Glossary

LangChain

What is LangChain?

LangChain is an open-source framework for Python and JavaScript that developers use to build applications based on language models. It provides unified interfaces to many model providers as well as building blocks for tool calling, agents, document processing and retrieval-augmented generation. Since version 1.0 (October 2025), LangChain's agents are built on LangGraph. Typical uses include chatbots that search internal documents and agents that call external services.

Topics

  • Framework
  • Development
  • Open source

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Related terms

LangGraph

LangGraph is an open-source framework from the LangChain ecosystem for modelling AI agents and multi-step processes as a graph of nodes and edges. A shared state is passed from step to step, and loops, branches, checkpointing and interruptions for human approval are built in. Version 1.0 was released in October 2025. One example is an agent that researches a topic, writes a draft and waits for approval before sending it.

  • Framework
  • Agents
  • +1

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

  • RAG
  • knowledge search
  • +1

Model chaining

Model chaining means connecting several AI models or AI steps in sequence so that the output of one step becomes the input of the next. Complex tasks can thus be broken down into manageable sub-tasks that can be checked and improved individually. Example: one step summarises a customer enquiry, a second assigns it to a department and a third drafts the reply.

  • orchestration
  • flows
  • +1

Crew AI

CrewAI is an open-source Python framework for combining several AI agents into a team (a 'crew'). Each agent is given a role, a goal and suitable tools; tasks are distributed and results passed on. In addition, 'flows' provide tightly controlled, event-driven workflows. For example, a research agent gathers information and a writing agent turns it into a report.

  • multi-agent
  • framework
  • +1