LangGraph is an open-source framework for building controllable and resilient language agents as stateful graphs. It targets developers seeking to create sophisticated AI agents with custom architectures, long-term memory, and human-in-the-loop capabilities for complex tasks, offering enhanced reliability and visibility over simpler orchestration methods.

How It Works

LangGraph enables agent orchestration by representing agent workflows as graphs, allowing for custom architectures and state management. It provides low-level primitives for building agents, supporting features like moderation checks, human-in-the-loop approvals, and persistence of context for long-running workflows. This approach offers greater control and extensibility compared to rigid abstractions, facilitating the creation of scalable multi-agent systems.

Quick Start & Requirements

• Install: pip install -U langgraph
• Example dependency: pip install langchain[anthropic] for ReAct agent.
• Resources: Requires Python. Specific LLM provider API keys may be needed for agent execution.
• Docs: https://langgraph.ai/
• Tutorials: https://langgraph.ai/tutorials

Highlighted Details

• Used in production by companies like Replit, Uber, and LinkedIn.
• First-class streaming support for token-by-token and intermediate step visibility.
• Integrates seamlessly with the LangChain ecosystem for enhanced observability (LangSmith) and deployment (LangGraph Platform).
• Inspired by Pregel, Apache Beam, and NetworkX.

Maintenance & Community

• Developed by LangChain Inc.
• Community resources include LangChain Academy, tutorials, templates, and how-to guides.

Licensing & Compatibility

• License: MIT.
• Compatibility: Can be used standalone or integrated with LangChain products.

Limitations & Caveats

The framework is designed for developers and may require a learning curve for complex agent implementations. While it can be used without LangChain, leveraging the broader ecosystem is recommended for full benefits.