--- title: "hivemind vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-hivemind-vs-sindresorhus-awesome" tools: ["activeloopai-hivemind", "sindresorhus-awesome"] --- # hivemind vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick hivemind when license: hivemind is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, hivemind is Apache-2.0. [hivemind](https://deeplake.ai/hivemind) reports 1.5k GitHub stars, 92 forks, and 41 open issues, last pushed Jul 11, 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 [hivemind's repository](https://github.com/activeloopai/hivemind) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [hivemind](/tools/activeloopai-hivemind.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Hivemind turns your traces into reusable skills across agents | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,466 | 484,026 | | Forks | 92 | 35,799 | | Open issues | 41 | 92 | | Language | TypeScript | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hivemind](/tools/activeloopai-hivemind.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 41 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/activeloopai-hivemind/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose hivemind if… - License: hivemind is Apache-2.0, awesome is CC0-1.0. - Tags unique to hivemind: ai, ai-agents, ai-memory, anthropic. - Also covers AI Agents, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, hivemind is Apache-2.0. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 1.5k) - visibility, not fit. ## When NOT to use hivemind - 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. - 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 hivemind and awesome? hivemind: Hivemind turns your traces into reusable skills across agents. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose hivemind over awesome? Choose hivemind over awesome when License: hivemind is Apache-2.0, awesome is CC0-1.0; Tags unique to hivemind: ai, ai-agents, ai-memory, anthropic; Also covers AI Agents, Vector Databases. ### When should I choose awesome over hivemind? Choose awesome over hivemind when License: awesome is CC0-1.0, hivemind is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 1.5k) - visibility, not fit. ### When should I avoid hivemind? 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. 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 hivemind or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,466). Stars measure visibility, not whether either tool fits your constraints. ### Are hivemind and awesome open source? Yes - both are open-source projects on GitHub (hivemind: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to hivemind or awesome? GraphCanon lists graph-backed alternatives at [hivemind alternatives](/tools/activeloopai-hivemind/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([hivemind markdown twin](/tools/activeloopai-hivemind/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/activeloopai-hivemind-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hivemind or awesome? hivemind: 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 hivemind and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hivemind trust report](/tools/activeloopai-hivemind/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-hivemind`](/api/graphcanon/graph?tool=activeloopai-hivemind) - 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/_