PrivateGPT

PrivateGPT suits developers building private AI applications around models they operate or select. Its strength is a flexible API for documents, data, and tools; its trade-off is that you must supply and maintain the inference server.

Overview

PrivateGPT is a free, open-source API layer that runs as a local server. It connects to OpenAI-compatible inference servers such as Ollama, LM Studio, llama.cpp, and vLLM rather than running models itself. That separation gives developers room to choose their model setup, but means PrivateGPT is not a complete, ready-to-use AI service.

By default, application data—including ingested documents, vector-store data, models, and caches—is stored under a local home directory. Developers can also connect to remote compatible servers, so local storage does not by itself ensure that inference stays on the same machine. Optional observability can emit traces for LLM calls, embedding requests, and retrieval steps, with console, Arize Phoenix, and Opik backends.

Key features

The Messages API supports streaming, asynchronous processing, and token counting, giving application developers controls for building beyond a basic prompt-and-response flow. File and artifact ingestion feed retrieval-augmented generation, and citations let users trace answers to source material.

PrivateGPT extends beyond document retrieval with structured access to databases and tabular data such as CSVs. Its documented capabilities also include web search and extraction, code execution, custom tools, and MCP connectors; MCP usage is documented through the Messages API. This breadth makes it a useful application layer for varied workflows, though the compatibility table marks skills as basic or in progress and prompt caching and OAuth or organizations as unsupported.

A browser workbench at /ui can help developers test, demonstrate, and inspect API requests. It is explicitly a local demonstrator, not a secure shared interface: chats and settings are stored in browser localStorage, and the workbench has no access control.

Pricing

The PrivateGPT plan costs nothing. It provides the open-source API layer for self-hosting and connects to a separate OpenAI-compatible inference server. That makes it a straightforward fit for developers who can manage their own deployment and model infrastructure; it is not a bundled model-hosting plan.

Platforms

PrivateGPT supports API use and self-hosted deployment, with package installation instructions for macOS, Linux, and Windows, plus Docker installation. The package route requires Python 3.11; Python 3.10 and Python 3.12 or later are unsupported. That narrow version requirement is a practical constraint when fitting it into an existing development environment.

Who it's for

PrivateGPT is best suited to developers who want an open-source layer for private AI products, need document retrieval with citations, or want to combine document workflows with structured data and tools. Its API and deployment choices are more compelling for teams building an application than for people looking for a finished chat product. The maker links to a community Discord for users.

Pros and cons

• Open-source and free: self-hosting avoids a PrivateGPT plan charge and gives developers control over deployment.
• Broad developer workflow support: the API combines document ingestion, retrieval with citations, tabular data access, and tool integrations.
• Inference is a separate dependency: users must provide an OpenAI-compatible model server, adding setup and operational responsibility.
• Workbench is not access-controlled: localStorage-based chats and settings make it a poor choice as a shared production interface.
• Python version constraint: package installation requires Python 3.11 and excludes both Python 3.10 and 3.12 or later.

Alternatives

For a broader comparison, see Retrieval-Augmented Generation Tools.

• AnythingLLM is worth considering if you want an alternative with Android support and a self-hosted Docker option, or prefer its freemium plans.
• Flowise may suit readers seeking a free tier with stated flow, prediction, and storage limits, plus a free trial.
• I3K RAG Enterprise offers a free AGPL-3.0 self-hosted Community plan with Linux, ARM64, or Windows support.
• RAGFlow is an option for readers who want a free plan with stated app, team, dataset-storage, and monthly-credit limits.
• txtai is another free, open-source API and self-hosted option, installable with pip or Docker.
• RAGStack may fit developers looking for a free open-source RAG package using Astra DB Serverless or Apache Cassandra as a vector store.
• LlamaIndex is an alternative if you want its open-source LlamaIndex and Workflows projects, with LlamaParse offered as a commercial platform.
• OpenRAG provides an AGPL-3.0 self-hosted option deployed on your own infrastructure.

Verdict

Choose PrivateGPT if you are building an application and want a free, open-source API layer for document, data, and tool workflows around an inference server you control. Look elsewhere if you need models included or a secure, access-controlled workbench rather than a local demonstrator.