memoripy vs EverOS
Local memory runtimes for AI agents compared
memoripy and EverOS are Python runtimes for giving AI agents persistent local memory. memoripy emphasizes sourced, versioned records and explainable recall, while EverOS centers on editable Markdown memory and local indexes that can be shared across apps and workflows.
memoripy: Evidence-Tracked Memory for AI Agents
Memoripy is a local Python memory runtime for AI agents that screens incoming evidence, tracks changing facts over time, and explains recall with citations. It suits teams that need auditable, scoped memory rather than a simple vector store.
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.
| memoripy | EverOS | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 694 | 13.4k |
| Forks | 59 | 926 |
| Last analyzed | Oct 9, 2026 | Oct 7, 2026 |
Key differences
- memoripy applies admission policies and preserves evidence, immutable versions, and temporal validity; EverOS stores conversations and context in editable Markdown with local SQLite and LanceDB indexes.
- memoripy provides recall receipts and audits for evidence gaps, conflicting facts, sensitive data, and retrieval feedback loops; EverOS offers file synchronization, offline reflection, and a source-backed Knowledge Wiki when configured.
- memoripy scopes memory by user, agent, run, project, organization, and namespace; EverOS scopes retrieval by user, agent, app, project, and session.
- memoripy's core works without a required third-party model provider, though broader extraction requires a supplied model or extractor; EverOS's documented server setup needs Python 3.12 or newer and an LLM provider.
- memoripy lists 695 stars and 60 forks; EverOS lists 13.4k stars and 926 forks, and its project health notes regular commits and releases.
- Both use Python and Apache-2.0; EverOS lists integrations with agent platforms and workflow tools, while memoripy offers optional HTTP service, MCP, and gateway tooling.
Choose memoripy if you…
- need sourced, versioned memory with explanations for recalled results.
- want controls for admission, temporal validity, data scope, and store auditing.
- prefer a core that works without a required third-party model provider.
Choose EverOS if you…
- want memory stored as editable Markdown with local indexes.
- need to share user-owned memory across apps, tools, or workflows.
- want optional integrations for multimodal ingestion, offline reflection, or agent platforms.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.