

• Langfuse Documentation: langfuse.com/docs
• Laminar Documentation: lmnr.ai/docs
• LangSmith Documentation: smith.langchain.com
• MCP-use Issues: GitHub Issues