In this blog piece, you’ll learn why and how we built LangGraph for production agents—focusing on control, durability, and the core features needed to scale.

Langchain is a publication focusing on programming languages, language design, and compiler development. Readers can explore articles covering topics such as language features, syntax design, and compiler optimization techniques. Additionally, they can learn about programming language theory, language implementation challenges, and practical applications of language design principles.

LangChain

LangGraph was designed as a low-level agent framework prioritizing production readiness over ease of getting started. Built to address LangChain's feedback about customization and scaling challenges, it focuses on six core features: parallelization, streaming, task queues, checkpointing, human-in-the-loop capabilities, and tracing. The framework uses a structured execution model based on the Pregel algorithm with channels and nodes, enabling deterministic concurrency and fault tolerance. Performance scales gracefully with agent complexity while maintaining low latency, making it suitable for production deployments at companies like LinkedIn, Uber, and Klarna.

Building LangGraph: Designing an Agent Runtime from first principles