VeRO: Versioning Rewards and Observations

VeRO is an evaluation harness for using coding agents to optimize LLM-based agents and workflows. It treats agent code as a versioned artifact — making changes, evaluating results, and hill-climbing toward better performance using git version control.

Paper: VeRO: An Evaluation Harness for Agents to Optimize Agents

Repository Structure

vero/
├── vero/               # Core library (scale-vero)
├── vero-agents/        # Agent implementations (benchmarking targets)
├── vero-benchmarking/  # Benchmarking scripts and analysis
└── LICENSE

vero/

The core optimization framework. Provides:

• Policy — orchestrates the optimization loop (agent + evaluator + git)
• Agents — VeroAgent (OpenAI Agents SDK) and ClaudeCodeAgent (Claude Agent SDK)
• Evaluator — runs task evaluations in isolated subprocess environments
• Tools — MCP-based tools for agents (bash, file I/O, experiment runner, dataset viewer, etc.)
• Traces — session analysis and LLM-based trace interpretation

cd vero && uv sync --extra optimize

See vero/README.md for full documentation.

vero-agents/

Agent implementations used as optimization targets:

See vero-agents/README.md for details.

vero-benchmarking/

Scripts and infrastructure for running optimization experiments:

cd vero-benchmarking && uv sync --all-extras

# Run an optimization experiment
uv run python scripts/run_benchmark.py --scaffold claude-code-vmf --model sonnet --task math

# Build datasets
./scripts/build_datasets.sh

See vero-benchmarking/README.md for full documentation.

Quick Start

Prerequisites

• Python 3.11+
• uv
• Git
• Access to an LLM provider (via LiteLLM, OpenAI, Anthropic, etc.)

Install

git clone <repo-url> && cd vero

# Install core library
cd vero && uv sync --extra optimize

# Install benchmarking tools
cd ../vero-benchmarking && uv sync --all-extras

Run Your First Optimization

from agents import Agent as OAIAgent
from vero.policy import Policy
from vero.agents.vero import VeroAgent

policy = Policy(
    project_path="/path/to/my-agent",
    dataset="/path/to/my-dataset",
    agent=VeroAgent(
        oai_agent=OAIAgent(name="VeroAgent", model="anthropic/claude-sonnet-4-5-20250929"),
    ),
    task="main",
    train_budget=10,
    max_turns=200,
)

best = await policy.run()
print(f"Best commit: {best.commit}, score: {best.score}")

Citation

@article{ursekar2026vero,
  title={VeRO: An Evaluation Harness for Agents to Optimize Agents},
  author={Ursekar, Varun and Shanker, Apaar and Chatrath, Veronica and Xue, Yuan (Emily) and Denton, Sam},
  journal={arXiv preprint arXiv:2602.22480},
  year={2026}
}

License

MIT