LangChain is an open-source software framework for building applications powered by large language models, providing abstractions for chaining prompts, connecting models to external data and tools, and constructing agents that reason over multiple steps. First released in October 2022 by developer Harrison Chase, it became one of the most widely adopted developer frameworks of the early generative AI boom, with bindings in Python and JavaScript and a large ecosystem of integrations.
The framework organizes LLM applications as composable "chains": sequences of calls to models, prompts, parsers, and external services. Its most common use case is retrieval-augmented generation, where a chain retrieves relevant documents from a vector database before passing them to a model as context, reducing Hallucination (AI) and grounding answers in a specific corpus.
History
LangChain's release in October 2022 coincided almost exactly with the run-up to ChatGPT, and interest in the framework accelerated sharply after the launch of ChatGPT the following month, as developers rushed to build their own applications on top of OpenAI's and other labs' APIs. LangChain Inc was founded in 2023 and raised seed funding as the project became a default reference point in tutorials on building retrieval and agent applications. The framework later introduced LangChain Expression Language (LCEL) for composing chains declaratively, and in 2024 launched LangGraph, a library for building stateful, cyclic agent workflows, alongside LangSmith, an observability and debugging platform, reflecting a broader pivot from beginner tutorials toward production-grade tooling.
Architecture and features
LangChain's core abstractions include chains, prompts, memory, agents, tools, retrievers, and output parsers, meant to be mixed and matched across different model providers and data sources. It offers a wide integration surface, connecting to models from many providers, vector stores, and hundreds of external tools, an area later complemented by the emergence of the open Model Context Protocol as a standardized alternative for wiring tools and data sources to models across the industry. Its memory modules, which give conversational agents persistence across turns, were an early attempt at the broader problem later described under the heading of context engineering.
Reception and criticism
LangChain is widely credited with accelerating prototyping of RAG and agent applications and with popularizing shared vocabulary, such as "chains," "agents," and "tools," across the LLM application ecosystem. It has also drawn sustained criticism from parts of the developer community for excessive abstraction layering, opaque error messages, and frequent breaking changes to its API. A widely shared 2023 essay titled "The Problem With LangChain" argued that many production teams did not need the framework at all and were better served calling model APIs directly. Competing frameworks, including the retrieval-focused LlamaIndex and various lower-level SDKs, emerged partly in response to these critiques, and by the mid-2020s many teams used LangChain selectively, for orchestration or specifically for LangGraph, rather than adopting it as a full-stack solution.