Terminal-Bench-2.0#

Overview#

Terminal-Bench v2 is a command-line benchmark suite that evaluates AI agents on 89 real-world, multi-step terminal tasks. Tasks range from compiling and debugging to system administration, running within isolated containers with rigorous validation.

Task Description#

Task Type: Command-Line Agent Evaluation
Input: Terminal task specification
Output: Task completion via agent actions
Domains: System administration, compilation, debugging, file operations

Key Features#

89 real-world terminal tasks
Multi-step task completion requirements
Isolated container execution environment
Binary scoring (0/1) with auto-validation
Multiple agent types supported (terminus-2, claude-code, codex, etc.)

Evaluation Notes#

Requires Python>=3.12 and pip install harbor==0.1.28
Environment options: docker, daytona, e2b, modal
Configurable agent types and timeout settings
Maximum turns configurable (default: 200)
Usage Example

Properties#

Property	Value
Benchmark Name	`terminal_bench_v2`
Dataset ID	terminal-bench-2.git
Paper	N/A
Tags	`Coding`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics#

Statistics not available.

Sample Example#

Sample example not available.

Prompt Template#

Prompt Template:

{question}

Extra Parameters#

Parameter	Type	Default	Description
`environment_type`	`str`	`docker`	Environment type for running the benchmark. Choices: [‘docker’, ‘daytona’, ‘e2b’, ‘modal’]
`agent_name`	`str`	`terminus-2`	Agent type to be used in Harbor. Choices: [‘oracle’, ‘terminus-2’, ‘claude-code’, ‘codex’, ‘qwen-coder’, ‘openhands’, ‘opencode’, ‘mini-swe-agent’]
`timeout_multiplier`	`float`	`1.0`	Timeout multiplier. If timeout errors occur, consider increasing this value.
`max_turns`	`int`	`200`	Maximum number of turns for the agent to complete the task.

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets terminal_bench_v2 \
    --limit 10  # Remove this line for formal evaluation

Using Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['terminal_bench_v2'],
    dataset_args={
        'terminal_bench_v2': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)