schema_version = "1.4"
artifacts = [
    { source = "/var/lib/are", service = "grader" },
]

[task]
name = "bespokelabs/cpu-decoder-graph-executor"
version = "1.1.0"
description = ""
keywords = [
    "autoresearch",
    "machine-learning",
    "benchmark",
]
authors = [
    { name = "Bespoke Labs" },
]

[metadata]
category = "Model Training and ML Infrastructure"
subcategory = "Systems and Efficiency"
task_objective = [
    "Optimize",
    "Implement",
]
artifact_type = [
    "Single script or program",
    "Codebase",
]
expert_time_estimate_hours = 4.0
are_version = "1.1"
budget_seconds = 86400

[verifier]
timeout_sec = 600.0
environment_mode = "separate"

[[verifier.collect]]
service = "grader"
command = "/opt/are/venv/bin/python /opt/are/drain.py"
timeout_sec = 3600.0

[verifier.environment]
network_mode = "no-network"
build_timeout_sec = 600.0
cpus = 1
memory_mb = 1024
gpus = 0

[agent]
network_mode = "allowlist"
allowed_hosts = [
    "api.openai.com",
    "api.anthropic.com",
    "grader",
]
timeout_sec = 86400.0
user = "agent"

[environment]
network_mode = "public"
build_timeout_sec = 3600.0
os = "linux"
cpus = 8
memory_mb = 14336
storage_mb = 153600
gpus = 0
mcp_servers = [
    { name = "are", transport = "streamable-http", url = "http://grader:8000/mcp" },
]

[environment.env]