memoripy vs Memori
Evidence-tracked memory compared with persistent LLM memory
memoripy and Memori both add memory to AI agents and LLM applications. memoripy emphasizes evidence screening, scoped retrieval, and auditable history in a local Python runtime, while Memori focuses on structuring interaction and execution context, with Python and TypeScript SDKs and cloud or database deployment options.
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.
Memori: Give AI Agents Persistent Memory
Memori adds persistent, structured memory to LLM applications and agents, capturing conversations and execution context for later recall. It suits teams building stateful agents who want a memory layer that works across models and existing infrastructure.
| memoripy | Memori | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | NOASSERTION |
| Stars | 694 | 17.1k |
| Forks | 59 | 3.6k |
| Last analyzed | Oct 9, 2026 | Oct 3, 2026 |
Key differences
- memoripy screens incoming evidence and supports versioned facts, historical queries, citations, and audit tools; Memori captures and recalls structured memory from LLM interactions and execution context.
- memoripy is a Python project; Memori offers Python and TypeScript SDKs.
- memoripy's core needs no external model or database, with PostgreSQL and service integrations optional; Memori offers managed cloud or bring-your-own-database deployment, and its basic SDK flow depends on external LLM and Memori credentials.
- memoripy lists an Apache-2.0 license; Memori's supplied license field is NOASSERTION, while its README states Apache 2.0, so verify the repository terms.
- memoripy reports 694 stars and 59 forks and notes slow issue handling; Memori reports 17.1k stars and 3.6k forks.
Choose memoripy if you…
- need evidence screening, citations, and audit tools for recalled memories.
- want local Python memory with scoped retrieval and version history.
- need a core setup that does not require an external model or database.
Choose Memori if you…
- want Python and TypeScript SDKs for persistent agent memory.
- need to capture sessions, execution context, and structured memory from LLM interactions.
- want options for managed cloud or using your own database.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.