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Use this cookbook when you know the benchmark and harness but need to choose where tasks should run.

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Decision Table

ChooseWhen
host_processThe benchmark is lightweight, local, or service-backed.
dockerYou need local container reproducibility.
modalYou want registry-image cloud sandboxes and provider-managed compute.
daytonaYou want remote development-style sandboxes for coding or terminal tasks.

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Docker Smoke Test

agentcompass run \
  swebench_verified \
  mini_swe_agent \
  glm-5.2 \
  --env docker \
  --benchmark-params '{"sample_ids":["astropy__astropy-12907"]}' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat \
  --task-concurrency 1

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Remote Smoke Test

export DAYTONA_API_KEY="..."

agentcompass run \
  terminal_bench_2 \
  terminus2 \
  your-model \
  --env daytona \
  --benchmark-params '{"sample_ids":["overfull-hbox"]}' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY"

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Image And Workspace Rules

For recipe-backed benchmarks, image and workspace are usually inferred from task metadata. For custom tasks, pass --env-params. 
agentcompass run <benchmark> <harness> <model> --env docker --env-params '{"image":"python:3.13-slim","workspace":"/workspace"}'