--- title: "agent-opt vs SPPO" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-opt-vs-uclaml-sppo" tools: ["future-agi-agent-opt", "uclaml-sppo"] --- # agent-opt vs SPPO *GraphCanon updated Aug 24, 2026* ## Verdict 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; pick SPPO if sPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. [agent-opt](https://app.futureagi.com) reports 71 GitHub stars, 7 forks, and 0 open issues, last pushed Jun 30, 2026. [SPPO](https://uclaml.github.io/SPPO/) has 589 stars, 48 forks, and 15 open issues, last pushed Jan 23, 2025. Figures are from public GitHub metadata via [agent-opt's repository](https://github.com/future-agi/agent-opt) and [SPPO's repository](https://github.com/uclaml/SPPO). | | [agent-opt](/tools/future-agi-agent-opt.md) | [SPPO](/tools/uclaml-sppo.md) | | --- | --- | --- | | Tagline | Open Source Library for Automated Optimization of AI Agent Workflows | Official implementation of Self-Play Preference Optimization for fine-tuning large language models via RLHF | | Stars | 71 | 589 | | Forks | 7 | 48 | | Open issues | 0 | 15 | | Language | Python | Python | | 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. | SPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-opt](/tools/future-agi-agent-opt.md) | [SPPO](/tools/uclaml-sppo.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 35d | 578d | | Open issues (now) | 0 | 15 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-agent-opt/trust.md) | [trust report](/tools/uclaml-sppo/trust.md) | ## 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. ## Decision facts: SPPO - **Pricing:** freemium - **Adopt for:** SPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. - **License detail:** Apache-2.0 ## Choose when ### Choose agent-opt if… - Tags unique to agent-opt: agent, ai-agents, aioptimization, automation. - Also covers AI Agents, Evaluation & Observability. - - When your project needs seamless CI/CD integration alongside automated optimization ### Choose SPPO if… - Tags unique to SPPO: deep-learning, fine-tuning, large language models, rlhf. - Also covers LLM Frameworks, Model Training. - Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences. ## 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 ## When NOT to use SPPO - Avoid SPPO if your project does not require or benefit from reinforcement learning mechanisms or the fine-tuning specifics provided through self-play methods. - Do not use SPPO in scenarios where simpler model tuning approaches without self-play are adequate for achieving project goals, as it might introduce unnecessary complexity. ## Common questions ### What is the difference between agent-opt and SPPO? agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. SPPO: Official implementation of Self-Play Preference Optimization for fine-tuning large language models via RLHF. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-opt over SPPO? Choose agent-opt over SPPO when Tags unique to agent-opt: agent, ai-agents, aioptimization, automation; Also covers AI Agents, Evaluation & Observability; - When your project needs seamless CI/CD integration alongside automated optimization. ### When should I choose SPPO over agent-opt? Choose SPPO over agent-opt when Tags unique to SPPO: deep-learning, fine-tuning, large language models, rlhf; Also covers LLM Frameworks, Model Training; Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences. ### 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 ### When should I avoid SPPO? Avoid SPPO if your project does not require or benefit from reinforcement learning mechanisms or the fine-tuning specifics provided through self-play methods. Do not use SPPO in scenarios where simpler model tuning approaches without self-play are adequate for achieving project goals, as it might introduce unnecessary complexity. ### Is agent-opt or SPPO more popular on GitHub? SPPO has more GitHub stars (589 vs 71). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-opt and SPPO open source? Yes - both are open-source projects on GitHub (agent-opt: Apache-2.0, SPPO: Apache-2.0). ### Where can I find alternatives to agent-opt or SPPO? GraphCanon lists graph-backed alternatives at [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) and [SPPO alternatives](/tools/uclaml-sppo/alternatives) ([agent-opt markdown twin](/tools/future-agi-agent-opt/alternatives.md), [SPPO markdown twin](/tools/uclaml-sppo/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/future-agi-agent-opt-vs-uclaml-sppo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-opt or SPPO? agent-opt: Steady. SPPO: Dormant. 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 agent-opt and SPPO? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-opt trust report](/tools/future-agi-agent-opt/trust); [SPPO trust report](/tools/uclaml-sppo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-opt`](/api/graphcanon/graph?tool=future-agi-agent-opt) - 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/_