AI & Machine Learning · Langfuse + Claude
Langfuse MCP with Claude: Monitor and Improve Your AI Applications
The short answer
Claude works with Langfuse through the Model Context Protocol (MCP), an open standard that acts as a secure adapter between the two. Once connected with an API key, Claude can read traces, review evaluation results, manage prompts and analyse metrics when a person asks in natural language. Access is limited to what the organisation permits, and consequential changes can require human approval.
What is Langfuse?
Langfuse is an open source platform for engineering large language model (LLM) applications. It records traces, which are step-by-step logs of how an AI application handled a request. It also stores evaluations, managed prompts and performance metrics. Engineering teams, product managers and quality leads use it to find out why an AI chatbot or agent gave a poor answer, and to improve it over time with clearer data.
How Claude connects to Langfuse through MCP
The Model Context Protocol (MCP) is an open standard that lets Claude securely use other software. In plain terms, it is a universal adapter between Claude and a tool. The organisation decides which information Claude may see and which actions it may take, and the connection can be switched off at any time.
Once Claude is connected to Langfuse, it can typically look up recent traces, filter them for failed or slow responses, read evaluation scores, and compare prompt versions. A team member might ask, "Show me the conversations from last week where the chatbot gave the wrong refund policy," and Claude can gather the relevant records and summarise the pattern.
Permissions and approvals keep people in charge. The connection uses a Langfuse API key with least-privilege access, so Claude sees only the projects the team selects. Changes such as publishing a new prompt version are designed to wait for a named person to approve them, and every action is recorded in audit logs.
What you can automate
Investigating customer complaints
A customer support lead reports that the AI assistant gave inconsistent answers about delivery times. Claude searches the relevant Langfuse traces, groups the failed responses by type, and highlights the prompt step that most often produced the error. The support team then has a clear starting point for correction, rather than reading hundreds of logs by hand.
Reviewing evaluation results
A quality manager wants to know whether last month's model change improved accuracy. Claude reads the evaluation scores in Langfuse, compares them against the previous period, and summarises where results improved and where they fell. The report helps the manager decide whether to keep the change or roll it back.
Managing prompt versions
A product team maintains dozens of prompts used by different features. Claude can list current prompt versions, show the differences between them, and draft a summary of what changed. Any new version is reviewed by a named approver before it goes live, so the team keeps control of wording that affects customers.
Tracking cost and performance trends
A finance or operations head wants a weekly view of how the AI application is performing. Claude analyses metrics such as response times and usage volumes in Langfuse and produces a short written briefing with the main changes and anything that needs attention. Leaders receive the insight without needing to learn the dashboard.
Preparing for a launch
Before releasing a new AI feature, a head of department wants confidence that it behaves well on real cases. Claude helps gather a sample of past traces covering common and difficult questions, and reports the evaluation outcomes. The launch decision is then made by people using evidence the team can trace and audit.
An end-to-end workflow: Weekly quality review for a customer support chatbot
TriggerEvery Monday at 8:00, a scheduled task asks Claude to prepare the weekly quality review.
Claude retrieves the past week's traces from Langfuse for the support chatbot project.
It filters for conversations with low evaluation scores or escalations to a human agent.
Claude groups the issues by topic, such as refunds, delivery or account access, and notes which prompt versions were in use.
It drafts a summary and posts the findings to the team's Microsoft 365 Teams channel, with links to the source records held in Langfuse.
The quality lead reviews the summary and creates tickets in the ticketing system for issues that need engineering or content fixes.
Any proposed prompt change is held for approval before it is published.
OutcomeThe team spots emerging problems within days instead of weeks, and each fix is tracked from the first complaint to the approved change. Typical results include faster reviews and clearer accountability, though outcomes vary by organisation.
How we set it up
Free automation audit
We review your current use of Langfuse and your AI applications to identify the workflows worth automating, such as quality reviews, prompt governance or reporting. You leave with a clear list of priorities and an honest view of what is practical.
Secure connection with an API key
We connect Claude to your Langfuse project using an API key created for this purpose. The key is stored securely and scoped to the projects you choose, following least-privilege access so Claude sees only what it needs.
Workflow design
We design the end-to-end process across Langfuse and your other systems, such as Microsoft 365, your CRM or ticketing tool. We agree which actions Claude can take alone and which require a named person to approve.
Testing on real cases
We test the workflow against past traces and evaluations from your own applications. We check accuracy, refine the instructions, and confirm that approvals and audit logs work as intended before anything goes live.
Launch, monitoring and training
We launch with monitoring and audit logging in place, then train your team on asking questions and reviewing Claude's outputs. Support continues after launch so the workflow keeps improving as your applications change.
Who benefits
- Head of AI or engineering
- Faster diagnosis of why an AI application gave a poor answer, using traces gathered and summarised automatically.
- Quality and compliance lead
- Regular, documented reviews of evaluation results with an audit trail of every change and approval.
- Product manager
- Clear summaries of prompt changes and performance trends, without needing to navigate technical dashboards.
- Operations or finance director
- Concise weekly briefings on usage and performance that support budgeting and planning decisions.
Security and control
- Least-privilege API key access, limited to the Langfuse projects you select.
- Human approval required before consequential actions such as publishing a prompt version.
- Audit logs recording what Claude accessed and what it proposed or changed.
- Regular testing against your own data, with monitoring after launch to catch drift or errors.
- Clear rules about which data may be processed and where, agreed before connection.
For teams in the US, UK, India and the Middle East
Organisations in the United States, the United Kingdom, India and the Middle East each have different data protection rules, including the Digital Personal Data Protection Act, 2023 in India and data protection laws in the UAE and Saudi Arabia. We set up the connection to respect the rules that apply to your data. Claude can be hosted in India, the United States, the United Kingdom or the European Union, and we choose the hosting region to meet your legal and contractual requirements.
Learn more about Claude and MCP
Anthropic's AI model and assistant.
Secure links between Claude and business tools.
How engineers build and change software with Claude.
Hosted agents that run multi-step work.
The open standard Claude uses to connect to tools.
Frequently asked questions
What does the Langfuse MCP server do?
The Langfuse MCP server lets an AI assistant such as Claude access Langfuse data through a standard, controlled interface. It can typically retrieve traces, evaluation results, prompt information and metrics when asked. It acts as the secure link between the assistant and your Langfuse project, so you do not need to write custom code for each question.
Is connecting Langfuse to Claude secure?
It can be, when set up carefully. The connection uses an API key with least-privilege access, limited to the projects you choose. Actions can require human approval, and audit logs record activity. We also agree data handling and hosting arrangements before launch, so the setup fits your security and legal requirements.
What can Claude do with Langfuse once connected?
Typically, Claude can search and filter traces, review evaluation scores, compare prompt versions and analyse metrics, then explain the results in plain English. It can also draft summaries and reports. The exact actions available depend on the permissions you grant and how the workflow is designed.
How long does setup take?
A focused setup for a single project often takes a few weeks, including the audit, connection, testing and training. Larger deployments across several applications or systems take longer. We confirm a realistic timeline during the free automation audit, once we understand your current setup.
Which Langfuse plan or permissions are needed?
Generally, you need a Langfuse account with an API key that has access to the projects you want Claude to work with. The specific plan and permission levels depend on your Langfuse arrangement and your needs. We check your current setup during the audit and advise on any changes needed before connection.