--- title: "agent-opt vs FLAML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-opt-vs-microsoft-flaml" tools: ["future-agi-agent-opt", "microsoft-flaml"] --- # agent-opt vs FLAML *GraphCanon updated Oct 3, 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 FLAML if fLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting. [agent-opt](https://app.futureagi.com) reports 75 GitHub stars, 9 forks, and 0 open issues, last pushed Jun 30, 2026. [FLAML](https://microsoft.github.io/FLAML/) has 4.4k stars, 569 forks, and 180 open issues, last pushed Oct 1, 2026. Figures are from public GitHub metadata via [agent-opt's repository](https://github.com/future-agi/agent-opt) and [FLAML's repository](https://github.com/microsoft/FLAML). | | [agent-opt](/tools/future-agi-agent-opt.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Tagline | Open Source Library for Automated Optimization of AI Agent Workflows | A fast library for AutoML and tuning | | Stars | 75 | 4,401 | | Forks | 9 | 569 | | Open issues | 0 | 180 | | Language | Python | Jupyter Notebook | | 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. | FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-opt](/tools/future-agi-agent-opt.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 95d | 1d | | Archived on GitHub | Yes | No | | Open issues (now) | 0 | 180 | | Stars delta | +4 (30d) | +16 (30d) | | Full report | [trust report](/tools/future-agi-agent-opt/trust.md) | [trust report](/tools/microsoft-flaml/trust.md) | ## Shared compatibility - **Python**: [agent-opt](/tools/future-agi-agent-opt.md) - Python runtime; [FLAML](/tools/microsoft-flaml.md) - Python runtime ## 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: FLAML - **Adopt for:** FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting. ## Choose when ### Choose agent-opt if… - agent-opt is primarily Python; FLAML is Jupyter Notebook. - License: agent-opt is Apache-2.0, FLAML is MIT. - 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 ### Choose FLAML if… - FLAML is primarily Jupyter Notebook; agent-opt is Python. - License: FLAML is MIT, agent-opt is Apache-2.0. - Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning. - Also covers Model Training. - FLAML ships Docker support for self-hosted deployment. - When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML. ## 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 FLAML - When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available. - If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting. - For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations. ## Common questions ### What is the difference between agent-opt and FLAML? agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. FLAML: A fast library for AutoML and tuning. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-opt over FLAML? Choose agent-opt over FLAML when agent-opt is primarily Python; FLAML is Jupyter Notebook; License: agent-opt is Apache-2.0, FLAML is MIT; 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 choose FLAML over agent-opt? Choose FLAML over agent-opt when FLAML is primarily Jupyter Notebook; agent-opt is Python; License: FLAML is MIT, agent-opt is Apache-2.0; Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning; Also covers Model Training; FLAML ships Docker support for self-hosted deployment; When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML. ### 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 FLAML? When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available. If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting. For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations. ### Is agent-opt or FLAML more popular on GitHub? FLAML has more GitHub stars (4,401 vs 75). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-opt and FLAML open source? Yes - both are open-source projects on GitHub (agent-opt: Apache-2.0, FLAML: MIT). ### Where can I find alternatives to agent-opt or FLAML? GraphCanon lists graph-backed alternatives at [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) and [FLAML alternatives](/tools/microsoft-flaml/alternatives) ([agent-opt markdown twin](/tools/future-agi-agent-opt/alternatives.md), [FLAML markdown twin](/tools/microsoft-flaml/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-microsoft-flaml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-opt or FLAML? agent-opt: Archived. FLAML: 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 agent-opt and FLAML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-opt trust report](/tools/future-agi-agent-opt/trust); [FLAML trust report](/tools/microsoft-flaml/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/_