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The first in a sequence of PRs focusing on improving performance in core. We're starting with reducing import times for common structures, hence the benchmarks here. The benchmark looks a little bit complicated - we have to use a process so that we don't suffer from Python's import caching system. I tried doing manual modification of `sys.modules` between runs, but that's pretty tricky / hacky to get right, hence the subprocess approach. Motivated by extremely slow baseline for common imports (we're talking 2-5 seconds): <img width="633" alt="Screenshot 2025-04-09 at 12 48 12 PM" src="https://github.com/user-attachments/assets/994616fe-1798-404d-bcbe-48ad0eb8a9a0" /> Also added a `make benchmark` command to make local runs easy :). Currently using walltimes so that we can track total time despite using a manual proces.
84 lines
5.0 KiB
Markdown
84 lines
5.0 KiB
Markdown
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> [!NOTE]
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> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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LangChain is a framework for building LLM-powered applications. It helps you chain
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together interoperable components and third-party integrations to simplify AI
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application development — all while future-proofing decisions as the underlying
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technology evolves.
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```bash
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pip install -U langchain
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```
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To learn more about LangChain, check out
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[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
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advanced customization or agent orchestration, check out
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[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
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controllable agent workflows.
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## Why use LangChain?
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LangChain helps developers build applications powered by LLMs through a standard
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interface for models, embeddings, vector stores, and more.
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Use LangChain for:
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- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
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external / internal systems, drawing from LangChain’s vast library of integrations with
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model providers, tools, vector stores, retrievers, and more.
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- **Model interoperability**. Swap models in and out as your engineering team
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experiments to find the best choice for your application’s needs. As the industry
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frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
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losing momentum.
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## LangChain’s ecosystem
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While the LangChain framework can be used standalone, it also integrates seamlessly
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with any LangChain product, giving developers a full suite of tools when building LLM
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applications.
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To improve your LLM application development, pair LangChain with:
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- [LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
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observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
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visibility in production, and improve performance over time.
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- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
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reliably handle complex tasks with LangGraph, our low-level agent orchestration
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framework. LangGraph offers customizable architecture, long-term memory, and
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human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
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Uber, Klarna, and GitLab.
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- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
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and scale agents effortlessly with a purpose-built deployment platform for long
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running, stateful workflows. Discover, reuse, configure, and share agents across
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teams — and iterate quickly with visual prototyping in
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[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
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## Additional resources
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- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
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guided examples on getting started with LangChain.
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- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
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snippets for topics such as tool calling, RAG use cases, and more.
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- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
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concepts behind the LangChain framework.
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- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
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navigating base packages and integrations for LangChain.
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