ai-memory vs EverOS
Portable AI agent memory compared
ai-memory and EverOS both keep agent memory in editable Markdown and provide indexes for retrieval across sessions. ai-memory focuses on coding-agent capture, handoffs, and a self-hosted shared service, while EverOS offers a Python runtime for user and agent memory across apps and workflows.
ai-memory: Share Long-Term Memory Across Coding Agents
A self-hosted Rust service that captures coding-agent activity into a searchable, git-backed Markdown wiki and hands work between agents. It suits developers and teams who want portable project memory without requiring LLM calls.
EverOS: Give AI Agents Portable Long-Term Memory
EverOS is a Python memory runtime that stores agent conversations and context in editable Markdown, with local indexes for retrieval. It suits teams building agents that need user-owned memory to persist across apps and sessions.
| ai-memory | EverOS | |
|---|---|---|
| Language | Rust | Python |
| License | MIT | Apache-2.0 |
| Stars | 9.1k | 13.4k |
| Forks | 632 | 926 |
| Last analyzed | Oct 10, 2026 | Oct 7, 2026 |
Key differences
- ai-memory is written in Rust and stores Markdown in a git-backed wiki with SQLite as a derived search index; EverOS is written in Python and uses Markdown with local SQLite and LanceDB indexes.
- ai-memory captures coding-agent prompts, tool calls, and session boundaries through lifecycle hooks, and supports typed handoffs; EverOS stores conversations, files, and agent trajectories, and separates user memory from agent memory.
- ai-memory can run locally or as a shared multi-user server with authentication and audit logs; EverOS is described as local-first and its Python library and API can serve multiple tools and workflows.
- ai-memory's default capture, search, and handoff paths do not require an LLM API key; EverOS's documented server setup requires an LLM provider, while embeddings and reranking are optional.
- ai-memory is licensed under MIT; EverOS is licensed under Apache-2.0.
- Both projects are active, but ai-memory cautions that it is new and changing quickly; EverOS describes regular commits and releases, with work spread across a relatively small contributor pool.
Choose ai-memory if you…
- need coding-agent lifecycle capture and claim-once handoffs.
- want a self-hosted shared server with per-person attribution and an audit log.
- prefer a default memory workflow that does not require an LLM API key.
Choose EverOS if you…
- want a Python runtime for memory shared across apps, tools, and workflows.
- need retrieval scoped by user, agent, app, project, or session.
- want optional offline reflection or multimodal ingestion for supported content types.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.