optim-agent

optim-agent lets Claude Code / Codex / OpenCode tune real system parameters by reading your code, proposing trials, and recording measured objective results. Use it when your system exposes configurable parameters and a measurable objective. It combines what each parameter means with what the trial history shows, then proposes the next configuration to evaluate. Objective evaluations remain authoritative: optim-agent proposes values, validates them against the declared space, records outcomes, and falls back to safe sampling when an agent reply is invalid.

Why optim-agent

• Semantic proposals - coding agents reason over parameter meanings, study context, and observed outcomes instead of treating every dimension as an anonymous coordinate.
• Small-budget leverage - useful when evaluations are expensive and classical surrogates are still data-starved.
• Agent CLI upside - proposal quality can improve as the underlying coding agents improve, such as moving from GPT-5.5 to GPT-5.6, without changing your optimization code.
• Auditable decisions - JSON/SQLite studies retain configurations, outcomes, states, context, and optional agent rationale.
• Bounded execution - the agent only proposes values; optim-agent validates them against the declared space, and invalid output falls back to safe sampling.

Install

Install the Codex skill:

$skill-installer install https://github.com/Optim-Agent/optim-agent

Install the Claude Code plugin:

claude plugin marketplace add Optim-Agent/optim-agent && claude plugin install optim-agent@optim-agent

Install the Python package:

# Stable release from PyPI
python -m pip install optim-agent

# Latest source from GitHub
python -m pip install "optim-agent @ git+https://github.com/Optim-Agent/optim-agent.git"

Requires one authenticated agent CLI on PATH: claude, codex, or opencode.

Quickstart

import optim_agent as oa

def objective(trial):
    threshold = trial.suggest_float(
        "threshold", 0.05, 0.95,
        context="decision threshold; higher values trade recall for precision",
    )
    budget = trial.suggest_int(
        "budget", 10, 200, log=True,
        context="compute or operating budget; larger values may improve quality",
    )
    return evaluate_system(threshold=threshold, budget=budget)  # domain code

study = oa.create_study(
    direction="maximize",
    sampler=oa.AgentSampler(
        backend="claude",  # or "codex" / "opencode"
        effort="high",
        context="maximize system quality under a strict operating-cost budget",
        history=5,
        explicit_reasoning=True,
        qualitative_notes=True,
    ),
    storage="study.json",  # optional: persist and resume
    summarize=True,  # optional: agent-written result summary after the last trial
)
study.optimize(objective, n_trials=20)
print(study.best_value, study.best_params)
print(study.summary)  # the summary agent's narration of the finished study

Optional context gives domain meaning to the study and parameters. Provide it study-wide on AgentSampler(context=...), per parameter on suggest_*(..., context=...), or both.

Where It Applies

For reinforcement learning, optim-agent tunes the system around the learning loop; it does not replace the policy-learning algorithm.

Optimization Trajectory

This seed-0 Branin trace compares TPE and GPT-5.5 under the same 10-trial budget, with incumbent objective values after each trial. It is a trajectory illustration; aggregate benchmark results and reproduction commands follow.

Optimizing Math Functions without Context: Branin-2D and Ackley-5D

Hard-function agents receive no supplied task context: only generic x1...x5 parameter names, numeric bounds, and trial history. Runs use 10 trials over five seeds; Random and TPE are unchanged baselines.

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