Metadata-Version: 2.4
Name: benchflow
Version: 0.6.5
Summary: Multi-turn agent benchmarking with ACP — run any agent, any model, any provider.
Project-URL: Homepage, https://github.com/benchflow-ai/benchflow
Project-URL: Repository, https://github.com/benchflow-ai/benchflow
Project-URL: Issues, https://github.com/benchflow-ai/benchflow/issues
Project-URL: Discord, https://discord.gg/mZ9Rc8q8W3
Project-URL: Changelog, https://github.com/benchflow-ai/benchflow/blob/main/CHANGELOG.md
Author-email: Xiangyi Li <xiangyi@benchflow.ai>, Kyoung Whan Choe <choe.kyoung@gmail.com>
Maintainer-email: Xiangyi Li <xiangyi@benchflow.ai>, Kyoung Whan Choe <choe.kyoung@gmail.com>
License: Apache-2.0
License-File: LICENSE
Keywords: acp,agent-evaluation,benchmark,llm-agents,multi-turn,skillsbench
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.12
Requires-Dist: agent-client-protocol>=0.10
Requires-Dist: anyio>=4.0
Requires-Dist: httpx>=0.27.0
Requires-Dist: litellm[proxy]==1.89.0
Requires-Dist: packaging>=24
Requires-Dist: pydantic>=2.7
Requires-Dist: pyyaml>=6.0
Requires-Dist: rich>=13.0
Requires-Dist: tomli-w>=1.0
Requires-Dist: typer>=0.9
Provides-Extra: bedrock
Requires-Dist: boto3>=1.40; extra == 'bedrock'
Provides-Extra: deepagents
Requires-Dist: deepagents>=0.6; extra == 'deepagents'
Requires-Dist: langchain-openai>=0.2; extra == 'deepagents'
Provides-Extra: dev
Requires-Dist: pre-commit>=3.7; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.24.0; extra == 'dev'
Requires-Dist: pytest>=9.0.3; extra == 'dev'
Requires-Dist: ruff>=0.7.0; extra == 'dev'
Requires-Dist: ty>=0.0.1a1; extra == 'dev'
Provides-Extra: judge
Requires-Dist: anthropic>=0.40; extra == 'judge'
Requires-Dist: google-genai>=1.0; extra == 'judge'
Requires-Dist: openai>=1.40; extra == 'judge'
Provides-Extra: sandbox-daytona
Requires-Dist: daytona>=0.184.0; extra == 'sandbox-daytona'
Requires-Dist: tenacity>=8.0; extra == 'sandbox-daytona'
Provides-Extra: sandbox-modal
Requires-Dist: modal>=0.73; extra == 'sandbox-modal'
Requires-Dist: tenacity>=8.0; extra == 'sandbox-modal'
Provides-Extra: train
Requires-Dist: transformers==5.6.2; extra == 'train'
Provides-Extra: trl
Requires-Dist: datasets>=2.19.0; extra == 'trl'
Requires-Dist: trl>=0.24.0; extra == 'trl'
Description-Content-Type: text/markdown

<div align="center">
  <h1>BenchFlow</h1>
  <p>The universal environment framework — a benchmark is just a frozen environment.</p>
  <a href="https://pypi.org/project/benchflow/" target="_blank">
    <img src="https://img.shields.io/badge/PyPI-benchflow-3775A9?style=for-the-badge&logo=pypi&logoColor=white" alt="PyPI package">
  </a>
  <a href="https://discord.gg/mZ9Rc8q8W3" target="_blank">
    <img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord">
  </a>
</div>

## What

BenchFlow is a universal environment framework: it runs AI agents against task environments and scores them through one hardened contract. **A benchmark is just a frozen environment** — point BenchFlow at any of them, drive it with *any* ACP agent, and run single-agent, multi-agent, or multi-round patterns over the same Scene-based lifecycle.

- **Run any benchmark** — three-layer routing runs supported frameworks natively, translates unknown formats and proves equivalence with a parity gate, or runs a bespoke harness as-is; every layer emits one scored-trajectory contract. See [Run any benchmark](./docs/running-any-benchmark.md)
- **Any ACP agent** — Gemini CLI, Claude Code, Codex, OpenCode, OpenHands, Pi, or your own
- **Single + multi + progressive** — single-agent / multi-agent (coder + reviewer, simulated user) / multi-round with a Python `BaseUser` callback
- **Loop strategies** — wrap any agent in a `--loop-strategy` (`verify-retry`, `self-review`); every rollout captures a per-iteration reward + token trajectory, so you can plot capability against cost (can a cheap model + loops match an expensive one at equal token spend?)
- **`task.md` tasks** — one file (YAML frontmatter + prompt body) replaces the split `task.toml` + `instruction.md` layout; author with `bench tasks init` / `check` / `migrate` / `export`
- **Hosted environments** — run external PrimeIntellect / Verifiers environments through `--source-env`, without converting them to BenchFlow tasks
- **Sandboxes** — Docker locally, Daytona for parallel cloud runs (orphaned sandboxes auto-reaped at eval start), Modal for serverless/GPU-backed task environments
- **Hardened verifier** — defaults block BenchJack/Meerkat-style reward-hacking; tasks opt out per-feature
- **Training-ready output** — every scored rollout emits ATIF (`trainer/atif.json`) and ADP (`trainer/adp.jsonl`) trajectory records next to the Verifiers/ORS (OpenReward) reward record

## Quickstart

```bash
# Install or upgrade to the latest stable BenchFlow CLI
uv tool install --python 3.12 --upgrade benchflow

# Run a benchmark: any task source, any ACP agent, any sandbox
export GEMINI_API_KEY=...            # or claude login / codex --login for subscription auth
bench eval run \
    --source-repo benchflow-ai/skillsbench --source-path tasks \
    --agent gemini --model gemini-3.1-flash-lite-preview \
    --sandbox docker
```

Each run writes a per-task `result.json` (rewards + trajectory + token usage) and a job `summary.json` (pass-rate, cost, and — for looped runs — a pass@iteration convergence curve). New here? Start with [Getting started](./docs/getting-started.md), or paste the [agent quickstart prompt](./docs/agent-quickstart.md) into Claude Code / Codex / Gemini CLI and let it drive the whole thing.

## Install

Install or upgrade to the latest stable release from PyPI with `uv`:

```bash
uv tool install --python 3.12 --upgrade benchflow
```

- Confirm with `bench --version`.
- BenchFlow CLI releases require Python 3.12 or newer. Keep `--python 3.12`
  in the install command so `uv` does not resolve an older Python-compatible
  package that lacks the CLI entrypoints.
- If you see `Executables already exist: bench, benchflow`, re-run with `uv tool install --python 3.12 --upgrade --force benchflow` to replace stale entrypoints from an older install.
- For Daytona or Modal extras, install the relevant optional package, for example `uv tool install --python 3.12 --upgrade 'benchflow[sandbox-daytona]'`.

Internal users wanting the newest preview from `main` install the [internal preview channel](./docs/release.md) (`uv tool install --python 3.12 --prerelease allow --upgrade benchflow`).

**Requirements & auth.** Install [uv](https://docs.astral.sh/uv/); the
`--python 3.12` flag lets it provision a compatible interpreter for the tool
install. Set `DAYTONA_API_KEY` for Daytona or configure Modal auth for Modal;
export an agent API key (`GEMINI_API_KEY`, `ANTHROPIC_API_KEY`, …) or use
subscription auth (`claude login` / `codex --login`). Provider-prefixed models
may need provider-specific credentials; Azure Foundry uses `AZURE_API_KEY` +
`AZURE_API_ENDPOINT`.

## Documentation

Start with [Getting started](./docs/getting-started.md), then [Concepts](./docs/concepts.md) for the mental model. Prefer to have an AI coding agent run the whole quickstart for you? Paste the [agent quickstart prompt](./docs/agent-quickstart.md) into Claude Code, Codex CLI, or Gemini CLI. Then by goal:

| If you want to… | Read |
|------------------|------|
| Run an eval on an existing task | [Getting started](./docs/getting-started.md) |
| Understand how BenchFlow runs *any* benchmark (the three-layer model) | [Run any benchmark](./docs/running-any-benchmark.md) |
| Have an AI agent install + run the quickstart end to end | [Agent quickstart prompt](./docs/agent-quickstart.md) |
| Understand Rollout / Scene / Role / Verifier | [Concepts](./docs/concepts.md) |
| Author a new task | [Task authoring](./docs/task-authoring.md) |
| Author a task in the native `task.md` format | [Native task.md authoring](./docs/task-authoring-task-md.md) |
| Run a hosted PrimeIntellect / Verifiers environment | [CLI reference](./docs/reference/cli.md) |
| Multi-agent: coder + reviewer, simulated user, BYOS, stateful envs | [Use cases](./docs/use-cases.md) |
| Multi-round single-agent (progressive disclosure, oracle access) | [Progressive disclosure](./docs/progressive-disclosure.md) |
| Skill evaluation (when the artifact is a skill, not a workspace) | [Skill eval](./docs/skill-eval.md) |
| Understand the security model | [Sandbox hardening](./docs/sandbox-hardening.md) |
| Use public vs internal preview SDK releases | [Release channels](./docs/release.md) |
| CLI flags + commands | [CLI reference](./docs/reference/cli.md) |
| Python API surface | [Python API reference](./docs/reference/python-api.md) |

Notebooks and runnable example scripts live under [`docs/examples/`](./docs/examples/) so examples stay versioned with the docs that explain them.

> **`bench agent` vs `bench eval adopt`.** `bench agent list` / `bench agent show`
> inspect **registered AI agents** (the solver programs like Claude Code or
> Gemini CLI). Onboarding a third-party benchmark into `benchmarks/<name>/` is a
> separate workflow — `bench eval adopt <source>` scaffolds and drives the
> conversion, and `bench eval adopt <name> --verify` parity-gates it. (The legacy
> `bench agent create|run|verify` commands still work as deprecated aliases.)
> See the [CLI reference](./docs/reference/cli.md#bench-eval-adopt) for details.

## Benchmark task sources

Benchmark datasets live in external Git repos and are referenced with two fields:

```yaml
# benchmarks/harvey-lab/harvey-lab-gemini-flash-lite.yaml
source:
  repo: benchflow-ai/benchmarks    # GitHub org/repo
  path: datasets/harvey-lab/tasks  # optional subpath within repo
  ref: main                         # optional branch/tag
agent: gemini
model: gemini/gemini-3.1-flash-lite-preview
```

Run any benchmark via the CLI:

```bash
# From a YAML config (shipped with the repo)
bench eval run --config benchmarks/harvey-lab/harvey-lab-gemini-flash-lite.yaml

# Inline — mirrors the YAML source fields
bench eval run \
    --source-repo benchflow-ai/skillsbench --source-path tasks \
    --agent gemini --model gemini-3.1-flash-lite-preview --sandbox daytona --concurrency 64
```

Repos are cloned and cached locally under `.cache/datasets/` on first use.

Hosted environments are another source type. Instead of a repo, pass
`--source-env` with the environment's pinned source version to run an external
PrimeIntellect / Verifiers environment on its own native harness — BenchFlow
preserves the hosted identity (`env_uid`, `hub_url`) and still writes the shared
rollout output contract. See the [CLI reference](./docs/reference/cli.md) for
the full hosted-environment command shape.

Downstream projects should depend on the public PyPI release by default. For
internal validation before the next public release, install or lock the internal
preview channel with prereleases enabled; see [Release channels](./docs/release.md).

## Authoring tasks

A task is one `task.md` (YAML frontmatter for config + a markdown prompt body)
plus `environment/` and `verifier/` sidecars. The `bench tasks` commands cover
the authoring lifecycle:

```bash
bench tasks init my-task                 # scaffold a task.md package under tasks/
bench tasks check tasks/my-task          # validate (default --level structural)
bench tasks migrate legacy-task/ --remove-legacy  # convert old split packages to task.md
bench tasks export tasks/my-task out/             # write a compatibility export + loss report
```

See [Native task.md authoring](./docs/task-authoring-task-md.md) and the
[task standard](./docs/task-standard.md).

## Featured

- **Progressive disclosure on SWE-bench Pro** — the `BaseUser` abstraction drives a multi-round rollout: terse round-0 prompt → failing-test hints → full spec. 5/5 oracle on Daytona, runnable demo at [`docs/examples/swebench_pro_progressive_disclosure.ipynb`](./docs/examples/swebench_pro_progressive_disclosure.ipynb). See [Progressive disclosure](./docs/progressive-disclosure.md).

## Audience

- **Eval researchers / paper writers** → [Getting started](./docs/getting-started.md) → [Concepts](./docs/concepts.md) → [Use cases](./docs/use-cases.md)
- **Task authors** → [Task authoring](./docs/task-authoring.md) → [Sandbox hardening](./docs/sandbox-hardening.md)
- **Agent builders integrating with benchflow** → [Concepts](./docs/concepts.md) → [Python API reference](./docs/reference/python-api.md) → [`benchflow.agents.registry`](./src/benchflow/agents/registry.py)
- **External benchmark adapters** → [Task authoring](./docs/task-authoring.md) → [Progressive disclosure](./docs/progressive-disclosure.md#comparison-with-multi-agent-simulated-user)

## Contributing

PRs welcome. Open against `main`. CI runs ruff + tests on every PR; please run `ruff check .` and `pytest tests/` locally first.

Release channels are documented in [Release channels](./docs/release.md). In
short: merges to `main` publish an internal preview after CI passes, while a
matching release tag publishes the public release.

## License

Apache-2.0.
