ai-memory vs memoripy
Agent memory systems compared
ai-memory and memoripy both give AI agents persistent memory, but they focus on different needs. ai-memory centers on a searchable, git-backed Markdown wiki and work handoffs, while memoripy emphasizes evidence screening, versioned facts, scoped retrieval, and citations.
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.
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.
| ai-memory | memoripy | |
|---|---|---|
| Language | Rust | Python |
| License | MIT | Apache-2.0 |
| Stars | 9.1k | 694 |
| Forks | 632 | 59 |
| Last analyzed | Oct 10, 2026 | Oct 9, 2026 |
Key differences
- ai-memory captures coding-agent activity and supports handoffs between agents; memoripy focuses on admitting, tracking, and auditing evidence and changing facts.
- ai-memory stores its source of truth in git-backed Markdown with a SQLite search index; memoripy uses typed, versioned records and does not require a database for its core.
- ai-memory is written in Rust and licensed under MIT; memoripy is written in Python and licensed under Apache-2.0.
- ai-memory can run locally or as a shared server for multiple machines and teammates; memoripy is a local runtime with optional service, MCP, gateway, and PostgreSQL extensions.
- ai-memory’s default capture, search, and handoff paths avoid LLM API calls, with optional LLM features; memoripy’s core also needs no external model, while its deterministic extractor has stated limits.
- ai-memory has 9.1k stars and 632 forks; memoripy has 694 stars and 59 forks. Both are unarchived, and each has upgrade considerations, including rapid change for ai-memory and v4 development for memoripy.
Choose ai-memory if you…
- want portable, inspectable project memory in Markdown and git.
- need coding-agent activity capture and typed handoffs across sessions or machines.
- want a shared server that separates project knowledge from personal handoffs.
Choose memoripy if you…
- need evidence-backed recall with citations and current versus historical facts.
- want scoped retrieval, admission controls, and audit tools for memory quality.
- need a local Python memory runtime with optional MCP, service, or PostgreSQL integrations.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.