Sayou – Open-source Dropbox for AI agents

6 points

4 months ago

success of openclaw is incredible, and I really loved playing with it, but of course the problem is the security risk running on my desktop. okay for personal use for fun, not at all for professional use.

Once agents run in the cloud (and they will — you're not giving your marketing manager a terminal), they need somewhere to read and write. Right now there's nothing. Your company's knowledge is locked in Notion, Slack, Google Drive, and the agent can see none of it unless someone copy-pastes it into the chat window every single time.

sayou is basically Dropbox for agents. It syncs your SaaS tools into a single workspace of versioned Markdown files, and any agent connects to it over MCP. Multiple agents share the same workspace. one does research and writes findings, another picks them up to draft a report, a third sends it over email. The human just makes decisions.

Why files and not a database? Honestly we tried a database first. It sucked. Agents don't think in rows. When you ask an agent to research competitor pricing, the natural output is a document with sections and citations, not 15 entries in a table. Markdown with YAML frontmatter turned out to be the sweet spot — you get structured metadata for querying and rich content that both agents and humans can actually read.

I also built a benchmark, Structured Agent Memory Benchmark(SAMB) to test how different memory systems like mem0 and zep compare to sayou in a real world scenario of agents running tasks. If you are writing code, stuff like "why did we choose bcrypt over Argon2?" and "what changed between the first and second architecture review?" — the kind of things you'd actually need an agent to recall.

  Results against Mem0 and Zep:

  sayou    67.0%   (files + FTS5)
  Mem0     18.5%   (embeddings)
  Zep      14.2%   (knowledge graph)

  3.6x. The biggest gap was on decision reasoning — 68% vs 8%. Turns out embedding similarity is great for vibes-based retrieval but terrible when you need a specific paragraph from a specific document. Files with full-text search let agents grep, read sections, and follow references. Simple and it works.

Honestly it is my first time building a benchmark so i would love other engineers to try sayou as their agent memory system, but also try to compare sayou fairly for agent task completions. If you're building agents that need to persist and share knowledge, please take a look at sayou's opensource project at https://github.com/pixell-global/sayou

4 comments

Results against Mem0 and Zep: sayou 67.0% (files + FTS5) Mem0 18.5% (embeddings) Zep 14.2% (knowledge graph) 3.6x. The biggest gap was on decision reasoning — 68% vs 8%. Turns out embedding similarity is great for vibes-based retrieval but terrible when you need a specific paragraph from a specific document. Files with full-text search let agents grep, read sections, and follow references. Simple and it works.