langfuse

Interactive CV with AI chat integration. Built with React 19, TypeScript, Claude API. Chat with my AI avatar about my experience.

End-to-end AI short-video production pipeline. FastAPI orchestration + Spring Boot gateway with multi-model failover, circuit breaker, metering, and full-stack observability. AI quality gating: prompt anchoring, CLIP consistency, AV sync auto-rescue.

A modern template for agentic orchestration — built for rapid iteration and scalable deployment using highly customizable, community-supported tools like MCP, LangGraph, and more.

🪢 Langfuse Python SDK

Agent Skills for Langfuse, the open source LLM engineering platform for tracing, prompt management, and evaluation

Community-maintained Kubernetes config and Helm chart for Langfuse

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

🟪 Open-source runtime that ships any LangGraph or Google ADK agent as a production-ready FastAPI service. Bundled , AG-UI copilotkit API, chat UI, 15+ guardrails, MCP, OpenTelemetry, OIDC. One pip install. Self-hosted, no vendor lock-in.

Agentic AI 智能体开发实战

Model Context Protocol (MCP) Server for Langfuse Prompt Management. This server allows you to access and manage your Langfuse prompts through the Model Context Protocol.

🪢 Langfuse JS/TS SDK

Visualize live LangGraph execution and see how your agent thinks as it runs.

To simplify and streamline LLM operations, empowering developers and organizations to harness the full potential of large language models with ease.

Production-ready Next.js template for building AI agents with LangGraph.js. Features MCP integration for dynamic tool loading, human-in-the-loop tool approval, persistent conversation memory with PostgreSQL, and real-time streaming responses. Built with TypeScript, React, Prisma, and Tailwind CSS.

A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability

A comprehensive toolkit for deploying production-ready Generative AI infrastructure on Amazon EKS. Includes pre-configured components for: 🚀 AI Gateway (LiteLLM) 🤖 LLM Serving (vLLM, SGLang, Ollama) 📊 Vector Databases, 🔍 Embedding Models (TEI) 📈 Observability (Langfuse, Phoenix) etc. Fast-track your GenAI deployment with Kubernetes

Self-hosting Langfuse on Amazon ECS with Fargate using CDK Python

Pluggable extension packages for the Pi AI-agent runtime. Each package exposes LLM-callable tools via MCP — covering IM workspace integrations (Lark, DingTalk, WeCom), browser & computer automation, persistent memory, telemetry, task scheduling, image generation, web access, security policies, and more.

🪢 Auto-generated Java Client for Langfuse API

This project applies the core knowledge from the LLMOps module, including the design and implementation of the API Layer, Inference Layer, Observability Layer, Cache Layer, Guardrails Layer, Routing Layer, and the Data Ingestion Pipeline.