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Description

Introducing a comprehensive managed PaaS that features a low-code visual builder, VectorDB, along with integrations for various applications and models, designed for the creation, testing, and deployment of high-performance AI applications on the edge. This solution eliminates inefficient and error-prone tasks, allowing users to simply drag and drop models, applications, data, and agents to discover the most effective combinations. You can deploy solutions in less than 60 seconds while significantly reducing latency. The platform supports seamless observation, testing, and iteration processes, ensuring that you maintain visibility and utilize tools that guarantee precision and dependability. Make informed, data-driven decisions with detailed reports on requests, LLM interactions, and usage analytics, while also accessing real-time traces by node. The experimentation feature simplifies the optimization of various elements, including embeddings, prompts, and models, ensuring continuous enhancement. This platform provides everything necessary to launch and iterate at scale, backed by a vibrant community of innovative builders who share valuable insights and experiences. The collective effort distills the most effective tips and techniques for developing AI applications, resulting in an elegant solution that enables the creation of agentic systems with the efficiency of a large team. Furthermore, its intuitive and user-friendly interface fosters seamless collaboration and management of AI applications, making it accessible for everyone involved.

Description

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data