Langfuse
AI Infrastructure & MLOps
Open-source LLM observability and analytics
Langfuse is an open-source observability and analytics platform designed for large language model (LLM) applications. It provides developers with tools to trace execution, run evaluations, manage prompts, and track performance metrics across LLM-powered systems. The platform integrates with popular frameworks and libraries including LangChain, OpenAI, LlamaIndex, and LiteLLM, making it accessible to teams using various LLM stacks. Langfuse enables users to debug application behavior, understand model performance, and identify issues in production environments. Core features include distributed tracing capabilities for tracking requests through complex LLM pipelines, evaluation frameworks for assessing output quality, centralized prompt management for version control and deployment, and comprehensive metrics collection for monitoring application health. Langfuse serves developers and teams building LLM applications who need visibility into their systems' behavior and performance. The open-source model allows organizations to self-host the platform, while a cloud version is available for those preferring managed infrastructure. The project emphasizes transparency and community-driven development, with its codebase available on GitHub. This approach appeals to teams seeking observability solutions they can audit, customize, and extend to meet specific requirements.
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Run and deploy AI models with a cloud API
Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior an
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