ProbOS

Alpha — ProbOS is under active development. APIs will change, features may break, and documentation may lag behind the code. Contributions and feedback welcome.

Probabilistic agent-native OS runtime — an operating system kernel where every component is an autonomous agent, coordination happens through consensus, and the system learns from its own behavior. Agents are organized as the crew of a starship — departments, ranks, chain of command, and a human Captain.

"What if an OS didn't execute instructions — it negotiated them?"

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What Is This?

ProbOS reimagines the OS as a mesh of probabilistic agents rather than deterministic processes. Instead of syscalls, you speak natural language. Instead of a scheduler, agents self-organize through Hebbian learning and trust networks. Instead of permissions, destructive operations require multi-agent consensus.

[~60 agents | health: 0.95] probos> read pyproject.toml and tell me about this project

  ✓ t1: read_file

  This project is ProbOS v0.4.0, a probabilistic agent-native OS runtime...

Design Philosophy

Traditional operating systems use rigid, deterministic mechanisms: syscalls, schedulers, ACLs. ProbOS replaces each with a probabilistic, self-organizing equivalent:

Traditional OS	ProbOS Equivalent
Syscalls	Natural language decomposed into intent DAGs
Process scheduler	Attention-based priority scoring with Hebbian learning
File permissions / ACLs	Multi-agent consensus voting with red team verification
Process table	Agent registry with health monitoring and auto-recycling
IPC	Pub/sub intent bus with concurrent fan-out
Cron / scheduled tasks	Dreaming engine — offline consolidation during idle periods
Command history	Episodic memory with semantic recall
Shell aliases	Workflow cache — learned shortcuts for repeated patterns

Every agent maintains a confidence score and trust reputation. The system doesn't just execute operations — it deliberates, verifies, and learns.

How It Works

When you type natural language:

1. Working memory assembles system state (agent health, trust scores, Hebbian weights, capabilities) within a token budget
2. Episodic recall finds similar past interactions for context (top-3 by keyword-overlap cosine similarity)
3. Workflow cache checks for previously successful DAG patterns (exact match, then fuzzy with pre-warm intents)
4. LLM decomposer converts text into a TaskDAG — a directed acyclic graph of typed intents with dependencies
5. Attention manager scores tasks: urgency × relevance × deadline_factor × dependency_bonus
6. DAG executor runs independent intents in parallel, respects dependency ordering
7. Consensus gates destructive operations through multi-agent voting + red team verification
8. Reflection (optional) sends execution results back to the LLM for synthesis
9. Hebbian router strengthens successful agent-intent pairings, weakens failures
10. Episodic memory stores the interaction for future recall
11. Workflow cache stores successful patterns to bypass the LLM on repeat queries
12. Dreaming engine consolidates learning during idle periods — replays episodes, prunes weak connections, adjusts trust scores, pre-warms likely upcoming intents

Quick Links

• :material-rocket-launch: Getting Started

  Install ProbOS and launch the interactive shell in under a minute.

• :material-layers-triple: Architecture

  Seven layers from Substrate to Experience, plus Federation and Knowledge.

• :material-robot: Agents

  ~60 agents across 36 pools in 6 departments. Sovereign identity, episodic memory, trust networks, and a Ward Room where agents communicate and collaborate.

• :fontawesome-brands-discord: Discord

  Join the community.