--- title: "agent-zero vs openpi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agent0ai-agent-zero-vs-physical-intelligence-openpi" tools: ["agent0ai-agent-zero", "physical-intelligence-openpi"] --- # agent-zero vs openpi *GraphCanon updated Jul 12, 2026* ## Verdict Pick agent-zero if agent-zero is a Python-based autonomous agent framework that uses Docker for deployment and supports integration with LLM providers such as OpenAI Codex via OAuth; pick openpi if openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. [agent-zero](https://agent-zero.ai) reports 18k GitHub stars, 3.7k forks, and 235 open issues, last pushed Jul 10, 2026. [openpi](https://github.com/Physical-Intelligence/openpi) has 13k stars, 2.2k forks, and 312 open issues, last pushed Jun 16, 2026. Figures are from public GitHub metadata via [agent-zero's repository](https://github.com/agent0ai/agent-zero) and [openpi's repository](https://github.com/Physical-Intelligence/openpi). | | [agent-zero](/tools/agent0ai-agent-zero.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Tagline | Agent Zero AI framework | Repository for running AI models with GPU requirements specified. | | Stars | 18,393 | 12,742 | | Forks | 3,680 | 2,187 | | Open issues | 235 | 312 | | Language | Python | Python | | Adopt for | Agent-zero is a Python-based autonomous agent framework that uses Docker for deployment and supports integration with LLM providers such as OpenAI Codex via OAuth. | openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-zero](/tools/agent0ai-agent-zero.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 25d | | Open issues (now) | 235 | 312 | | Security scan | 99 low (99 low) | No lockfile | | Full report | [trust report](/tools/agent0ai-agent-zero/trust.md) | [trust report](/tools/physical-intelligence-openpi/trust.md) | ## Decision facts: agent-zero - **Pricing:** unknown - The repository does not explicitly state any pricing information. - **Requirements:** Requires Docker; Requires Docker setup and can be configured to leverage existing Docker environments. - **Adopt for:** Agent-zero is a Python-based autonomous agent framework that uses Docker for deployment and supports integration with LLM providers such as OpenAI Codex via OAuth. ## Decision facts: openpi - **Adopt for:** openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. ## Choose when ### Choose agent-zero if… - License: agent-zero is Other, openpi is Apache-2.0. - Pricing: The repository does not explicitly state any pricing information.. - Requirements: Requires Docker; Requires Docker setup and can be configured to leverage existing Docker environments.. - Tags unique to agent-zero: agent, ai, assistant, autonomous. - Also covers AI Agents. - * When setting up agents in SSH sessions, servers, recovery shells, or requiring scriptable installation processes. ### Choose openpi if… - License: openpi is Apache-2.0, agent-zero is Other. - Tags unique to openpi: fine-tuning, lora, model parallelism, nvidia gpu. - Also covers Model Training. - When you have an NVIDIA GPU with at least 8 GB of VRAM for inference or at least 22.5 GB to fine-tune models using LoRA (Low-Rank Adaptation) on a single GPU. ## When NOT to use agent-zero - * When your deployment environment does not support or require Dockerization for agent operations. - * In scenarios where OAuth-based integration with third-party language model providers is undesirable or impractical. - * For installations that do not align well with the provided `/a0/usr` directory mapping conventions (e.g., specific data directories are required). ## When NOT to use openpi - When your project is not compatible with Ubuntu 22.04 or if you do not have access to a supported GPU configuration. - If you need to support multi-node training, as this capability has yet to be implemented in the current version of openpi. ## Common questions ### What is the difference between agent-zero and openpi? agent-zero: Agent Zero AI framework. openpi: Repository for running AI models with GPU requirements specified.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-zero over openpi? Choose agent-zero over openpi when License: agent-zero is Other, openpi is Apache-2.0; Pricing: The repository does not explicitly state any pricing information.; Requirements: Requires Docker; Requires Docker setup and can be configured to leverage existing Docker environments.; Tags unique to agent-zero: agent, ai, assistant, autonomous; Also covers AI Agents; * When setting up agents in SSH sessions, servers, recovery shells, or requiring scriptable installation processes. ### When should I choose openpi over agent-zero? Choose openpi over agent-zero when License: openpi is Apache-2.0, agent-zero is Other; Tags unique to openpi: fine-tuning, lora, model parallelism, nvidia gpu; Also covers Model Training; When you have an NVIDIA GPU with at least 8 GB of VRAM for inference or at least 22.5 GB to fine-tune models using LoRA (Low-Rank Adaptation) on a single GPU. ### When should I avoid agent-zero? * When your deployment environment does not support or require Dockerization for agent operations. * In scenarios where OAuth-based integration with third-party language model providers is undesirable or impractical. * For installations that do not align well with the provided `/a0/usr` directory mapping conventions (e.g., specific data directories are required). ### When should I avoid openpi? When your project is not compatible with Ubuntu 22.04 or if you do not have access to a supported GPU configuration. If you need to support multi-node training, as this capability has yet to be implemented in the current version of openpi. ### Is agent-zero or openpi more popular on GitHub? agent-zero has more GitHub stars (18,393 vs 12,742). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-zero and openpi open source? Yes - both are open-source projects on GitHub (agent-zero: Other, openpi: Apache-2.0). ### Where can I find alternatives to agent-zero or openpi? GraphCanon lists graph-backed alternatives at [agent-zero alternatives](/tools/agent0ai-agent-zero/alternatives) and [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) ([agent-zero markdown twin](/tools/agent0ai-agent-zero/alternatives.md), [openpi markdown twin](/tools/physical-intelligence-openpi/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/agent0ai-agent-zero-vs-physical-intelligence-openpi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-zero or openpi? agent-zero: Very active. openpi: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for agent-zero and openpi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-zero trust report](/tools/agent0ai-agent-zero/trust); [openpi trust report](/tools/physical-intelligence-openpi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agent0ai-agent-zero`](/api/graphcanon/graph?tool=agent0ai-agent-zero) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_