--- title: "holaOS vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/holaboss-ai-holaos-vs-pguso-agents-from-scratch" tools: ["holaboss-ai-holaos", "pguso-agents-from-scratch"] --- # holaOS vs agents-from-scratch *GraphCanon updated Aug 26, 2026* ## Verdict Pick holaOS if holaOS is tailored to work environments seeking a local-first AI assistant that retains context indefinitely; pick agents-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. [holaOS](https://holaos.ai) reports 11k GitHub stars, 712 forks, and 6 open issues, last pushed Aug 21, 2026. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 954 stars, 240 forks, and 3 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [holaOS's repository](https://github.com/holaboss-ai/holaOS) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [holaOS](/tools/holaboss-ai-holaos.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Local-first super agent for work that learns and retains context. | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 10,870 | 954 | | Forks | 712 | 240 | | Open issues | 6 | 3 | | Language | TypeScript | Python | | Adopt for | HolaOS is tailored to work environments seeking a local-first AI assistant that retains context indefinitely. | agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [holaOS](/tools/holaboss-ai-holaos.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 4d | 18d | | Open issues (now) | 6 | 3 | | Stars delta | +5.3k (30d) | Unknown | | Open issues delta | +4 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/holaboss-ai-holaos/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: holaOS - **Adopt for:** HolaOS is tailored to work environments seeking a local-first AI assistant that retains context indefinitely. ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose holaOS if… - holaOS is primarily TypeScript; agents-from-scratch is Python. - License: holaOS is Other, agents-from-scratch is MIT. - Tags unique to holaOS: agent, ai-agent, local-first, memory. - When you require an agent that operates entirely locally without relying on cloud storage to maintain user privacy and data security. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; holaOS is TypeScript. - License: agents-from-scratch is MIT, holaOS is Other. - Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.. - Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm. - Also covers Developer Tools. - You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ## When NOT to use holaOS - Avoid if your setup strictly requires cloud integration or synchronization across multiple devices which holaOS does not offer due to its local-first focus. - Not suitable when a mobile-friendly solution is needed as the tool supports workspace contexts primarily and may lack mobility-specific features. ## When NOT to use agents-from-scratch - You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. - If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ## Common questions ### What is the difference between holaOS and agents-from-scratch? holaOS: Local-first super agent for work that learns and retains context.. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose holaOS over agents-from-scratch? Choose holaOS over agents-from-scratch when holaOS is primarily TypeScript; agents-from-scratch is Python; License: holaOS is Other, agents-from-scratch is MIT; Tags unique to holaOS: agent, ai-agent, local-first, memory; When you require an agent that operates entirely locally without relying on cloud storage to maintain user privacy and data security. ### When should I choose agents-from-scratch over holaOS? Choose agents-from-scratch over holaOS when agents-from-scratch is primarily Python; holaOS is TypeScript; License: agents-from-scratch is MIT, holaOS is Other; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm; Also covers Developer Tools; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ### When should I avoid holaOS? Avoid if your setup strictly requires cloud integration or synchronization across multiple devices which holaOS does not offer due to its local-first focus. Not suitable when a mobile-friendly solution is needed as the tool supports workspace contexts primarily and may lack mobility-specific features. ### When should I avoid agents-from-scratch? You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ### Is holaOS or agents-from-scratch more popular on GitHub? holaOS has more GitHub stars (10,870 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are holaOS and agents-from-scratch open source? Yes - both are open-source projects on GitHub (holaOS: Other, agents-from-scratch: MIT). ### Where can I find alternatives to holaOS or agents-from-scratch? GraphCanon lists graph-backed alternatives at [holaOS alternatives](/tools/holaboss-ai-holaos/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([holaOS markdown twin](/tools/holaboss-ai-holaos/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/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/holaboss-ai-holaos-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, holaOS or agents-from-scratch? holaOS: Very active. agents-from-scratch: 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 holaOS and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [holaOS trust report](/tools/holaboss-ai-holaos/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=holaboss-ai-holaos`](/api/graphcanon/graph?tool=holaboss-ai-holaos) - 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/_