Overview
LangGraph is a framework for building controllable, production-grade AI agents with graph-based workflows. It extends LangChain by providing primitives for orchestrating multi-step, non-linear processes.
Key Statistics
Overall Rating
4.2/5
GitHub Stars
19,900
Last Updated
2025-10
Version
0.2.62
Features
Graph-based workflows
Graph-based workflows capabilities
State management
State management capabilities
Multi-agent orchestration
Multi-agent orchestration capabilities
Getting Started
Installation
pip install langgraph
Quick Start
Define nodes and edges for workflow graph
Code Example
from langgraph.prebuilt import create_react_agent
Pros & Cons
Advantages
Full control over agent behavior with low-level primitives
Excellent for complex non-linear workflows
Built-in state persistence and memory management
Production-proven by major companies (Klarna Uber LinkedIn)
Strong streaming and observability features
Human-in-the-loop support is first-class
Can be used standalone or with LangChain
MIT licensed with commercial platform option
Limitations
Steeper learning curve than LangChain
Requires understanding of graph theory concepts
May be overkill for simple linear workflows
Smaller community than LangChain (but growing)
Some advanced features require LangGraph Platform
Documentation still maturing compared to LangChain
More complex setup for basic use cases
Technical Details
Primary Language
Python
Supported Languages
License
MIT
Enterprise Ready
Yes
Community Size
Large
Pricing
Open Source + Commercial
Open source MIT. LangGraph Platform for enterprise deployment
Performance Metrics
easeOfUse
3/5
scalability
5/5
documentation
4/5
community
4/5
performance
5/5
Common Use Cases
Complex customer support workflows with escalation
Multi-agent research and analysis systems
Task management and orchestration
Long-running business process automation
Interactive assistants with memory
Decision support systems with conditional logic
Collaborative agent systems
Workflows requiring human oversight and approval