--- title: "ROLL vs agent-opt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-future-agi-agent-opt" tools: ["alibaba-roll", "future-agi-agent-opt"] --- # ROLL vs agent-opt *GraphCanon updated Aug 7, 2026* ## Verdict Pick ROLL if efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided; pick agent-opt if agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. [ROLL](https://alibaba.github.io/ROLL/) reports 3.4k GitHub stars, 304 forks, and 120 open issues, last pushed Aug 7, 2026. [agent-opt](https://app.futureagi.com) has 71 stars, 7 forks, and 0 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [agent-opt's repository](https://github.com/future-agi/agent-opt). | | [ROLL](/tools/alibaba-roll.md) | [agent-opt](/tools/future-agi-agent-opt.md) | | --- | --- | --- | | Tagline | Scaling Library for Reinforcement Learning with Large Language Models | Open Source Library for Automated Optimization of AI Agent Workflows | | Stars | 3,354 | 71 | | Forks | 304 | 7 | | Open issues | 120 | 0 | | Language | Python | Python | | Adopt for | Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. | Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [agent-opt](/tools/future-agi-agent-opt.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 35d | | Open issues (now) | 120 | 0 | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/future-agi-agent-opt/trust.md) | ## Decision facts: ROLL - **Adopt for:** Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. ## Decision facts: agent-opt - **Adopt for:** Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. ## Choose when ### Choose ROLL if… - Tags unique to ROLL: agentic, rlhf, rlvr. - Also covers Model Training. - When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. ### Choose agent-opt if… - Tags unique to agent-opt: agent, ai-agents, aioptimization, automation. - Also covers AI Agents. - - When your project needs seamless CI/CD integration alongside automated optimization ## When NOT to use ROLL - Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. - Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ## When NOT to use agent-opt - - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ## Common questions ### What is the difference between ROLL and agent-opt? ROLL: Scaling Library for Reinforcement Learning with Large Language Models. agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose ROLL over agent-opt? Choose ROLL over agent-opt when Tags unique to ROLL: agentic, rlhf, rlvr; Also covers Model Training; When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. ### When should I choose agent-opt over ROLL? Choose agent-opt over ROLL when Tags unique to agent-opt: agent, ai-agents, aioptimization, automation; Also covers AI Agents; - When your project needs seamless CI/CD integration alongside automated optimization. ### When should I avoid ROLL? Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ### When should I avoid agent-opt? - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ### Is ROLL or agent-opt more popular on GitHub? ROLL has more GitHub stars (3,354 vs 71). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and agent-opt open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, agent-opt: Apache-2.0). ### Where can I find alternatives to ROLL or agent-opt? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [agent-opt markdown twin](/tools/future-agi-agent-opt/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-future-agi-agent-opt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or agent-opt? ROLL: Very active. agent-opt: Steady. 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 agent-opt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [agent-opt trust report](/tools/future-agi-agent-opt/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/_