Graphiti vs PrivateGPT
Graphiti
github.comPrivateGPT
privategpt.devSide by side
| Figure | Graphiti | PrivateGPT |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Retrieval-Augmented Generation Tools | 8.5 (#7) | 8.6 (#4) |
| API | The repository describes a REST API service built with FastAPI. | Its Messages API supports streaming, asynchronous processing, and token counting. |
| Deployment | The maker’s Graphiti page describes the framework as runnable locally and distinguishes it from Zep’s managed commercial platform. | PrivateGPT can be installed as a Python package, from source, or with Docker, and runs as a local server. |
| Purpose | Graphiti is an open-source framework for building and querying temporal context graphs for AI agents. | PrivateGPT provides an open-source API layer for building private AI applications on your own infrastructure. |
| Retrieval | Hybrid retrieval combines semantic embeddings, BM25 keyword search, and graph traversal. | The API supports file and artifact ingestion and retrieval with citations. |
Plans
In their own words
Graphiti
Graphiti is an open-source framework for building and querying temporal context graphs for AI agents. It records validity periods for facts, allowing applications to ask what is true now or what was true at a previous point in time. New structured and unstructured information is integrated incrementally rather than requiring a complete graph rebuild. Derived entities and relationships remain linked to their source episodes for provenance. Retrieval combines semantic search, keyword search, and graph traversal, with result reranking. Developers can define entity and edge types with Pydantic models or let structure emerge from incoming data. Supported graph backends listed by the project are Neo4j, FalkorDB, and Amazon Neptune; Kuzu is deprecated. Graphiti includes FastAPI REST and MCP services, supports several LLM providers, runs locally, and is self-hosted. It requires Python 3.10 or newer and an LLM API key. The Apache-2.0 framework is free.
PrivateGPT
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.