benchflow 0.2.1

Multi-turn agent benchmarking with ACP — run any agent, any model, any provider.

BenchFlow

Multi-turn agent benchmarking with ACP

What

BenchFlow runs AI coding agents against benchmark tasks and captures their full trajectory. It combines Harbor (environments, verifier, orchestration) with ACP (multi-turn agent communication).

The agent runs inside a sandboxed environment (Docker or Daytona). BenchFlow connects to it via ACP over a live stdio pipe. You can send one prompt or many — the agent stays alive between prompts, maintaining full context.

Install

pip install benchflow

Requires Python 3.12+ and Docker (or a Daytona API key for cloud sandboxes).

Quick Start

source .env  # ANTHROPIC_API_KEY (auto-inherited by SDK)

# Run a single task
benchflow run -t path/to/task -a claude-agent-acp -e daytona

# Run a full benchmark (89 tasks, 64 concurrent)
benchflow job -t .ref/terminal-bench-2 -e daytona -c 64

# List available agents
benchflow agents

# View results
benchflow metrics jobs/
benchflow view jobs/my-job/my-trial/

SDK

from benchflow import SDK, Job, JobConfig, collect_metrics

result = await SDK().run(task_path="path/to/task", agent="claude-agent-acp")
print(result.rewards)  # {"reward": 1.0}

Single task, multi-turn, full benchmark jobs, and programmatic metrics — see docs/getting-started.md.

CLI

benchflow run -t path/to/task -a claude-agent-acp   # single task
benchflow job -t tasks/ -a claude-agent-acp -c 1    # benchmark job
benchflow metrics jobs/                              # aggregate results
benchflow view jobs/my-job/my-trial/                # trajectory viewer

Full flag reference for all 8 subcommands: docs/cli-reference.md.

Agents

Any ACP-compatible agent works. Registered agents are auto-installed in sandboxes.

benchflow agents              # list registered agents
benchflow run -t task/ -a pi-acp -e daytona

See docs/architecture.md for the full tested agent × model/provider matrix and how to add your own.

Environments

Environment	Concurrency	Notes
docker	~4	Local Docker. Limited by network exhaustion.
daytona	64+	Cloud sandboxes. Requires DAYTONA_API_KEY.

How it Works

BenchFlow starts a sandboxed environment, connects to the agent via ACP over a live stdio pipe, sends one or more prompts (the agent retains full context between turns), then runs the verifier and captures the full trajectory.

See docs/architecture.md for SDK run phases, ACP protocol details, and the registry pattern.

Task Format

Tasks follow the Harbor task format:

my-task/
├── task.toml              # timeouts, resources, metadata
├── instruction.md         # what the agent should do
├── environment/
│   └── Dockerfile         # sandbox setup
├── tests/
│   └── test.sh            # verifier → reward.txt
└── solution/              # optional reference solution

Full task.toml schema, verifier contract, and a worked example: docs/task-authoring.md.

Results

Every run produces structured output:

jobs/{job_name}/{trial_name}/
├── config.json              # SDK.run() parameters (agent, model, environment)
├── result.json              # rewards, agent, timing breakdown
├── timing.json              # {environment_setup, agent_setup, agent_execution, verifier, total}
├── prompts.json             # prompts sent
├── agent/
│   ├── install-stdout.txt   # agent install output
│   └── {agent_name}.txt     # agent stderr/debug output (hyphens → underscores)
├── trajectory/
│   └── acp_trajectory.jsonl # tool calls + agent thoughts
└── verifier/
    └── reward.txt           # reward value

Benchmarks

Tasks are auto-downloaded on first run (cloned into .ref/).

SkillsBench (86 tasks — tool use, file editing, API calls):

python benchmarks/run_skillsbench.py benchmarks/skillsbench-claude-glm5.yaml   # Claude
python benchmarks/run_skillsbench.py benchmarks/skillsbench-codex-gpt54.yaml   # Codex

Terminal-Bench 2 (89 tasks — shell, git, compilers, daemons):

python benchmarks/run_tb2.py benchmarks/tb2_single-codex-gpt54.yaml      # single-turn
python benchmarks/run_tb2.py benchmarks/tb2_multiturn-codex-gpt54.yaml   # multi-turn

Shipped configs use environment: daytona and concurrency: 8. For local Docker: --env docker --concurrency 1.

Benchmark	Agent	Model	Score
TB2 single-turn	codex-acp	GPT-5.4*	69.7% (62/89)
TB2 single-turn	claude-agent-acp	Sonnet 4.6	58.4% (52/89)
TB2 multi-turn	codex-acp	GPT-5.4*	62.9% (56/89)
TB2 multi-turn	claude-agent-acp	Haiku 4.5	37.1% (33/89)
SkillsBench	codex-acp	GPT-5.4*	37.2% (32/86)

*GPT-5.4 runs used effort=medium.

Skills

BenchFlow ships a Claude Code skill in .claude/skills/benchflow/ that teaches agents how to use the framework. Place skills in ~/.claude/skills/ (or bake into task Dockerfiles) for auto-discovery.

Validation tasks in .claude/skills/benchflow/tasks/ confirm agents can use the skill correctly.

Architecture

ACP client, job orchestration, multi-agent registry, trajectory capture, skills, viewer, and CLI — see docs/architecture.md.

Citation

If you use BenchFlow in academic work, please cite:

@software{BenchFlow_Team_BenchFlow_2026,
author = {{BenchFlow Team}},
month = mar,
title = {{BenchFlow: Multi-turn agent benchmarking with ACP}},
url = {https://github.com/benchflow-ai/benchflow},
year = {2026}
}

License

Apache License 2.0 — see LICENSE.