ai-memory
15,729
Stars
1,157
Forks
71
Watchers
26
Open Issues
MIT
许可证
Rust
语言
2026-05-21
创建时间
2026-11-12
最近更新
项目能做什么
为终端编码 Agent 提供长期记忆支持,并方便不同 Agent 厂商间无缝交接上下文的解决方案。
来源:基于仓库元数据
· 内容以仓库实际 README 为准
使用说明 查看原始 README 原仓库文档
中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/logo-dark.png">
<img alt="ai-memory" src="docs/logo-light.png" width="480">
</picture>
</p>
> 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](https://aimemory.sh)**
[](https://aimemory.sh)
[](https://github.com/akitaonrails/ai-memory/releases/latest)
[](rust-toolchain.toml)
[](LICENSE)
## 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.
ai-memory removes those limits.
- **It follows you across agents.** More than twenty harnesses (Claude Code,
Codex, Cursor, Gemini CLI, OpenCode, Grok, Devin, Kimi, Kiro, and others)
feed one shared memory. Quit Claude Code mid-task, open Codex in the same
directory, and the next agent picks up a handoff that says where you left
off, what failed, and what is still open. Handoffs are a typed protocol:
each one has an owner and is claimed exactly once.
- **It can carry the session, not just a summary.** Start agents with
`ai-memory run`: quit Claude Code, run `ai-memory run codex`, and Codex gets
the shared workstream plus a private, temporary local projection of Claude's
visible native history for exact details the bounded startup packet omitted.
The remote server receives only the harness and session identity, never the
local transcript path or body. Direct harness launches behave exactly as
before.
- **It follows you across projects.** A small profile of how you usually
work (your package manager, test layout, architecture habits) reaches
every new repository at session start, so you stop re-explaining yourself.
It sits below each repository's rules file as a default. It is on by
default for a single user, and on a shared server it is opt-in and private
per person. See [`docs/cross-project-profile.md`](docs/cross-project-profile.md).
- **It follows you across machines.** Memory lives in a server you run, on
the same laptop, a homelab box, or anywhere else, so the project you left
on the desktop is the project you resume on the laptop, with the same
knowledge and the same open questions.
- **It works for a team.** Point everyone at one server, and what one
person's sessions learn, everyone's agents can retrieve. Knowledge is
shared per project; personal handoffs sta
阅读完整原文
项目深度分析 规则生成(AI 未配置)
综合评分 48.4 分。该项目在“个人可实施性”维度得 35 分,“付费可能性”维度得 32.0 分,差异化空间 70.0 分(评分依据见评分卡)。
面向细分需求的二次开发产品
| 产品构想 | 基于 ai-memory 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。 |
| 目标用户 | 该细分场景用户 |
| 难度/周期 | 中 · 2~6 周 |
| 收费模式 | 买断制 / 订阅制 / 广告 |
| 竞争风险 | 细分需求规模需验证 |
| 依据 | AI 推测:基于项目主题标签的判断 |
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心
竞品与相似项目 GitHub Search 真实检索
相似项目数据正在完善中,暂不展示(避免误导)
项目优势与风险
| 技术成熟度 | 创建于 2026-05-21,近 30 天仍在推送,发布过 100 个 Release |
| 社区活跃 | Star 15,729 / Fork 1,157 / Issue 26(数据支持,可自行核实) |
| 许可证 | MIT |
| 主要风险 | 上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价 |
安装与使用
此项目未提供 Docker 镜像,安装步骤以原仓库 README 为准(涉及依赖安装与环境配置,对新手门槛较高)。