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Vinkius

Langfuse (LLM Tracing & Evals) Connector for AI agents.

10 live capabilities

Monitor LLM observability and prompt management in your workspace.

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Why people use Langfuse (LLM Tracing & Evals)

Langfuse LLM Observability: Stop digging through logs to fix broken AI chains

With this Connector, you just tell your agent to find the last failed trace. It pulls the telemetry, shows you the payload, and highlights the bottleneck in seconds. You get the answer without ever leaving your editor.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a direct line to your LLM production data without leaving your workspace.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Debugging a failed chain

    An engineer asks for the last failed trace to see the exact payload and where the logic broke using `get_trace`.

  2. Real-world use case 02

    Cost auditing

    A product owner asks for today's total spend and the most expensive model using `get_daily_metrics`.

  3. Real-world use case 03

    Prompt versioning

    A data scientist wants to see the system instructions for a specific support prompt using `list_prompts`.

Complete set · 10capabilities

The complete Langfuse (LLM Tracing & Evals) capability set.

These are the exact actions your AI can choose when you ask it to work with Langfuse (LLM Tracing & Evals).

Capability set01 / 03

01—04

4 capabilities in this set.

Part of 10 available through Langfuse (LLM Tracing & Evals).

  1. 01 Capability

    Create score

    Attach a 1-5 star rating or automated metric to a trace or observation. This helps you track model quality.

  2. 02 Capability

    List sessions

    List high-level user sessions that group multiple traces. This helps you see multi-turn user interactions.

  3. 03 Capability

    List traces

    List all traces tracking your LLM API sessions. This gives you a quick overview of all recent AI activity.

  4. 04 Capability

    Get trace

    Pull the complete telemetry and nested graph for a single trace. This helps you see the exact flow of an AI request.

Capability set02 / 03

05—07

3 capabilities in this set.

Part of 10 available through Langfuse (LLM Tracing & Evals).

  1. 05 Capability

    Get daily metrics

    Get rolled-up USD costs and aggregated latency statistics. Use this to monitor your daily infrastructure spend.

  2. 06 Capability

    Create observation

    Add a new LLM observation like a span or event to a trace. This helps you record specific events during a run.

  3. 07 Capability

    Get observation

    Retrieve specific span or generation context from a trace. It lets you see exactly what happened at a specific step.

Capability set03 / 03

08—10

3 capabilities in this set.

Part of 10 available through Langfuse (LLM Tracing & Evals).

  1. 08 Capability

    List observations

    List all raw observation objects across different traces. Use this to gather data from multiple runs at once.

  2. 09 Capability

    List prompts

    Extract all actively managed prompt templates and versions. This lets you audit your production instructions.

  3. 10 Capability

    List scores

    List all explicit scores mapped to quality or cost algorithms. Use this to see how your models are performing over time.

Set up in minutes

One URL. Then ask Langfuse (LLM Tracing & Evals) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Langfuse (LLM Tracing & Evals) from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_sTXOynCd5hRTA7WbjY0nBUsTjmjwRIVY989VA7VU/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Langfuse (LLM Tracing & Evals), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Langfuse (LLM Tracing & Evals) for the conversation.

Where the request belongs

Work Langfuse can move forward.

Built around the request

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.

01

LLM Engineer

Debugs complex chains and checks token latencies during a sprint.

02

Product Owner

Monitors daily AI spend and user satisfaction scores.

03

Data Scientist

Audits evaluation metrics and manages prompt versions.

When one Connector is not enough

Combine Langfuse with the systems that finish the task.

View all recipes
01

MCP Recipe for AI Inference Monitoring

Your GPT-4 API takes 4 seconds per response , Groq returns the same quality answer in 180 milliseconds, Langfuse traces every call, and Sheets shows the latency-cost comparison that makes your product feel instant

 02

Monitor AI Agent Performance Using Connectors

Your agents run in production but you cannot explain why one failed at 3am , fix that

 03

Route AI Requests to the Fastest Model via MCP

You run everything on GPT-4o because choosing a model per task is hard , your agent benchmarks Groq and Mistral against your actual workloads

 04

Track LLM Cost vs Quality Using Connectors

Your OpenAI bill grew from $200 to $2,400 in 2 months and you have no idea which feature caused it , because you track API spend at the account level, not at the prompt level

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

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01 6 capabilities

LangSmith (LLM Observability & Hub)

Monitor LLM apps via LangSmith. track traces, audit prompt templates, and manage evaluation datasets.

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02 3 capabilities

LangSmith

Observability and evaluation platform for LLM applications. monitor traces, debug agent runs, and track performance metrics across your AI stack.

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Datadog AI (LLM Observability) logo
03 10 capabilities

Datadog AI (LLM Observability)

Monitor LLM performance via Datadog. track token usage, audit prompts, and monitor AI model metrics directly from any AI agent.

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Helicone (LLM Observability) logo
04 10 capabilities

Helicone (LLM Observability)

Monitor LLM usage via Helicone. track requests, analyze costs, measure latency, and manage prompts.

View Connector
Chainlit logo
05 6 capabilities

Chainlit

Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.

View Connector
Portkey logo
06 10 capabilities

Portkey

AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.

View Connector

Bring your own AI

Change the model, client or framework. Keep Langfuse connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Langfuse.

The practical details behind the request, access and result.

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 capabilities 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 capability 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 capability 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 capability 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.

One connection away

Give your agent a direct line to Langfuse.

Connect Langfuse once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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