momomemory

momomemory / momo

Public

Momo is a self-hostable AI memory system written in Rust — inspired by SuperMemory

20
1
100% credibility
Found Feb 17, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

Momo is an open-source, self-hostable system that stores, searches, and visualizes long-term memories for AI agents via a web console and simple commands.

How It Works

1
📖 Discover Momo

You hear about Momo, a helpful tool that gives AI a long-term memory like a digital notebook for important facts and conversations.

2
🚀 Get it running

With one simple command, you launch Momo on your computer, and it starts up quietly in the background.

3
🌐 Open your dashboard

Visit a web page on your computer to see Momo's friendly control panel ready for action.

4
💭 Add memories

Type in facts, conversations, or upload files like PDFs and images so Momo remembers what matters to you.

5
🔍 Search everything

Ask questions like 'What do I prefer?' and Momo instantly pulls up relevant memories from your notebook.

6
🕸️ Explore connections

View a visual map showing how your memories link together, revealing patterns and relationships.

🧠 Perfect AI recall

Your AI companion now remembers everything forever, making chats smarter and more personal every time.

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Star Growth

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AI-Generated Review

What is momo?

Momo is a self-hostable AI memory system built in Rust, giving AI agents persistent, searchable long-term memory without needing an external vector database—everything runs in a single binary via LibSQL's native vector search. Inspired by SuperMemory, it ingests documents, conversations, and media (like PDFs, images via OCR, audio/video transcription), builds knowledge graphs, detects contradictions, and generates user profiles. Spin it up with one Docker command and hit the web console at localhost:3000 or API at /api/v1.

Why is it gaining traction?

It stands out for multi-tenant isolation via container tags, autonomous features like intelligent forgetting and inference-derived insights, and local-first AI pipelines (FastEmbed, Whisper) alongside OpenAI/Ollama support. Developers dig the MCP server for Supermemory workflows, AST-aware code chunking, and reranking for precise hybrid search—no vendor lock-in, just Docker-ready privacy. At 16 stars, it's early but hooks self-hosters tired of managed RAG services.

Who should use this?

AI agent builders needing private, scalable memory (e.g., per-user graphs in chatbots). Indie devs prototyping RAG apps without cloud bills. Teams handling diverse inputs like codebases, podcasts, or docs, wanting versioning and profiling out-of-box.

Verdict

Grab momo 4.4.1 from GitHub if you're evaluating self-hosted memory—its Rust efficiency and Docker simplicity shine for prototypes, despite 1.0% credibility from low stars and nascent docs. Mature enough for tinkering, but watch for production hardening.

(187 words)

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