Getting Started

Get AgentLens running and capturing agent events in under 5 minutes.

Prerequisites

• Node.js ≥ 20.0.0
• An MCP-compatible AI agent (Claude Desktop, Cursor, or any MCP client)

Step 1: Start the Server

The fastest way to start is with npx:

npx @agentlensai/server

The server starts on http://localhost:3400 with a SQLite database (no external dependencies).

You'll see:

🔍 AgentLens server listening on http://localhost:3400
📦 Database: ./agentlens.db (SQLite WAL mode)
🔓 Auth: disabled (set AUTH_DISABLED=false for production)

Production Setup

For production, install it as a dependency:

npm install @agentlensai/server
# or
pnpm add @agentlensai/server

Step 2: Create an API Key

API keys authenticate your agents with the AgentLens server.

curl -X POST http://localhost:3400/api/keys \
  -H "Content-Type: application/json" \
  -d '{"name": "my-first-agent"}'

Response:

{
  "id": "01HXYZ...",
  "key": "als_a1b2c3d4e5f6...",
  "name": "my-first-agent",
  "scopes": ["*"],
  "createdAt": "2026-02-08T00:00:00.000Z"
}

Save your key!

The raw API key (als_...) is only shown once. Copy it now — the server stores only a SHA-256 hash.

Step 3: Configure Your MCP Agent

Add AgentLens as an MCP server in your agent's configuration.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "agentlens": {
      "command": "npx",
      "args": ["@agentlensai/mcp"],
      "env": {
        "AGENTLENS_API_URL": "http://localhost:3400",
        "AGENTLENS_API_KEY": "als_your_key_here",
        "AGENTLENS_AGENT_NAME": "claude-desktop"
      }
    }
  }
}

Cursor

Edit .cursor/mcp.json in your project root (or globally in ~/.cursor/mcp.json):

{
  "mcpServers": {
    "agentlens": {
      "command": "npx",
      "args": ["@agentlensai/mcp"],
      "env": {
        "AGENTLENS_API_URL": "http://localhost:3400",
        "AGENTLENS_API_KEY": "als_your_key_here",
        "AGENTLENS_AGENT_NAME": "cursor-agent"
      }
    }
  }
}

Custom MCP Agent

If you're building your own agent with the MCP SDK:

{
  "mcpServers": {
    "agentlens": {
      "command": "npx",
      "args": ["@agentlensai/mcp"],
      "env": {
        "AGENTLENS_API_URL": "http://localhost:3400",
        "AGENTLENS_API_KEY": "als_your_key_here",
        "AGENTLENS_AGENT_NAME": "my-custom-agent",
        "AGENTLENS_AGENT_VERSION": "1.0.0",
        "AGENTLENS_ENVIRONMENT": "development"
      }
    }
  }
}

Step 4: Use AgentLens Tools in Your Agent

Once connected, the agent can use these MCP tools:

Start a Session

Tool: agentlens_session_start
Arguments: { "agentId": "my-agent", "agentName": "My Agent", "tags": ["dev"] }
→ Returns: { "sessionId": "01HXYZ..." }

Log Events

Tool: agentlens_log_event
Arguments: {
  "sessionId": "01HXYZ...",
  "eventType": "tool_call",
  "payload": { "toolName": "search", "callId": "c1", "arguments": { "query": "test" } }
}

End a Session

Tool: agentlens_session_end
Arguments: { "sessionId": "01HXYZ...", "reason": "completed" }

Query Events (Agent Self-Inspection)

Tool: agentlens_query_events
Arguments: { "sessionId": "01HXYZ...", "limit": 10 }
→ Returns: array of recent events in this session

Step 5: Open the Dashboard

Navigate to http://localhost:3400 in your browser.

You'll see:

• Overview — Metrics grid with total sessions, events, error rates, and cost
• Sessions — List of all agent sessions with status, duration, and event counts
• Session Detail — Click any session to see a vertical timeline of every event
• Events Explorer — Filterable list of all events across all sessions
• Analytics — Charts for event trends, cost breakdown, and agent comparisons
• Alerts — Configure and view alert rules and history

Alternative: Python Auto-Instrumentation

If you're building in Python, you can skip MCP entirely and use auto-instrumentation — one line captures every LLM call across 9 providers:

pip install agentlensai[all-providers]   # all 9 providers
# or pick specific ones:
pip install agentlensai[openai]          # just OpenAI
pip install agentlensai[bedrock,ollama]  # Bedrock + Ollama

import agentlensai

agentlensai.init(
    url="http://localhost:3400",
    api_key="als_your_key",
    agent_id="my-agent",
    integrations="auto",  # auto-discovers all installed provider SDKs
)

# Every LLM call is now captured automatically
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)
# ^ Logged: model, tokens, cost, latency, full prompt/completion

agentlensai.shutdown()

Supported providers: OpenAI, Anthropic, LiteLLM, AWS Bedrock, Google Vertex AI, Google Gemini, Mistral AI, Cohere, and Ollama. See the Python SDK README for full provider documentation.

Next Steps

• Configuration Reference — Environment variables and options
• MCP Integration Guide — Deep dive into the MCP server
• API Reference — Full REST API documentation
• Integrations — Connect AgentGate and FormBridge