ReMe vs Memori
Persistent memory for AI agents: ReMe vs Memori
ReMe and Memori both help AI agents retain useful information across sessions. ReMe centers on local, editable Markdown memory, while Memori provides structured memory through SDKs, managed cloud, and bring-your-own-database options.
ReMe: Build a File-Based Memory Workspace for AI Agents
ReMe is a Python memory-management kit for agents, built around a local-first, self-evolving knowledge base stored as Markdown files. It helps agent users capture conversations and resources, refine durable knowledge, and retrieve relevant context across supported agent integrations.
Memori: Persist Structured Memory for AI Agents
Memori is LLM-agnostic memory infrastructure that turns agent conversations and execution into structured, persistent state. Its Python and TypeScript SDKs support managed cloud, single-tenant cloud, VPC, and on-premises deployments.
| ReMe | Memori | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | — |
| Stars | 3.7k | 17.2k |
| Forks | 321 | 3.8k |
| Last analyzed | Oct 23, 2026 | Oct 19, 2026 |
Key differences
- ReMe stores memories as readable Markdown files with frontmatter and wikilinks; Memori organizes memory by entity, process, and session.
- ReMe offers CLI, HTTP, MCP, and Python interfaces; Memori offers Python and TypeScript SDKs, integrations, and MCP connectivity.
- ReMe is local-first, with a local service and workspace by default; Memori offers managed cloud and BYODB options.
- ReMe lists its license as Apache-2.0; Memori lists NOASSERTION in repository facts, while its README states Apache 2.0, so its terms should be verified.
- ReMe reports 3.5k stars and 306 forks; Memori reports 17.1k stars and 3.6k forks.
- ReMe supports optional embedding retrieval alongside BM25 and wikilink search; Memori describes structured memory and advanced augmentation for facts, preferences, events, and relationships.
Choose ReMe if you…
- want human-readable memory files that you can inspect and edit directly.
- need a local-first workspace with several access interfaces for compatible agents.
- prefer Markdown notes, linked knowledge, and optional semantic retrieval.
Choose Memori if you…
- need Python and TypeScript SDKs for existing LLM applications.
- want managed cloud or the option to connect your own database.
- need memory organized around entities, processes, and sessions, with a dashboard for memories and analytics.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.