Langfuse (LLM Tracing & Evals) MCP, Ready to Go
Connect your AI agents to Langfuse to monitor costs, trace LLM outputs, and manage prompts using Claude or Cursor. Get real-time production data.
No credit card required. Experience the power of this integration risk-free.
Monitor LLM observability and prompt management in your workspace.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Langfuse (LLM Tracing & Evals) MCP Server?
Average time for the server to become ready for requests over the last 8 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Langfuse MCP: 10 Tools for LLM Observability
Query traces, manage prompt templates, and track AI costs with these 10 tools.
Get trace
Pull the complete telemetry and nested graph for a single trace. This helps you see the exact flow of an AI request.
Get daily metrics
Get rolled-up USD costs and aggregated latency statistics. Use this to monitor your daily infrastructure spend.
Create observation
Add a new LLM observation like a span or event to a trace. This helps you record specific events during a run.
Get observation
Retrieve specific span or generation context from a trace. It lets you see exactly what happened at a specific step.
List observations
List all raw observation objects across different traces. Use this to gather data from multiple runs at once.
List prompts
Extract all actively managed prompt templates and versions. This lets you audit your production instructions.
Create score
Attach a 1-5 star rating or automated metric to a trace or observation. This helps you track model quality.
List scores
List all explicit scores mapped to quality or cost algorithms. Use this to see how your models are performing over time.
List sessions
List high-level user sessions that group multiple traces. This helps you see multi-turn user interactions.
List traces
List all traces tracking your LLM API sessions. This gives you a quick overview of all recent AI activity.
Langfuse LLM Observability: Stop digging through logs to fix broken AI chains
This is for the engineers and product folks who are tired of manually hunting through logs to figure out why a production agent is hallucinating or costing too much.
LLM Engineer
Debugs complex chains and checks token latencies during a sprint.
Product Owner
Monitors daily AI spend and user satisfaction scores.
Data Scientist
Audits evaluation metrics and manages prompt versions.
Frequently Asked Questions
How does Langfuse MCP help with LLM costs? +
It lets you query your real-time spending and daily metrics through your AI client. You can see your total USD costs and average latency spikes without having to log into a separate dashboard.
Can I use Langfuse MCP to see my prompt versions? +
Yes, you can query your prompt vault to see active templates, system instructions, and specific versions. This makes it much easier to audit how your agent's instructions are changing.
How do I find why my AI agent is hallucinating with Langfuse MCP? +
You can ask your agent to pull the telemetry for a specific failed trace. This shows you the exact payload and nested graph, helping you pinpoint exactly where the logic went off track.
Does Langfuse MCP track my token usage? +
Yes, it pulls token counts and usage metrics from your traces. You can ask your agent to summarize your token usage or check the costs of specific sessions.
Can I add human feedback to traces using Langfuse MCP? +
You can attach scores or human feedback to specific traces or observations. This allows you to monitor model grounding and accuracy directly through your AI client.
What is the best way to monitor multi-turn user sessions with Langfuse MCP? +
You can use the session monitoring tools to extract correlated user sessions. This helps you see the boundaries of multi-turn interactions and understand how users are actually using your agent.
Can I see the exact system instruction for a specific prompt version? +
Yes. Use the list_prompts tool to browse your managed templates. Your agent can retrieve the exact text and variables for any deployed prompt version, making it easy to audit AI logic through natural conversation.
How do I log human feedback for a specific trace? +
Use the create_score tool by providing the Trace ID and a JSON payload defining the score name (e.g. 'user-satisfaction') and value. Your agent will attach this structured data directly to the Langfuse record.
Can my agent report on my LLM spending for the current day? +
Absolutely. The get_daily_metrics tool retrieves aggregated USD costs and average latency metrics from Langfuse. Your agent can summarize these statistics to help you monitor your infrastructure budget in real-time.
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