Langfuse
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Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
DocuCommit is a self-hosted documentation platform that stores every page as plain Markdown in your own Git repository. There is no database: every edit is a real commit, so revision history, diffs, and restore come from Git itself, not from a vendor's revision table.
Non-developers get a WYSIWYG editor that writes clean Markdown (toggle to source anytime), a guided three-pane merge when two people edit the same page, and paragraph comments stored in a sidecar file so the Markdown stays clean. Developers get files they can grep, and AI agents can read the entire knowledge base with a git clone. No integration layer, no sync pipeline.
Includes full-text search, draw.io and Mermaid diagrams stored next to the Markdown, and one-click export to Markdown, HTML, and PDF. A desktop app for editing, plus a read-only server (Docker) that publishes the docs to the whole team. Leaving costs nothing: the repo is already yours, so there is nothing to migrate out of.
A startup from Sweden.
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In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / 19 days ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / about 1 month ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 2 months ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 2 months ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / 2 months ago
Helicone AI - Open-source LLM Observability for Developers
BookStack - An open source knowledge management application that's focused on ease of use.
LangSmith - Build and deploy LLM applications with confidence
GitBook - Modern Publishing, Simply taking your books from ideas to finished, polished books.
LangChain - Framework for building applications with LLMs through composability
Confluence - Confluence is content collaboration software that changes how modern teams work