# Langfuse

```json
{
  "name": "Langfuse",
  "slug": "langfuse",
  "url": "https://composio.dev/toolkits/langfuse",
  "markdown_url": "https://composio.dev/toolkits/langfuse.md",
  "logo_url": "https://logos.composio.dev/api/langfuse",
  "categories": [
    "ai & machine learning"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-08-20T15:32:52.559Z"
}
```

![Langfuse logo](https://logos.composio.dev/api/langfuse)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Langfuse MCP or direct API to inspect traces, review evals, manage prompts, and analyze metrics through natural language.

## Summary

Langfuse is an open source LLM engineering platform for traces, evals, prompt management, and metrics.
It helps teams debug, monitor, and improve LLM applications with clear observability data.

## Categories

- ai & machine learning

## Toolkit Details

- Tools: 10

## Images

- Logo: https://logos.composio.dev/api/langfuse

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for Langfuse.
  - Setup:
    - Configure Api Key credentials for Langfuse.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- List recent Langfuse traces with errors
- Compare production prompt versions in Langfuse
- Review Langfuse metrics for latency spikes

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LANGFUSE_CREATE_SCORE` | Create Score | Attach an evaluation score to exactly one Langfuse trace, session, or dataset run, optionally narrowing a trace score to an observation. |
| `LANGFUSE_GET_PROMPT` | Get Prompt | Retrieve a Langfuse text or chat prompt by name, deployment label, or exact version, with optional dependency resolution. |
| `LANGFUSE_LIST_ANNOTATION_QUEUES` | List Annotation Queues | List annotation queues configured for human evaluation in the connected Langfuse project. |
| `LANGFUSE_LIST_DATASET_ITEMS` | List Dataset Items | List dataset items, optionally scoped to a dataset, source trace or observation, or historical dataset version. |
| `LANGFUSE_LIST_DATASETS` | List Datasets | List datasets available to the connected Langfuse project. |
| `LANGFUSE_LIST_EXPERIMENTS` | List Experiments | List Langfuse experiments active in a required time range, optionally including metadata and directly attached scores. |
| `LANGFUSE_LIST_MODELS` | List Models | List Langfuse-managed and project-custom model pricing definitions used for usage and cost calculation. |
| `LANGFUSE_LIST_OBSERVATIONS` | List Observations | Search Langfuse observations such as generations, spans, events, agents, and tool calls, selecting only the field groups needed. |
| `LANGFUSE_LIST_SCORES` | List Scores | Search numeric, boolean, categorical, text, and correction scores with subject and annotation context. |
| `LANGFUSE_QUERY_METRICS` | Query Metrics | Run an aggregate Langfuse metrics query over observations or numeric, boolean, or categorical scores. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK
```python
pip install composio_openai
```

```typescript
npm install @composio/openai
```

#### Path 1, Step 2: Initialize Composio and Create Tool Router Session

Import and initialize Composio client, then create a Tool Router session
```python
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
openai = OpenAI()
session = composio.create(user_id='your-user-id')
```

```typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
```

#### Path 1, Step 3: Execute Langfuse Tools via Tool Router with Your Agent

Get tools from Tool Router session and execute Langfuse actions with your Agent
```python
tools = session.tools
response = openai.responses.create(
  model='gpt-4.1',
  tools=tools,
  input=[{
    'role': 'user',
    'content': 'List recent Langfuse traces with errors and summarize common failure patterns'
  }]
)
result = composio.provider.handle_tool_calls(
  response=response,
  user_id='your-user-id'
)
print(result)
```

```typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'List recent Langfuse traces with errors and summarize common failure patterns'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK for Python or TypeScript
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
```

#### Path 2, Step 2: Initialize Client and Create Tool Router Session

Import and initialize the Composio client, then create a Tool Router session for Langfuse
```python
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

composio = Composio(api_key='your-composio-api-key')
session = composio.create(user_id='your-user-id')
url = session.mcp.url
```

```typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
```

#### Path 2, Step 3: Connect to AI Agent

Use the MCP server with your AI agent (Anthropic Claude or Mastra)
```python
import asyncio

options = ClaudeAgentOptions(
    permission_mode='bypassPermissions',
    mcp_servers={
        'tool_router': {
            'type': 'http',
            'url': url,
            'headers': {
                'x-api-key': 'your-composio-api-key'
            }
        }
    },
    system_prompt='You are a helpful assistant with access to Langfuse tools.',
    max_turns=10
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query('List recent Langfuse traces with errors and summarize common failure patterns')
        async for message in client.receive_response():
            if hasattr(message, 'content'):
                for block in message.content:
                    if hasattr(block, 'text'):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'List recent Langfuse traces with errors and summarize common failure patterns'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
```

## Why Use Composio?

### 1. AI Native Langfuse Integration

- Supports both Langfuse MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable trace, eval, prompt, and metric workflows
- Rich coverage for reading, writing, and querying your Langfuse observability data

### 2. Managed Auth

- Secure API key handling so you don't have to hard-code Langfuse credentials
- Central place to manage, scope, and revoke Langfuse access
- Per user and per environment credentials for safer agent deployments

### 3. Agent Optimized Design

- Tools are tuned for AI agents, so they can work with Langfuse traces and metrics more reliably
- Clear execution logs show what your agent ran, when it ran, and on whose behalf
- Less glue code, fewer brittle integrations, and faster debugging loops

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Langfuse
- Scoped, least privilege access to Langfuse resources
- Full audit trail of agent actions to support review, security checks, and compliance

## Use Langfuse with any AI Agent Framework

Choose a framework you want to connect Langfuse with:

None listed.

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## Frequently Asked Questions

### Do I need my own developer credentials to use Langfuse with Composio?

Yes, Langfuse requires you to configure your own API key. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)