LangChain
LangChain is an open-source framework for AI agents and LLM applications. It connects language models to tools, data and systems through a unified interface – and is the entry point to the ecosystem of LangGraph, DeepAgents and LangSmith.
- Project
- LangChain
- Publisher
- LangChain
- Repository
- github.com/langchain-ai/langchain
- Documentation
- docs.langchain.com/
- Languages
- Python, TypeScript
- Category
- AI agent frameworks
What is LangChain?
LangChain is a framework for building agents and applications on top of large language models. It offers a standardised interface for models, embeddings, vector stores and tools, plus a large number of integrations. LangChain is available for Python and JavaScript/TypeScript.
A core benefit is interchangeability: models can be swapped without rewriting the application. This keeps decisions open while the model landscape keeps evolving.
LangChain, LangGraph and DeepAgents
The three projects are layers of the same stack. LangGraph is the runtime for stateful agents modelled as a graph. LangChain provides components, integrations and a lightweight agent layer on top. DeepAgents is an opinionated agent harness that already includes planning, sub-agents, a filesystem and context management. LangSmith complements the ecosystem with tracing, evaluation and deployment.
What LangChain is suited for
- Agents that call tools and business systems via APIs
- Retrieval-augmented generation (RAG) over your own documents and data
- Fast prototyping with changing models and providers
- Applications that are later extended with LangGraph for state and control
Using LangChain effectively in an AI-first business model
A framework speeds up implementation, but it does not replace deciding which outcome an agent should be accountable for. Before building integrations, define the agent’s goal, mandate and permissions: what may it prepare, what may it recommend, what may it only execute with approval?
In operation, traceability and evaluation count. Log tool calls and decisions, evaluate entire workflows rather than single answers, and connect measurement to the metrics your business success depends on – in sales, for example, repeat orders or reactivated customer relationships rather than messages sent.
From tool to outcome
In AI-first coaching you clarify which outcomes AI agents should be accountable for in your go-to-market and within which framework. REVAGO then designs, builds and runs your system. You pay for results – not for activity.
Frequently asked questions
What is LangChain?
LangChain is an open-source framework for AI agents and LLM applications. It offers a unified interface to language models, embeddings, vector stores and tools, plus many integrations, and is available for Python and JavaScript/TypeScript.
What is the difference between LangChain and LangGraph?
LangGraph is the low-level runtime for long-running, stateful agents modelled as a graph, with durable execution and human-in-the-loop. LangChain builds on it and provides components, integrations and a simple agent layer. Use LangGraph directly for complex, controlled workflows and LangChain for fast agents with many integrations.
Is LangChain suitable for agents in sales?
LangChain can connect agents to CRM, e-mail or knowledge bases. Whether an agent is effective in sales, however, depends less on the framework than on clear goals, approval rules, well-maintained customer data and ongoing evaluation in operation.
Further reading in the wiki
A neutral overview, not a recommendation of individual providers. REVAGO is not affiliated with the listed projects. Descriptions are based on the projects’ public information; features and licences may change.