ai-memory: open-source long-term memory across AI coding agents
The akitaonrails/ai-memory project, trending on GitHub, describes itself as a long-term memory solution for AI coding agent CLIs that carries context between agents from different vendors. Its README says a user can quit Claude Code mid-task, start OpenAI Codex in the same directory and continue without re-explaining the architecture, failed approaches or open questions. It claims more than twenty harnesses feed one shared memory — including Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Grok, Devin, Kimi and Kiro — with memory stored as a git-backed wiki of plain Markdown files on a self-run server, plus multi-user auth and an audit log. These are the project's own claims and have not been independently evaluated.
It targets the fact that coding agents' built-in memory stays on one machine and one tool, proposing a self-hosted, plain-text memory store shared across agents, projects and machines.
Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors
Long-term memory for AI coding agents. Quit Claude Code mid-task, start OpenAI Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions.
Website: aimemory.sh
Why ai-memory
Your coding agent probably has a memory feature already. Claude Code takes its own notes, Cursor remembers some things, and every platform is adding more. They all have the same limits: the notes live on one machine, belong to one agent, and drop out of view when you switch tools or hand the work to a teammate.
Some source content is omitted. Continue reading on the original site.
#ai-memory#Claude Code#OpenAI Codex#Cursor#Gemini CLI#OpenCode#Devin#Kimi#Kiro