Comparison
agent-opt vs FLAML
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.
Markdown twin · agent-opt alternatives · FLAML alternatives
GraphCanon updated Oct 3, 2026
8views this month
Trust & integrity
| Signal | agent-opt | FLAML |
|---|---|---|
| Maintenance | Archived (95d since push) As of Oct 3, 2026 · github_public_v1 | Very active (1d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- agent-opt
- Open Source Library for Automated Optimization of AI Agent Workflows
- FLAML
- A fast library for AutoML and tuning
Stars
- agent-opt
- 75
- FLAML
- 4.4k
Forks
- agent-opt
- 9
- FLAML
- 569
Open issues
- agent-opt
- 0
- FLAML
- 180
Language
- agent-opt
- Python
- FLAML
- Jupyter Notebook
Adopt for
- agent-opt
- 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
- FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting.
Persona
- agent-opt
- -
- FLAML
- -
Runtime
- agent-opt
- -
- FLAML
- -
License
- agent-opt
- Apache-2.0
- FLAML
- MIT
Last pushed
- agent-opt
- Jun 30, 2026
- FLAML
- Oct 1, 2026
Categories
- agent-opt
- AI Agents, Evaluation & Observability
- FLAML
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- agent-opt
- Archived (8%)
- FLAML
- Very active (96%)
Days since push
- agent-opt
- 95d
- FLAML
- 1d
Archived on GitHub
- agent-opt
- Yes
- FLAML
- No
Open issues (now)
- agent-opt
- 0
- FLAML
- 180
Stars delta
- agent-opt
- +4 (30d)
- FLAML
- +16 (30d)
Full report
- agent-opt
- Trust report
- FLAML
- Trust report
Shared compatibility
- Python · agent-opt: Python runtime · FLAML: Python runtime
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (future-agi/agent-opt) · observed Oct 3, 2026
- GitHub forks (future-agi/agent-opt) · observed Oct 3, 2026
- Last push (future-agi/agent-opt) · observed Jun 30, 2026
- License file (Apache-2.0) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/FLAML) · observed Oct 2, 2026
- GitHub forks (microsoft/FLAML) · observed Oct 2, 2026
- Last push (microsoft/FLAML) · observed Oct 1, 2026
- License file (MIT) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agent-opt 75 · FLAML 4.4k (synced Oct 3, 2026).
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 and FLAML alternatives (agent-opt markdown twin, FLAML markdown twin), 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 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; FLAML trust report.