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.

DevOS is what happens when you stop thinking of AI as "a tool" and start thinking of it as "an employee." The premise: AI coding agents in 2026 are technically remarkable. Devin can ship features autonomously. Cursor + Claude Code accelerate every senior engineer. Copilot writes half the boilerplate. But every existing AI agent product treats AI as a tool you call — invoked from an IDE, prompted in a chat, given a one-shot task. That's the wrong frame for how teams actually work. Real teams don't have "tools" — they have employees. Employees: Have specialized roles (frontend, backend, QA, design, copy, sysadmin) Take tickets off a sprint board Attend standups and report blockers Hand off work to teammates with context Open PRs, get code reviews, respond to comments Have a track record visible across sprints DevOS treats AI agents the same way. Three layers in one product: 1. The marketplace. Browse specialized AI agents by role. Each comes with role-specific prompting, tool integrations, and a track record. Hire as many as your team's working style needs. Agents are configured for their role — a "frontend dev" agent ships React/Vue with the team's design system patterns; a "copywriter" agent ships marketing pages in your brand voice; a "QA" agent writes Playwright tests and triages bugs. 2. The sprint board. Linear/Jira-style kanban with full agile machinery: sprints, epics, backlog grooming, sprint planning, retrospectives. Agents appear as assignable team members. Drag a ticket onto an agent the same way you'd assign it to a human dev. The agent picks it up, posts an estimate, asks clarifying questions if needed, and starts working. 3. The communication layer. Standups are automated — every morning agents post what they shipped yesterday, what they're doing today, and what's blocking them. PRs are opened in your real repo. Code reviews happen in real GitHub / GitLab. Slack/Discord/Telegram integration so humans can talk to agents like teammates