Perenna

A lightweight, Git-backed permanent memory for AI agents. Claude Code, Codex, ChatGPT, Cursor, and other MCP clients can share durable memories without sharing a vendor account or conversation history.

• Separate MCP tools for reading, writing, and deleting memories
• Local stdio and single-user OAuth-protected Streamable HTTP transports
• Human-readable Markdown stored in an independent Git repository
• Local Vexor retrieval index that can always be rebuilt from Git
• Cross-process locking for multiple local agent processes

Why Perenna?

Your memory should follow you, not the agent you happen to be using.

Claude Code, Codex, Cursor, and ChatGPT keep memory in separate silos. Switch agents and your memory disappears. Switch machines and local memory stays behind.

Perenna gives them one shared, Git-backed memory. Local agents and ChatGPT can connect to the same self-hosted Perenna service, while every durable memory stays ordinary Markdown you can inspect, edit, version, and back up yourself.

Mem0's self-hosted stack is much heavier, while its hosted Free Plan currently allows customer content to be used for model training and product improvement.

Perenna is different by design: no account, no proprietary memory cloud, no lock-in. Just your memories, in your Git repository, on infrastructure you control.

Quickstart

Install with your AI agent

Paste this into Claude Code, Codex, ChatGPT Desktop, Cursor, or another coding agent with terminal and local MCP configuration access:

Open the following URL, read the complete instructions, and follow them to
install and connect Perenna:
https://raw.githubusercontent.com/scarletkc/Perenna/main/docs/guides/agent-installation.md

Install a published release

Perenna requires Python 3.12+, Git, and uv.

uv tool install perenna

Install the optional memory behavior skill for the local client:

perenna skill install --agent codex
# or
perenna skill install --agent claude-code

Repeat --agent in one command when both clients should receive the skill. The configuration reference documents user and project scope, destinations, and replacement safeguards.

Codex and Claude Code can instead install the combined Skill and MCP connection from Perenna's repository Marketplace. Follow the Plugin setup guide and choose one setup path per client.

Perenna needs a working Vexor embedding provider. For interactive provider selection and configuration, run:

uvx vexor init

Perenna automatically reuses ~/.vexor/config.json. When using process-level configuration, make sure the MCP server receives VEXOR_CONFIG_JSON plus VEXOR_API_KEY or the selected provider's key from its host environment. Remote providers receive memory text and search queries.

If you choose local embeddings, also install Perenna's local extra:

uv tool install "perenna[local]"

Vexor provider configuration covers remote and local setup. From the environment that starts the MCP client, verify the selected provider with:

uvx vexor doctor

Configure an MCP client to start:

perenna mcp --source <client-name>

Perenna creates its local data under ~/.perenna/ unless another home is configured.

To import, publish, or fast-forward compatible history through a private Git repository, run:

perenna sync setup <repository-url>

Install from source for development

git clone https://github.com/scarletkc/Perenna.git
cd Perenna
uv tool install .

Documentation

Start with the documentation index, then follow the path for your task:

• Getting started
• Client setup
• Plugin setup
• Self-hosting for ChatGPT
• Using permanent memory
• perenna-memory Agent Skill
• Configuration reference
• Architecture
• Development guide

License

MIT