--- title: "agents-from-scratch vs agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pguso-agents-from-scratch-vs-wshobson-agents" tools: ["pguso-agents-from-scratch", "wshobson-agents"] --- # agents-from-scratch vs agents *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick agents if the agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) reports 1.0k GitHub stars, 251 forks, and 4 open issues, last pushed Jul 25, 2026. [agents](https://sethhobson.com) has 40k stars, 4.2k forks, and 12 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch) and [agents's repository](https://github.com/wshobson/agents). | | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Tagline | Build AI agents locally without relying on frameworks or cloud APIs. | Multi-harness agentic plugin marketplace for various AI agents | | Stars | 1,017 | 39,823 | | Forks | 251 | 4,246 | | Open issues | 4 | 12 | | Language | Python | Python | | 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. | The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | MIT | | Categories | AI Agents, Developer Tools | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 56d | 1d | | Open issues (now) | 4 | 12 | | Stars delta | +63 (30d) | +895 (30d) | | Open issues delta | +1 (30d) | +7 (30d) | | Full report | [trust report](/tools/pguso-agents-from-scratch/trust.md) | [trust report](/tools/wshobson-agents/trust.md) | ## 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. ## Decision facts: agents - **Adopt for:** The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot ## Choose when ### Choose agents-from-scratch if… - 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. - 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. ### Choose agents if… - Tags unique to agents: agent-skills, agentic-ai, automation, workflows. - You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments - More GitHub stars (40k vs 1.0k) - visibility, not fit. ## 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. ## When NOT to use agents - You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support - Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ## Common questions ### What is the difference between agents-from-scratch and agents? agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. agents: Multi-harness agentic plugin marketplace for various AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose agents-from-scratch over agents? Choose agents-from-scratch over agents when 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; 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 choose agents over agents-from-scratch? Choose agents over agents-from-scratch when Tags unique to agents: agent-skills, agentic-ai, automation, workflows; You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments; More GitHub stars (40k vs 1.0k) - visibility, not fit. ### 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. ### When should I avoid agents? You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ### Is agents-from-scratch or agents more popular on GitHub? agents has more GitHub stars (39,823 vs 1,017). Stars measure visibility, not whether either tool fits your constraints. ### Are agents-from-scratch and agents open source? Yes - both are open-source projects on GitHub (agents-from-scratch: MIT, agents: MIT). ### Where can I find alternatives to agents-from-scratch or agents? GraphCanon lists graph-backed alternatives at [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) and [agents alternatives](/tools/wshobson-agents/alternatives) ([agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/alternatives.md), [agents markdown twin](/tools/wshobson-agents/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/pguso-agents-from-scratch-vs-wshobson-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agents-from-scratch or agents? agents-from-scratch: Steady. agents: Very 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 agents-from-scratch and agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust); [agents trust report](/tools/wshobson-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pguso-agents-from-scratch`](/api/graphcanon/graph?tool=pguso-agents-from-scratch) - 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/_