LangGraph
Graph-based agent framework
📋 Overview
LangGraph enables stateful multi-step AI workflows with graph nodes and conditional edges. It provides a comprehensive solution for modern AI workflows. The tool is designed for both beginners and advanced users. It supports multiple deployment options including cloud and self-hosted. LangGraph is a framework for building stateful, multi-step AI workflows using graph-based architectures. It enables developers to create complex agent workflows with conditional branching, parallel execution, and state management.
✨ Key Features
- •Graph-based stateful workflows
- •Conditional edges for decision-making
- •Works with any LLM provider
- •10K+ stars
- •Active community and development
- •Comprehensive documentation
🎯 The Problem It Solves
You need agents that maintain state across steps and make decisions.
🔧 How It Works
Define a graph with nodes (LLM calls) and edges (transitions).
🚀 Installation & Quick Start
Installation
pip install langgraphQuick Start
- pip install langchain langgraph
- Define StateGraph with nodes and edges
- graph = StateGraph(State)
- app = graph.compile()
- app.invoke(input)
✅ Pros
- •Most flexible agent architecture
- •Production-ready
- •Great debugging tools
- •Active community
- •Comprehensive documentation
- •Production-ready
❌ Cons
- •Steep learning curve
- •Complex for simple use cases
- •Learning curve for new users
💬 Practitioner Verdict
“Most powerful agent framework.”
Self-Hosted (Free)
Open source, MIT/Apache licensed. Run it yourself.
⭐ Star & Clone on GitHubFree forever. Your infrastructure, your data.
📊 Specifications
- Language
- Python
- License
- MIT
- Platform
- Linux, macOS, Windows
- Supported Models
- REST API, CLI
💰 Pricing Reality
100% free OSS.