langfuselisted

# Langfuse Skill Debug AI agents and LLM applications through Langfuse observability. This skill is the agent-facing companion to `langfuse-mcp`. It tells Claude Code and Codex when to use Langfuse, which MCP tool to call first, and how to move from broad trace discovery to a concrete root-cause hypothesis. **Triggers:** langfuse, traces, debug AI, find exceptions, set up langfuse, what went wrong, why is it slow, datasets, evaluation sets ## What This Skill Provides - Setup steps for connecting `langfuse-mcp` to Claude Code or Codex. - Playbooks for exception triage, trace inspection, latency analysis, sessions, prompts, and datasets. - A quick reference for the highest-value MCP tools. - Links to full setup and tool references for deeper troubleshooting. Use the playbooks before guessing at individual tools. Start broad, identify the relevant trace/session/observation, then drill into the exact failure or slow path. ## Setup **Step 1:** Get credentials from https://cloud.langfuse.com → Settings → API Keys If self-hosted, use your instance URL for `LANGFUSE_HOST` and create keys there. **Step 2:** Install MCP (pick one): Requires Python 3.10 or newer. CI verifies Python 3.10 through 3.14. ```bash # Claude Code (project-scoped, shared via .mcp.json) claude mcp add \ --scope project \ --env LANGFUSE_PUBLIC_KEY=pk-... \ --env LANGFUSE_SECRET_KEY=sk-... \ --env LANGFUSE_HOST=https://cloud.langfuse.com \ langfuse -- uvx langfuse-mcp # Codex CLI (user-scoped