A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"langfuse": {
"env": {
"LANGFUSE_HOST": "https://cloud.langfuse.com",
"LANGFUSE_PUBLIC_KEY": "pk-...",
"LANGFUSE_SECRET_KEY": "sk-...",
"LANGFUSE_MCP_DEFAULT_OUTPUT_MODE": "full_json_file"
},
"args": [
"langfuse-mcp"
],
"command": "uvx"
}
}
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A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability
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Agent-facing Model Context Protocol server and skill for Langfuse observability.
Use langfuse-mcp from Claude Code, Codex, Cursor, or any MCP client to query traces, inspect generations, debug exceptions, analyze sessions, manage prompts, browse datasets, and understand what your AI agents did in production.
langfuse agent skill for ready-made debugging playbooks.Langfuse is where your traces live. langfuse-mcp makes that telemetry directly usable by agents that need to answer questions like "what failed?", "why was this slow?", "which prompt version ran?", or "what happened in this user's session?"
Comparison with official Langfuse MCP (as of Jan 2026):
| langfuse-mcp | Official | |
|---|---|---|
| Traces & Observations | Yes | No |
| Sessions & Users | Yes | No |
| Exception Tracking | Yes | No |
| Prompt Management | Yes | Yes |
| Dataset Management | Yes | No |
| Annotation Queues | Yes | No |
| Scores (v2) | Yes | No |
| Selective Tool Loading | Yes | No |
This project provides a full observability toolkit — traces, observations, sessions, exceptions, prompts, datasets, annotation queues, and scores — while the official MCP focuses on prompt management.
Requires uv (for uvx) and Python 3.10 or newer. CI verifies Python 3.10 through 3.14.
Get credentials from Langfuse Cloud → Settings → API Keys. If self-hosted, use your instance URL for LANGFUSE_HOST.
# Claude Code (project-scoped, shared via .mcp.json)
claude mcp add \
-e LANGFUSE_PUBLIC_KEY=pk-... \
-e LANGFUSE_SECRET_KEY=sk-... \
-e LANGFUSE_HOST=https://cloud.langfuse.com \
--scope project \
langfuse -- uvx langfuse-mcp
# Codex CLI (user-scoped, stored in ~/.codex/config.toml)
codex mcp add langfuse \
--env LANGFUSE_PUBLIC_KEY=pk-... \
--env LANGFUSE_SECRET_KEY=sk-... \
--env LANGFUSE_HOST=https://cloud.langfuse.com \
-- uvx langfuse-mcp
To pin a CI-verified interpreter explicitly, add --python 3.14 before langfuse-mcp.
Restart your CLI, then verify with /mcp (Claude Code) or codex mcp list (Codex).
This repo ships a first-party langfuse skill for Claude Code and Codex. The skill gives agents concrete playbooks for trace debugging, exception triage, latency analysis, prompt management, and dataset work.
Install it when you want the agent to know when to reach for Langfuse and which MCP tools to call first.
Via skills (recommended):
npx skills add avivsinai/langfuse-mcp -g -y
Via skild:
npx skild install @avivsinai/langfuse -t claude -y
Manual install:
cp -r sk
... [View full README on GitHub](https://github.com/avivsinai/langfuse-mcp#readme)