--- title: "LazyLLM vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lazyagi-lazyllm-vs-sindresorhus-awesome" tools: ["lazyagi-lazyllm", "sindresorhus-awesome"] --- # LazyLLM vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick LazyLLM when license: LazyLLM is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, LazyLLM is Apache-2.0. [LazyLLM](https://docs.lazyllm.ai/) reports 3.9k GitHub stars, 396 forks, and 46 open issues, last pushed Jul 10, 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 [LazyLLM's repository](https://github.com/LazyAGI/LazyLLM) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [LazyLLM](/tools/lazyagi-lazyllm.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Easiest and laziest way for building multi-agent LLMs applications. | 😎 Curated list of awesome topics including hardware resources | | Stars | 3,856 | 484,026 | | Forks | 396 | 35,799 | | Open issues | 46 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LazyLLM](/tools/lazyagi-lazyllm.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 11d | | Open issues (now) | 46 | 92 | | Owner type | Organization | User | | Security scan | 31 low (31 low) | No lockfile | | Full report | [trust report](/tools/lazyagi-lazyllm/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose LazyLLM if… - License: LazyLLM is Apache-2.0, awesome is CC0-1.0. - Tags unique to LazyLLM: deep-learning, agents, finetuning, data. - Also covers AI Agents. ### Choose awesome if… - License: awesome is CC0-1.0, LazyLLM is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 3.9k) - visibility, not fit. ## When NOT to use LazyLLM - 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. ## 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 LazyLLM and awesome? LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose LazyLLM over awesome? Choose LazyLLM over awesome when License: LazyLLM is Apache-2.0, awesome is CC0-1.0; Tags unique to LazyLLM: deep-learning, agents, finetuning, data; Also covers AI Agents. ### When should I choose awesome over LazyLLM? Choose awesome over LazyLLM when License: awesome is CC0-1.0, LazyLLM is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 3.9k) - visibility, not fit. ### When should I avoid LazyLLM? 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. ### 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 LazyLLM or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 3,856). Stars measure visibility, not whether either tool fits your constraints. ### Are LazyLLM and awesome open source? Yes - both are open-source projects on GitHub (LazyLLM: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to LazyLLM or awesome? GraphCanon lists graph-backed alternatives at [LazyLLM alternatives](/tools/lazyagi-lazyllm/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([LazyLLM markdown twin](/tools/lazyagi-lazyllm/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/lazyagi-lazyllm-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LazyLLM or awesome? LazyLLM: 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 LazyLLM and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LazyLLM trust report](/tools/lazyagi-lazyllm/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lazyagi-lazyllm`](/api/graphcanon/graph?tool=lazyagi-lazyllm) - 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/_