LangGraph

LangGraph is an open-source framework for building stateful, multi-actor applications powered by large language models, designed specifically for creating sophisticated AI agent architectures. Built as an extension of LangChain, LangGraph enables developers to create cyclical graphs essential for developing AI agents that can persist through failures, collaborate with humans, and maintain comprehensive memory across sessions.

Key Features:

• Stateful Multi-Agent Architecture - Enables creation of stateful, multi-actor applications using LLMs with cyclical graph structures for complex agent runtimes
• Durable Execution - Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off
• Human-in-the-Loop Integration - Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution with time-travel debugging capabilities
• Comprehensive Memory Management - Built-in memory stores conversation histories and maintains context over time, enabling rich, personalized interactions across sessions
• Graph-Based Workflow Control - Visual representation of workflows as graphs with nodes and edges, offering structured approach to complex task orchestration
• Real-Time Streaming - Native token-by-token streaming and streaming of intermediate steps, showing agent reasoning and actions in real time
• Production-Ready Deployment - Scalable infrastructure designed specifically for stateful, long-running workflows with horizontal scaling and intelligent caching
• LangGraph Platform - Integrated deployment and scaling service with visual prototyping studio, APIs for agent UXs, and configurable assistants
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Use Cases:

• Complex multi-agent systems requiring coordination and collaboration
• Agentic RAG systems with document grading and query rewriting
• Research assistants with autonomous data summarization and insight extraction
• Code migration and developer platform automation tools
• SQL query generation agents for data accessibility
• Customer service agents with persistent context and handoff capabilities
• Scientific research and analysis workflows

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