PhyAgentOS-core vs rai
PhyAgentOS-core is much bigger: 2.1k stars against 583. Over the days we have tracked them PhyAgentOS-core moved +22.1% and rai +5%, so PhyAgentOS-core is growing faster right now.
They split the axes: PhyAgentOS-core leads on context depth and noise control and customization and setup ease, rai on model freedom.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Decouples cognition from physical hardware with a session-centered runtime and small target adapters so the same agent codebase runs across sim and real robots.
- Stars
- 2.1k
- Tracked growth
- +22.1%
- Maturity
- ●●●●●
- Last commit
- 2h ago
- Language
- Python
- License
- MIT
- Cost to run
- Free, self-hosted (local compute)
A ROS2-native, vendor-agnostic embodied agent framework focused on integrating multi-modal and agentic capabilities directly on robots.
- Stars
- 583
- Tracked growth
- +5%
- Maturity
- ●●●●●
- Last commit
- 9m ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Open-source, self-hosted; model API costs may apply
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | PhyAgentOS-core | rai |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Workspace-wide protocol | ●●●●● Diff only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Multi-layer safety | ●●●●● None |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Plugins + YAML configs | ●●●●● Config file |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Fully self-hosted | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Specific policy endpoints | ●●●●● Bring-your-own-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Quick CLI onboarding | ●●●●● Manual setup |
Which one to pick
Pick PhyAgentOS-core if…
Safety-first — built-in multi-layer validation (critic → preflight → target-side SafetyGuard) and auditable file protocols make it suitable for real-robot deployment.
- Context depth: Workspace-wide protocol (4/5 against 1/5)
- Noise control: Multi-layer safety (5/5 against 1/5)
- Customization: Plugins + YAML configs (5/5 against 3/5)
- Setup ease: Quick CLI onboarding (4/5 against 2/5)
Pick rai if…
Robot-first — pick RAI when you need a ROS2-integrated, extensible framework to add multi-agent, multi-modal AI capabilities to physical robots.
- Model freedom: Bring-your-own-key (5/5 against 3/5)
What people want from each one
PhyAgentOS/PhyAgentOS-core
RobotecAI/rai
Questions people ask
Is PhyAgentOS-core better than rai?
They split the axes: PhyAgentOS-core leads on context depth and noise control and customization and setup ease, rai on model freedom. PhyAgentOS-core is worth picking when safety-first — built-in multi-layer validation (critic → preflight → target-side SafetyGuard) and auditable file protocols make it suitable for real-robot deployment.
Which of PhyAgentOS-core and rai keeps my code private?
PhyAgentOS-core: Fully self-hosted (5/5). rai: Self-hostable (4/5).
What does each one cost to run?
PhyAgentOS-core: Free, self-hosted (local compute). rai: Open-source, self-hosted; model API costs may apply.
Full profiles: PhyAgentOS/PhyAgentOS-core and RobotecAI/rai. Everything else in Robotics & embodied.