--- title: "XAgent vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openbmb-xagent-vs-sindresorhus-awesome" tools: ["openbmb-xagent", "sindresorhus-awesome"] --- # XAgent vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick XAgent when license: XAgent is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, XAgent is Apache-2.0. [XAgent](https://blog.x-agent.net/blog/xagent/) reports 8.5k GitHub stars, 902 forks, and 64 open issues, last pushed Aug 12, 2024. [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 [XAgent's repository](https://github.com/OpenBMB/XAgent) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [XAgent](/tools/openbmb-xagent.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | An Autonomous LLM Agent for Complex Task Solving | 😎 Curated list of awesome topics including hardware resources | | Stars | 8,522 | 484,026 | | Forks | 902 | 35,799 | | Open issues | 64 | 92 | | Language | Python | - | | 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._ | | [XAgent](/tools/openbmb-xagent.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 698d | 11d | | Open issues (now) | 64 | 92 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/openbmb-xagent/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose XAgent if… - License: XAgent is Apache-2.0, awesome is CC0-1.0. - Tags unique to XAgent: python. - Also covers AI Agents, Vector Databases. - XAgent ships Docker support for self-hosted deployment. ### Choose awesome if… - License: awesome is CC0-1.0, XAgent is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 8.5k) - visibility, not fit. ## When NOT to use XAgent - Last GitHub push was 699 days ago (dormant maintenance, Aug 12, 2024). Validate activity before betting a new project on XAgent. - 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 XAgent and awesome? XAgent: An Autonomous LLM Agent for Complex Task Solving. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose XAgent over awesome? Choose XAgent over awesome when License: XAgent is Apache-2.0, awesome is CC0-1.0; Tags unique to XAgent: python; Also covers AI Agents, Vector Databases; XAgent ships Docker support for self-hosted deployment. ### When should I choose awesome over XAgent? Choose awesome over XAgent when License: awesome is CC0-1.0, XAgent is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 8.5k) - visibility, not fit. ### When should I avoid XAgent? Last GitHub push was 699 days ago (dormant maintenance, Aug 12, 2024). Validate activity before betting a new project on XAgent. 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 XAgent or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 8,522). Stars measure visibility, not whether either tool fits your constraints. ### Are XAgent and awesome open source? Yes - both are open-source projects on GitHub (XAgent: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to XAgent or awesome? GraphCanon lists graph-backed alternatives at [XAgent alternatives](/tools/openbmb-xagent/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([XAgent markdown twin](/tools/openbmb-xagent/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/openbmb-xagent-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, XAgent or awesome? XAgent: 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 XAgent and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [XAgent trust report](/tools/openbmb-xagent/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openbmb-xagent`](/api/graphcanon/graph?tool=openbmb-xagent) - 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/_