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

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

🪢 Langfuse Python SDK - Instrument your LLM app with decorators or low-level SDK and get detailed tracing/observability. Works with any LLM or framework

🪢 Langfuse documentation -- Langfuse is the open source LLM Engineering Platform. Observability, evals, prompt management, playground and metrics to debug and improve LLM apps

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.

🟪 Open source Agent Governance Platform that turns any LangGraph or ADK agent into a production-ready service. Supports: AG-UI, CopilotKit API, OpenTelemetry, MCP, memory, guardrails, SSO, RBAC.

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

🪢 Langfuse JS/TS SDKs - Instrument your LLM app and get detailed tracing/observability. Works with any LLM or framework

Agentic AI 智能体开发实战

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.

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

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

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

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.

This repository provides resources and guidelines to facilitate the integration of Open-WebUI and Langfuse, enabling seamless monitoring and management of AI model usage statistics.

AI agent simulation framework

SynthGenAI - Package for Generating Synthetic Datasets using LLMs.

🪢 Auto-generated Java Client for Langfuse API

Langfuse Go SDK

An n8n community node that brings Langfuse observability to your OpenAI chat workflows.