friendliai/langchain

friendliai/langchain

Description: 🦜🔗 Build context-aware reasoning applications

Language: Jupyter Notebook

License: MIT

Stars: 0

Forks: 0

Open issues: 0

Created: 2024-02-15T05:26:45Z

Pushed: 2025-04-05T21:25:43Z

Default branch: master

Fork: yes

Parent repository: langchain-ai/langchain

Archived: no

README:

 [](https://codespaces.new/langchain-ai/langchain)

> [!NOTE] > Looking for the JS/TS library? Check out LangChain.js.

LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.

pip install -U langchain

To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.

Why use LangChain?

LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.

Use LangChain for:

• Real-time data augmentation. Easily connect LLMs to diverse data sources and

external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.

• Model interoperability. Swap models in and out as your engineering team

experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.

LangChain’s ecosystem

While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.

To improve your LLM application development, pair LangChain with:

• LangSmith - Helpful for agent evals and

observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.

• LangGraph - Build agents that can

reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.

• LangGraph Platform - Deploy

and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.

Additional resources

• Tutorials: Simple walkthroughs with

guided examples on getting started with LangChain.

• How-to Guides: Quick, actionable code

snippets for topics such as tool calling, RAG use cases, and more.

• Conceptual Guides: Explanations of key

concepts behind the LangChain framework.

• API Reference: Detailed reference on

navigating base packages and integrations for LangChain.