--- title: "awesome-prompts vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ai-boost-awesome-prompts-vs-pguso-agents-from-scratch" tools: ["ai-boost-awesome-prompts", "pguso-agents-from-scratch"] --- # awesome-prompts vs agents-from-scratch *GraphCanon updated Aug 12, 2026* ## Verdict Pick awesome-prompts if awesome-prompts is a collection of rated GPT prompts from the GPTs Store with a focus on engineering, attacks, protections, and academic papers; 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. [awesome-prompts](https://awesomegpt.vip) reports 8.4k GitHub stars, 798 forks, and 35 open issues, last pushed Jul 11, 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 [awesome-prompts's repository](https://github.com/ai-boost/awesome-prompts) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [awesome-prompts](/tools/ai-boost-awesome-prompts.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Curated chatgpt prompts and advanced prompt engineering papers | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 8,440 | 954 | | Forks | 798 | 240 | | Open issues | 35 | 3 | | Language | - | Python | | Adopt for | awesome-prompts is a collection of rated GPT prompts from the GPTs Store with a focus on engineering, attacks, protections, and academic papers. | 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 | GPL-3.0 | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | Developer Tools | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-prompts](/tools/ai-boost-awesome-prompts.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 18d | | Open issues (now) | 35 | 3 | | Full report | [trust report](/tools/ai-boost-awesome-prompts/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: awesome-prompts - **Adopt for:** awesome-prompts is a collection of rated GPT prompts from the GPTs Store with a focus on engineering, attacks, protections, and academic papers. ## 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 awesome-prompts if… - License: awesome-prompts is GPL-3.0, agents-from-scratch is MIT. - Tags unique to awesome-prompts: awesome-list, chatgpt, prompt-attack, prompt-protect. - Need detailed prompt engineering resources ### Choose agents-from-scratch if… - License: agents-from-scratch is MIT, awesome-prompts is GPL-3.0. - 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 AI Agents. - 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 awesome-prompts - Seeking a platform for generating new prompts rather than reviewing existing ones - In search of direct support tools or services, not just informational content - Requiring real-time collaboration on prompt creation and experimentation ## 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 awesome-prompts and agents-from-scratch? awesome-prompts: Curated chatgpt prompts and advanced prompt engineering papers. 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 awesome-prompts over agents-from-scratch? Choose awesome-prompts over agents-from-scratch when License: awesome-prompts is GPL-3.0, agents-from-scratch is MIT; Tags unique to awesome-prompts: awesome-list, chatgpt, prompt-attack, prompt-protect; Need detailed prompt engineering resources. ### When should I choose agents-from-scratch over awesome-prompts? Choose agents-from-scratch over awesome-prompts when License: agents-from-scratch is MIT, awesome-prompts is GPL-3.0; 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 AI Agents; 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 awesome-prompts? Seeking a platform for generating new prompts rather than reviewing existing ones In search of direct support tools or services, not just informational content Requiring real-time collaboration on prompt creation and experimentation ### 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 awesome-prompts or agents-from-scratch more popular on GitHub? awesome-prompts has more GitHub stars (8,440 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-prompts and agents-from-scratch open source? Yes - both are open-source projects on GitHub (awesome-prompts: GPL-3.0, agents-from-scratch: MIT). ### Where can I find alternatives to awesome-prompts or agents-from-scratch? GraphCanon lists graph-backed alternatives at [awesome-prompts alternatives](/tools/ai-boost-awesome-prompts/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([awesome-prompts markdown twin](/tools/ai-boost-awesome-prompts/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/ai-boost-awesome-prompts-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, awesome-prompts or agents-from-scratch? awesome-prompts: 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 awesome-prompts and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-prompts trust report](/tools/ai-boost-awesome-prompts/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ai-boost-awesome-prompts`](/api/graphcanon/graph?tool=ai-boost-awesome-prompts) - 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/_