LangChain: framework for agents and LLM-powered apps
LangChain is a framework for building agents and LLM-powered applications.
Verified
Core facts
Read by a script from the official API, not rewritten by a model.
- License
- MIT
- Languages
- Python
- Maintainer
- langchain-ai
- Verified
What it is
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development. LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more. The project describes it as future-proofing decisions as the underlying technology evolves.
Main features
Under real-time data augmentation, the project says LLMs can be connected to diverse data sources and external/internal systems.
Under model interoperability, the project says models can be swapped in and out as an engineering team experiments.
Under rapid prototyping, the project says it has a modular, component-based architecture for building and iterating on LLM applications.
Under production-ready features, the project says it has built-in support for monitoring, evaluation, and debugging through integrations like LangSmith.
The project says it has an ecosystem of integrations, templates, and community-contributed components.
The project says it offers abstraction levels from high-level chains for quick starts to low-level components for fine-grained control.
Getting started
For more advanced customization or agent orchestration, the README points to LangGraph, a framework for building controllable agent workflows.
LangSmith is for developing, debugging, and deploying AI agents and LLM applications.
uv add langchainfrom langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")Who it is for
The LangChain framework can be used standalone. Deep Agents is a higher-level package built on LangChain for agents with built-in capabilities such as planning, subagents, and file system usage. Deep Agents is used to build agents that can plan, use subagents, and leverage file systems for complex tasks.
LangGraph is a low-level agent orchestration framework. Integrations cover chat and embedding models, tools and toolkits, and more. LangSmith provides agent evals, observability, and debugging for LLM apps.
LangSmith Deployment is a purpose-built platform for long-running, stateful workflows, used to deploy and scale agents. The documentation provides conceptual overviews and guides. The LangChain ecosystem overview explains how LangChain, LangGraph, and Deep Agents fit together.
The API reference is a reference for public classes, functions, and types. Discussions is a community forum for technical questions, ideas, and feedback. LangChain Academy offers free courses on LangChain libraries and products, made by the LangChain team.
The Code of Conduct sets out community guidelines and standards.
Related tools
Related tools listed on this site.
博思白板
The site describes it as a new-generation AI online whiteboard combining mind maps, flowcharts and team co-creation.
LongCat
Model and API platform. This review reads the official LongCat-2.0 technical article and its linked API product page, covering code understanding/generation, reasoning and interaction purposes; the original chat UI was not reviewed in operation.
讯飞星辰MaaS
The public primary description positions iFLYTEK Xingchen MaaS as a developer-facing model fine-tuning service, covering data processing, model optimization, evaluation and deployment/hosting. This review did not run those console workflows.
MMLU
The original benchmark and code repository for Measuring Massive Multitask Language Understanding, using multidisciplinary tests to analyze model knowledge and understanding. Its paper appeared at ICLR 2021.
FlagEval
Evaluation toolkit and open platform for large models. Its public project covers foundation models, training and fine-tuning algorithms, with language, vision, audio and multimodal evaluation directions.
OpenCompass
Model evaluation platform with language and multimodal leaderboards, benchmark resources, and documentation.
HELM
Stanford CRFM’s open-source holistic model-evaluation framework, unifying datasets, model interfaces and multiple metrics. Its README states that it entered maintenance mode on 2026-06-01.
Sources
This page is written from the official sources below. Open them to check the original text.
- GitHub READMEhttps://github.com/langchain-ai/langchainFetched
Verified ·
This page is written by AI from the official sources above and checked automatically. This site has not tested it. Found a mistake? Tell us.