Comparison
awesome-mcp-servers vs AutoGL
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.
Markdown twin · awesome-mcp-servers alternatives · AutoGL alternatives
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Trust & integrity
| Signal | awesome-mcp-servers | AutoGL |
|---|---|---|
| Maintenance | Very active (6d since push) As of today · github_public_v1 | Slowing (233d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | No lockfile As of today · none |
Tagline
- awesome-mcp-servers
- A collection of MCP servers.
- AutoGL
- An autoML framework & toolkit for machine learning on graphs.
Stars
- awesome-mcp-servers
- 91k
- AutoGL
- 1.1k
Forks
- awesome-mcp-servers
- 13k
- AutoGL
- 123
Open issues
- awesome-mcp-servers
- 2.6k
- AutoGL
- 20
Language
- awesome-mcp-servers
- -
- AutoGL
- Python
Adopt for
- awesome-mcp-servers
- -
- AutoGL
- -
Persona
- awesome-mcp-servers
- -
- AutoGL
- -
Runtime
- awesome-mcp-servers
- -
- AutoGL
- -
License
- awesome-mcp-servers
- MIT
- AutoGL
- Apache-2.0
Last pushed
- awesome-mcp-servers
- Jul 4, 2026
- AutoGL
- Nov 20, 2025
Categories
- awesome-mcp-servers
- Developer Tools
- AutoGL
- Developer Tools, Model Training
Trust and health
Maintenance
- awesome-mcp-servers
- Very active (96%)
- AutoGL
- Slowing (36%)
Days since push
- awesome-mcp-servers
- 6d
- AutoGL
- 233d
Open issues (now)
- awesome-mcp-servers
- 2.6k
- AutoGL
- 20
Owner type
- awesome-mcp-servers
- User
- AutoGL
- Organization
Security scan
- awesome-mcp-servers
- No MCP manifest
- AutoGL
- No lockfile
Full report
- awesome-mcp-servers
- Trust report
- AutoGL
- Trust report
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.
When NOT to use awesome-mcp-servers
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (punkpeye/awesome-mcp-servers) · observed Jul 11, 2026
- GitHub forks (punkpeye/awesome-mcp-servers) · observed Jul 11, 2026
- Last push (punkpeye/awesome-mcp-servers) · observed Jul 4, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUMNLab/AutoGL) · observed Jul 11, 2026
- GitHub forks (THUMNLab/AutoGL) · observed Jul 11, 2026
- Last push (THUMNLab/AutoGL) · observed Nov 20, 2025
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-mcp-servers 91k · AutoGL 1.1k (synced Jul 11, 2026).
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 and AutoGL alternatives (awesome-mcp-servers markdown twin, AutoGL markdown twin), 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 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; AutoGL trust report.