A framework for orchestrating LLM workflows, agents and tools.
LangChain is a framework for cleanly connecting the building blocks of an AI application, namely models, vector stores, agents and calls to external tools. As soon as a feature goes beyond a single prompt and spans several steps, it takes a lot of recurring boilerplate off our hands and keeps the flow easy to follow. Because it unifies providers behind a common interface, you can swap one model for another without rebuilding half the application. That keeps complex AI workflows clear and easy to maintain.
More in the documentationWe use LangChain when an AI feature becomes multi-step, such as a RAG pipeline that first searches and then summarises, or an agent that calls tools in the right order. The unified interface lets us swap the model in the background without touching your logic. That keeps the project open to the fast pace of change in the AI field.
Good to know
Do not reach for LangChain by reflex. For a single call to a model the bare provider SDK is leaner and more transparent; its value only begins where you genuinely need to chain several steps, tools and data sources together.
More tools we work with in the same area.
OpenAI
GPT models for text, analysis and intelligent features.
Anthropic
Claude models for secure, high-performance AI integrations.
RAG
Retrieval-augmented generation for AI answers based on your own data.
Vector Databases
Semantic search and knowledge bases for AI applications.
Hugging Face
Open-source models and inference for AI that fits you exactly.
MCP
Model Context Protocol for a clean connection between AI and your tools.
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