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⚡ Building applications with LLMs through composability ⚡
4f69094b51
Most easily reviewed with the "hide whitespace" option toggled. Seeing 10-50% speed ups in import time for common structures 🚀 The general purpose of this PR is to lazily import structures within `langchain_core.XXX_module.__init__.py` so that we're not eagerly importing expensive dependencies (`pydantic`, `requests`, etc). Analysis of flamegraphs generated with `importtime` motivated these changes. For example, the one below demonstrates that importing `HumanMessage` accidentally triggered imports for `importlib.metadata`, `requests`, etc. There's still much more to do on this front, and we can start digging into our own internal code for optimizations now that we're less concerned about external imports. <img width="1210" alt="Screenshot 2025-04-11 at 1 10 54 PM" src="https://github.com/user-attachments/assets/112a3fe7-24a9-4294-92c1-d5ae64df839e" /> I've tracked the improvements with some local benchmarks: ## `pytest-benchmark` results | Name | Before (s) | After (s) | Delta (s) | % Change | |-----------------------------|------------|-----------|-----------|----------| | Document | 2.8683 | 1.2775 | -1.5908 | -55.46% | | HumanMessage | 2.2358 | 1.1673 | -1.0685 | -47.79% | | ChatPromptTemplate | 5.5235 | 2.9709 | -2.5526 | -46.22% | | Runnable | 2.9423 | 1.7793 | -1.163 | -39.53% | | InMemoryVectorStore | 3.1180 | 1.8417 | -1.2763 | -40.93% | | RunnableLambda | 2.7385 | 1.8745 | -0.864 | -31.55% | | tool | 5.1231 | 4.0771 | -1.046 | -20.42% | | CallbackManager | 4.2263 | 3.4099 | -0.8164 | -19.32% | | LangChainTracer | 3.8394 | 3.3101 | -0.5293 | -13.79% | | BaseChatModel | 4.3317 | 3.8806 | -0.4511 | -10.41% | | PydanticOutputParser | 3.2036 | 3.2995 | 0.0959 | 2.99% | | InMemoryRateLimiter | 0.5311 | 0.5995 | 0.0684 | 12.88% | Note the lack of change for `InMemoryRateLimiter` and `PydanticOutputParser` is just random noise, I'm getting comparable numbers locally. ## Local CodSpeed results We're still working on configuring CodSpeed on CI. The local usage produced similar results. |
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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.