langgraph

Practical system design, tools, and hands-on resources for building Gen-AI agents & agentic AI systems.

AI Agent 教学仓库 | 系统化 LangChain、RAG、LangGraph、MCP 全栈实战代码 | 万字博客详解 | 开源可运行示例 | 从零构建智能体

Swytchcode demo projects with different agents

🤖 An open-source, AI agent-native research canvas application that performs real-time search with HITL (Human in The Loop) capabilities, powered by CopilotKit, Tavily and LangGraph

Aurora — Open source AI-powered agentic incident management & root cause analysis for SREs. LangGraph agents investigate across AWS, Azure, GCP, Kubernetes. Integrates with PagerDuty, Datadog, Grafana, Slack and More. Apache 2.0.

Unified LangChain documentation.

LC-StudyLab 是一个完整演示 LangChain v1.0 全家桶能力的开源项目，整合了 LangGraph、DeepAgents、RAG 检索增强生成、Guardrails 安全校验与流式输出智能体等核心特性，帮助开发者系统掌握 LangChain v1 的所有关键组件

五星大厨:全面Multi-Agent 的客服机器人，基于langraph实现，txt2sql ,txt2cypher, lightrag, 多模态 等

Build LangGraph agents like Next.js apps.

Building a Multi-Agent AI System with LangGraph and LangSmith

Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)

Open-source spreadsheets platform for deep research and document processing

Local execution trees for TypeScript AI agents. agent-inspect helps you understand what happened inside an AI agent run — locally. It turns manual steps, tool calls, LLM calls, structured logs, failures, durations, and run metadata into readable execution trees you can inspect from the terminal. It is built for TypeScript/Node.js developers..

Demo of a customer service agent (Cymbal Air) using LangGraph, Tools, and RAG to interact with Google Cloud Databases via MCP Toolbox.

基于LangChain和LangGraph实现的AI Agent配置平台

👾 下一代透明智能体架构 | Next-Gen Transparent Agent Architecture 🔍 全行为审计 | 🛡️ 两段式安全调用 | 🧠 双水位记忆 | ⏰ 心跳任务 📊 P0 级事故率降低 80% | 兼容 OpenClaw + Claude Code 技能生态

基于 LangChain/LangGraph 的 ReAct Agent ，结合 RAG、工具调用与 Streamlit 界面，面向智能客服与报告生成场景。

📽️ Code from tutorials presented on the "Code AI with Rok" YouTube channel

Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.

A practical AI agents handbook covering agent systems, agentic workflows, LangGraph, MCP/A2A, context engineering, agent memory, evaluation, observability, and multi-agent architecture. Current trend focus: Gemini Interactions API and managed agents, emerging agent runtimes, and production AI workflow patterns.