Changelog

OpenAI has released a major SDK update. The latest Langfuse integration now supports both the new and old versions, ensuring optimal backwards compatibility with other packages.

The JSON viewer is flexible but not super simple to read. Starting with OpenAI Chat Messages you can now view payloads in a pretty format to read and debug even faster.

LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together. Langfuse natively supports the new expression language.

Accurate token counts and costs for GPT-4 Turbo

Optional SSO enforcement on an organizational level for advanced security.

You can now upgrade to the latest version of Langfuse by pulling the latest container. Database migrations are applied automatically on startup. Optionally, one-click deploy via Railway.

RAGAS is an open-source package focused on evaluating RAG pipelines. With this new cookbook, you can use RAGAS evals on your production data in Langfuse.

Drop-in replacement for OpenAI's Python SDK to get full visibility by changing only one line of code.

The Python SDK uses Pydantic to validate the data you send to Langfuse at run time before it is asynchronously sent to the Langfuse API. Previously, the Python SDK used Pydantic v1 leading to incompatibilities with projects using Pydantic v2.

We've removed a performance bottleneck in the API authentication which affected all API requests.

New core dashboard with analytics on token usage, latencies, and scores/metrics.

If you pass JSON as a completion, it is rendered in the Langfuse UI.

Browse traces and users super-⚡️. Jump between traces with keyboard shortcuts (k & j).

Use the new filter builder to filter across all sorts of attributes. State is persisted in the URL, so you can share your filters with others.

Inputs and outputs of spans and generations can be complex, now it is much easier to read them. Click to copy any value.

Collect sets of inputs and expected outputs in Langfuse to evaluate your LLM app. Use evaluations to benchmark different experiments.

Filter generations and export them as CSV, JSON, or OpenAI-JSONL

Run model-based evaluations on your production data in Langfuse using the Python SDK.

You can now make your traces public to discuss them with your team or share them more publicly.

All users on cloud.langfuse.com have access to analytics dashboards. More analytics to come soon in core Langfuse project.

In addition to token counts, usage can now be displayed in USD. Available for many popular models.

Compare metrics across releases of your application or version of your chain/prompt. Releases are automatically tracked for major hosting providers.

Now the Langchain integration is also available for Javascript.

Self-hosting is now easier than ever with our prebuilt Docker images.

We've built the Q&A chatbot for the Langfuse docs, now everyone can access its production traces. Create an account on Langfuse Cloud and play with the Q&A chatbot to see your own traces.

Get usage data from Langfuse API for use in your own application and dashboards. Optionally broken down by user or use case.

Do 'human-in-the-loop' evaluation by adding manual scores right in the Langfuse UI.

LLM applications/chains/agents running on Langchain can now be traces via the native integration.

Share projects with coworkers and manage permissions.

New user interface to browse complex traces in a nested way.

For early-users on Langfuse Cloud, we are releasing an alpha version of our analytics dashboards.

For OpenAI and Anthropic models token counts are now automatically calculated. This is helpful for streaming endpoints that do not return usage data.

After working on the initial version for 2 months, we are excited to launch Langfuse today!