--- title: "agentset vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-sindresorhus-awesome" tools: ["agentset-ai-agentset", "sindresorhus-awesome"] --- # agentset vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick agentset when license: agentset is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, agentset is MIT. [agentset](https://agentset.ai) reports 2.0k GitHub stars, 182 forks, and 12 open issues, last pushed Jul 6, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [agentset's repository](https://github.com/agentset-ai/agentset) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [agentset](/tools/agentset-ai-agentset.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more. | 😎 Curated list of awesome topics including hardware resources | | Stars | 2,027 | 484,026 | | Forks | 182 | 35,799 | | Open issues | 12 | 92 | | Language | TypeScript | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | LLM Frameworks, AI Agents, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 5d | 11d | | Open issues (now) | 12 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose agentset if… - License: agentset is MIT, awesome is CC0-1.0. - Tags unique to agentset: llms, ai-sdk, embeddings, genai. - Also covers AI Agents, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, agentset is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 2.0k) - visibility, not fit. ## When NOT to use agentset - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use awesome - 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 agentset and awesome? agentset: The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentset over awesome? Choose agentset over awesome when License: agentset is MIT, awesome is CC0-1.0; Tags unique to agentset: llms, ai-sdk, embeddings, genai; Also covers AI Agents, Vector Databases. ### When should I choose awesome over agentset? Choose awesome over agentset when License: awesome is CC0-1.0, agentset is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 2.0k) - visibility, not fit. ### When should I avoid agentset? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is agentset or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 2,027). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and awesome open source? Yes - both are open-source projects on GitHub (agentset: MIT, awesome: CC0-1.0). ### Where can I find alternatives to agentset or awesome? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/agentset-ai-agentset-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentset or awesome? agentset: Very active. awesome: 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 agentset and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentset-ai-agentset`](/api/graphcanon/graph?tool=agentset-ai-agentset) - 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/_