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# Building LangGraph: Designing an Agent Runtime from first principles

**[LangChain](https://daily.dev/sources/langchain)** · 21 min read · 39 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.langchain.com/building-langgraph/>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#langgraph](https://daily.dev/tags/langgraph), [#llm](https://daily.dev/tags/llm)

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