ReMe vs Memori
Persistent memory for AI agents compared
ReMe and Memori both help AI agents retain useful context across sessions. ReMe centers on editable Markdown files in a local workspace, while Memori provides a structured memory layer with SDKs and managed-cloud or 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: 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.
| ReMe | Memori | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | NOASSERTION |
| Stars | 3.7k | 17.1k |
| Forks | 321 | 3.6k |
| Last analyzed | Oct 23, 2026 | Oct 3, 2026 |
Key differences
- ReMe stores memories as readable, editable Markdown with frontmatter and wikilinks; Memori structures them as attributes, events, facts, preferences, relationships, rules, and skills.
- ReMe offers CLI, HTTP, MCP, and Python interfaces; Memori offers Python and TypeScript SDKs and integrations for supported models and frameworks.
- ReMe defaults to a local service and workspace, with optional embeddings; Memori offers managed Memori Cloud or a bring-your-own-database option.
- ReMe is licensed Apache-2.0 in the supplied project facts; Memori's supplied license field is NOASSERTION, while its README states Apache 2.0, so verify the repository license terms.
- Memori has more reported stars and forks than ReMe: 17.1k and 3.6k, compared with ReMe's 3.5k and 306.
- ReMe's LLM-powered memory processing requires LLM API configuration, though some file operations and search work without it; Memori's basic SDK flow depends on external LLM and Memori credentials, and requires attribution to create memories.
Choose ReMe if you…
- want memories stored as portable Markdown files that people can inspect and edit.
- need a local workspace with CLI, HTTP, MCP, or Python access.
- prefer BM25 and wikilink search, with embeddings available as an optional addition.
Choose Memori if you…
- need Python and TypeScript SDKs for an application or agent.
- want structured memory that can capture sessions, entities, processes, and execution context.
- plan to use a managed cloud option or connect your own database.
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.