--- title: "awesome-mcp-servers vs AutoGL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/punkpeye-awesome-mcp-servers-vs-thumnlab-autogl" tools: ["punkpeye-awesome-mcp-servers", "thumnlab-autogl"] --- # awesome-mcp-servers vs AutoGL *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-mcp-servers when license: awesome-mcp-servers is MIT, AutoGL is Apache-2.0; pick AutoGL when license: AutoGL is Apache-2.0, awesome-mcp-servers is MIT. [awesome-mcp-servers](https://glama.ai/mcp/servers) reports 91k GitHub stars, 13k forks, and 2.6k open issues, last pushed Jul 4, 2026. [AutoGL](http://mn.cs.tsinghua.edu.cn/AutoGL/) has 1.1k stars, 123 forks, and 20 open issues, last pushed Nov 20, 2025. Figures are from public GitHub metadata via [awesome-mcp-servers's repository](https://github.com/punkpeye/awesome-mcp-servers) and [AutoGL's repository](https://github.com/THUMNLab/AutoGL). | | [awesome-mcp-servers](/tools/punkpeye-awesome-mcp-servers.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Tagline | A collection of MCP servers. | An autoML framework & toolkit for machine learning on graphs. | | Stars | 90,602 | 1,135 | | Forks | 12,821 | 123 | | Open issues | 2,557 | 20 | | Language | - | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mcp-servers](/tools/punkpeye-awesome-mcp-servers.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 233d | | Open issues (now) | 2.6k | 20 | | Owner type | User | Organization | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/punkpeye-awesome-mcp-servers/trust.md) | [trust report](/tools/thumnlab-autogl/trust.md) | ## Choose when ### Choose awesome-mcp-servers if… - License: awesome-mcp-servers is MIT, AutoGL is Apache-2.0. - Tags unique to awesome-mcp-servers: ai, mcp. - More GitHub stars (91k vs 1.1k) - visibility, not fit. ### Choose AutoGL if… - License: AutoGL is Apache-2.0, awesome-mcp-servers is MIT. - Tags unique to AutoGL: automl, deep-learning, graph-neural-networks, hyper-parameter-optimization. - Also covers Model Training. ## When NOT to use awesome-mcp-servers - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## When NOT to use AutoGL - Last GitHub push was 234 days ago (slowing maintenance, Nov 20, 2025). Validate activity before betting a new project on AutoGL. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between awesome-mcp-servers and AutoGL? awesome-mcp-servers: A collection of MCP servers.. AutoGL: An autoML framework & toolkit for machine learning on graphs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mcp-servers over AutoGL? Choose awesome-mcp-servers over AutoGL when License: awesome-mcp-servers is MIT, AutoGL is Apache-2.0; Tags unique to awesome-mcp-servers: ai, mcp; More GitHub stars (91k vs 1.1k) - visibility, not fit. ### When should I choose AutoGL over awesome-mcp-servers? Choose AutoGL over awesome-mcp-servers when License: AutoGL is Apache-2.0, awesome-mcp-servers is MIT; Tags unique to AutoGL: automl, deep-learning, graph-neural-networks, hyper-parameter-optimization; Also covers Model Training. ### When should I avoid awesome-mcp-servers? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### When should I avoid AutoGL? Last GitHub push was 234 days ago (slowing maintenance, Nov 20, 2025). Validate activity before betting a new project on AutoGL. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is awesome-mcp-servers or AutoGL more popular on GitHub? awesome-mcp-servers has more GitHub stars (90,602 vs 1,135). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mcp-servers and AutoGL open source? Yes - both are open-source projects on GitHub (awesome-mcp-servers: MIT, AutoGL: Apache-2.0). ### Where can I find alternatives to awesome-mcp-servers or AutoGL? GraphCanon lists graph-backed alternatives at [awesome-mcp-servers alternatives](/tools/punkpeye-awesome-mcp-servers/alternatives) and [AutoGL alternatives](/tools/thumnlab-autogl/alternatives) ([awesome-mcp-servers markdown twin](/tools/punkpeye-awesome-mcp-servers/alternatives.md), [AutoGL markdown twin](/tools/thumnlab-autogl/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/punkpeye-awesome-mcp-servers-vs-thumnlab-autogl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mcp-servers or AutoGL? awesome-mcp-servers: Very active. AutoGL: Slowing. 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-mcp-servers and AutoGL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mcp-servers trust report](/tools/punkpeye-awesome-mcp-servers/trust); [AutoGL trust report](/tools/thumnlab-autogl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=punkpeye-awesome-mcp-servers`](/api/graphcanon/graph?tool=punkpeye-awesome-mcp-servers) - 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/_