0xK3vin

0xK3vin / MegaMemory

Public

Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.

agentic-coding ai ai-agents claude-code code-context
58
6
100% credibility
Found Feb 10, 2026 at 24 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

MegaMemory provides a persistent memory system for AI coding assistants to store, query, and visualize project concepts, architecture, decisions, and relationships across sessions.

How It Works

1
📖 Discover MegaMemory

You hear about a handy tool that gives your coding AI buddy a forever memory for your project's features, designs, and choices.

2
🛠️ Add it to your computer

You install the memory helper with one easy step, and it grabs a small brain model to understand ideas.

3
🔗 Connect to your coding space

You choose your favorite AI coding workspace and link the memory tool in moments—it sets everything up automatically.

4
🚀 AI starts remembering

Your AI checks the memory at the start of each session, recalls relevant details before tasks, and saves new learnings afterward, feeling like it knows your project inside out.

5
🌐 Explore the memory map

You open a simple web page to see a colorful map of your project's ideas, connections, and how everything fits together.

6
🔄 Blend memories from branches

When working across different project versions, you merge the memories so nothing gets lost.

🎉 Smarter coding forever

Your AI now remembers across all sessions, works faster and better, and your projects feel alive with persistent knowledge.

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AI-Generated Review

What is MegaMemory?

MegaMemory builds a persistent knowledge graph for coding agents, storing concepts like features, modules, and decisions in a per-project SQLite database with github persistent storage. TypeScript MCP server exposes tools for semantic search via natural language queries, creating/updating nodes, and linking relationships—agents "understand" context before tasks and record changes after, retaining memory across sessions. CLI handles installs for opencode/Claude Code, web explorer visualizes graphs, and merge resolves branch conflicts.

Why is it gaining traction?

No external APIs needed—in-process embeddings enable fast semantic search over concepts agents write themselves, beating ephemeral chat histories or static code indexes. Git-friendly merge with AI resolution keeps knowledge consistent across persistent empires github branches, plus one-command editor setups hook devs tired of re-explaining projects. Low 20 stars, but practical loop (list roots, understand, update) fits agent workflows cleanly.

Who should use this?

Devs wielding opencode or Claude Code on mid-sized repos, needing persistent project manager memory for architecture and decisions without constant code rereads. Teams facing merge hell on knowledge divergence, or solo coders tracking patterns/configs over long hauls.

Verdict

Grab it if you're in MCP agents—installer and docs punch above 1.0% credibility/20 stars, making early maturity a non-issue for trials. Scale watch needed as graphs grow past 10k nodes.

(187 words)

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