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Nuno Campos
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[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs.
Inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/), LangGraph lets you coordinate and checkpoint multiple chains (or actors) across cyclic computational steps using regular python functions (or [JS](https://github.com/langchain-ai/langgraphjs)). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/).
The main use is for adding **cycles** and **persistance** to your LLM application. If you only need quick Directed Acyclic Graphs (DAGs), you can already accomplish this using [LangChain Expression Language](https://python.langchain.com/docs/expression_language/).
The main use is for adding **cycles** and **persistence** to your LLM application. If you only need quick Directed Acyclic Graphs (DAGs), you can already accomplish this using [LangChain Expression Language](https://python.langchain.com/docs/expression_language/).
Cycles are important for agentic behaviors, where you call an LLM in a loop, asking it what action to take next.