PrivateGPT

What is PrivateGPT?

PrivateGPT is an open-source API layer designed to transform local AI models into production-ready applications. It provides a comprehensive set of higher-level building blocks, including RAG (Retrieval-Augmented Generation), skills, tools, MCP (Model Context Protocol), and text-to-SQL capabilities. By following the Claude API model, PrivateGPT enables developers to build private AI products without relying on cloud APIs or rebuilding backend primitives from scratch.

Key features include a built-in workbench UI for testing and demos, available at /ui, and full compatibility with any OpenAI-compatible inference server via the OPENAI_API_BASE environment variable. The API endpoints follow the Anthropic API specification, making integration seamless for developers familiar with that ecosystem. PrivateGPT does not run models itself; instead, it connects to local LLM servers like Ollama, providing flexibility in model choice.

Benefits include complete data privacy, as all processing occurs locally, and reduced dependency on external services. Use cases range from internal knowledge bases and document analysis to AI-powered customer support and code generation. The API supports advanced features like RAG for contextual question answering, text-to-SQL for natural language database queries, and MCP for structured interactions.

Technical details: PrivateGPT requires a running OpenAI-compatible LLM server. Installation is straightforward via pip or Docker. The API is the core product, while the UI serves as a demonstrator for quick testing. It is production-tested, powering Zylon, an on-premise AI platform used by enterprises globally. Developers can build custom applications on top of the API, integrating with existing tools that support local OpenAI-compatible providers.

Use Cases

Key Features

open source API layer local AI model integration RAG capabilities built in workbench UI OpenAI compatible inference Anthropic API compatibility text to SQL support MCP support complete data privacy reduced external dependencies

Who is it for?

developers, AI engineers, data scientists, privacy, focused teams, enterprise IT, customer support teams, code generation users

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