--- title: "oneflow vs awesome-mcp-servers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/oneflow-inc-oneflow-vs-punkpeye-awesome-mcp-servers" tools: ["oneflow-inc-oneflow", "punkpeye-awesome-mcp-servers"] --- # oneflow vs awesome-mcp-servers *GraphCanon updated Jul 12, 2026* ## Verdict Pick oneflow when license: oneflow is Apache-2.0, awesome-mcp-servers is MIT; pick awesome-mcp-servers when license: awesome-mcp-servers is MIT, oneflow is Apache-2.0. [oneflow](http://www.oneflow.org) reports 9.4k GitHub stars, 1.0k forks, and 645 open issues, last pushed Dec 4, 2025. [awesome-mcp-servers](https://glama.ai/mcp/servers) has 91k stars, 13k forks, and 2.6k open issues, last pushed Jul 4, 2026. Figures are from public GitHub metadata via [oneflow's repository](https://github.com/Oneflow-Inc/oneflow) and [awesome-mcp-servers's repository](https://github.com/punkpeye/awesome-mcp-servers). | | [oneflow](/tools/oneflow-inc-oneflow.md) | [awesome-mcp-servers](/tools/punkpeye-awesome-mcp-servers.md) | | --- | --- | --- | | Tagline | OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. | A collection of MCP servers. | | Stars | 9,409 | 90,602 | | Forks | 1,013 | 12,821 | | Open issues | 645 | 2,557 | | Language | C++ | - | | Adopt for | OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [oneflow](/tools/oneflow-inc-oneflow.md) | [awesome-mcp-servers](/tools/punkpeye-awesome-mcp-servers.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 219d | 6d | | Open issues (now) | 645 | 2.6k | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/oneflow-inc-oneflow/trust.md) | [trust report](/tools/punkpeye-awesome-mcp-servers/trust.md) | ## Decision facts: oneflow - **Adopt for:** OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. ## Choose when ### Choose oneflow if… - License: oneflow is Apache-2.0, awesome-mcp-servers is MIT. - Tags unique to oneflow: neural-networks, deep-learning, distributed, machine-learning. - Also covers Model Training. - OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ### Choose awesome-mcp-servers if… - License: awesome-mcp-servers is MIT, oneflow is Apache-2.0. - Tags unique to awesome-mcp-servers: ai, mcp. - Also covers Developer Tools. ## When NOT to use oneflow - Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. - If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. - OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ## When NOT to use awesome-mcp-servers - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## Common questions ### What is the difference between oneflow and awesome-mcp-servers? oneflow: OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.. awesome-mcp-servers: A collection of MCP servers.. See the comparison table for live GitHub stats and shared categories. ### When should I choose oneflow over awesome-mcp-servers? Choose oneflow over awesome-mcp-servers when License: oneflow is Apache-2.0, awesome-mcp-servers is MIT; Tags unique to oneflow: neural-networks, deep-learning, distributed, machine-learning; Also covers Model Training; OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ### When should I choose awesome-mcp-servers over oneflow? Choose awesome-mcp-servers over oneflow when License: awesome-mcp-servers is MIT, oneflow is Apache-2.0; Tags unique to awesome-mcp-servers: ai, mcp; Also covers Developer Tools. ### When should I avoid oneflow? Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ### When should I avoid awesome-mcp-servers? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### Is oneflow or awesome-mcp-servers more popular on GitHub? awesome-mcp-servers has more GitHub stars (90,602 vs 9,409). Stars measure visibility, not whether either tool fits your constraints. ### Are oneflow and awesome-mcp-servers open source? Yes - both are open-source projects on GitHub (oneflow: Apache-2.0, awesome-mcp-servers: MIT). ### Where can I find alternatives to oneflow or awesome-mcp-servers? GraphCanon lists graph-backed alternatives at [oneflow alternatives](/tools/oneflow-inc-oneflow/alternatives) and [awesome-mcp-servers alternatives](/tools/punkpeye-awesome-mcp-servers/alternatives) ([oneflow markdown twin](/tools/oneflow-inc-oneflow/alternatives.md), [awesome-mcp-servers markdown twin](/tools/punkpeye-awesome-mcp-servers/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/oneflow-inc-oneflow-vs-punkpeye-awesome-mcp-servers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, oneflow or awesome-mcp-servers? oneflow: Slowing. awesome-mcp-servers: 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 oneflow and awesome-mcp-servers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [oneflow trust report](/tools/oneflow-inc-oneflow/trust); [awesome-mcp-servers trust report](/tools/punkpeye-awesome-mcp-servers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=oneflow-inc-oneflow`](/api/graphcanon/graph?tool=oneflow-inc-oneflow) - 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/_