Comparison
Awesome-Prompt-Engineering vs agents
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.
Markdown twin · Awesome-Prompt-Engineering alternatives · agents alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | Awesome-Prompt-Engineering | agents |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4w · github_public_v1 | Very active (1d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · github_public_v1 | Not a fork · Personal account As of 6d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- Awesome-Prompt-Engineering
- 6.2k
- agents
- 39k
Forks
- Awesome-Prompt-Engineering
- 734
- agents
- 4.1k
Open issues
- Awesome-Prompt-Engineering
- 94
- agents
- 5
Language
- Awesome-Prompt-Engineering
- TypeScript
- agents
- Python
Adopt for
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
- agents
- 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
- Awesome-Prompt-Engineering
- -
- agents
- -
Runtime
- Awesome-Prompt-Engineering
- -
- agents
- -
License
- Awesome-Prompt-Engineering
- Apache-2.0
- agents
- MIT
Last pushed
- Awesome-Prompt-Engineering
- Jul 27, 2026
- agents
- Aug 18, 2026
Categories
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
- agents
- AI Agents, Developer Tools
Trust and health
Days since push
- Awesome-Prompt-Engineering
- 0d
- agents
- 1d
Open issues (now)
- Awesome-Prompt-Engineering
- 94
- agents
- 5
Stars delta
- Awesome-Prompt-Engineering
- Unknown
- agents
- +860 (30d)
Open issues delta
- Awesome-Prompt-Engineering
- Unknown
- agents
- +2 (30d)
Owner type
- Awesome-Prompt-Engineering
- Organization
- agents
- User
Full report
- Awesome-Prompt-Engineering
- Trust report
- agents
- Trust report
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
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
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (wshobson/agents) · observed Aug 19, 2026
- GitHub forks (wshobson/agents) · observed Aug 19, 2026
- Last push (wshobson/agents) · observed Aug 18, 2026
- License file (MIT) · observed Aug 19, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Prompt-Engineering 6.2k · agents 39k (synced Jul 28, 2026).
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 and agents alternatives (Awesome-Prompt-Engineering markdown twin, agents markdown twin), 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 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; agents trust report.