Practical overview
When it fits
Use it when your system exposes tunable parameters and each trial can be evaluated with a measurable objective, especially when the parameter meanings and previous results can inform further proposals.
Who it is for
- Algorithm engineers tuning systems with configurable parameters.
- Developers who can provide evaluation code and a measurable objective.
- Researchers running model, inference, quantitative, reinforcement-learning or scientific experiments.
Documented uses
- Define bounded continuous, integer or categorical parameters for a system.
- Ask a coding-agent CLI to propose configurations informed by parameter meaning and previous trials.
- Evaluate configurations against a scalar objective and retain trial outcomes.
- Review the best recorded parameters and value; optionally request a study summary.
Requirements & limits
The documented setup installs the Python package and requires one authenticated Claude Code, Codex or OpenCode CLI on PATH. The repository identifies the license as MIT.
- You must provide the objective function and system-specific evaluation; example objectives are not built-in integrations.
- The coding agent proposes values, while the objective evaluation supplies trial results.
- Invalid proposals are validated against the declared search space and fall back to safe sampling.
- Study-wide or per-parameter context can communicate domain meaning to the proposal process.
- JSON or SQLite storage can retain study data; study summaries are an optional agent-generated feature.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Define and run an agent-guided parameter-tuning study
Use a coding-agent CLI to propose parameter values for an objective function and record measured trial results.
Before you start
- Python package
optim-agentinstalled. - One authenticated agent CLI (
claude,codex, oropencode) available onPATH. - A system with configurable parameters and an objective function that returns a measurable result.
Steps
Define an objective function that declares bounded parameters with
trial.suggest_floatortrial.suggest_int, optionally adding context to explain their meaning.Create a study with
oa.create_study, setting its direction and anoa.AgentSamplerbackend matching the available agent CLI.Call
study.optimize(objective, n_trials=20)to evaluate the requested trials.Inspect
study.best_valueandstudy.best_params; optionally setstorageto persist the study andsummarize=Trueto request a study summary.
Expected outcome
The study evaluates configurations and exposes the best measured value and corresponding parameters; optional storage preserves study data.
Guide limitations
- The objective function and domain-specific evaluation must be provided by the user.
- The agent proposes parameter values; optim-agent validates them against the declared search space, and invalid replies fall back to safe sampling.
- The documented example uses a finite trial count and does not establish that a particular objective will improve.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- Optim-Agent/optim-agent · Official homepage title · retained in its source language

