Changelog

Two hours ago, OpenAI released the latest version of their o3-mini model. Langfuse now supports this model in both the LLM playground and cost tracking.

Quickly access different Langfuse features and switch between projects via the Cmd+K menu.

Automatically remove event data outside of a specified retention period.

Langfuse now has a new API Reference with interactive examples.

Complex prompts are easier to edit with our new prompt editor, which supports variable highlighting.

Trace sampling is now supported in the JS SDK and integrations.

Document your updates to prompts

Create your Datasets in seconds

Configure additional HTTP headers for LLM API keys to include custom headers in requests to provider endpoints, particularly useful for OpenAI-compliant LLM proxies.

See a detailed comparison of changes between prompt versions. Optionally, you can also review changes before creating a new prompt version.

As part of our ongoing effort to improve Langfuse for larger teams, we are excited to announce that audit logs are now available on Enterprise/Teams plans.

After running for some time in public beta, we are excited to announce that the PostHog integration is now fully available. It runs on an hourly basis and is available to all Langfuse plans and can be self-hosted.

Langfuse now supports cost tracking all usage types such as cached tokens, audio tokens, reasoning tokens, etc.

Langfuse v3 is now stable and ready for production use when self-hosting Langfuse, including many scalability and architectural improvements.

To make it easier to get started with the Langfuse JS/TS SDK, we've created an extensive end-to-end example notebook. It includes a general introduction and examples for Anthropic, OpenAI SDK and LangChain.

Langfuse now supports GitHub Enterprise and Keycloak as SSO providers on Langfuse Cloud and self-hosted instances

Comprehensive guides for tracing Google Vertex AI and Gemini models with Langfuse

Langfuse now supports Google Vertex AI incl. Gemini models for LLM Playground and Evaluations.

Move fast on prompts without breaking things! Run experiments on Datasets and directly compare evaluation results side-by-side. Experimentation speeds up the feedback loop when working on prompts and prevents regressions when making rapid changes.

We've completely rebuilt the documentation for Datasets and Evals to make it easier to get started with offline evaluation. To celebrate Launch Week, we've also summarized all the documentation improvements we've made over the past year.

Add multi-modal attachments to LLM traces in Langfuse to observe, and view them within the Langfuse UI.

Introducing support for managed LLM-as-a-judge evaluators for dataset experiments.

After running experiments on datasets, you can now compare results side-by-side, view metrics, and peek into details of each dataset item across runs.

Easily use the Langfuse documentation in Cursor and other LLM editors via the new llms.txt file.

Langfuse Prompt Management now integrates natively with the Vercel AI SDK. Version and release prompts in Langfuse, use them via Vercel AI SDK, monitor metrics in Langfuse.

We've updated the sidebar in the Langfuse UI collapsible. This change provides more screen space for viewing traces and other complex pages.

Configure SDK-side masking to redact sensitive information from inputs and outputs sent to the Langfuse server.

Langfuse now supports Amazon Bedrock for LLM Playground and Evaluations.

Tool calling makes Langfuse Evals reliable. Previously, only OpenAI models were supported. With this update, you can use any tool-calling LLM when setting up an LLM-as-a-judge evaluator.

Manage your annotation tasks with ease using our new workflow tooling. Create queues, add traces to them, and get a simple UI to review and label LLM application traces in Langfuse.

Large traces can be hard to read. We've added aggregated latency and cost information to the every span level to make it easier to spot outliers and debug the LLM application.

See previews of all commonly asked questions and feature requests for each page across the Langfuse documentation.

Langfuse is now available on AWS Marketplace, making it the premier choice for AWS customers looking for a powerful LLM observability, analytics and evaluation platform.

Langfuse now provides an integration example for DSPy, offering observability for this powerful framework that optimizes language model prompts and weights.

Prompt management just got more powerful for Langchain users by linking Langfuse prompts to Langchain executions.

Deploy Langfuse behind a custom base path for more flexible self-hosting configurations.

Day 1 support for OpenAI's o1 models including tracking token counts and USD spend.

Automatically initialize Langfuse resources on startup using environment variables, streamlining self-hosted deployments and infrastructure-as-code integrations.

Langfuse prompt management now guarantees instant access to prompts after first use while refreshing the cached version in the background.

Add multiple modalities to a single trace, including text and images.

Add comments to traces, observations, sessions and prompts via the Langfuse UI.

Explore customizable charts for aggregate and time series analytics, grouped by score type, source and name.

You can now replace the documentation, support and feedback links in the UI with references to your internal resources. This helps roll out Langfuse in your organization and ensures that users can find the information they need.

Porter now allows you to deploy a production-grade Langfuse cluster via their Add-On marketplace. If you use Porter, this is the easiest way to get started with self-hosted Langfuse.

Introducing organizations to manage projects and permissions across larger teams working in Langfuse.

Langfuse now supports tracing OpenAI Structured Outputs.

The AI SDK now supports OpenTelemetry-based instrumentation. With the new LangfuseExporter, you can easily collect these traces in Langfuse.

Tables are at the core of Langfuse and we finally made them resizable.

Day 1 support for OpenAI GPT-4o mini including tracking token counts and USD spend.

Display trace or observation inputs and outputs in Markdown format. Enhance the readability of Markdown elements such as headers, lists, blocks of code, tables, and links.