Rememb
Description
Persistent memory for AI agents — local, portable, zero config. Works with Cursor, Windsurf, Claude via MCP.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README

[](https://glama.ai/mcp/servers/LuizEduPP/Rememb) [](https://lobehub.com/mcp/luizedupp-rememb)
Operate AI agents without losing context between sessions. `rememb` is a local-first persistent memory layer: structured entries, keyword search, versioning, diff, restore, and audit trail — no cloud service required.

The problem
Teams using agents at real velocity rarely fail because they lack generation. They fail because operating agents every day creates context debt:
- too much re-explaining project facts every session
- too little durable memory outside the chat window
- too little audit trail for why something changed
- too much noise when recalling the right context
Every team or solo developer operating agents professionally hits this wall:
Session 1: "We're using PostgreSQL, auth at src/auth/, prefer async patterns."
Session 2: Agent starts from zero. You explain everything again.
Session 3: Same thing.
Existing solutions often center on hosted memory layers, API keys, or opaque context pipelines. What you actually need is to **resume the next session with the minimum correct context and a trail you can inspect**.
`rememb` is built around four memory problems:
- durable facts and decisions instead of session-only chat memory
- keyword search instead of rereading everything (agents judge relevance)
- non-destructive versioning instead of silent overwrites
- local-first audit trail for AI work, not opaque cloud logs
Install
pip install rememb
Quick Start
With MCP (recommended)
Zero friction. No CLI commands. Na
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