// HACKER NEWS — CYBERSECURITY
CEO fired developers to make room for AI. Developers create open source AI CEO
An AI system that acts as your company's virtual executive team — a senior advisor with Harvard MBA-level knowledge, customized for your specific business.
A walkthrough of Open Executive in action — watch on YouTube.
Developed by sentelabs.ai Open Executive provides a single coherent executive voice backed by eight specialist AI agents:
All responses come from one consistent executive voice. The internal agent architecture is never exposed to the user. Beyond Q&A, the system maintains episodic memory of past decisions and initiatives across sessions, and a built-in scheduler can proactively surface follow-ups and time-sensitive actions.
Knowledge — Two retrieval layers per specialist call: (1) built-in MBA-level Markdown (knowledge/builtin/, git-tracked) seeded into ChromaDB at startup, and (2) your uploaded company documents chunked and stored in a separate company_docs collection. RAG context is injected into the user turn, never the cached system prompt.
Episodic memory — After every response, a background claude-haiku-4-5 pass extracts key decisions, initiatives, and advice into SQLite. The next session opens with a block so the Executive remembers what it recommended last month.
Scheduler — A built-in job runner claims due actions via UPDATE … RETURNING to prevent double-firing. The API must run as a single instance; do not horizontally scale it without gating the scheduler first.
Prompt caching — The system prompt is structured so the Executive persona, company profile, and knowledge index are cached separately (up to 85% cache hit rate after the first few turns). No dynamic content ever goes in a cached block.
Open http://localhost:3000 to start chatting with your executive. The API runs on port 8000 and the UI on 3000.
First run: requires Python 3.11+ and Node 22+. The initial uv sync pulls heavy
ML dependencies (ChromaDB + sentence-transformers/PyTorch), and the first boot
downloads a small embedding model (~90 MB) to build the local vector index — so the
first make dev takes a few minutes before the app is ready. Subsequent starts are fast.