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LangChain LangChain provides the engineering platform and open source frameworks developers use to build, test, and deploy reliable AI agents
langchain-ai langchain: The platform for reliable agents. - GitHub LangChain is a framework for building agents and LLM-powered applications It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves
Home - Docs by LangChain LangChain is the platform for agent engineering AI teams at Replit, Clay, Rippling, Cloudflare, Workday, and more trust LangChain’s products to engineer reliable agents Quickly get started building agents, with any model provider of your choice
LangChain - Wikipedia LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis
What Is LangChain? Examples and definition | Google Cloud What is LangChain? LangChain is an open-source orchestration framework that simplifies building applications with large language models (LLMs) It provides tools and components to connect LLMs
LangChain overview - Docs by LangChain LangChain is the easiest way to start building agents and applications powered by LLMs With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more
What is LangChain? - IBM LangChain is an open source orchestration framework for application development using large language models (LLMs) Available in both Python- and Javascript-based libraries, LangChain’s tools and APIs simplify the process of building LLM-driven applications like chatbots and AI agents
Using LangChain and LangGraph to Build a RAG-Powered Chatbot Learn how to build a RAG-powered chatbot using LangChain and LangGraph in this comprehensive code walkthrough This guide explains the complete workflow from document indexing with vector embeddings to stateful conversation management, including detailed explanations of key LangGraph concepts like StateGraph, persistent checkpointing, and thread-based configuration for managing conversation