Is io.github.LuizEduPP/rememb safe to use?

io.github.LuizEduPP/rememb has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.LuizEduPP/rememb?

io.github.LuizEduPP/rememb can be installed by cloning its GitHub repository and following the setup instructions in the README.

io.github.LuizEduPP/rememb

Persistent memory for AI agents — local JSON, zero config, no server required.

UnknownNot tested

AI / ML

GitHub

16FNo CVEsunknown

Quick Install

{
  "mcpServers": {
    "io-github-luizedupp-rememb": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

Setup guide

No install config available. Check the server's README for setup instructions.

Are you the author?

Add this badge to your README to show your security score and help users find safe servers.

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[![MCPpedia Score](https://mcppedia.org/api/badge/io-github-luizedupp-rememb)](https://mcppedia.org/s/io-github-luizedupp-rememb)

Should you use this server?

Persistent memory for AI agents — local JSON, zero config, no server required.

✓

Is it safe?

No package registry to scan.

No authentication — any process on your machine can connect.

License not specified.

Is it maintained?

Commit history unknown.

Will it work with my client?

Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.

README

AI agents forget everything between sessions. rememb gives them persistent memory — local, portable, and works with any agent.

The problem

Every dev using AI 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 (Mem0, Zep, Letta) require servers, API keys, and cloud accounts.
You just want the agent to remember your project.

Install

pip install rememb[mcp]        # Recommended — includes MCP server
pip install rememb             # CLI only
pip install rememb[mcp,semantic,pdf]  # All features

Quick Start

With MCP (recommended)

Zero friction. No CLI commands. Native IDE integration.

1. Add to your IDE's MCP config:

{
  "mcpServers": {
    "rememb": {
      "command": "rememb",
      "args": ["mcp"]
    }
  }
}

2. Restart your IDE.

The agent now automatically reads memory at session start, writes when learning something new, and searches when needed.

Without MCP

rememb rules   # Print generic rules for AI agents

Copy the output to your editor's rules file (.windsurfrules, .cursorrules, CLAUDE.md, etc.)

How it works

.rememb/
  entries.json   ← structured memory (project, actions, systems, user, context)
  meta.json      ← project metadata

A JSON file in your project. Your agent reads it at the start of every session.

User: "We're using PostgreSQL, auth at src/auth/, async patterns"
Agent: [rememb_write] → Saved

[New session]
Agent: [rememb_read]  → Context loaded
Agent: "I see you're using PostgreSQL with auth at src/auth/..."

Search uses local semantic embeddings (no API, no cloud). Falls back to keyword search if embeddings aren't available.

Memory sections

Section	What to store
`project`	Tech stack, architecture, goals
`actions`	What was done, decisions made
`systems`	Services, modules, integrations
`requests`	User preferences, recurring asks
`user`	Name, style, expertise, preferences
`context`	Anything else relevant

CLI

rememb init                     # Initialize memory store
rememb write "text"             # Add entry (--section, --tags)
rememb read                     # List all entries (--section, --agent)
rememb search "query"           # Semantic/keyword search (--top)
rememb edit <id>                # Update entry (--content, --section, --tags)
rememb delete <id>              # Remove entry
rememb clear --yes              # Delete all entries
rememb import <folder>          # Import .md/.txt/.pdf files
rememb rules                    # Show generic rules for AI agents

Design

Local first — plain JSON file in your project
Portable — copy .rememb/ anywhere, it works
Agnostic — any agent, any IDE (MCP or CLI)
No lock-in — no servers, no API keys, no accounts

Contributing

git clone https://github.com/LuizEduPP/Rememb
cd rememb
pip install -e ".[dev]"

PRs welcome. Issues welcome. Stars welcome. 🌟

License

MIT

Test This Server

No automated test available for this server. Check the GitHub README for setup instructions.

Help improve this page

This server is missing a description. Tools and install config are also missing.If you've used it, help the community.

Add information

Score Breakdown

MCPpedia Score

Click each category to see evidence

16F

Last scored 6h ago

How we score →

Security

3/30

No known vulnerabilities.

○

Known CVEs — No package registry to scan

○

Tool safety — No tools to analyze

○

Tool poisoning — No tools to analyze

○

Injection vectors — No tools to analyze

✓

Tool stability — Tool definitions stable

○

Dependency health — No package to analyze

✗

License — No license specified

○

Authentication — No authentication required

○

Repository — active repo

CVEs checked daily via OSV.dev. Score algorithm is open source.

Reviews

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is io.github.LuizEduPP/rememb safe to use?: io.github.LuizEduPP/rememb has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.LuizEduPP/rememb?: io.github.LuizEduPP/rememb can be installed by cloning its GitHub repository and following the setup instructions in the README.
What AI clients work with io.github.LuizEduPP/rememb?: io.github.LuizEduPP/rememb is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.

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Discussion

Start a conversation

Ask a question, share a tip, or report an issue.

Has anyone used this with Cursor?How do you handle auth?Any alternatives?

AI agents forget everything between sessions. rememb gives them persistent memory — local, portable, and works with any agent.

The problem

Every dev using AI 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 (Mem0, Zep, Letta) require servers, API keys, and cloud accounts.
You just want the agent to remember your project.

Install

pip install rememb[mcp]        # Recommended — includes MCP server
pip install rememb             # CLI only
pip install rememb[mcp,semantic,pdf]  # All features

Quick Start

With MCP (recommended)

Zero friction. No CLI commands. Native IDE integration.

1. Add to your IDE's MCP config:

{
  "mcpServers": {
    "rememb": {
      "command": "rememb",
      "args": ["mcp"]
    }
  }
}

2. Restart your IDE.

The agent now automatically reads memory at session start, writes when learning something new, and searches when needed.

Without MCP

rememb rules   # Print generic rules for AI agents

Copy the output to your editor's rules file (.windsurfrules, .cursorrules, CLAUDE.md, etc.)

How it works

.rememb/
  entries.json   ← structured memory (project, actions, systems, user, context)
  meta.json      ← project metadata

A JSON file in your project. Your agent reads it at the start of every session.

User: "We're using PostgreSQL, auth at src/auth/, async patterns"
Agent: [rememb_write] → Saved

[New session]
Agent: [rememb_read]  → Context loaded
Agent: "I see you're using PostgreSQL with auth at src/auth/..."

Search uses local semantic embeddings (no API, no cloud). Falls back to keyword search if embeddings aren't available.

Memory sections

Section	What to store
`project`	Tech stack, architecture, goals
`actions`	What was done, decisions made
`systems`	Services, modules, integrations
`requests`	User preferences, recurring asks
`user`	Name, style, expertise, preferences
`context`	Anything else relevant

CLI

rememb init                     # Initialize memory store
rememb write "text"             # Add entry (--section, --tags)
rememb read                     # List all entries (--section, --agent)
rememb search "query"           # Semantic/keyword search (--top)
rememb edit <id>                # Update entry (--content, --section, --tags)
rememb delete <id>              # Remove entry
rememb clear --yes              # Delete all entries
rememb import <folder>          # Import .md/.txt/.pdf files
rememb rules                    # Show generic rules for AI agents

Design

Local first — plain JSON file in your project
Portable — copy .rememb/ anywhere, it works
Agnostic — any agent, any IDE (MCP or CLI)
No lock-in — no servers, no API keys, no accounts

Contributing

git clone https://github.com/LuizEduPP/Rememb
cd rememb
pip install -e ".[dev]"

PRs welcome. Issues welcome. Stars welcome. 🌟

License

MIT