--- title: "Made-With-ML vs circle-guard-bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-whitecircle-circle-guard-bench" tools: ["gokumohandas-made-with-ml", "whitecircle-circle-guard-bench"] --- # Made-With-ML vs circle-guard-bench *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; circle-guard-bench is Python; pick circle-guard-bench when circle-guard-bench is primarily Python; Made-With-ML is Jupyter Notebook. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [circle-guard-bench](https://whitecircle.ai) has 70 stars, 5 forks, and 0 open issues, last pushed Mar 7, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [circle-guard-bench's repository](https://github.com/whitecircle/circle-guard-bench). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | First-of-its-kind AI benchmark for evaluating the protection capabilities of large language model (LLM) guard systems (guardrails and safeguards) | | Stars | 48,703 | 70 | | Forks | 7,661 | 5 | | Open issues | 27 | 0 | | Language | Jupyter Notebook | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) | | --- | --- | --- | | Days since push | 132d | 129d | | Open issues (now) | 27 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/whitecircle-circle-guard-bench/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) - Python runtime ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; circle-guard-bench is Python. - License: Made-With-ML is MIT, circle-guard-bench is Apache-2.0. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers AI Agents. ### Choose circle-guard-bench if… - circle-guard-bench is primarily Python; Made-With-ML is Jupyter Notebook. - License: circle-guard-bench is Apache-2.0, Made-With-ML is MIT. - Tags unique to circle-guard-bench: ai, benchmark, benchmarking, guardrail. - Also covers Inference & Serving. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - 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. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use circle-guard-bench - Last GitHub push was 130 days ago (slowing maintenance, Mar 7, 2026). Validate activity before betting a new project on circle-guard-bench. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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 Made-With-ML and circle-guard-bench? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. circle-guard-bench: First-of-its-kind AI benchmark for evaluating the protection capabilities of large language model (LLM) guard systems (guardrails and safeguards). See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over circle-guard-bench? Choose Made-With-ML over circle-guard-bench when Made-With-ML is primarily Jupyter Notebook; circle-guard-bench is Python; License: Made-With-ML is MIT, circle-guard-bench is Apache-2.0; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents. ### When should I choose circle-guard-bench over Made-With-ML? Choose circle-guard-bench over Made-With-ML when circle-guard-bench is primarily Python; Made-With-ML is Jupyter Notebook; License: circle-guard-bench is Apache-2.0, Made-With-ML is MIT; Tags unique to circle-guard-bench: ai, benchmark, benchmarking, guardrail; Also covers Inference & Serving. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. 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. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid circle-guard-bench? Last GitHub push was 130 days ago (slowing maintenance, Mar 7, 2026). Validate activity before betting a new project on circle-guard-bench. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is Made-With-ML or circle-guard-bench more popular on GitHub? Made-With-ML has more GitHub stars (48,703 vs 70). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and circle-guard-bench open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, circle-guard-bench: Apache-2.0). ### Where can I find alternatives to Made-With-ML or circle-guard-bench? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [circle-guard-bench alternatives](/tools/whitecircle-circle-guard-bench/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [circle-guard-bench markdown twin](/tools/whitecircle-circle-guard-bench/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/gokumohandas-made-with-ml-vs-whitecircle-circle-guard-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or circle-guard-bench? Made-With-ML: Slowing. circle-guard-bench: 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 Made-With-ML and circle-guard-bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [circle-guard-bench trust report](/tools/whitecircle-circle-guard-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_