pixell-global

pixell-global / sayou

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The persistent workspace for AI agents

26
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100% credibility
Found Feb 20, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Sayou provides a persistent, file-based workspace where AI agents can save, search, and version notes, research, and decisions across multiple sessions.

How It Works

1
🔍 Discover a smart note-taker for your AI helper

While using your favorite AI coding app like Claude, you learn about a way to make your AI remember notes across chats.

2
⚙️ Add it with one easy step

Click install in your app or run a simple command, and your AI helper connects to a personal notebook.

3
💾 Save your first idea

Ask your AI to research something and save it as a note — it creates a organized file with details you can always find.

4
🔄 Come back tomorrow

Start a new chat and ask your AI what you saved before — it pulls up the exact note instantly.

5
📝 Build and update over time

Keep adding research, updates, or lists; everything versions automatically so you never lose old ideas.

🎉 Your knowledge grows forever

Your AI's notebook becomes a growing library of insights, decisions, and projects that stick around session after session.

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AI-Generated Review

What is sayou?

Sayou gives AI agents persistent workspaces to store research notes, code snippets, decisions, and docs as versioned Markdown files with YAML frontmatter—ensuring knowledge compounds across sessions instead of vanishing. Built in Python, it exposes 11 MCP tools like workspace_write, workspace_read, and workspace_search via CLI, async Python API, or REST server, with SQLite/MySQL backends and optional S3 storage. Agents get full context on one read, auto-summarized by token budget, plus semantic search and a simple KV store.

Why is it gaining traction?

Unlike vector DBs like Pinecone or memory layers like Mem0 that trap facts in embeddings, sayou feels like a real filesystem—agents write structured files they can grep, diff, and time-travel, perfect for persistent workspaces in tools like Claude Code or Cursor. The MCP integration auto-installs with one command, hooking session starts/ends for passive capture, and SAMB benchmarks prove it crushes conversation recall on real workflows like auth builds or churn analysis. Devs love the audit trail and zero-schema flexibility over rigid databases.

Who should use this?

Backend devs building multi-session AI agents for coding/research, PMs debugging churn via persistent data workspaces, or teams needing github persistent storage for agent outputs without S3 hacks. Ideal for Express.js auth flows, BigQuery analysis, or any Claude/Cursor workflow where agents need to recall decisions across days.

Verdict

Early beta (15 stars, 1.0% credibility) with excellent docs and benchmarks, but light on tests—grab for prototyping persistent agent workspaces now, watch for prod scaling. Worth the pip install if you're tired of agents forgetting mid-project. (198 words)

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