LibreAI / AI Agents / PhyAgentOS-core
// agents · PhyAgentOS/PhyAgentOS-core

PhyAgentOS-core

Recursive self-improving physical agent OS — verified, evidence-based execution for robots and simulators

★ 2.8k stars⑂ 128 forksMITupdated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

PhyAgentOS-core is an open-source ai agents project by PhyAgentOS (PhyAgentOS/PhyAgentOS-core on GitHub). It has 2,776 GitHub stars and 128 forks, is released under the MIT license, and was last updated on GitHub on 8 October 2026.

GitHub stars
2,776
Forks
128
License
MIT
Last updated on GitHub
8 October 2026
Category
AI Agents
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Clarify your goal

    Define an embodied task with a visible goal and success criteria — PAOS verification modes (audit, enforce, recovery) are built around explicit criteria.

  2. Get PhyAgentOS

    Clone the repo and install it in editable mode; install Dora CLI v0.4.1 only if you will run managed Forge Skill profiles.

  3. Initialize and configure

    Run paos onboard, then edit ~/.PhyAgentOS/config.json with your model provider (e.g. OpenRouter) and Forge/verification settings, and install the Forge Skills you need.

  4. Run your first task

    Start an installed Skill Runtime, then paos agent -m "..." with a task, and check the verdict and evidence afterwards.

// features

Why people love it

One execution boundary

Robot actions enter only through the versioned Forge Gateway Tool API (Query/Action/Session) — the agent never reaches directly into a simulator or hardware SDK.

Evidence before verdict

Validated images and optional robot state are captured around actions with source, sequence, time and SHA-256 digest metadata.

Action-agnostic verification

The verifier judges goal, criteria, constraints, execution facts and evidence — with off, audit, enforce and recovery modes.

Planner-owned recovery

A recovery verdict appends a bounded PlanRevision to the same task; unknown effects are reconciled, never blindly retried.

Crash-safe persistence

SQLite WAL transactions persist AgentTask, PlanRevision, Query records and Gateway invocation references.

Scoped self-improvement

Verified tasks produce reusable workflow Skills and clustered Lessons, loaded only with the matching Skill, with guarded promotion.

Advertisement
// run it

Run it yourself

  1. Clone and install

    Clone PhyAgentOS-core and install it editable, exactly as the 5-minute quick start shows.

    git clone https://github.com/PhyAgentOS/PhyAgentOS-core.git
    cd PhyAgentOS-core
  2. Initialize the workspace

    Onboard to create ~/.PhyAgentOS/config.json and the default workspace, then add your provider API key to the config.

    paos onboard
  3. Start the agent

    Run the interactive CLI (or paos gateway for channels/Cron/Heartbeat) and give it a verifiable task.

    paos agent
    # one-shot: paos agent -m "Inspect Forge capabilities, then place the object in the target area and verify the visible result."
// use cases

What you can build

Advertisement
// faq

Questions, answered

Who maintains PhyAgentOS?

The PhyAgentOS project team maintains it; the project's site is phy-agent-os.x-era.com.

Is it free?

Yes, it is MIT-licensed. You supply your own model provider and any robot/simulator hardware or software.

Does it need a paid API key?

The config example uses a provider such as OpenRouter with your API key, so model costs depend on your provider and model choice.

How does it compare to a plain agent framework?

General frameworks plan and call tools. PAOS adds a single governed execution gateway for physical actions, captured evidence, a separate verifier, and experience that only comes from verified tasks.

How do I get started?

pip install -e . from a clone, run paos onboard, configure a provider in config.json, install a Forge Skill, and run paos agent.

// keep exploring

Similar tools

AI Agents

agent-qa

A self-improving QA agent that writes tests in plain English.

★ 902 starsNOASSERTION
View guide →
AI Agents

prime-agent

A self-improving agent for coding workflows and long-running autonomous tasks

★ 21.6k starsNOASSERTION
View guide →
AI Agents

agent-beacon

Self-improving memory layer that works across AI agent harnesses.

★ 1.8k starsMIT
View guide →
AI Agents

OpenAgentsControl

AI agent framework for plan-first development workflows with approval-based execution. Multi-language support (TypeScript, Python, Go, Rust) with automatic t…

★ 4.9k starsMIT
View guide →
AI Agents

webarena-verified

WebArena-Verified: the audited, deterministically scored release of the WebArena web-agent benchmark.

★ 62 starsApache-2.0
View guide →
AI Agents

morphik-core

An open-source multimodal retrieval engine for agent pipelines.

★ 3.7k starsNOASSERTION
View guide →
// alternatives

Alternatives in AI Agents

The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if PhyAgentOS-core is not the right fit.

Get PhyAgentOS-core

Free and open — grab the source, star the repo, and start creating.