Flowise vs PrivateGPT

Flowise is an open source platform for building generative AI agents and large language model workflows. Its Assistant, Chatflow, and Agentflow visual builders cover assistants, chatbots, single- and multi-agent systems, and workflow orchestration. Listed capabilities include execution logs, visual debugging, evaluations, human review, and retrieval-augmented generation indexing pipelines. Documentation describes connections to more than 100 sources, tools, vector databases, and memories, including LangChain, LlamaIndex, and Zapier Zaps. Developers can use APIs, an embedded chat widget, and TypeScript or Python SDKs. Deployment options include cloud and on-premises setups, as well as self-hosting through npm or Docker. Listed security controls include role-based access, single sign-on, encrypted credentials, secret managers, rate limiting, and restricted domains. The project is winding down: active feature development ceased July 29, 2026; repository archival is listed for August 10; and end of life is August 31, 2026. Source code remains under Apache 2.0. Free costs nothing; Starter costs $35 a month.

PrivateGPT is a free, open-source API layer for building private AI applications on your own infrastructure. It does not run models itself: it connects to a separate OpenAI-compatible inference server, which can be local or remote. The Messages API supports streaming, asynchronous processing, and token counting. Document workflows include file and artifact ingestion, retrieval with citations, and tools for querying databases and analyzing tabular data. The documented capabilities also include web search and extraction, code execution, custom tools, and MCP connectors. A browser workbench at `/ui` can be used for demos and inspecting API requests, but it stores chats and settings in browser localStorage and has no access control; the documentation describes it as a local demonstrator. PrivateGPT can be installed as a Python package, from source, or with Docker, and runs as a local server. Package installation requires Python 3.11; Python 3.10 and 3.12 or later are unsupported. Platforms include Linux, macOS, Windows, web, API, and self-hosted environments.