--- title: "awesome vs dspy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sindresorhus-awesome-vs-stanfordnlp-dspy" tools: ["sindresorhus-awesome", "stanfordnlp-dspy"] --- # awesome vs dspy *GraphCanon updated Jul 12, 2026* ## Verdict Pick awesome when license: awesome is CC0-1.0, dspy is MIT; pick dspy when license: dspy is MIT, awesome is CC0-1.0. [awesome](https://github.com/sindresorhus/awesome) reports 484k GitHub stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. [dspy](https://dspy.ai) has 36k stars, 3.1k forks, and 571 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome's repository](https://github.com/sindresorhus/awesome) and [dspy's repository](https://github.com/stanfordnlp/dspy). | | [awesome](/tools/sindresorhus-awesome.md) | [dspy](/tools/stanfordnlp-dspy.md) | | --- | --- | --- | | Tagline | 😎 Curated list of awesome topics including hardware resources | A framework for programming language models | | Stars | 484,026 | 36,036 | | Forks | 35,799 | 3,082 | | Open issues | 92 | 571 | | Language | - | Python | | Adopt for | - | Evaluate DSPy based on its unique approach of programming language models via Python, making it an option that steps away from traditional prompting methods. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome](/tools/sindresorhus-awesome.md) | [dspy](/tools/stanfordnlp-dspy.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 0d | | Open issues (now) | 92 | 571 | | Owner type | User | Organization | | Full report | [trust report](/tools/sindresorhus-awesome/trust.md) | [trust report](/tools/stanfordnlp-dspy/trust.md) | ## Decision facts: dspy - **Adopt for:** Evaluate DSPy based on its unique approach of programming language models via Python, making it an option that steps away from traditional prompting methods. ## Choose when ### Choose awesome if… - License: awesome is CC0-1.0, dspy is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 36k) - visibility, not fit. ### Choose dspy if… - License: dspy is MIT, awesome is CC0-1.0. - Tags unique to dspy: programming framework, language-models, ai-development. - When you aim to leverage a comprehensive framework designed specifically for programming and developing with language models rather than just prompting them. ## 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. ## When NOT to use dspy - When your project strictly requires real-time interaction and feedback through traditional prompting methods, as DSPy's framework is focused on a programming approach which may not be suitable for all - In scenarios where the flexibility of prompt-based interactions with language models is preferred over strict programming methodologies. ## Common questions ### What is the difference between awesome and dspy? awesome: 😎 Curated list of awesome topics including hardware resources. dspy: A framework for programming language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome over dspy? Choose awesome over dspy when License: awesome is CC0-1.0, dspy is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 36k) - visibility, not fit. ### When should I choose dspy over awesome? Choose dspy over awesome when License: dspy is MIT, awesome is CC0-1.0; Tags unique to dspy: programming framework, language-models, ai-development; When you aim to leverage a comprehensive framework designed specifically for programming and developing with language models rather than just prompting them. ### 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. ### When should I avoid dspy? When your project strictly requires real-time interaction and feedback through traditional prompting methods, as DSPy's framework is focused on a programming approach which may not be suitable for all In scenarios where the flexibility of prompt-based interactions with language models is preferred over strict programming methodologies. ### Is awesome or dspy more popular on GitHub? awesome has more GitHub stars (484,026 vs 36,036). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome and dspy open source? Yes - both are open-source projects on GitHub (awesome: CC0-1.0, dspy: MIT). ### Where can I find alternatives to awesome or dspy? GraphCanon lists graph-backed alternatives at [awesome alternatives](/tools/sindresorhus-awesome/alternatives) and [dspy alternatives](/tools/stanfordnlp-dspy/alternatives) ([awesome markdown twin](/tools/sindresorhus-awesome/alternatives.md), [dspy markdown twin](/tools/stanfordnlp-dspy/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/sindresorhus-awesome-vs-stanfordnlp-dspy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome or dspy? awesome: Active. dspy: 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 awesome and dspy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome trust report](/tools/sindresorhus-awesome/trust); [dspy trust report](/tools/stanfordnlp-dspy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=sindresorhus-awesome`](/api/graphcanon/graph?tool=sindresorhus-awesome) - 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/_