Langfuse November Update - Langfuse

Langfuse November Update

Agent Tracing, Model Pricing Tiers in Cost Tracking, Score Analytics, Langfuse MCP Server & more

Marc Klingen

What a month!

We have just wrapped up Launch Week 4 ( full recap here) with major updates to Agent Observability, Model Pricing Tiers in Cost Tracking, new Score Analytics to align Evaluations, a new hosted MCP server, and so much more...

Major Updates to Agent Observability

We have made agent tracing & evals much more powerful by introducing:

  1. improved tool call visibility with inline details and arguments (available and selected tools)
  2. a unified Trace Log View that simplifies scrolling and searching through agent observations
  3. added more observation types which add meaning to agent spans
  4. Agent Graphs generally available to visualize complex executions across Agent frameworks and custom implementations.

Read more about the updates here

Model Pricing Tiers in Cost Tracking

Langfuse now supports pricing tiers for models with context-dependent pricing, enabling accurate cost calculation for models like Claude Sonnet 4.5, Gemini 2.5 Pro, Gemini 3 Pro Preview that charge different rates based on input token count. We have added pre-configured pricing tiers for 3 commonly used models. Alternatively, you can configure any number of custom pricing tiers via the Langfuse UI or API.

Read more about the updates here

Score Analytics to align Evaluations

Score Analytics now provides comprehensive tools for analyzing and comparing evaluation scores across your LLM application. Whether you’re validating that different LLM judges agree, checking if human annotations align with automated evaluations, or exploring score distributions and trends, Score Analytics gives you the insights you need to trust your evaluation process.

Read more about the updates here

Schema Enforcement for Dataset Items

You can now add JSON Schema validation to your datasets to ensure all dataset items conform to the expected structure. This helps maintain data quality, catch errors early, and ensure consistency across your team when building and maintaining test datasets.

Read more about the updates here

MCP Server for Prompt Management

Langfuse now includes a hosted MCP server built directly into the platform (StreamableHTTP, authenticated with Langfuse API keys). You can use it to iterate on Prompts with Claude Code or let production agents fetch them dynamically as needed. We will extend it to the rest of the Langfuse data platform in the future.

Read more about the updates here

New Integrations

Fixes & Improvements

User Story: Merck

The oldest pharmaceutical company in the world, Merck, is leading innovation in their field. Langfuse is powering 80+ of their AI project teams globally to ship large scale AI Applications and Agents. Read the story.

"Generative AI will only earn enterprise trust when we can see what's happening under the hood. Langfuse enables us to track every prompt, response, cost, and latency in real time, turning black-box models into auditable, optimizable assets."

Walid Mehanna, Chief Data & AI Officer at Merck

Some Pointers