A low-level agent orchestration framework for building stateful, multi-agent AI systems with full control over workflows, memory, and human-in-the-loop oversight.
LangGraph is an MIT-licensed agent orchestration framework that provides granular, low-level primitives for designing fully customizable agent architectures including single-agent, multi-agent, and hierarchical control flows. It features built-in persistent memory stores that maintain conversation histories across sessions. The framework supports native token-by-token streaming, human-in-the-loop interrupt and approval workflows, and avoids black-box abstractions, letting teams build solutions tailored to their specific requirements. Production adoption includes Klarna, Lyft, LinkedIn, and Cloudflare.
Building multi-agent orchestration systems with complex control flows
Creating conversational AI agents with persistent cross-session memory
Implementing human-in-the-loop approval workflows
Developing long-running, fault-tolerant agent applications
Reduced agent development time through reusable orchestration primitives
Improved agent reliability through built-in state management and persistence
Increased flexibility avoiding vendor lock-in
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