Awesome LangGraph
An index of the LangChain + LangGraph ecosystem: concepts, projects, tools, templates, and guides for LLM & multi-agent apps.
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🦜🕸️ Awesome LangGraph & LangChain Ecosystem 
The definitive index of frameworks, templates, and real-world projects for teams that want to build, observe, evaluate, and deploy stateful, tool-using AI agents with the LangChain + LangGraph stack.
Whether you’re prototyping your first workflow or operating production systems, this list maps the full lifecycle of agent development, from building with core libraries and integrations, to observing runs with platform tooling, evaluating quality and behavior, and deploying reliable agent applications.
What you’ll find
- Core frameworks: LangChain, LangGraph, Deep Agents, and LangSmith
- Resources for building, observing, evaluating, and deploying agent systems
- Integrations & MCP tooling across models, vector stores, loaders, and tools
- Official LangChain/LangGraph projects and prebuilt agent libraries
- Community projects grouped by use case (RAG, web automation, research, finance, etc.)
- Starter templates and learning resources to get productive fast
Contributions welcome—see the Contributing Guide.
Table of Contents
- 🦜🕸️ Awesome LangGraph & LangChain Ecosystem
- 🌟 Community Projects
- 🧬 Bio & Health
- 🌐 Web Automation & Scraping
- 📊 Business Intelligence & Market Research
- 🖥️ Chat Interfaces & GUIs
- ☁️ Cloud & DevOps
- 🤖 Coding/Dev Agents
- 🛎️ Customer Ops
- 📊 Data Platforms
- 🧮 Data Science
- 🛠️ Developer Tools
- 📦 Packages
- 💰 Finance & Fintech
- 🎯 Marketing
- 🎥 Media & Podcasts
- 🤖 Robotics & Embodied AI
- 📚 RAG & Document Processing
- 🔬 Research Agents
- 🌍 Sustainability
- 📋 Templates & Starters
- 🏢 Workplace & Productivity
- 📚 Learning Resources
- 📖 Additional Resources
- 👥 Communities
- 🤝 Contributing
- 🙏 Acknowledgments
🌐 What is the LangChain/Graph Ecosystem
The LangChain/Graph Ecosystem is a comprehensive suite of frameworks and platforms for building, deploying, and managing LLM-powered applications. While LangGraph can be used standalone, it integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
Source: LangChain Documentation
Ecosystem Components:
🔗 LangChain - Provides integrations and composable components to streamline LLM application development. Contains agent abstractions built on top of LangGraph.
🕸️ LangGraph - The core framework for building stateful, multi-agent systems with complex workflows, collaboration, and memory management.
🧠 Deep Agents - An agent harness for building agents that can plan, decompose complex tasks, use subagents, manage large context with filesystem tools, and persist long-term memory.
🛠️ LangSmith - The platform layer for observing, evaluating, and deploying AI agents and LLM applications with tracing, prompt engineering, Agent Server deployment, sandboxes, and operational tooling.
🧩 LangSmith Fleet - A no-code platform for creating and managing AI agents from templates, connecting apps and accounts, automating routine work, and keeping humans in control with approvals and oversight.
🤝 LangChain Integrations & Partners - Third-party integrations and provider packages that extend LangChain's capabilities across the AI ecosystem. These integration packages provide standardized interfaces to work with popular AI services, databases, and tools.
🦜 LangChain 🔗
LangChain is the foundational framework for building applications with Large Language Models (LLMs). It provides standardized interfaces, reusable components, and extensive integrations that enable developers to create sophisticated AI applications through composable building blocks.
▫️ Core Components and Usage ▫️
Essential building blocks and advanced capabilities for LangChain applications - from fundamental components to sophisticated AI features.
Core Components
Essential building blocks for LangChain applications
| Component | Description |
|---|---|
| 🤖 Agents | Decision-making systems that use LLMs to determine which actions to take |
| 🧠 Models | Unified interfaces for LLMs and embedding models across providers |
| 💬 Messages | Structured communication format between components |
| 🛠️ Tools | External function calls and integrations for agents |
| 🧭 Short-term Memory | Working memory for maintaining conversation context |
| ⚡ Streaming | Real-time response processing for partial results |
Advanced Usage
Advanced capabilities and techniques for sophisticated AI applications
| Feature | Description |
|---|---|
| 🧠 Long-term Memory | Persistent memory that survives across sessions |
| 🛡️ Guardrails | Safety checks and policy enforcement for agent inputs, outputs, and tool usage |
| 🎯 Context Engineering | Techniques for optimizing prompts and context management |
| 📋 Structured Output | Generate responses in specific formats and schemas |
| 🔗 Model Context Protocol | Standardized tool integration and context sharing |
| 👥 Human-in-the-Loop | Approval workflows and interrupt-based human oversight for sensitive agent actions |
| 🤝 Multi-agent | Coordinated systems with multiple AI agents |
| 🔍 Retrieval | Advanced document retrieval and RAG patterns |
| ⚙️ Runtime | Production deployment and runtime management |
| 🔧 Middleware | Custom processing layers and request/response modification |
▫️ LangChain Libraries ▫️
| Package | Python | TypeScript | Description |
|---|---|---|---|
| LangChain | langchain | langchain | Main framework with chains, agents, retrieval methods, and cognitive architecture |
| LangChain Core | langchain-core | @langchain/core | Base abstractions and runtime for the entire ecosystem |
| Community | langchain-community | @langchain/community | Third-party integrations and community contributions |
| MCP Adapters | langchain-mcp-adapters | - | Make Anthropic MCP tools compatible with agents |
| Text Splitters | langchain-text-splitters | @langchain/textsplitters | Document processing and text splitting utilities |
| Experimental | langchain-experimental | @langchain/experimental | Beta features and experimental components |
| CLI Tools | langchain-cli | - | Command line interface for project management |
| Legacy | langchain-legacy | - | Legacy components from pre-v1.0 (Python only) |
▫️ LangChain Documentation ▫️
Access the official LangChain documentation across the current unified docs experience and legacy redirect URLs:
| Docs | Python | JavaScript | Notes |
|---|---|---|---|
| Current Open Source Docs | Overview | Overview | Current unified LangChain OSS docs on docs.langchain.com |
| Legacy Redirects | Legacy Entry | Legacy Entry | Older URLs that now redirect to the current overview docs |
AI-accessible documentation format for LLMs and IDEs - LangChain now exposes a unified llms.txt entrypoint on docs.langchain.com for programmatic access to the latest documentation across LangChain, LangGraph, LangSmith, and API references.
Available Files
| Scope | llms.txt | llms-full.txt |
|---|---|---|
| Unified LangChain Docs | docs.langchain.com/llms.txt | N/A |
| Legacy Redirects | python.langchain.com/llms.txt, js.langchain.com/llms.txt | N/A |
Format Differences
llms.txt- Unified index file with links and summaries for the latest LangChain ecosystem docs- Legacy
llms.txtURLs - Older Python and JavaScript endpoints that currently redirect to the unified docs file
⚠️ Review Output: Even with up-to-date documentation, current models may not always generate correct code. Always review generated code before production use.
🦜 LangGraph 🕸️
LangGraph is an open-source framework for building AI agents and multi-agent systems as graphs, and is a core part of the LangChain Ecosystem. It focuses on agent orchestration, enabling sophisticated AI applications that can maintain state, coordinate multiple agents, and handle complex reasoning processes through graph-based workflows.
▫️ Core Features ▫️
| Capability | Description | Key Features |
|---|---|---|
| 💾 Persistence | State persistence across executions and failures | Checkpointing, state recovery, session continuity |
| 🔄 Durable Execution | Build agents that persist through failures and run for extended periods | Automatic resume, failure recovery, long-running workflows |
| ⚡ Streaming | Real-time execution with partial results and live updates | Token streaming, progress tracking, responsive UX |
| ⏸️ Interrupts | Pause graph execution for human input, review, and intervention | Approval checkpoints, state editing, resumable workflows |
| ⏰ Time Travel | Navigate through agent execution history and states | State debugging, execution replay, historical analysis |
| 🧠 Add and Manage Memory | Comprehensive memory management for stateful agents | Short-term working memory, long-term persistence, memory optimization |
| 📊 Subgraphs | Nested graph structures for complex workflow composition | Modular workflows, reusable components, hierarchical execution |
| 🧪 Testing | Validate graph behavior with unit and partial-execution testing patterns | Node testing, partial execution, checkpointer-based test setup |
| 👀 Observability | Trace and debug graph execution with LangSmith | Execution traces, state inspection, runtime monitoring |
▫️ LangGraph Libraries and SDKs ▫️
| Package | Python | TypeScript | Description |
|---|---|---|---|
| LangGraph | langgraph | @langchain/langgraph | Core graph-based agent orchestration framework |
| LangGraph CLI | langgraph-cli | @langchain/langgraph-cli | Command line interface for LangGraph development and deployment |
| LangGraph SDK | Python SDK | JavaScript SDK | Official SDKs for interacting with LangGraph Platform(Server) and deployed applications |
▫️ LangGraph Documentation ▫️
Access the official LangGraph documentation across the current unified open source docs:
| Docs | Python | JavaScript | Notes |
|---|---|---|---|
| Current Open Source Docs | Overview | Overview | Current LangGraph OSS docs on docs.langchain.com |
| Quickstart | Quickstart | Quickstart | Build a first LangGraph app with tools, memory, and streaming |
AI-accessible documentation format for LLMs and IDEs - LangGraph documentation is part of the unified docs.langchain.com docs experience, so the primary llms.txt entrypoint is shared across the broader ecosystem docs.
| Scope | llms.txt | llms-full.txt |
|---|---|---|
| Unified LangChain Docs | docs.langchain.com/llms.txt | N/A |
🧠 Deep Agents
Deep Agents is an open-source agent harness for building agents that can plan, delegate to subagents, manage context with filesystem tools, and persist long-term memory for complex, multi-step tasks.
▫️ Core Capabilities ▫️
| Capability | Description | Key Features |
|---|---|---|
| 🗂️ Planning & Task Decomposition | Break large tasks into manageable steps and track progress | Built-in todo system, adaptive planning, multi-step execution |
| 📁 Context Management | Offload and manage large context through filesystem tools | Virtual filesystem, file read/write/edit tools, context compaction |
| 🧩 Subagents | Delegate work to specialized agents for context isolation | Built-in task delegation, specialized subtasks, cleaner main-agent context |
| 🧠 Memory | Persist useful information across threads and sessions | Long-term memory, memory stores, cross-thread recall |
| ✋ Human-in-the-Loop | Add approval checkpoints for sensitive operations | Approval controls, resumable execution, tool-level oversight |
| 🧪 Sandboxes | Execute code in isolated environments when needed | Sandbox backends, secure execution, remote runtime options |
| ⚡ Streaming | Stream intermediate output and agent progress in real time | Live progress updates, streamed responses, responsive UX |
| 🛠️ Skills | Extend agents with reusable, task-specific expertise | Skill directories, progressive disclosure, custom instructions |
▫️ Deep Agents SDK and CLI ▫️
| Package | Python | TypeScript | Description |
|---|---|---|---|
| Deep Agents SDK | deepagents | deepagents | Standalone open-source library for building agents with planning, subagents, filesystem tools, and long-term memory |
| Deep Agents CLI | CLI Docs | CLI Docs | Open-source terminal coding agent built on the Deep Agents SDK |
▫️ Deep Agents Documentation ▫️
Access the official Deep Agents documentation across the current unified open source docs:
| Docs | Python | JavaScript | Notes |
|---|---|---|---|
| Current Open Source Docs | Overview | Overview | Current Deep Agents OSS docs on docs.langchain.com |
| Quickstart | Quickstart | Quickstart | Build a first deep agent with tools, planning, and memory |
| CLI | CLI Overview | CLI Overview | Terminal coding agent built on the Deep Agents SDK |
AI-accessible documentation format for LLMs and IDEs - Deep Agents documentation is available through the same unified docs.langchain.com docs index used across the LangChain ecosystem.
| Scope | llms.txt | llms-full.txt |
|---|---|---|
| Unified LangChain Docs | docs.langchain.com/llms.txt | N/A |
🦜 LangSmith 🛠️
LangSmith is a framework-agnostic platform for building, debugging, evaluating, and deploying AI agents and LLM applications. It works with or without LangChain's open-source frameworks and brings observability, prompt engineering, deployment, and operational tooling into one workflow.
🔹 Core Features 🔹
| Feature | Description | Key Capabilities |
|---|---|---|
| 🚀 Deployment | Deploy agents as production-ready Agent Servers | Managed deployment, scaling, agent runtime, production hosting |
| 📊 Observability | Gain visibility into each step your application takes to debug faster and improve reliability | Tracing, dashboards, alerts, insights, automations |
| 📈 Evaluation | Measure and track application quality over time | Offline experiments, online evaluators, LLM-as-judge, evaluator tooling |
| ✏️ Prompt Engineering | Iterate on prompts with built-in versioning and collaboration | Prompt testing, versioning, collaboration, commit history |
| 🧪 Studio | Design, test, and refine applications end-to-end in a visual interface | Visual development, prompt iteration, debugging workflows |
| 🛡️ Sandboxes | Run code and tools in isolated execution environments | Secure execution, sandbox SDK, controlled runtime environments |
| 🏢 Platform Setup | Configure LangSmith for cloud, hybrid, or self-hosted usage | Admin controls, compliance, data governance, infrastructure options |
🔹 LangSmith SDK 🔹
| Package | Python | TypeScript | Description |
|---|---|---|---|
| LangSmith Client SDK | langsmith | langsmith | Official SDK for LangSmith platform integration |
🔹 LangSmith Documentation 🔹
Access the official LangSmith platform documentation:
| Docs Area | Documentation | Notes |
|---|---|---|
| Overview | LangSmith Docs | Main entrypoint for the LangSmith platform |
| Deployment | Deployment | Deploy agents as Agent Servers in production |
| Observability | Observability | Tracing, dashboards, alerts, and monitoring workflows |
| Evaluation | Evaluation | Offline and online evaluation workflows for AI systems |
| Prompt Engineering | Prompt Engineering | Prompt iteration, testing, and versioning |
| Studio | Studio | Visual interface for building and refining apps |
| Sandboxes | Sandboxes | Isolated execution environments for tools and code |
| Agent Server | Agent Server | Runtime and API surface for deployed agents |
AI-accessible documentation format for LLMs and IDEs - LangSmith is also covered by the unified docs.langchain.com docs index for programmatic access.
| Scope | llms.txt | llms-full.txt |
|---|---|---|
| Unified LangChain Docs | docs.langchain.com/llms.txt | N/A |
▪️ LangSmith Claude Code Plugins - LangSmith observability plugins for Claude Code ▪️
Integrates LangSmith tracing and observability directly into Claude Code workflows. Connect your Claude Code sessions to LangSmith to capture traces, monitor runs, and evaluate agent behavior from within your IDE.
| Repository | langsmith-claude-code-plugins |
| GitHub Stars |  |
| Last Commit |  |
[!TIP] Get started with LangSmith for free! Sign up at langchain.com/langsmith and follow the deployment quickstart, observability quickstart, or evaluation quickstart depending on where you want to start.
🧩 LangSmith Fleet
LangSmith Fleet is a no-code workspace for building AI agents for real work. Start from a template or describe your goal in plain English, connect tools like Gmail, Google Calendar, Slack, Exa, Tavily, and remote MCP servers, and let the agent automate routine workflows while keeping humans in control.
🔹 Core Features 🔹
| Feature | Description | Key Capabilities |
|---|---|---|
| 🧱 No-Code Agent Builder | Create and manage agents without writing code | Template-based setup, AI-assisted configuration, editable instructions |
| 🔌 Connected Tools | Bring context and actions into agents through app integrations | Gmail, Google Calendar, Slack, Tavily, Exa, remote MCP servers |
| 📡 Channels | Use agents where your team already works | Slack and Microsoft Teams integrations, in-channel workflows |
| 🗓️ Schedules | Run recurring tasks automatically | Daily briefings, recurring reports, timed automations |
| ✅ Access & Oversight | Keep humans in control for important operations | Approval flows, oversight controls, safer automation |
| 🛠️ Skills | Extend agents with reusable task-specific behavior | Specialized instructions, reusable workflows, scoped capabilities |
🔹 LangSmith Fleet Documentation 🔹
Access the official LangSmith Fleet documentation:
| Docs Area | Documentation | Notes |
|---|---|---|
| Overview | LangSmith Fleet | Main entrypoint for Fleet |
| Quickstart | Quickstart | Build your first Fleet agent |
| Essentials | Essentials | Connections, automation, memory, and approvals |
| Templates | Templates | Browse ready-made starter agents and customize them |
| Tools | Tools | Connect apps and services to your agents |
| Channels | Channels | Use Fleet in Slack and Teams workflows |
| Schedules | Schedules | Trigger recurring tasks and automations |
🔹 Fleet Templates 🔹
Fleet includes ready-made templates for practical workflows such as:
- Daily calendar brief
- Email assistant
- LinkedIn recruiter
- Social media AI monitor
- Competitor research
Browse the full template catalog here:
🦜 LangChain Integrations & Partners 🤝
Third-party integrations and provider packages that extend LangChain's capabilities across the AI ecosystem. These integration packages provide standardized interfaces to work with popular AI services, databases, and tools.
🔸 Chat Models 🔸
Language models that use message sequences as input/output for conversational AI. Support tool calling, structured output, streaming, and multimodal inputs for building sophisticated chat applications.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Tools & Toolkits 🔸
Enable agents to interact with external systems. Define input schemas for tool calling and executing actions. Support web search, database queries, file operations, browser control, and API integrations.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Middleware 🔸
Control and customize agent execution at every step. Add logging, retries, guardrails, human approval, rate limiting, prompt transforms, and other execution-time behavior.
- Python: Browse middleware
- JavaScript: Browse middleware
🔸 Sandboxes 🔸
Run agent-generated code in isolated execution environments. Sandboxes provide safer boundaries for shell access, filesystem operations, and code execution.
- Python: Browse providers
- JavaScript: -
🔸 Checkpointers 🔸
Persistence backends for LangGraph state. Save and resume agent state across interactions using in-memory, SQL, Redis, MongoDB, cloud, and other checkpoint stores.
- Python: Browse providers
- JavaScript: -
🔸 Retrievers 🔸
Advanced retrieval strategies that combine dense and sparse search methods. Enable sophisticated document retrieval patterns including hybrid search, reranking, and context-aware retrieval for RAG applications.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Text Splitters 🔸
Break large documents into smaller, manageable chunks. Maintain semantic coherence while fitting within model context windows. Essential for RAG pipelines and document processing workflows.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Embedding Models 🔸
Transform raw text into fixed-length vectors that capture semantic meaning. Enable machines to compare and search text based on meaning rather than exact words. Essential for retrieval, semantic search, and ranking workflows.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Vector Stores 🔸
Databases optimized for storing and querying high-dimensional vectors using similarity metrics. Used with embedding models to power semantic search, retrieval, and knowledge base applications.
- Python: Browse providers
- JavaScript: Browse providers
🔸 Document Loaders 🔸
Integrations for ingesting data from files, websites, databases, APIs, and cloud systems. Transform diverse sources into LangChain Document objects for downstream processing.
- Python: Browse providers
- JavaScript: Browse providers
🟢 Official LangGraph Projects 🦜
Examples of applications and tools built with the LangChain ecosystem, ranging from experimental projects to production-ready solutions that showcase different capabilities and use cases.
🟩 Specialized Agent Libraries 🤖
Pre-built agent packages for specific use cases and interaction patterns:
▪️ Computer Use Agent - Automate computer interactions and GUI tasks ▪️
Advanced agent for automating computer interactions and GUI tasks. Provides sophisticated screen interaction capabilities, click/type automation, and visual reasoning for complex desktop automation workflows.
| Python | langgraph-cua-py |
| TypeScript | langgraph-cua |
| GitHub Stars |  |
| Last Commit |  |
▪️ Swarm Agent - Coordinate multiple specialized agents ▪️
Multi-agent coordination system for distributed task execution. Enables dynamic task distribution and collective intelligence across multiple specialized agents working together on complex problems.
| Python | langgraph-swarm-py |
| TypeScript | langgraph-swarm |
| GitHub Stars |  |
| Last Commit |  |
▪️ Supervisor - Hierarchical multi-agent coordination ▪️
Agent orchestration and workflow supervision system. Provides advanced multi-agent coordination, task delegation, and workflow management for complex hierarchical agent architectures.
| Python | langgraph-supervisor-py |
| TypeScript | langgraph-supervisor |
| GitHub Stars |  |
| Last Commit |  |
▪️ MCP Adapters - Integrate Anthropic MCP tools with agents ▪️
Model Context Protocol integration for LangChain agents. Provides tool compatibility and protocol bridging to seamlessly integrate Anthropic MCP tools with agent workflows.
| Python | langchain-mcp-adapters |
| TypeScript | -- |
| GitHub Stars |  |
| Last Commit |  |
▪️ LangMem - Build agents with persistent learning capabilities ▪️
Advanced memory management system for persistent agent learning. Enables memory management, experience replay, and adaptation for agents that learn and improve over time.
| Python | langmem |
| TypeScript | -- |
| GitHub Stars |  |
| Last Commit |  |
▪️ BigTool - Handle large-scale tool ecosystems ▪️
Large-scale tool management and optimization system. Provides advanced tool management and intelligent selection optimization for efficiently handling complex tool ecosystems.
| Python | langgraph-bigtool |
| TypeScript | -- |
| GitHub Stars |  |
| Last Commit |  |
▪️ Deep Agents - Complex, long-term planning and execution ▪️
Advanced agents for complex, long-term planning and execution. Features sophisticated planning tools, sub-agent orchestration, file system access, and detailed prompts for handling complex multi-step tasks.
| Python | deepagents |
| TypeScript | -- |
| GitHub Stars |  |
| Last Commit |  |
▪️ Open Deep Research - Automated PhD-level research agent ▪️
Configurable deep research agent for automated research tasks. Performs comprehensive research across multiple sources with multi-model support, MCP compatibility, and evaluation benchmarks. Achieves performance comparable to popular research agents on Deep Research Bench leaderboard.
| Python | open_deep_research |
| TypeScript | -- |
| GitHub Stars |  |
| Last Commit |  |
🟩 Apps & Agents 📱
Complete application examples showcasing different patterns and use cases:
▪️ LangConnect - Managed RAG service with FastAPI integration ▪️
Managed RAG service with FastAPI and PostgreSQL/pgvector integration. Features document collection management, semantic search, and Docker deployment support for production-ready RAG applications.
| Repository | langconnect |
| GitHub Stars |  |
| Last Commit |  |
▪️ ChatLangChain - Documentation assistant with RAG-based search ▪️
Documentation assistant powered by RAG-based semantic search with intelligent query analysis. Features automated content indexing, duplicate prevention, GenUI, and sophisticated document tracking system.
| Repository | chat-langchain |
| GitHub Stars |  |
| Last Commit |  |
▪️ Agent Inbox - Centralized interface for AI agent interactions ▪️
Centralized interface for AI agent interactions featuring real-time communication. Includes interrupt handling and configurable response systems for both local and cloud deployments.
| Repository | agent-inbox |
| GitHub Stars |  |
| Last Commit |  |
▪️ Python Fullstack - All-in-one chatbot template with modern UI ▪️
All-in-one chatbot template combining React-style agents with modern UI. Built with FastHTML components and Claude 3, featuring single-deployment architecture and extensible tools.
| Repository | langgraph-fullstack-python |
| GitHub Stars |  |
| Last Commit |  |
▪️ LangChain Next.js - Next.js starter template for LangChain.js ▪️
Next.js starter template showcasing LangChain.js modules. Includes streaming chat, structured output, multi-step agents, and RAG implementations with Vercel AI SDK integration.
| Repository | langchain-nextjs-template |
| GitHub Stars |  |
| Last Commit |  |
▪️ Multi-Modal Researcher - Research and podcast generation workflow ▪️
Research and podcast generation workflow using LangGraph with Gemini 2.5 model family. Features video understanding, Google search integration, and multi-speaker text-to-speech for creating comprehensive research reports and audio podcasts.
| Repository | multi-modal-researcher |
| GitHub Stars |  |
| Last Commit |  |
▪️ Deep Agents UI - Next.js interface for Deep Agents ▪️
Next.js interface for Deep Agents with streaming support and LangGraph Platform integration. Generic AI agents capable of handling tasks of varying complexity with customizable UI components.
| Repository | deep-agents-ui |
| GitHub Stars |  |
| Last Commit |  |
▪️ Gen UI Computer Use - Generative UI web app for Computer Use Agents ▪️
A Generative UI web app for interacting with Computer Use Agents (CUA). Uses the @langchain/langgraph-cua prebuilt package and features a modern interface for computer automation and task management.
| Repository | gen-ui-computer-use |
| GitHub Stars |  |
| Last Commit |  |
▪️ Social Media Agent - Generates social media posts from URLs ▪️
Generates Twitter & LinkedIn posts from URLs with optional human review. Features content analysis, platform-specific formatting, and approval workflows for social media content creation.
| Repository | social-media-agent |
| GitHub Stars |  |
| Last Commit |  |
▪️ Open SWE - Open-source asynchronous coding agent ▪️
Open-source asynchronous coding agent built on LangGraph for long-running, autonomous software engineering tasks. Supports Anthropic and OpenAI models and is designed for complex, multi-step code generation and modification workflows.
| Repository | open-swe |
| GitHub Stars |  |
| Last Commit |  |
🟩 Development Tools 🛠️
Development tools for building, visualizing, and deploying LangGraph applications:
▪️ LangGraph Generator - Code generation and project scaffolding ▪️
Automated code generation tool for LangGraph projects. Converts YAML configurations to working code, provides project scaffolding, and automates boilerplate creation for rapid development.
| Repository | langgraph-gen-py |
| GitHub Stars |  |
| Last Commit |  |
🌟 Community Projects
Discover amazing open-source projects and innovative tools built by the community using the LangGraph ecosystem. These projects showcase real-world applications across diverse domains and use cases, demonstrating the versatility and power of LangGraph and LangChain frameworks.
Want to add your project? See our Contributing Guide for details on how to submit your LangGraph/LangChain project to this collection.
🧬 Bio & Health
Healthcare, medical diagnosis, genomic research, and scientific research agents
| Project | Description | GitHub Stars |
|---|---|---|
| souvikmajumder26/Multi-Agent-Medical-Assistant | AI-powered multi-agent system for medical diagnosis, research, and patient interaction, featuring LLMs, RAG, and human-in-the-loop validation |   |
| ArcInstitute/SRAgent | Multi-agent framework for automating genomic research and RNA sequencing workflows from scientific databases |   |
🌐 Web Automation & Scraping
Browser control, web task automation, and data extraction
| Project | Description | GitHub Stars |
|---|---|---|
| hrithikkoduri/WebRover | Autonomous agent for automating web tasks and research |   |
| esinecan/agentic-ai-browser | Web automation agent with behavioral caching, DOM fidelity, and success pattern recording |   |
| browser-use/browser-use | Library for AI agents to control websites and automate tasks |   |
| stanford-mast/blast | High-performance serving engine for browser-augmented LLM applications with auto-scaling and OpenAI-compatible API |   |
| ScrapeGraphAI/scrapecraft | Visual editor for building scraping workflows with LangGraph, bulk scraping, and live streaming |   |
| nickhawn/news-agent | News crawler that personalizes daily summaries with Tavily and memory |   |
| hermesagent/langchain-hermes | LangChain tool that screenshots any URL and returns a base64 image for multimodal LLM analysis. No signup required for basic use. |   |
📊 Business Intelligence & Market Research
Business analysis, market research, and strategic intelligence tools
| Project | Description | GitHub Stars |
|---|---|---|
| oba2311/analyst_agent | Marketing analysis agent with trend/sentiment analysis and report generation |   |
🖥️ Chat Interfaces & GUIs
Frontend applications, chat interfaces, and graphical user interfaces for AI agents
| Project | Description | GitHub Stars |
|---|---|---|
| GaiZhenbiao/ChuanhuChatGPT | GUI for ChatGPT/LLMs with agent support, web search, and knowledge base features |   |
| CopilotKit/open-multi-agent-canvas | Multi-agent chat interface with travel/research examples and MCP servers |   |
| teddynote-lab/LangConnect-Client | Streamlit RAG client with document management, semantic/hybrid search, and MCP integration |   |
☁️ Cloud & DevOps
Cloud infrastructure management, deployment automation, and cloud resource management
| Project | Description | GitHub Stars |
|---|---|---|
| eosho/ARMA | Azure Resource Management Assistant with multi-agent architecture for resource provisioning and ARM template validation |   |
🤖 Coding/Dev Agents
AI agents specifically designed for software development, code generation, and programming assistance
| Project | Description | GitHub Stars |
|---|---|---|
| KodyKendall/LlamaBot | Web development coding agent for creating HTML/CSS/JavaScript projects and business landing pages |   |
| AbhinavTheDev/coding-agent | Development tool that uses LangGraph agents to aid coding workflow with natural language |   |
| langtalks/swe-agent | Software engineering multi-agent system with researcher and developer agents for automated code implementation |   |
🛎️ Customer Ops
Customer support, CRM systems, service management, and customer interaction automation
| Project | Description | GitHub Stars |
|---|---|---|
| kargarisaac/telegram_link_summarizer_agent | Telegram bot that summarizes shared links using LangGraph and multi-tool extraction |   |
| gotohuman/gotohuman-langgraph-lead-example | Sales email drafting with human-in-the-loop review |   |
| raminmohammadi/ai-agent-smart-assist | Knowledge base + classification + Q&A for support teams (FAISS + RAG) |   |
📊 Data Platforms
Data analysis, visualization, business intelligence, and data processing agents
| Project | Description | GitHub Stars |
|---|---|---|
| starpig1129/AI-Data-Analysis-MultiAgent | Multi-agent data analysis with visualization and report generation |   |
| project-ryoma/ryoma | Data agent framework for analysis, engineering, and visualization with LangChain integration |   |
| bididi-badidi/FYP-Data-Analysis-With-LLM | Automated data analysis workflows using LangGraph agents — end-to-end pipeline for dataset exploration, statistical analysis, and insight generation with LLMs |   |
🧮 Data Science
Machine learning, statistical analysis, and data science workflow automation
| Project | Description | GitHub Stars |
|---|---|---|
| business-science/ai-data-science-team | AI-powered data science team for common tasks |   |
| leockl/sklearn-diagnose | AI-powered diagnostic tool for analyzing and debugging scikit-learn machine learning models |   |
| RichardKaranuMbuti/ScienceBridge | Scientific research accelerator that analyzes datasets, generates hypotheses, and validates them through code |   |
🛠️ Developer Tools
Development frameworks, toolkits, and development infrastructure
| Project | Description | GitHub Stars |
|---|---|---|
| akamai/patchdiff-ai | AI-powered patch diff analysis tool for automated code change review and security analysis |   |
| sanjeed5/ai-conversation-simulator | Simulated conversations to test assistants; LangSmith integration |   |
| DiTo97/deepagents-backends | Backend services and infrastructure for deploying and managing Deep Agents applications |   |
| emanueleielo/compact-middleware | Claude Code's compaction engine as a drop-in DeepAgents middleware |   |
| JoshuaC215/agent-service-toolkit | Toolkit for deploying agents with FastAPI and Streamlit |   |
| HyperbolicLabs/Hyperbolic-AgentKit | Agent kit with Blockchain/compute features |   |
| googleapis/genai-toolbox | Infra for agent ↔ DB connectivity (security, observability, pooling) |   |
| Darwin-lfl/langmanus | Automation framework with web search, crawling, Python execution |   |
| hinthornw/trustcall | Tenacious tool calling on LangGraph |   |
| langasync/langasync | Asynchronous utilities and extensions for LangChain enabling high-performance async workflows |   |
| andrestorres123/delve | Taxonomy generator for unstructured data |   |
| Bessouat40/RAGLight | Modular RAG/Agentic RAG library for multiple providers |   |
| teddynote-lab/langgraph-mcp-agents | MCP integration toolkit for LangGraph agents |   |
| cryxnet/deepmcpagent | MCP-first agent framework (LangChain/LangGraph) over HTTP/SSE |   |
| cubaseuser123/Cellwise-LanGraph-NoteBook-Agent | LangGraph agent that auto-documents Jupyter notebooks in real-time as cells execute, via IPython hooks |   |
| Azzedde/brainstormers | Curated chains for structured brainstorming |   |
| rsrini7/mermaid2gif | Tool for converting Mermaid diagrams to animated GIFs with optional AI-powered diagram generation |   |
| zamalali/langchain-code | Multi-LLM CLI tool with ReAct & Deep modes for code analysis, feature implementation, and bug fixing |   |
| Mediaeater/swarm.at | Settlement protocol that hash-chains every LangGraph node execution into an immutable audit ledger with trust-tiered agent identities |   |
| eosho/langchain_terminal_agent | Safe, human-in-the-loop terminal assistant built with LangChain that executes shell commands under human review and oversight |   |
| davialabs/davia | Interactive, editable documentation platform designed for coding agents — turns agent outputs into a live, queryable knowledge base |   |
| proactive-agent/langgraphics | Real-time LangGraph execution visualizer — animates live agent graph traversal node-by-node with LangSmith/Langfuse tracing integration |   |
| dkondo/agent-tackle-box | Terminal debugger for LangGraph & LangChain agents — inspect state, tool calls, and semantic breakpoints with a Textual TUI and Python program stepping |   |
| YaVendio/olive | Transforms plain Python functions into remote tools accessible by AI agents, simplifying tool-server creation for LangGraph and other agent frameworks |   |
| sardanaaman/langgraph-compass | Intelligent follow-up question generation module for conversational LangGraph agents — improves dialogue continuity and user intent resolution |   |
| Sean-V-Dev/HMLR-Agentic-AI-Memory-System | Persistent living-memory layer for AI agents — stores and retrieves context across sessions to support long-running agentic workflows |   |
| chopratejas/headroom | Context-optimization proxy layer for LLM applications — compresses token usage, manages context windows, and provides an OpenAI-compatible API for LangChain, MCP, and FastAPI stacks |   |
| tb8412/qae-langgraph-example | Deterministic pre-execution safety certification for LangGraph agents with auditable certificates and constraint-based routing |   |
📦 Packages
Installable packages and libraries for extending LangGraph workflows
| Package | Description | Registry |
|---|---|---|
| MOSS LangGraph | Cryptographic signing for LangGraph workflows. Add tamper-proof audit trails with ML-DSA-44 post-quantum signatures to node outputs and state transitions. |  |
| langchain-colony | LangChain integration for The Colony — the AI agent internet. Provides LangChain-native tools for agents to post, comment, vote, message, and interact on a social platform with 780+ AI agents. |  |
💰 Finance & Fintech
Financial analysis, trading, banking, investment research, and business intelligence
| Project | Description | GitHub Stars |
|---|---|---|
| virattt/ai-hedge-fund | Multi-agent trading system with LangChain |   |
| sagar-n/deepagents | Stock research assistant with specialized analysis agents |   |
| AKMessi/AI-IPO-Analyst | IPO analysis agent with PDF parsing and market data enrichment |   |
| johnsonhk88/AI-Bank-Statement-Document-Automation-By-LLM-And-Personal-Finanical-Analysis-Prediction | Bank statement parsing + personal finance analysis with multi-agent workflow |   |
| OzorOwn/defi-mcp | DeFi MCP server with 12 tools: real-time crypto prices, multi-chain wallet balances (9 chains), DEX quotes via Jupiter and Li.Fi, and token search across 275+ assets |   |
| AKMessi/dealscout | VC due-diligence AI agent with debating sub-agents — a product analyst and market analyst argue opposing views before reaching a joint investment conclusion |   |
🎯 Marketing
Marketing analysis, content strategy, and promotional automation
No active community listings currently.
🎥 Media & Podcasts
Content creation, media processing, podcasts, multimedia generation, and voice processing
| Project | Description | GitHub Stars |
|---|---|---|
| souzatharsis/podcastfy | Turns multi-modal content into podcast-style dialogues |   |
| wassim249/YT-Navigator | Navigate and search across YouTube channel content |   |
| benjichat/voice_agent_base | Voice agent with STT/TTS and web search in a React UI |   |
| von-development/voice-file-agent | Voice-controlled file manager with LangGraph ReAct |   |
🤖 Robotics & Embodied AI
Robotics applications, embodied AI, and physical world interaction
| Project | Description | GitHub Stars |
|---|---|---|
| RobotecAI/rai | Flexible multi-agent framework for developing and deploying Embodied AI features in robotics with multi-modal interaction support |   |
📚 RAG & Document Processing
Retrieval-Augmented Generation, document chatbots, and knowledge base systems
| Project | Description | GitHub Stars |
|---|---|---|
| GiovanniPasq/agentic-rag-for-dummies | A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes |   |
| Goodnight77/Just-RAG | Agentic RAG with LangGraph + Qdrant |   |
| IlyaRice/RAG-Challenge-2 | RAG with custom PDF parsing, parent retrieval, and reranking |   |
| LexStack-AI/LexReviewer | LangGraph-powered legal PDF RAG service with hybrid vector+BM25 retrieval, streaming chat, and citation-aware answers with bounding-box references for highlighting. |   |
| TAMustafa/Local_Chat_RAG | Local RAG chat (Ollama) with sources and modern UI |   |
| bRAGAI/bRAG-langchain | Tutorial series on RAG from basics to advanced |   |
| catio-tech/graphqa | Natural language graph analysis framework for querying graphs without complex query languages |   |
| zamalali/DeepGit | Agentic workflow for intelligent GitHub repo discovery with hybrid retrieval and re-ranking |   |
🔬 Research Agents
AI research assistants, academic tools, and automated research workflows
| Project | Description | GitHub Stars |
|---|---|---|
| bytedance/deer-flow | Deep research framework with search/crawl/Python tools |   |
| duartecaldascardoso/article-explainer | Multi-agent system for understanding research articles with AI-powered explanations and Swarm architecture |   |
| MODSetter/SurfSense | Research agent integrating personal KBs and external sources |   |
| Intelligent-Internet/ii-researcher | Deep search agent with BAML, multi-provider scraping, async flows |   |
| pogjester/company-research-agent | Company research pipeline with streaming and filtering |   |
| jolovicdev/shandu | Source evaluation and knowledge synthesis |   |
| LearningCircuit/local-deep-research | Local deep research with multiple LLMs and web searches |   |
| assafelovic/gpt-researcher | Report-oriented research agent with citations |   |
| Just-Curieous/Curie | Agent for empirical experimentation in ML/systems |   |
| iblameandrew/local-deepsearch-academic | Academic paper discovery with S2 + RAPTOR indexing |   |
| andrestorres123/breeze-agent | Streamlined research flow inspired by STORM on LangGraph |   |
| bernatsampera/event-deep-research | Multi-agent biographical research system extracting structured timeline events from historical figures |   |
| microsoft/RD-Agent | R&D automation for data mining, paper analysis, model tuning |   |
| SalesforceAIResearch/enterprise-deep-research | Steerable enterprise research stack with planner, specialized search agents, and web UI on LangGraph |   |
| xyin-anl/Nodeology | Simplified scientific workflow builder |   |
| lgesuellip/researcher_agent | Turns websites into LLM-ready research content with automated documentation indexing |   |
| guy-hartstein/company-research-agent | Agentic company due-diligence tool powered by LangGraph and Tavily; multi-agent framework with Gemini/GPT for deep company research and financial analysis |   |
| OpenDCAI/Paper2Any | Converts papers, text, or topics into editable research figures, technical route diagrams, and presentation slides using LangGraph agents |   |
🌍 Security & Governance
Action verification, provenance tracking, fail-closed mechanisms, prompt injection protection
| Project | Description | GitHub Stars |
|---|---|---|
| LembaGang/headless-oracle-v5 | Fail-closed market status MCP. Pauses stateful LangGraph agents from entering retry loops during closed markets or DST shifts. |   |
| pic-standard | Local-first protocol for provenance & intent verification before agent actions (fail-closed, verifiable evidence). |   |
| sidclawhq/platform | Approval and audit layer for LangGraph agents. Intercepts tool calls, evaluates them against policies, and holds them for human review before execution. Hash-chain audit trail, 13 framework integrations. Apache 2.0. |   |
🌍 Sustainability
Environmental impact, green technology, and sustainability analysis
No active community listings currently.
📋 Templates & Starters
Ready-to-use project templates, boilerplates, and starter kits for building LangGraph applications
| Project | Description | GitHub Stars |
|---|---|---|
| emanueleielo/deepagents-open-lovable | Open-source Deep Agents implementation with Lovable platform integration for complex planning and execution workflows |   |
| wassim249/fastapi-langgraph-agent-production-ready-template | FastAPI template for LangGraph agents (logging, persistence, security) |   |
| gtesei/agentic_design_patterns | A comprehensive, hands-on collection of design patterns for building robust agentic AI systems using LangChain and LangGraph |   |
| NicholasGoh/fastapi-mcp-langgraph-template | FastAPI template with LangGraph + MCP and streaming UX |   |
| lgesuellip/langgraph-whatsapp-agent | Template for WhatsApp agents with LangGraph and MCP |   |
| ac12644/langgraph-starter-kit | Production-ready multi-agent starter kit with 6 patterns (Supervisor, Swarm, HITL, Structured Output, Research, RAG), 5 LLM providers, MCP integration, CLI scaffolder, and Fastify server |   |
🏢 Workplace & Productivity
Office automation, productivity tools, and workplace management
| Project | Description | GitHub Stars |
|---|---|---|
| ashumishra2104/AI_Travel_agent_Streamlit | Streamlit-based AI travel agent with weather, search, currency conversion, and YouTube integration tools |   |
| eduly-ai/eduly | AI-powered educational platform for personalized learning experiences and tutoring assistance |   |
| emanueleielo/ciana-parrot | Self-hosted AI personal assistant with Telegram integration, scheduled tasks, multi-provider LLM support, and MCP server integration |   |
| tavily-ai/meeting-prep-agent | Meeting prep with calendars, search, and profile research |   |
| temporal-cortex/mcp | Deterministic calendar scheduling MCP server with LangGraph examples — datetime resolution, multi-calendar availability, and atomic booking |   |
| zamalali/InboxHero | Email triage with attachment reading and reply drafting |   |
| khoj-ai/khoj | Self-hosted second brain for docs and web |   |
| raj-maharajwala/AI_Agent_Chatbot_Synapse | Bundle of search/productivity/data agents with UI |   |
| pipeshub-ai/pipeshub-ai | Fully extensible workplace AI platform for enterprise search and workflow automation; connects Gmail, Slack, Notion, Google Drive with LangGraph-powered agents and knowledge-graph RAG |   |
📚 Learning Resources
Official — LangChain Academy
Foundation Courses
| Name | Description |
|---|---|
| Monitoring Production Agents | Learn how to monitor and improve agents in production, track costs, analyze traces, and watch quality and latency over time. |
| Building Reliable Agents | Take an agent from first run to production-ready system through iterative cycles of improvement with LangSmith. |
| Introduction to LangChain - Python | Learn how to build AI agents with LangChain using pre-built architectures and model integrations, then debug them with LangSmith Observability. |
| Introduction to Agent Observability & Evaluations | Learn the essentials of agent observability and evaluations with LangSmith. |
| Introduction to LangGraph - Python | Learn the basics of LangGraph for building agentic and multi-agent applications with more precision and control. |
Project Courses
| Name | Description |
|---|---|
| Deep Agents | Learn the fundamental characteristics of Deep Agents and implement your own Deep Agent for complex, long-running tasks. |
| Ambient Agents with LangGraph | Build an ambient email agent with LangGraph and evaluate it with LangSmith. |
| Deep Research with LangGraph | Build a deep research agent with LangGraph and evaluate its performance with LangSmith. |
Quickstart Courses
| Name | Description |
|---|---|
| LangSmith Agent Builder | Start creating no-code agents for real work using everyday language. |
| LangSmith Essentials | Learn the essentials of LangSmith for continuous testing and improvement with live production data. |
| LangGraph Essentials - TypeScript | Learn LangGraph essentials in TypeScript by building an email workflow. |
| LangGraph Essentials - Python | Learn LangGraph essentials in Python by building an email workflow. |
| LangChain Essentials - TypeScript | Learn the core elements of LangChain in TypeScript, including create_agent, tools, MCP, streaming, and structured outputs. |
| LangChain Essentials - Python | Learn the core elements of LangChain in Python, including create_agent, tools, MCP, streaming, and structured outputs. |
Community Courses
- LangGraph — Develop LLM-Powered AI Agents (Udemy) — Practical LangGraph agent patterns. Instructor: emarco177.
📖 Additional Resources
A compact list of tutorials, workshops, blogs, and case studies you can keep growing over time.
Courses
| Name | Description |
|---|---|
| Ava WhatsApp Agent Course | Build a WhatsApp agent with voice processing, image generation, and long-term memory using LangGraph. |
Examples
| Name | Description |
|---|---|
| GenAI Agents | A collection of agent implementation examples and patterns. |
| Generative AI | Comprehensive Generative AI resource hub covering roadmaps, LangChain/LangGraph projects, RAG, multi-agent patterns, MCP, and interview/coding prep. |
Tutorials
| Name | Description |
|---|---|
| RAG Techniques | Several RAG implementations and step-by-step walkthroughs. |
Workshops
| Name | Description |
|---|---|
| Grounding RAG Applications Workshop | Hands-on RAG chatbot + travel planning agents with JavaScript and Elasticsearch. |
Blogs
| Name | Description |
|---|---|
| LinkedIn — Practical Text-to-SQL | Search & discovery and analytics use cases with text-to-SQL at LinkedIn. |
| Rakuten — From Hype to Real Tools | Partnership details and production applications with LangChain. |
| Komodo Health — Enterprise Assistant | Enterprise assistant enabling domain workflows in healthcare. |
| Cisco Outshift — DevOps Agent via REST | Building a React agent app for DevOps tasks using REST APIs. |
| Elastic — Security GenAI Features | Generative AI features for security workflows. |
Talks
| Name | Description |
|---|---|
| Uber — AI-Driven Developer Productivity | How Uber boosts developer productivity and code generation with AI. |
Docs
| Name | Description |
|---|---|
| GitLab — Duo Workflow | Architecture documentation for code-generation workflows. |
Case Studies
| Name | Description |
|---|---|
| Klarna — Domain Copilot | Production copilot for domain-specific tasks. |
| Minimal — Multi-Agent Customer Support | Customer support system built with LangGraph and LangSmith. |
| OpenRecovery — Clinical Copilot | Clinical copilot for healthcare operations. |
| AppFolio — Embedded Copilots | Product-embedded copilots in a real-estate platform. |
| Infor — Product Copilots & Support | Embedded product experiences, support, and copilots. |
| AirTop — Browser Automation for Agents | Agentic browser automation platform. |
| Athena Intelligence — Research & Summarization | Research and summarization workflows in production. |
| Captide — Agentic Equity Research | Equity research using LangGraph + LangSmith agentic workflows. |
👥 Communities
| Name | Type | Description |
|---|---|---|
| LangChain Community | Official Community | Central hub for announcements, discussions, events, and channels to connect with the LangChain/LangGraph ecosystem. |
| LangChain Forum | Forum | Community discussion forum for asking questions, sharing ideas, and connecting with other LangChain and LangGraph builders. |
🤝 Contributing
We welcome additions and fixes! Please read the Contributing Guide for the submission process, formatting rules, and category guidelines before opening an issue or pull request.
🙏 Acknowledgments
Special thanks to the @langchain-ai team for building an outstanding framework and ecosystem that enables developers to create powerful AI applications.