Terminal-Bench 2 Verified

Terminal-Bench 2 Verified is the verified Terminal-Bench 2 subset hosted on Hugging Face. It follows the same task execution and verifier flow as Terminal-Bench 2, normally with terminus2.

How it works

Load tasks. AgentCompass clones the Hugging Face dataset and retrieves its Git LFS objects before loading the task directories.
Run the agent. The recipe prepares each task’s container image and workspace, then the terminal harness solves the instruction.
Verify the result. The Harbor verifier executes tests/test.sh; a reward of 1 marks the task correct.

git-lfs must be installed in the process that loads the dataset. Without it, the verified task assets cannot be retrieved.

Parameters

You can run all tasks in the default dataset without --benchmark-params. For task selection and aggregation options, see the shared Benchmark parameter reference. Configure phase deadlines and timeout multipliers through --execution-params; see run controls.

Run examples

agentcompass run takes three positional arguments in order: Benchmark, Harness, and Model. The examples use terminal_bench_2_verified; harness choices are described below. Before running, make sure local Docker is available and set MODEL_NAME, MODEL_BASE_URL, and MODEL_API_KEY to the model under test, API endpoint, and API key.

Recommended harness

The recommended terminal agent is terminus2.

Smoke test (single task end-to-end)
Custom parameters
AgentCompass recommended config

Run the filter-js-from-html task to verify container preparation, inference, and scoring, leaving other parameters at their defaults.

agentcompass run \
  terminal_bench_2_verified \
  terminus2 \
  "$MODEL_NAME" \
  --env docker \
  --benchmark-params '{
    "sample_ids": ["filter-js-from-html"]
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY"

Evaluate every task in the default dataset with higher inference and evaluation timeout multipliers, for environments with slower model responses or tool execution.

agentcompass run \
  terminal_bench_2_verified \
  terminus2 \
  "$MODEL_NAME" \
  --env docker \
  --execution-params '{
    "evaluation_timeout_multiplier": 8,
    "run_timeout_multiplier": 16
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY"

Run a full evaluation with the AgentCompass recommended configuration. If model service conditions, deployment instance count, task concurrency, or model capability increase task duration, pass evaluation_timeout_multiplier and run_timeout_multiplier through --execution-params.

agentcompass run \
  terminal_bench_2_verified \
  terminus2 \
  "$MODEL_NAME" \
  --env docker \
  --task-concurrency 16 \
  --harness-params '{
    "max_turns": 300
  }' \
  --execution-params '{
    "run_timeout_seconds": 14400
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY"

Other optional harnesses

The commands below evaluate the full dataset. Configure the model variables for an OpenAI Responses API endpoint when using Codex, or an Anthropic Messages API endpoint when using Claude Code. codex and claude_code are two other harness options. Pass --recipe terminalbench2_verified_docker_ac to use the AgentCompass prebuilt image. It includes download dependencies such as Node.js, npm, curl, and wget for Codex, Claude Code, and similar harnesses.

Run with the official image
Use AgentCompass images

Omit --recipe to use the official task image. Because it does not include the Node bootstrap dependencies, provide the matching installation command explicitly.

# Codex
agentcompass run \
  terminal_bench_2_verified \
  codex \
  "$MODEL_NAME" \
  --env docker \
  --task-concurrency 16 \
  --harness-params '{
    "install_command": "apt-get update && apt-get install -y curl ca-certificates && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && apt-get install -y nodejs && npm install -g @openai/codex"
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-responses

# Claude Code
agentcompass run \
  terminal_bench_2_verified \
  claude_code \
  "$MODEL_NAME" \
  --env docker \
  --task-concurrency 16 \
  --harness-params '{
    "install_command": "apt-get update && apt-get install -y curl ca-certificates && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && apt-get install -y nodejs && npm install -g @anthropic-ai/claude-code"
  }' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol anthropic

Pass --recipe terminalbench2_verified_docker_ac to select the AgentCompass prebuilt image without explicitly providing the corresponding installation command.

# Codex
agentcompass run \
  terminal_bench_2_verified \
  codex \
  "$MODEL_NAME" \
  --env docker \
  --recipe terminalbench2_verified_docker_ac \
  --task-concurrency 16 \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-responses

# Claude Code
agentcompass run \
  terminal_bench_2_verified \
  claude_code \
  "$MODEL_NAME" \
  --env docker \
  --recipe terminalbench2_verified_docker_ac \
  --task-concurrency 16 \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol anthropic

Evaluation Results

For shared result conventions, see Run Directory, Aggregate Scores, and Task Files and Shared Fields.

Scoring Metrics

Terminal-Bench 2 Verified’s primary metric is binary correct, recording the verdict from the verification flow above. A verifier reward of exactly 1 maps to true; other valid reward values map to false. There is no partial credit. With the default configuration, each task has one attempt and the overall score is the pass rate over tasks with valid scores, ranging from 0 to 1; higher is better. See Metrics and Aggregation for repeated attempts, category aggregation, and scoring failure rules.

Task Results and Scoring Evidence

eval_raw_data under meta.benchmark preserves the Harbor verifier’s scoring evidence:

Field	Contents
`verify_result`	Raw verifier result, when available. Its `rewards` object contains the `reward` used to determine whether the task passed.
`testcase_output`	Contents of the verifier’s `test-stdout.txt`, including test output.
`verifier`	Start and finish times for verification.
`error`	Diagnostics when verification encounters a problem.

Using AgentCompass

Modules

Other Features

How it works

Parameters

Run examples

Recommended harness

Other optional harnesses

Evaluation Results

Scoring Metrics

Task Results and Scoring Evidence

​How it works

​Parameters

​Run examples

​Recommended harness

​Other optional harnesses

​Evaluation Results

​Scoring Metrics

​Task Results and Scoring Evidence

How it works

Parameters

Run examples

Recommended harness

Other optional harnesses

Evaluation Results

Scoring Metrics

Task Results and Scoring Evidence