LangGraph
A LangGraph Library is a LangChain graph-based programming library (in LangChain to enable the creation of stateful, multi-actor LLM applications).
- Context:
- It can create Cyclical Graphs that allow outputs from one node to feed back into another, enabling complex reasoning loops and agent-like behaviors with LLMs1†source4†source.
- It can manage state effectively, using an internal data structure that is passed and updated between nodes throughout the execution of the graph. This state management supports the sharing and persistence of information across different graph nodes3†source4†source.
- It can coordinate multiple actors or chains in sophisticated multi-step computations, facilitating complex interactions and workflows that involve multiple agents3†source.
- It can perform conditional branching where the output from one node determines the next node to execute, allowing for dynamic and adaptive application behaviors2†source.
- It can be compatible with the existing tools, agents, and components of the LangChain ecosystem, leveraging these resources to enhance functionality and utility in applications requiring looped or cyclical logic1†source4†source.
- It can be useful in developing agent-like systems where LLMs need to decide on actions in a cycle or loop, mirroring human-like decision-making processes1†source4†source.
- ...
- Example(s):
- 2024-01-04.
https://pypi.org/project/langgraph/0.0.8/. - 2024-05-07.
https://pypi.org/project/langgraph/0.0.47/. - ...
- 2024-01-04.
- Counter-Example(s):
- See: LangChain, Stateful Application, Graph-Based Programming, Agent-Like Systems.
References
LangGraph is a library built on top of LangChain that enables the creation of stateful, multi-actor applications with large language models (LLMs). It extends LangChain's functionality by introducing an easy way to create cyclical graphs, which are often needed for building agent-like systems or runtimes.[1][4]
The key features of LangGraph include:
1. **Cyclical Graphs**: LangGraph allows for the creation of cyclical graphs, where the output of one node can feed back into another node, enabling agent-like behaviors and reasoning loops with LLMs.[1][4]
2. **State Management**: LangGraph introduces the concept of state, which is an internal data structure that gets passed between nodes and updated as the graph executes. This state can be used to store and share information between different nodes in the graph.[3][4]
3. **Multi-Actor Coordination**: LangGraph enables the coordination of multiple chains or actors across multiple steps of computation, allowing for complex multi-agent interactions and workflows.[3]
4. **Conditional Branching**: LangGraph supports conditional branching, where the output of a node can be used to determine which node to execute next, enabling dynamic and adaptive behavior.[2]
LangGraph is designed to work seamlessly with the LangChain ecosystem and can leverage all the existing tools, agents, and components provided by LangChain. It is particularly useful for building agent-like systems, where an LLM is used to reason about the next action to take in a loop or cycle.[1][4]
Citations: [1] https://www.youtube.com/watch?v=qaWOwbFw3cs [2] https://www.youtube.com/watch?v=nmDFSVRnr4Q [3] https://github.com/langchain-ai/langgraph [4] https://blog.langchain.dev/langgraph/ [5] https://js.langchain.com/docs/langgraph