VeRO

A harness for agents to optimize programs, text, and agents

VeRO gives an optimizer something to edit, a controlled way to evaluate it, and durable memory of everything it tried. The target is anything you can put under Git and score — a program (one function to a whole repo), text (a prompt, spec, or config), or an agent (its scaffold, tools, and prompts).

VeRO runs the same version → evaluate → select loop over all of them. Where each candidate is produced and contained is a swappable backend, and Harbor is the recommended one: it runs the whole coding agent inside a reproducible, credential-isolated container and scores it against a trusted evaluation sidecar. That is the right default for optimizing agents and for any untrusted or reproducibility-critical run. Lighter local backends exist for trusted work that does not need containment.

Every candidate is a Git commit and stays selectable after it is scored. The loop is the same whichever backend produces and contains the candidate.

Install

uv sync --extra optimize        # or --all-extras for the full toolchain
uv run vero --help

The published distribution is scaleapi-vero (pip install scaleapi-vero); it imports as vero. Do not install scale-vero from PyPI — that name is an unrelated placeholder squatted in July 2026, not VeRO.

Python 3.11–3.13. 3.14 is excluded because litellm does not build there.

Quickstart — no credentials needed

The C matrix-multiplication example is deterministic and runs with no model credentials at all. Its editable target contains only C; a trusted external harness compiles it, checks correctness, and measures latency.

cd examples/c-matmul/target
git init -b main && git add .
git -c user.name=vero -c user.email=vero@localhost commit -m baseline
cd ..

uv run vero evaluate --config vero.toml     # score the baseline
uv run vero run --config vero.toml          # optimize

VeRO evaluates the baseline, gives an isolated worktree to the configured producer, evaluates its commit, selects the faster feasible result, and leaves the original target untouched.

Examples

Each is a complete, checked-in target plus harness — clone-and-run, not a sketch.

Run harness-conformance before spending a real benchmark on a new harness or model. Every harness addresses its provider differently, and it costs minutes to find that out instead of hours.

A real run, end to end

harbor-circle-packing runs a coding agent in a container, scores each candidate through a trusted sidecar, and finalizes on a test partition the agent never touches. One run with mini-swe-agent and claude-sonnet-5:

0.9598 → 2.5766 on the held-out partition, shipped: true, in about an hour. The best published result for 26 circles is ~2.635. The agent wrote a 15 KB Lubachevsky–Stillinger-style growth algorithm with LP refinement — no hardcoded coordinates.

Two details worth reading off the left panel. The red point is an infeasible candidate: it scored 2.5341 but overlapped, the harness rejected it on the valid == 1 constraint, and the agent's next commit was "Add safety margin to guarantee strict feasibility". And the last five evaluations are flat — it found the idea early, then polished.

The cautionary half. The same task run with codex and no prohibition on hardcoding scored 2.6360 in eight minutes — higher than the honest run — by copying the published Packomania table into 26 coordinate literals. It satisfies the objective exactly and held-out scoring cannot catch it, because every partition here holds one deterministic case, so a memorized answer transfers perfectly. The instruction now forbids it. The general lesson is the one this suite is built around: a fixed single-instance objective measures lookup and problem-solving identically, and only varying the instance across partitions separates them.

Regenerate the figure from any session directory:

python examples/circle-packing/make_figure.py <session-dir> -o results/progress.svg

Which backend

The target and evaluator do not have to be Python: external evaluators and producers connect over command protocols.

What you get

Where things are

For end-to-end agent-optimization benchmarks, see ../harness-opt-bench/, which also documents how each coding agent must be pointed at the gateway — the one thing that reliably costs a run when it is wrong.

Paper and reproduction

VeRO was introduced in VeRO: A Harness for Agents to Optimize Agents, accepted at ICML 2026. The paper studies agent-harness optimization; the current library generalizes the same version/evaluate/select loop to programs more broadly.

The frozen code for reproducing the paper is preserved on the paper/v1 branch and at the paper-v1 tag:

git checkout paper-v1

The same pre-v0.5 tree is also readable in place under ../legacy/. Note that it is scale-vero 0.4.7 and this is 0.5.0, both importing as vero, so they cannot share a virtualenv.

Development

uv sync --all-extras
uv run pytest tests/test_v05_*.py

VeRO is licensed under the MIT License.