--- title: "ROLL vs 12-factor-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-humanlayer-12-factor-agents" tools: ["alibaba-roll", "humanlayer-12-factor-agents"] --- # ROLL vs 12-factor-agents *GraphCanon updated Jul 12, 2026* ## Verdict Pick ROLL when rOLL is primarily Python; 12-factor-agents is TypeScript; pick 12-factor-agents when 12-factor-agents is primarily TypeScript; ROLL is Python. [ROLL](https://alibaba.github.io/ROLL/) reports 3.3k GitHub stars, 295 forks, and 119 open issues, last pushed Jul 11, 2026. [12-factor-agents](https://github.com/humanlayer/12-factor-agents) has 24k stars, 1.8k forks, and 26 open issues, last pushed Sep 21, 2025. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [12-factor-agents's repository](https://github.com/humanlayer/12-factor-agents). | | [ROLL](/tools/alibaba-roll.md) | [12-factor-agents](/tools/humanlayer-12-factor-agents.md) | | --- | --- | --- | | Tagline | Efficient and user-friendly scaling library for RL with LLMs | Principles for building production-ready LLM-powered software | | Stars | 3,292 | 24,036 | | Forks | 295 | 1,834 | | Open issues | 119 | 26 | | Language | Python | TypeScript | | Adopt for | - | A TypeScript-based framework focused on applying 12-factor principles to build production-ready software with large language models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The content and images are licensed under CC BY-SA 4.0, while the code is covered by the Apache 2.0 License. | | Categories | Model Training, Evaluation & Observability | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [12-factor-agents](/tools/humanlayer-12-factor-agents.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 292d | | Open issues (now) | 119 | 26 | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/humanlayer-12-factor-agents/trust.md) | ## Decision facts: 12-factor-agents - **Pricing:** freemium - Free to use with open-source licenses - **Requirements:** Min 4 GB RAM; Requires Docker; Requires a solid understanding of TypeScript and familiarity with concepts like prompt engineering and context window management. - **Adopt for:** A TypeScript-based framework focused on applying 12-factor principles to build production-ready software with large language models. - **License detail:** The content and images are licensed under CC BY-SA 4.0, while the code is covered by the Apache 2.0 License. ## Choose when ### Choose ROLL if… - ROLL is primarily Python; 12-factor-agents is TypeScript. - License: ROLL is Apache-2.0, 12-factor-agents is Other. - Tags unique to ROLL: rlhf, rlvr, agentic. - Also covers Model Training, Evaluation & Observability. ### Choose 12-factor-agents if… - 12-factor-agents is primarily TypeScript; ROLL is Python. - License: 12-factor-agents is Other, ROLL is Apache-2.0. - Pricing: Free to use with open-source licenses. - Requirements: Min 4 GB RAM; Requires Docker; Requires a solid understanding of TypeScript and familiarity with concepts like prompt engineering and context window management.. - Tags unique to 12-factor-agents: memory, llms, 12-factor, orchestration. - Also covers LLM Frameworks, AI Agents. - You are specifically developing AI agents or LLM-powered applications in TypeScript and need a structured guideline grounded in the 12-factor app principles. ## When NOT to use ROLL - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use 12-factor-agents - If your project requires languages other than TypeScript or if your application already has a strong foundation not necessarily aligning with the 12-factor app principles. - When you’re looking for comprehensive deployment automation tools rather than guidance on building LLM-powered agents and ensuring their reliability in production environments. ## Common questions ### What is the difference between ROLL and 12-factor-agents? ROLL: Efficient and user-friendly scaling library for RL with LLMs. 12-factor-agents: Principles for building production-ready LLM-powered software. See the comparison table for live GitHub stats and shared categories. ### When should I choose ROLL over 12-factor-agents? Choose ROLL over 12-factor-agents when ROLL is primarily Python; 12-factor-agents is TypeScript; License: ROLL is Apache-2.0, 12-factor-agents is Other; Tags unique to ROLL: rlhf, rlvr, agentic; Also covers Model Training, Evaluation & Observability. ### When should I choose 12-factor-agents over ROLL? Choose 12-factor-agents over ROLL when 12-factor-agents is primarily TypeScript; ROLL is Python; License: 12-factor-agents is Other, ROLL is Apache-2.0; Pricing: Free to use with open-source licenses; Requirements: Min 4 GB RAM; Requires Docker; Requires a solid understanding of TypeScript and familiarity with concepts like prompt engineering and context window management.; Tags unique to 12-factor-agents: memory, llms, 12-factor, orchestration; Also covers LLM Frameworks, AI Agents; You are specifically developing AI agents or LLM-powered applications in TypeScript and need a structured guideline grounded in the 12-factor app principles. ### When should I avoid ROLL? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid 12-factor-agents? If your project requires languages other than TypeScript or if your application already has a strong foundation not necessarily aligning with the 12-factor app principles. When you’re looking for comprehensive deployment automation tools rather than guidance on building LLM-powered agents and ensuring their reliability in production environments. ### Is ROLL or 12-factor-agents more popular on GitHub? 12-factor-agents has more GitHub stars (24,036 vs 3,292). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and 12-factor-agents open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, 12-factor-agents: Other). ### Where can I find alternatives to ROLL or 12-factor-agents? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [12-factor-agents alternatives](/tools/humanlayer-12-factor-agents/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [12-factor-agents markdown twin](/tools/humanlayer-12-factor-agents/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/alibaba-roll-vs-humanlayer-12-factor-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or 12-factor-agents? ROLL: Very active. 12-factor-agents: 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 ROLL and 12-factor-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [12-factor-agents trust report](/tools/humanlayer-12-factor-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll) - 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/_