--- title: "llm_agents vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mpaepper-llm-agents-vs-sindresorhus-awesome" tools: ["mpaepper-llm-agents", "sindresorhus-awesome"] --- # llm_agents vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick llm_agents when license: llm_agents is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, llm_agents is MIT. [llm_agents](https://www.paepper.com/blog/posts/intelligent-agents-guided-by-llms/) reports 1.1k GitHub stars, 85 forks, and 3 open issues, last pushed Jun 23, 2025. [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 [llm_agents's repository](https://github.com/mpaepper/llm_agents) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [llm_agents](/tools/mpaepper-llm-agents.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Build agents which are controlled by LLMs | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,050 | 484,026 | | Forks | 85 | 35,799 | | Open issues | 3 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm_agents](/tools/mpaepper-llm-agents.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 382d | 11d | | Open issues (now) | 3 | 92 | | Security scan | 32 low (32 low) | No lockfile | | Full report | [trust report](/tools/mpaepper-llm-agents/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose llm_agents if… - License: llm_agents is MIT, awesome is CC0-1.0. - Tags unique to llm_agents: llms, deep-learning, machine-learning, python. - Also covers AI Agents. ### Choose awesome if… - License: awesome is CC0-1.0, llm_agents is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 1.1k) - visibility, not fit. ## When NOT to use llm_agents - Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents. - 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 llm_agents and awesome? llm_agents: Build agents which are controlled by LLMs. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm_agents over awesome? Choose llm_agents over awesome when License: llm_agents is MIT, awesome is CC0-1.0; Tags unique to llm_agents: llms, deep-learning, machine-learning, python; Also covers AI Agents. ### When should I choose awesome over llm_agents? Choose awesome over llm_agents when License: awesome is CC0-1.0, llm_agents is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.1k) - visibility, not fit. ### When should I avoid llm_agents? Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents. 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 llm_agents or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,050). Stars measure visibility, not whether either tool fits your constraints. ### Are llm_agents and awesome open source? Yes - both are open-source projects on GitHub (llm_agents: MIT, awesome: CC0-1.0). ### Where can I find alternatives to llm_agents or awesome? GraphCanon lists graph-backed alternatives at [llm_agents alternatives](/tools/mpaepper-llm-agents/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([llm_agents markdown twin](/tools/mpaepper-llm-agents/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/mpaepper-llm-agents-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm_agents or awesome? llm_agents: Dormant. 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 llm_agents and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm_agents trust report](/tools/mpaepper-llm-agents/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mpaepper-llm-agents`](/api/graphcanon/graph?tool=mpaepper-llm-agents) - 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/_