memoripy vs Memori
Persistent memory for AI agents compared
memoripy and Memori give AI agents memory that persists beyond a single interaction. memoripy emphasizes sourced, versioned records and control over recall, while Memori captures conversations and agent execution as structured memory for later use.
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 structured, persistent memory to LLM applications by capturing agent execution and conversations. Its Python and TypeScript SDKs integrate with existing models and data infrastructure, with managed cloud and BYODB options.
| 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 focuses on evidence, admission policies, temporal validity, and explanations for recalled records; Memori focuses on capturing conversations and agent execution into structured state.
- memoripy provides scopes such as user, agent, run, project, and organization; Memori organizes memory by entity, process, and session.
- memoripy is a Python library and runtime with optional service and gateway tooling; Memori offers Python and TypeScript SDKs, managed cloud, and BYODB.
- memoripy lists an Apache-2.0 license; Memori lists NOASSERTION in its repository, while its README states Apache 2.0.
- The supplied project data lists 695 stars and 60 forks for memoripy, and 17.1k stars and 3.6k forks for Memori.
Choose memoripy if you…
- need auditable, sourced memory with version history and explanations for recall.
- want admission controls, detailed scope options, and tools to audit or correct stored records.
- prefer a local-first Python runtime that can work without a required third-party model provider.
Choose Memori if you…
- want to capture conversations and agent execution as persistent, structured memory.
- need Python and TypeScript SDKs, integrations with existing infrastructure, or managed cloud and BYODB options.
- want a dashboard for memories, analytics, and API keys, alongside a CLI for account and quota tasks.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.