Agent runtime, unified memory, system primitives, and event bus — build and deploy AI agents on the operating system designed for intelligence.
Everything you need from HeadyOS — powered by Sacred Geometry and Continuous Semantic Logic.
Managed execution environment for AI agents with hot-reload, sandbox isolation, WASM containment, and φ-scaled auto-scaling that adapts to workload patterns.
Shared 3D vector space across all running agents with cross-agent context sharing, semantic search, and CSL-gated access control for memory isolation.
File, network, and compute tools available to any agent via MCP protocol. 31 tools exposed through JSON-RPC 2.0 over SSE/stdio channels.
Real-time inter-agent communication with pub/sub, request-reply, and streaming patterns. φ-scaled backpressure prevents any single agent from overwhelming the bus.
A comprehensive look at what HeadyOS offers and how it works.
HeadyOS reimagines the operating system concept for the age of artificial intelligence. Traditional operating systems manage processes, files, and hardware. HeadyOS manages AI agents, vector memory, and intelligence. It provides the foundational runtime, primitives, and abstractions that autonomous agents need to execute tasks, communicate with each other, and persist learned knowledge.
The HeadyOS runtime executes AI agents in managed sandboxes with full lifecycle control. Each agent runs in a WASM containment boundary, preventing any agent from accessing resources outside its granted permissions. Agents can be started, stopped, hot-reloaded, and scaled independently.
The runtime provides automatic resource management using φ-scaled allocation. When an agent needs more compute, memory, or network bandwidth, HeadyOS allocates resources in golden-ratio increments (×1.618 per step), creating smooth scaling curves that avoid the oscillation problems of binary doubling. When demand decreases, resources are reclaimed using the inverse ratio (×0.618 per step).
Hot-reload enables live code updates without stopping running agents. When new agent code is deployed, HeadyOS creates a shadow instance, validates it against the agent's test suite, and atomically swaps traffic from the old instance to the new one. This zero-downtime deployment ensures agents remain available during updates.
All agents running on HeadyOS share access to a unified 3D vector memory space — a 384-dimensional persistent store backed by pgvector with octree spatial indexing. Each agent can read from the shared memory and write to its own memory partition, with CSL-gated access control determining visibility between agents.
Cross-agent context sharing enables collaborative intelligence. When Agent A discovers a useful pattern, it can write that pattern to shared memory where Agent B automatically discovers it during context pre-enrichment. This creates a form of collective learning where the intelligence of the system grows with every agent interaction.
HeadyOS exposes 31 tools through the Model Context Protocol (MCP) via JSON-RPC 2.0 over SSE/stdio channels. These tools cover file operations (read, write, search, watch), network operations (HTTP, WebSocket, DNS), compute operations (function execution, shell commands, WASM instantiation), memory operations (embed, search, store, delete), and system operations (health check, telemetry, configuration, secrets).
Every tool call is enriched by HeadyAutoContext before execution and after completion. This means when an agent reads a file, the file content is automatically embedded into vector memory. When an agent makes an HTTP request, the response is indexed for future semantic search. The operating system learns from every operation.
The HeadyOS event bus provides real-time inter-agent communication through three patterns: publish/subscribe for broadcast messages, request/reply for synchronous queries, and streaming for continuous data flows. The bus uses φ-scaled backpressure to prevent any single agent from overwhelming the system — when a consumer falls behind, the bus slows the producer by ψ (0.618) rate, creating smooth degradation instead of hard failures.
Events are typed and versioned, ensuring forward and backward compatibility as agents evolve. The bus supports topic hierarchies (e.g., agents.research.papers.new) with wildcard subscriptions, enabling agents to listen for broad categories or specific events.
HeadyOS provides a complete SDK for agent development in JavaScript/TypeScript, Python, and Rust. The SDK includes agent lifecycle management, memory access, MCP tool wrappers, event bus subscriptions, and testing utilities. An interactive CLI lets developers create, deploy, monitor, and debug agents from the terminal.
The HeadyOS IDE integration (via the Antigravity IDE) provides real-time agent monitoring, memory visualization, event stream inspection, and hot-reload triggers directly in the development environment. Developers can see their agents' vector memory evolve in real time as they test interactions.
The HeadyOS workflow in four steps.
Define your agent's capabilities, permissions, and initial context using the HeadyOS SDK. Deploy with a single CLI command.
HeadyOS manages your agent's lifecycle in a WASM sandbox with φ-scaled resource allocation and hot-reload capability.
Every agent action is indexed into shared 3D vector memory. Context pre-enrichment ensures your agent always has relevant information.
Event bus enables real-time communication between agents. Shared memory creates collective intelligence that grows over time.
Built on the Heady™ infrastructure — sacred geometry governs every parameter.
Nine interconnected platforms. One unified intelligence layer.
Real-world applications of HeadyOS.
Deploy research agents that continuously monitor academic papers, extract insights, and build knowledge graphs in shared vector memory.
Build code generation agents with access to your entire codebase through vector memory. Context-aware suggestions that understand your architecture.
Agent-powered infrastructure management. Deploy, monitor, scale, and heal services automatically using MCP system tools.
Create data processing agents that ingest, transform, enrich, and analyze data streams in real time through the event bus.
Compose complex workflows from simple agents. The event bus handles coordination, and shared memory provides context.
Comprehensive testing framework for AI agents. Simulate environments, inject test data, and verify agent behavior deterministically.
Everything you need to know about HeadyOS.
The SDK supports JavaScript/TypeScript, Python, and Rust for agent development. WASM containment means any language that compiles to WebAssembly can run on HeadyOS. The MCP protocol is language-agnostic.
Each agent has its own memory partition for private data. Shared memory uses CSL-gated access control — agents can read shared content above the 0.382 relevance threshold and write to shared space if their output meets the 0.618 quality gate.
Model Context Protocol (MCP) is a JSON-RPC 2.0 based protocol for exposing tools to AI agents over SSE or stdio channels. HeadyOS provides 31 MCP tools covering file, network, compute, memory, and system operations.
Yes. HeadyOS provides a local development runtime that emulates the full platform. Agents developed locally can be deployed to HeadyOS cloud with a single command. Local mode uses SQLite with vector extensions instead of pgvector.
When new agent code is deployed, HeadyOS creates a shadow instance, validates it against tests, and atomically swaps traffic. The old instance continues running until all in-flight requests complete, then gracefully shuts down.
HeadyOS offers a developer tier with 3 concurrent agents, 100MB vector memory, and 1000 MCP tool calls per day. Production tiers scale with usage. Enterprise plans include dedicated infrastructure and custom SLAs.
φ-scaled backpressure reduces producer rate by ψ (0.618) when consumers fall behind. This creates smooth degradation curves instead of hard failures. Topic-based routing ensures events reach only interested subscribers.
Yes. The network MCP tools support HTTP, WebSocket, and DNS operations. All external requests are logged, traced via OpenTelemetry, and indexed into vector memory by AutoContext.