Most AI assistants (Copilot, Cursor, Claude Code, etc.) can autocomplete, but they forget everything once the tab closes.
They don’t remember design reasoning, they can’t coordinate tasks, and every new prompt starts from zero.
AutomatosX is a local-first framework that fixes this by combining persistent memory, multi-agent orchestration, and governance into a single developer toolchain.
- Persistent Memory: Every decision, plan, and conversation is stored locally (SQLite + FTS5) for millisecond recall.
- Multi-Agent Collaboration: Specialized agents (frontend, backend, QA, security, product…) can delegate work, verify results, and operate as a small software team.
- Governance Controls: Each agent has clear permissions and delegation depth limits to prevent runaway recursion.
- Local-First Design: Runs entirely on your machine, zero token cost, zero privacy leakage.
- Dynamic Ability Loading: Agents only load capabilities they need, reducing prompt size and latency.
Why it matters: AI coding tools today act like smart calculators. AutomatosX gives them memory and structure, so they can manage evolving projects instead of isolated prompts. This bridges the gap between LLM assistants and true autonomous developer environments.
We see it as a missing “operating system layer” for agentic development — turning one developer into an orchestrated AI team that actually learns from its own history.
Most AI assistants (Copilot, Cursor, Claude Code, etc.) can autocomplete, but they forget everything once the tab closes. They don’t remember design reasoning, they can’t coordinate tasks, and every new prompt starts from zero.
AutomatosX is a local-first framework that fixes this by combining persistent memory, multi-agent orchestration, and governance into a single developer toolchain.
- Persistent Memory: Every decision, plan, and conversation is stored locally (SQLite + FTS5) for millisecond recall. - Multi-Agent Collaboration: Specialized agents (frontend, backend, QA, security, product…) can delegate work, verify results, and operate as a small software team. - Governance Controls: Each agent has clear permissions and delegation depth limits to prevent runaway recursion. - Local-First Design: Runs entirely on your machine, zero token cost, zero privacy leakage. - Dynamic Ability Loading: Agents only load capabilities they need, reducing prompt size and latency.
Why it matters: AI coding tools today act like smart calculators. AutomatosX gives them memory and structure, so they can manage evolving projects instead of isolated prompts. This bridges the gap between LLM assistants and true autonomous developer environments.
We see it as a missing “operating system layer” for agentic development — turning one developer into an orchestrated AI team that actually learns from its own history.
I’d love feedback on:
Multi-agent design trade-offs
Memory governance and privacy boundaries
Integration ideas with local or hybrid LLMs