--- title: "whatcanirun vs awesome-copilot" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fiveoutofnine-whatcanirun-vs-github-awesome-copilot" tools: ["fiveoutofnine-whatcanirun", "github-awesome-copilot"] --- # whatcanirun vs awesome-copilot *GraphCanon updated Jul 15, 2026* ## Verdict Pick whatcanirun when whatcanirun is primarily TypeScript; awesome-copilot is Python; pick awesome-copilot when awesome-copilot is primarily Python; whatcanirun is TypeScript. [whatcanirun](https://whatcani.run) reports 244 GitHub stars, 22 forks, and 3 open issues, last pushed May 4, 2026. [awesome-copilot](https://awesome-copilot.github.com/) has 36k stars, 4.5k forks, and 34 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [whatcanirun's repository](https://github.com/fiveoutofnine/whatcanirun) and [awesome-copilot's repository](https://github.com/github/awesome-copilot). | | [whatcanirun](/tools/fiveoutofnine-whatcanirun.md) | [awesome-copilot](/tools/github-awesome-copilot.md) | | --- | --- | --- | | Tagline | Find the best models and how to run them locally. | Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. | | Stars | 244 | 36,439 | | Forks | 22 | 4,544 | | Open issues | 3 | 34 | | Language | TypeScript | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [whatcanirun](/tools/fiveoutofnine-whatcanirun.md) | [awesome-copilot](/tools/github-awesome-copilot.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 71d | 0d | | Open issues (now) | 3 | 34 | | Owner type | User | Organization | | Full report | [trust report](/tools/fiveoutofnine-whatcanirun/trust.md) | [trust report](/tools/github-awesome-copilot/trust.md) | ## Choose when ### Choose whatcanirun if… - whatcanirun is primarily TypeScript; awesome-copilot is Python. - Tags unique to whatcanirun: apple-silicon, llamacpp, llm, local-llm. - Leaner open-issue backlog (3). ### Choose awesome-copilot if… - awesome-copilot is primarily Python; whatcanirun is TypeScript. - Tags unique to awesome-copilot: agent-skills, agents, ai, awesome. - Also covers AI Agents. ## When NOT to use whatcanirun - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use awesome-copilot - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between whatcanirun and awesome-copilot? whatcanirun: Find the best models and how to run them locally.. awesome-copilot: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.. See the comparison table for live GitHub stats and shared categories. ### When should I choose whatcanirun over awesome-copilot? Choose whatcanirun over awesome-copilot when whatcanirun is primarily TypeScript; awesome-copilot is Python; Tags unique to whatcanirun: apple-silicon, llamacpp, llm, local-llm; Leaner open-issue backlog (3). ### When should I choose awesome-copilot over whatcanirun? Choose awesome-copilot over whatcanirun when awesome-copilot is primarily Python; whatcanirun is TypeScript; Tags unique to awesome-copilot: agent-skills, agents, ai, awesome; Also covers AI Agents. ### When should I avoid whatcanirun? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid awesome-copilot? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is whatcanirun or awesome-copilot more popular on GitHub? awesome-copilot has more GitHub stars (36,439 vs 244). Stars measure visibility, not whether either tool fits your constraints. ### Are whatcanirun and awesome-copilot open source? Yes - both are open-source projects on GitHub (whatcanirun: MIT, awesome-copilot: MIT). ### Where can I find alternatives to whatcanirun or awesome-copilot? GraphCanon lists graph-backed alternatives at [whatcanirun alternatives](/tools/fiveoutofnine-whatcanirun/alternatives) and [awesome-copilot alternatives](/tools/github-awesome-copilot/alternatives) ([whatcanirun markdown twin](/tools/fiveoutofnine-whatcanirun/alternatives.md), [awesome-copilot markdown twin](/tools/github-awesome-copilot/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/fiveoutofnine-whatcanirun-vs-github-awesome-copilot.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, whatcanirun or awesome-copilot? whatcanirun: Steady. awesome-copilot: 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 whatcanirun and awesome-copilot? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [whatcanirun trust report](/tools/fiveoutofnine-whatcanirun/trust); [awesome-copilot trust report](/tools/github-awesome-copilot/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fiveoutofnine-whatcanirun`](/api/graphcanon/graph?tool=fiveoutofnine-whatcanirun) - 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/_