--- title: "Awesome-Prompt-Engineering vs agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-wshobson-agents" tools: ["promptslab-awesome-prompt-engineering", "wshobson-agents"] --- # Awesome-Prompt-Engineering vs agents *GraphCanon updated Aug 19, 2026* ## Verdict Pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license; 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 Code, Codex CLI, Cursor, OpenCode, GitHub Copilot. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) reports 6.2k GitHub stars, 734 forks, and 94 open issues, last pushed Jul 27, 2026. [agents](https://sethhobson.com) has 39k stars, 4.1k forks, and 5 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [agents's repository](https://github.com/wshobson/agents). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Tagline | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | Multi-harness agentic plugin marketplace for various AI agents | | Stars | 6,197 | 38,928 | | Forks | 734 | 4,145 | | Open issues | 94 | 5 | | Language | TypeScript | Python | | Adopt for | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | 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 | Apache-2.0 | MIT | | Categories | Developer Tools, Model Training | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 94 | 5 | | Stars delta | Unknown | +860 (30d) | | Open issues delta | Unknown | +2 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/wshobson-agents/trust.md) | ## Decision facts: Awesome-Prompt-Engineering - **Adopt for:** Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. ## 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 Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; agents is Python. - License: Awesome-Prompt-Engineering is Apache-2.0, agents is MIT. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt. - Also covers Model Training. - You need focused materials on GPT and related models for prompt engineering ### Choose agents if… - agents is primarily Python; Awesome-Prompt-Engineering is TypeScript. - License: agents is MIT, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to agents: agent-skills, agentic-ai, automation, workflows. - Also covers AI Agents. - 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 ## When NOT to use Awesome-Prompt-Engineering - The project requires languages other than TypeScript - Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ## 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 Awesome-Prompt-Engineering and agents? Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. 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 Awesome-Prompt-Engineering over agents? Choose Awesome-Prompt-Engineering over agents when Awesome-Prompt-Engineering is primarily TypeScript; agents is Python; License: Awesome-Prompt-Engineering is Apache-2.0, agents is MIT; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; Also covers Model Training; You need focused materials on GPT and related models for prompt engineering. ### When should I choose agents over Awesome-Prompt-Engineering? Choose agents over Awesome-Prompt-Engineering when agents is primarily Python; Awesome-Prompt-Engineering is TypeScript; License: agents is MIT, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to agents: agent-skills, agentic-ai, automation, workflows; Also covers AI Agents; 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. ### When should I avoid Awesome-Prompt-Engineering? The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ### 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 Awesome-Prompt-Engineering or agents more popular on GitHub? agents has more GitHub stars (38,928 vs 6,197). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and agents open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, agents: MIT). ### Where can I find alternatives to Awesome-Prompt-Engineering or agents? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [agents alternatives](/tools/wshobson-agents/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/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/promptslab-awesome-prompt-engineering-vs-wshobson-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Prompt-Engineering or agents? Awesome-Prompt-Engineering: Very active. 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 Awesome-Prompt-Engineering and agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [agents trust report](/tools/wshobson-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering`](/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering) - 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/_