--- title: "langroid vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langroid-langroid-vs-sindresorhus-awesome" tools: ["langroid-langroid", "sindresorhus-awesome"] --- # langroid vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick langroid when license: langroid is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, langroid is MIT. [langroid](https://langroid.github.io/langroid/) reports 4.1k GitHub stars, 381 forks, and 74 open issues, last pushed Jul 7, 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 [langroid's repository](https://github.com/langroid/langroid) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [langroid](/tools/langroid-langroid.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Harness LLMs with Multi-Agent Programming | 😎 Awesome lists about all kinds of interesting topics | | Stars | 4,056 | 484,026 | | Forks | 381 | 35,799 | | Open issues | 74 | 92 | | Language | Python | - | | Adopt for | - | A curated collection of resources on a variety of technological topics, emphasizing hardware and robotics. | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langroid](/tools/langroid-langroid.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 3d | 11d | | Open issues (now) | 74 | 92 | | Owner type | Organization | User | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/langroid-langroid/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Decision facts: awesome - **Adopt for:** A curated collection of resources on a variety of technological topics, emphasizing hardware and robotics. ## Choose when ### Choose langroid if… - License: langroid is MIT, awesome is CC0-1.0. - Tags unique to langroid: agents, ai, chatgpt, function-calling. - Also covers AI Agents, LLM Frameworks, Vector Databases. - langroid ships Docker support for self-hosted deployment. ### Choose awesome if… - License: awesome is CC0-1.0, langroid is MIT. - Tags unique to awesome: awesome, awesome-list, lists, resources. - Also covers Developer Tools. - When you need well-organized access to diverse technical subjects from IoT to robotics ## When NOT to use langroid - 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 - If seeking specific coding frameworks or libraries for software development rather than hardware-focused resources - In scenarios requiring real-time interactive support or forums, as the content is static lists without active discussion ## Common questions ### What is the difference between langroid and awesome? langroid: Harness LLMs with Multi-Agent Programming. awesome: 😎 Awesome lists about all kinds of interesting topics. See the comparison table for live GitHub stats and shared categories. ### When should I choose langroid over awesome? Choose langroid over awesome when License: langroid is MIT, awesome is CC0-1.0; Tags unique to langroid: agents, ai, chatgpt, function-calling; Also covers AI Agents, LLM Frameworks, Vector Databases; langroid ships Docker support for self-hosted deployment. ### When should I choose awesome over langroid? Choose awesome over langroid when License: awesome is CC0-1.0, langroid is MIT; Tags unique to awesome: awesome, awesome-list, lists, resources; Also covers Developer Tools; When you need well-organized access to diverse technical subjects from IoT to robotics. ### When should I avoid langroid? 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? If seeking specific coding frameworks or libraries for software development rather than hardware-focused resources In scenarios requiring real-time interactive support or forums, as the content is static lists without active discussion ### Is langroid or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 4,056). Stars measure visibility, not whether either tool fits your constraints. ### Are langroid and awesome open source? Yes - both are open-source projects on GitHub (langroid: MIT, awesome: CC0-1.0). ### Where can I find alternatives to langroid or awesome? GraphCanon lists graph-backed alternatives at [langroid alternatives](/tools/langroid-langroid/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([langroid markdown twin](/tools/langroid-langroid/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/langroid-langroid-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langroid or awesome? langroid: 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 langroid and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langroid trust report](/tools/langroid-langroid/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langroid-langroid`](/api/graphcanon/graph?tool=langroid-langroid) - 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/_