pip install agentbox-rl

AgentBox is an ART-native environment + rollout harness: isolated Docker sandboxes, structured tools (or pure shell), task seeding, verifiers, and multi-turn trajectories shaped for OpenPipe ART (messages_and_choices, rewards, groups) — for online GRPO with ART’s inference backend, offline SFT dumps, and the same real-rollout path for benchmarks.

It is not a trainer. ART (or TRL / Unsloth / verl) owns training; AgentBox owns the virtual env and trajectory collection.

Highlights

• ART-native trajectories — to_art_dict() / to_art(); GRPO groups via ParallelRunner
• One container per rollout — clean state, parallel isolation, labeled GC
• OpenAI tools protocol — Chat Completions + tool calling (ART-compatible wire format)
• Provider-agnostic models — Featherless, OpenRouter, vLLM, Ollama, ART backend client, …
• Builtins + custom tools — BaseTool / @tool, override-by-name
• Task seeding — starter_files + setup_commands before the agent starts
• Objective rewards — pytest / command verifiers (hybrid-ready with ART RULER)
• ParallelRunner — concurrent rollouts and GRPO-style groups
• Trajectory export — JSON, JSONL, ART-native dicts
• Typer CLI — doctor, generate, bench, traj, run, export, prune
• Real-rollout benchmarks — freeze tasks + env; multi-student scoring
• Task generation — batch + two-stage stubs + static/Docker/LLM QC (no private scripts)
• Trajectory HTML — official verifier vs agent self-check dashboard

Quick Start

Prerequisites: Python 3.11+, Docker daemon running, optional uv.

# Install from PyPI
pip install agentbox-rl

# Or install from source
pip install agentbox-rl[dev]

# For optional features:
pip install agentbox-rl[rich]     # progress bars
pip install agentbox-rl[art]      # live art.Trajectory
pip install agentbox-rl[generate] # DSPy task generation
pip install agentbox-rl[all]      # everything

# Development install from source
git clone https://github.com/konic-labs/agentbox.git
cd agentbox
uv venv .venv --seed --python 3.12
source .venv/bin/activate
uv pip install -e ".[dev]"

# Optional
uv pip install -e ".[rich]"      # progress bars
uv pip install -e ".[art]"       # live art.Trajectory
uv pip install -e ".[generate]"  # DSPy task generation

Sanity checks (no external LLM required):

agentbox version
agentbox doctor
uv run pytest tests/unit -q
python examples/hello_rollout.py

Minimal Python rollout (mock)

import asyncio
from agentbox import Task, Rollout
from agentbox.config import SandboxConfig, ResourceLimits
from agentbox.model import MockModelClient, ModelResponse
from agentbox.trajectory.schema import ToolCall, FunctionCall
import json

async def main():
    task = Task.from_json("examples/tasks/fix_fizzbuzz/task.json")
    fixed = 'def fizzbuzz(n):\n    return "Fizz" if n % 3 == 0 else str(n)\n'
    mock = MockModelClient([
        ModelResponse(
            content=None,
            tool_calls=[ToolCall(
                id="c1",
                function=FunctionCall(
                    name="write_file",
                    arguments=json.dumps({"path": "fizzbuzz.py", "content": fixed}),
                ),
            )],
        ),
        ModelResponse(content="done", tool_calls=[]),
    ])
    traj = await Rollout.run(
        task,
        model=mock,
        sandbox=SandboxConfig(
            limits=ResourceLimits(network_disabled=False),
        ),
    )
    print(traj.final_status, traj.reward)
    traj.save("out/traj.json")

asyncio.run(main())

Real model (Ollama)

agentbox run examples/tasks/fix_fizzbuzz/task.json \
  --model qwen2.5-coder:7b \
  --base-url http://localhost:11434/v1 \
  --api-key ollama \
  --network \
  --out trajectories/

from agentbox import Agent, Task, Rollout

agent = Agent(
    model="qwen2.5-coder:7b",
    base_url="http://localhost:11434/v1",
    api_key="ollama",
    max_steps=20,
)
traj = await Rollout.run(task=Task.from_json("examples/tasks/fix_fizzbuzz/task.json"), agent=agent)

Parallel / GRPO-style groups

from agentbox import ParallelRunner, Agent

runner = ParallelRunner(concurrency=16, agent=agent)
trajs = await runner.run_tasks(tasks, n_per_task=1)
groups = await runner.run_groups(tasks, group_size=4)
art = [[t.to_art_dict() for t in g] for g in groups]

Workflow

Step	What happens	Artifact
0. Task	Load or generate Task definition	task.json
1. Sandbox	Create labeled Docker container	running container
2. Seed	Write starter_files, run setup_commands	/workspace ready
3. Agent	Multi-turn tools via OpenAI protocol	messages + tool records
4. Verify	pytest / command exit code	reward
5. Record	Finalize trajectory, destroy container	JSON / ART dict

task → create sandbox → seed → agent loop → verifier → trajectory → destroy

Tools

Builtin	Role
list_files	List workspace paths
read_file	Read file content
write_file	Create / overwrite
edit_file	Exact one-occurrence replace (old_string / new_string)
run_command	Shell in container
run_tests	Default python -m pytest -q

Modes: structured (all builtins), shell (run_command only), custom.

Custom tools:

from agentbox.tools import tool

@tool(description="Count lines")
async def count_lines(sandbox, path: str) -> str:
    r = await sandbox.exec(f"wc -l -- {path}")
    return r.stdout.strip()

agent = Agent(model="...", base_url="...", custom_tools=[count_lines])

Same name overrides a builtin. Details: docs/tools.md.

Models & Providers

OpenAI Chat Completions + tools only. Configure any compatible endpoint:

Type	Examples	Config
External	Featherless, OpenRouter, Together, Fireworks	base_url + api_key
Local	vLLM, SGLang, Ollama /v1	base_url; api_key optional

Agent(model="glm-5.2", base_url="https://api.featherless.ai/v1", api_key="...")
Agent(model="Qwen/Qwen2.5-7B-Instruct", base_url="http://localhost:8000/v1")

Tasks

{
  "task_id": "fix_fizzbuzz_001",
  "description": "Fix fizzbuzz so tests pass.",
  "starter_files": { "fizzbuzz.py": "...", "test_fizzbuzz.py": "..." },
  "setup_commands": ["pip install -q pytest"],
  "verifier": {
    "type": "pytest",
    "command": "python -m pytest -q",
    "success_exit_code": 0
  },
  "metadata": { "difficulty": "easy", "tags": ["python"] }
}

Automated generation (optional):

from agentbox.tasks.generate import TaskGenerator, GenerateConfig

gen = TaskGenerator(GenerateConfig(
    model="glm-5.2",
    base_url="https://api.featherless.ai/v1",
    api_key="...",
    validate_in_docker=True,
))
task = await gen.generate(difficulty="easy", domain="python")

Trajectories & ART

AgentBox is built to drop into ART workflows:

Flow	AgentBox role	ART role
Online GRPO	Docker env, tools, tasks, optional verifier	Inference (vLLM/LoRA) + backend.train
Offline SFT	Bulk rollouts from any API	SFT on exported trajectories
Rewards	Verifier scores	Optional RULER on groups (hybrid OK)

traj.save("trajectories/run.json")
art = traj.to_art_dict()   # ART-native: messages_and_choices, reward, metrics, metadata
# pip install agentbox-rl[art]
# live = traj.to_art()     # openpipe-art Trajectory object

agentbox export traj.json --format art -o traj.art.json
python examples/art_integration.py

Production workflows

End-to-end lab path without private scripts:

# 0) Optional project config (teacher + students)
cp examples/agentbox.yaml ./agentbox.yaml   # edit endpoints

# 1) Probe tool-calling before a long suite
agentbox doctor --model MODEL --base-url http://localhost:8000/v1

# 2) Generate a QC-passed task set
agentbox generate batch -m MODEL --base-url URL -n 20 -c 8 --two-stage \
  -o generated/tasks

# 3) Freeze a suite and run multi-student bench
agentbox bench create suites/coding-v1 --from-tasks generated/tasks \
  --suite-id coding-v1 --name "coding v1"
agentbox bench run suites/coding-v1 \
  --student small=qwen3.5:0.8b@http://localhost:11434/v1 \
  --student big=MODEL@http://localhost:8000/v1 \
  --probe --out bench-results/run1

# 4) Inspect
agentbox bench show bench-results/run1/report.json --out REPORT.md
agentbox traj render bench-results/run1 -o traj-dash.html

# 5) ART offline groups
python examples/art_grpo_offline.py

Details: docs/generation.md · docs/art_grpo.md · docs/trajectories.md.

CLI Reference

agentbox doctor [--prune] [--model M --base-url URL]
agentbox build-image [--tag agentbox/sandbox:latest]
agentbox generate one|batch|validate-llm|validate-docker …
agentbox run TASK.json -m MODEL --base-url URL [--network] [--out trajectories/]
agentbox run-dir tasks/ -m MODEL --base-url URL -c 16 --n 4
agentbox bench create DIR --from-tasks tasks/ --suite-id ID --name NAME
agentbox bench freeze DIR && agentbox bench validate DIR --strict
agentbox bench run DIR -m MODEL --base-url URL --model-id LABEL -o bench-results/run1
agentbox bench run DIR --student id=model@url --probe --limit 5
agentbox bench show bench-results/run1/report.json --out extended.md
agentbox traj show traj.json
agentbox traj render bench-results/run1 -o traj-dash.html
agentbox export traj.json --format art -o out.json
agentbox prune

Full flag list: docs/cli.md · docs/benchmarks.md.

Benchmarks

Same Docker rollouts as training collection, frozen as a suite, scored against any OpenAI-compatible endpoint (local or external).

python examples/bench_run.py   # hermetic mock solver (Docker required)

agentbox bench run examples/benchmarks/coding-mini \
  --model-id ollama-qwen \
  --model qwen2.5-coder:7b \
  --base-url http://localhost:11434/v1 \
  --api-key ollama \
  --out bench-results/run1

Setup checks run after seed (env health); task verifiers own success/reward. Details: docs/benchmarks.md.

Sandbox image

Default: python:3.12-slim-bookworm (glibc; not Alpine).

agentbox build-image   # optional baked image with pytest + unix tools

Override: SandboxConfig(image="my-org/env:1.2") or presets in agentbox.sandbox.images.

Technical Docs

Doc	Concern
docs/index.md	Documentation map
docs/setup.md	Install & first runs
docs/architecture.md	Module boundaries & data flow
docs/configuration.md	All config fields
docs/sandbox.md	Docker lifecycle, jail, prune
docs/tools.md	Builtins & custom tools
docs/models.md	Providers & clients
docs/agent.md	Loop, timeouts, prompts
docs/tasks.md	Schema, seeder, verifier
docs/generation.md	Automated task generation
docs/trajectories.md	Formats & ART export
docs/runner.md	Rollout & parallel
docs/cli.md	CLI reference
docs/benchmarks.md	Real-rollout multi-model suites
docs/security.md	Isolation model
docs/development.md	Tests & extension

Development

uv pip install -e ".[dev]"
uv run pytest tests/unit -q
uv run pytest tests/integration -q -m docker
uv run agentbox doctor

Conventional Commits (feat:, fix:, docs:, test:, …). See docs/development.md.

Security (short)

Tools run only inside Docker; paths jailed under /workspace; network off by default; never mount the Docker socket into sandboxes. Details: docs/security.md.

License

Apache License 2.0. See LICENSE.