Key takeaways
- LangChain is a toolkit that helps developers connect language models to data, tools and multi-step workflows.
- It was created by Harrison Chase as an open-source project in October 2022 and grew into LangChain Inc.
- Runnables are LangChain's common interface: components you can invoke, stream, batch and chain with the pipe operator.
- CompiledGraph belongs to LangGraph, not LangChain; import it from the langgraph package.
- Alternatives include LlamaIndex, Haystack, DSPy, Semantic Kernel, provider agent SDKs and plain API calls.
In simple words, LangChain is a toolkit for building apps with AI language models: it provides ready-made pieces to connect a model to your documents, tools and APIs, and to chain steps together, so you write less glue code. It is available for Python and JavaScript and has a large ecosystem. I build RAG and AI products in Python, so this page explains what LangChain does and why it matters, who made it and when, where the company is, who uses it, runnables, a common LangGraph import question, and the best alternatives.
What is LangChain in simple words?
LangChain is a set of building blocks for AI applications. Instead of writing everything yourself, you use its components: model wrappers to talk to AI models, prompt templates, document loaders, text splitters, retrievers for searching your data, and tools that let models call functions.
Think of it like a box of connectors for AI apps: plug a model into your PDFs to make a document chatbot, or into a search API to make a research assistant. It works with many model providers and databases through a common interface, so you can switch components without rewriting your app. Its companion, LangGraph, handles more complex agents. For example, a document chatbot in LangChain is a loader, a splitter, an embedding model, a vector store retriever and a chat model wired together in a few dozen lines.
What does LangChain do exactly?
LangChain standardises how you call models, build prompts, retrieve documents, call tools and compose these steps into chains and agents. It provides hundreds of integrations with model providers, vector stores, document sources and tools.
Concretely, LangChain can: load documents from files, websites and databases; split them into chunks; embed and store them in vector stores; retrieve relevant chunks for questions; format prompts; call chat models with streaming; parse structured outputs; and run tool-calling agents. LangSmith, from the same company, traces and evaluates these applications.
Why is LangChain important?
LangChain was important in making LLM application patterns, such as RAG, tool calling and agents, accessible to many developers quickly, and it remains widely used because of its integrations, community and ecosystem, including LangGraph and LangSmith.
Also asked as: why langchain is important
Its common interfaces reduce vendor lock-in: switching from one model provider to another can be a small change. Tutorials, templates and community answers make learning faster. It also attracts criticism for abstraction complexity and frequent changes, which is why many teams use it selectively. Understanding it is valuable for AI engineers, even if you choose lighter tools for some projects.
Who made LangChain?
LangChain was created by Harrison Chase, who released it as an open-source project. He later co-founded the company LangChain, Inc., with Ankush Gola, to develop LangChain, LangGraph and the LangSmith platform.
The open-source project grew rapidly through community contributions, with thousands of contributors adding integrations and features. The company is venture-backed and offers commercial products for tracing, evaluation and deployment, while the core libraries remain open source under the MIT licence. Before LangChain, Chase worked on machine learning teams at startups including Robust Intelligence; the project began as a side effort to package common patterns for building with language models.
When was LangChain introduced?
LangChain was first released in October 2022, shortly before ChatGPT's launch, and its popularity exploded as developers rushed to build applications with language models in early 2023.
Also asked as: when langchain was introduced
The JavaScript version followed in early 2023. Major milestones include the split into modular packages such as langchain-core, the introduction of LangGraph for stateful agents, LangSmith for observability, and stable 1.0 releases focused on agent building. Because it evolved quickly, older tutorials often use outdated interfaces, so prefer current documentation. When reading a tutorial, check its date and imports: code using langchain.llms or LLMChain usually predates the modern packages, while langchain_core and provider packages such as langchain_openai signal current style.
Where is LangChain located?
LangChain, Inc. is a technology company based in the United States, and its team works across locations. The open-source project itself is global, developed on GitHub by contributors worldwide.
For practical purposes, the "location" that matters to developers is online: documentation on the LangChain website, code on GitHub, the LangChain Academy courses, and community forums. Company details, such as offices and leadership, are on LangChain's official website. Location also matters little for usage: the libraries run wherever your code runs, on a laptop, a server or a cloud function, and LangSmith offers hosted and self-hosted options for teams with data residency requirements.
Who uses LangChain?
LangChain is used by startups, enterprises, researchers and individual developers to build chatbots, RAG systems over company documents, coding and research assistants, data extraction pipelines, and agents that automate workflows. Its packages see very large download numbers.
Users range from students learning AI engineering to large companies running production systems, some using LangGraph and LangSmith for complex agent workflows and monitoring. Others use only specific components, such as document loaders or text splitters, within their own architectures. Its popularity means many job descriptions for AI engineers mention LangChain or LangGraph.
What are LangChain runnables?
Runnables are LangChain's standard interface for components: prompts, models, retrievers, parsers and custom functions all implement methods such as invoke, stream, batch and their async versions, so they can be combined consistently.
You compose runnables with the pipe operator, as in prompt | model | parser, creating a chain that is itself a runnable. Helpers such as RunnableParallel run branches concurrently, RunnablePassthrough forwards inputs, and RunnableLambda wraps your own functions. This design, called the LangChain Expression Language, gives every chain streaming, batching and tracing support automatically.
Can CompiledGraph be imported from LangChain?
No, CompiledGraph belongs to LangGraph, not LangChain. When you build a graph with LangGraph's StateGraph and call .compile(), you get a compiled graph object; import related classes from the langgraph package, whose module paths have changed across versions.
Also asked as: can compiledgraph be import from langchain
If an import fails, check your installed LangGraph version and its API reference, since classes such as CompiledStateGraph live under langgraph.graph.state in recent releases. In many cases you do not need to import the class at all: use the object returned by .compile() and call invoke or stream on it. Keep LangChain and LangGraph versions compatible.
What are the best LangChain alternatives?
Popular alternatives include LlamaIndex for data and retrieval-focused applications, Haystack for production search and RAG pipelines, DSPy for programmatically optimising prompts, Microsoft's Semantic Kernel, CrewAI and Agno for agents, provider SDKs with agent features, and plain API calls with small helper code.
Choose by need: LlamaIndex for document-heavy RAG; Haystack for structured pipelines; DSPy for systematic prompt optimisation; provider SDKs for simple, direct control; and LangGraph if you like LangChain's ecosystem but need explicit agent control. Many production teams use minimal frameworks, so understanding the underlying patterns matters more than any library. Whatever you choose, keep retrieval steps such as hybrid search, fusion and reranking inspectable, since those decide answer quality more than the framework does.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
