LangChain vs RAGFlow

LangChain is a free, MIT-licensed open-source framework for building agents with models and tools. Its create_agent harness combines a model, tools, prompt and middleware, with templates for common uses and a ReAct pattern. Middleware can add human approval, conversation compression, sensitive-data removal, guardrails, retries, routing and custom tool policies. Agents run on LangGraph’s durable runtime, which supports persistence, rewind, checkpointing and human-in-the-loop workflows. LangChain documents more than 1,000 integrations across chat and embedding models, tools, toolkits, document loaders and vector stores. Standard interfaces for chat models and embeddings support switching providers with minimal code changes. Documentation covers Python and JavaScript/TypeScript. LangSmith adds tracing, debugging and output evaluation. The framework supports API and self-hosted use. LangChain was started in 2023 and is headquartered in San Francisco.

RAGFlow is an open-source retrieval-augmented generation engine and agent platform for building a context layer for AI agents. Its ingestion pipeline processes multiple data formats into semantic representations for retrieval. Search combines vector search and BM25 with custom scoring and re-ranking, while visual workflows connect retrieval, tools, and MCPs for agent orchestration. Users can connect online, local, and OpenAI-compatible model providers for knowledge bases, chats, search, and agents. Documented connectors include Notion, Google Drive, S3, Microsoft Teams, REST API, and Sitemap. RAGFlow provides HTTP and Python APIs, and a chat assistant can be embedded in another webpage using an iframe. It offers cloud and self-hosted deployment; Docker deployment has a stated minimum of 4 CPU cores, 16 GB RAM, and 50 GB disk. The free plan includes five apps, one team member, 0.1 GB of dataset storage, and 500 monthly credits, but no API key. Starter costs $29 a month and includes an API key.