Agents today either forget everything when the session ends, or remember fragments out of context through vector search. Neither is how real work gets done.

I built sayou because I was tired of my agents losing their work between sessions — research reports, project docs, client records, all gone. Vector DBs can retrieve similar content but you can't version, diff, or structure what's stored there. Traditional databases force you to define schemas upfront for work that's inherently messy.

sayou is a file-based workspace for AI agents. Files with YAML frontmatter for structure, markdown for content, append-only versioning, full-text search, and an audit trail. It connects to any MCP-compatible client (Claude Code, Cursor, Windsurf) with 11 tools out of the box.

Stack: Python, SQLite for local dev, MySQL + S3 for production. No external dependencies for the base install.

What it looks like in practice: an agent writes research to research/competitor-pricing.md with status: draft in frontmatter. Next session, another agent searches for it, reads it, updates the status to reviewed. Every write is a new version. Nothing is overwritten.

Quick start: pip install sayou && sayou init --claude

Interested in the broader question too — how are you handling persistent state for multi-session agents? We think "agent workspace" is a missing category between memory systems and databases.