Optim Agent

optim-agent

Agentic system optimization with coding agents. Automate the iterative parameter-tuning work of an algorithm engineer.

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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.

Models	Systems	Research
Training, architecture, and RL experiments	Inference, latency, cost, control, and decision rules	Quant signals, simulations, and scientific workflows

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