Memmy

Memmy is a local-first shared memory and AI agent designed for people who work with multiple AI tools daily. It scans local AI collaboration history from applications such as Cursor, Claude, and Codex, then distills scattered conversations into structured memory covering user preferences, technical decisions, project background, and common pitfalls. This self-evolving memory foundation lets the desktop app, CLI, and external agents share the same long-term context while keeping all data on the user's device. Built as a desktop application for macOS and Windows, Memmy follows a three-step process: scan, organize, and inject. Users explicitly authorize the app to read local AI conversation history, after which Memmy deduplicates and categorizes the extracted information into a searchable memory base. When the user switches to any AI tool, Memmy injects the most relevant memories on demand, rather than dumping the entire history, enabling smooth context transfer between agents. Memmy is also an agent itself. Beyond memory management, it can be asked to merge plans discussed across different agents, turn technical decisions into documents, or continue unfinished work. The project is open source and emphasizes privacy: all memory stays local, nothing is uploaded to a remote server, users control which agents can access which memories, and every scan requires explicit consent that can be revoked at any time.

Category: AI Agent
Pricing: Unknown

Problem solved

People who rely on multiple AI assistants often lose context when switching tools; each AI remembers only its own chat history, so users must repeatedly restate preferences, decisions, and project details. Memmy solves this by creating a local, shared memory layer that captures, organizes, and injects relevant context across AI tools and agents, ensuring consistent personalization and continuity while preserving privacy.

Use cases

Remember user preferences and technical decisions across AI coding tools
Switch from Cursor to Claude or Codex without losing project context
Automatically turn scattered AI conversations into searchable structured memory
Inject only the most relevant memories into whichever AI agent is active
Ask Memmy to merge plans or decisions discussed across multiple agents
Create documents from technical decisions and project history
Maintain a private, local memory vault for sensitive AI work

Audience

Developers
ML Engineers
Data Scientists
Researchers
Startups

Pros

Local-first privacy: memory data stays on-device
Shared memory across multiple AI tools
Automatic structuring and deduplication of conversation history
Precise context injection rather than full history dumps
Open source and free to use

Cons

Requires scanning local AI conversation history, which may include sensitive content
Desktop app currently supports macOS and Windows only, with no Linux build listed
Memory quality depends on the coverage and compatibility of supported AI tools
Local-only storage makes cross-device or team sharing more difficult
The long-term pricing and roadmap are not clearly disclosed despite the current free open-source offering

Official website: https://memmy.bot/

Canonical LambdaBase page: https://www.lambdabase.com/tools/memmy

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Memmy

Unknown

• Updated Sep 1, 2026, 03:12 PM

Memmy is a local-first shared memory and AI agent designed for people who work with multiple AI tools daily.

Use Cases ▼

Remember user preferences and technical decisions across AI coding tools

Switch from Cursor to Claude or Codex without losing project context

Automatically turn scattered AI conversations into searchable structured memory

Inject only the most relevant memories into whichever AI agent is active

Ask Memmy to merge plans or decisions discussed across multiple agents

Create documents from technical decisions and project history

Maintain a private, local memory vault for sensitive AI work

What Problem It Solves ▼

Key Features ▼

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Local-first privacy: memory data stays on-device

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Shared memory across multiple AI tools

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Automatic structuring and deduplication of conversation history

✓

Precise context injection rather than full history dumps

✓

Open source and free to use

Overview

Full Description ▼

Built as a desktop application for macOS and Windows, Memmy follows a three-step process: scan, organize, and inject. Users explicitly authorize the app to read local AI conversation history, after which Memmy deduplicates and categorizes the extracted information into a searchable memory base. When the user switches to any AI tool, Memmy injects the most relevant memories on demand, rather than dumping the entire history, enabling smooth context transfer between agents.

Memmy is also an agent itself. Beyond memory management, it can be asked to merge plans discussed across different agents, turn technical decisions into documents, or continue unfinished work. The project is open source and emphasizes privacy: all memory stays local, nothing is uploaded to a remote server, users control which agents can access which memories, and every scan requires explicit consent that can be revoked at any time.