terminal-bench-1: Instructions file for Claude Code

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

Terminal-Bench (T-Bench) is a benchmark for testing AI agents in real terminal environments. It consists of a dataset of tasks and an execution harness that connects language models to a sandboxed terminal environment.

Architecture

Core Components

• terminal_bench/: Main Python package

  • cli/tb/: Command-line interface implementation
  • agents/: Agent implementations for various LLMs
  • harness/: Execution harness for running tasks
  • dataset/: Dataset handling and management
  • registry/: Task registry management
  • llms/: Language model integrations
  • terminal/: Terminal environment handling

• tasks/: Individual task implementations

  • Each task has its own directory with instruction, docker environment, test script, and reference solution
  • Tasks include Python, shell scripts, C, Rust, and other languages

• adapters/: Adapters for other benchmarks (SWE-bench, USACO, etc.)

Common Development Commands

Running Tasks

# Run the harness with specific agent and model
tb run --agent terminus --model anthropic/claude-3-7-latest

# Run specific tasks
tb run --task-id "task-name" --agent terminus --model model-name

# Run with dataset version
tb run --dataset terminal-bench-core==0.1.1

Testing

# Run all tests
uv run pytest tests/ -v

# Run specific test file
uv run pytest tests/unit/test_file.py -v

# Run with coverage
uv run pytest tests/ --cov

Linting and Code Quality

# Run Ruff linter
uvx ruff check .

# Fix auto-fixable issues
uvx ruff check . --fix

Task Management

# List available tasks
tb tasks list

# Create a new task (follow the documentation)
tb tasks create

Development Guidelines

Python Style

• Python 3.13+ is used
• Use type hints for all functions
• Private methods have _ prefix
• Use modular design, avoid deep nesting
• Use modern type annotations: list, dict, | instead of List, Dict, Union