feat(classic): add external benchmark adapters for GAIA, SWE-bench, and AgentBench

Integrate standard AI agent benchmarks into the direct_benchmark infrastructure
using a plugin-based adapter pattern:

- Add BenchmarkAdapter base class with setup(), load_challenges(), and evaluate()
- Implement GAIAAdapter for the GAIA benchmark (requires HF token)
- Implement SWEBenchAdapter for SWE-bench (requires Docker)
- Implement AgentBenchAdapter for AgentBench multi-environment benchmark
- Extend HarnessConfig with benchmark options (--benchmark, --benchmark-split, etc.)
- Modify ParallelExecutor to use adapter's evaluate() for external benchmarks
- Fix runner to record finish step (was being skipped, breaking answer extraction)
- Add optional benchmarks dependency group with datasets and huggingface-hub
- Increase default benchmark timeout to 900s

Usage:
  poetry run direct-benchmark run \
    --benchmark agent-bench \
    --benchmark-subset dbbench \
    --strategies one_shot \
    --models claude

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

classic/direct_benchmark/direct_benchmark/__main__.py

@@ -201,6 +201,35 @@ def cli():
     is_flag=True,
     help="Enable debug output.",
 )
+@click.option(
+    "--benchmark",
+    "-b",
+    "external_benchmark",
+    default=None,
+    help="Run external benchmark (gaia, swe-bench, agent-bench).",
+)
+@click.option(
+    "--benchmark-split",
+    default="validation",
+    help="Benchmark split (train, validation, test). Default: validation.",
+)
+@click.option(
+    "--benchmark-subset",
+    default=None,
+    help="Benchmark subset (difficulty level '1', repo name, environment).",
+)
+@click.option(
+    "--benchmark-limit",
+    type=int,
+    default=None,
+    help="Maximum number of benchmark challenges to load.",
+)
+@click.option(
+    "--benchmark-cache-dir",
+    type=click.Path(path_type=Path),
+    default=None,
+    help="Cache directory for benchmark datasets.",
+)
 def run(
     strategies: str,
     models: str,
@@ -231,6 +260,11 @@ def run(
     reset_models: tuple[str, ...],
     reset_challenges: tuple[str, ...],
     debug: bool,
+    external_benchmark: Optional[str],
+    benchmark_split: str,
+    benchmark_subset: Optional[str],
+    benchmark_limit: Optional[int],
+    benchmark_cache_dir: Optional[Path],
 ):
     """Run benchmarks with specified configurations."""
     # Handle timeout/cutoff options
@@ -254,15 +288,18 @@ def run(
         console.print(f"Available: {list(MODEL_PRESETS.keys())}")
         sys.exit(1)
 
-    # Find challenges directory
-    if challenges_dir is None:
+    # Find challenges directory (not required for external benchmarks)
+    if challenges_dir is None and not external_benchmark:
         challenges_dir = find_challenges_dir()
         if challenges_dir is None:
             console.print(
                 "[red]Could not find challenges directory. "
-                "Please specify with --challenges-dir[/red]"
+                "Please specify with --challenges-dir or use --benchmark[/red]"
             )
             sys.exit(1)
+    elif challenges_dir is None:
+        # External benchmark - use a placeholder path
+        challenges_dir = Path(".")
 
     # Set up paths
     if workspace is None:
@@ -308,6 +345,11 @@ def run(
         reset_strategies=list(reset_strategies) if reset_strategies else None,
         reset_models=list(reset_models) if reset_models else None,
         reset_challenges=list(reset_challenges) if reset_challenges else None,
+        external_benchmark=external_benchmark,
+        benchmark_split=benchmark_split,
+        benchmark_subset=benchmark_subset,
+        benchmark_limit=benchmark_limit,
+        benchmark_cache_dir=benchmark_cache_dir,
     )
 
     # Determine UI mode
@@ -331,7 +373,15 @@ def run(
         console.print(f"Strategies: {strategy_list}")
         console.print(f"Models: {model_list}")
         console.print(f"Parallel: {parallel}")
-        console.print(f"Challenges: {challenges_dir}")
+        if external_benchmark:
+            console.print(f"Benchmark: [cyan]{external_benchmark}[/cyan]")
+            console.print(f"  Split: {benchmark_split}")
+            if benchmark_subset:
+                console.print(f"  Subset: {benchmark_subset}")
+            if benchmark_limit:
+                console.print(f"  Limit: {benchmark_limit}")
+        else:
+            console.print(f"Challenges: {challenges_dir}")
         if categories:
             console.print(f"Categories: {categories}")
         if skip_categories:
@@ -441,6 +491,56 @@ def list_strategies():
         console.print(f"  - {s}")
 
 
+@cli.command()
+def list_benchmarks():
+    """List available external benchmarks."""
+    from .adapters import list_adapters
+
+    console.print("\n[bold]Available External Benchmarks[/bold]\n")
+
+    benchmarks = list_adapters()
+    if not benchmarks:
+        console.print("[dim]No benchmarks registered.[/dim]")
+        return
+
+    benchmark_info = {
+        "gaia": {
+            "name": "GAIA",
+            "description": "General AI Assistant Benchmark - reasoning tasks",
+            "splits": "validation, test",
+            "subsets": "1 (easy), 2 (medium), 3 (hard)",
+            "requires": "HF token (gated dataset)",
+        },
+        "swe-bench": {
+            "name": "SWE-bench",
+            "description": "Software Engineering Benchmark - GitHub issues",
+            "splits": "dev, test",
+            "subsets": "full, lite, verified, or repo name",
+            "requires": "Docker, swebench package",
+        },
+        "agent-bench": {
+            "name": "AgentBench",
+            "description": "Multi-environment agent benchmark",
+            "splits": "dev, test",
+            "subsets": "os, db, kg, card_game, ltp, web_shopping, ...",
+            "requires": "Varies by environment (Docker for os)",
+        },
+    }
+
+    for name in sorted(benchmarks):
+        info = benchmark_info.get(name, {})
+        console.print(f"[cyan]{name}[/cyan]:")
+        if info.get("description"):
+            console.print(f"  {info['description']}")
+        if info.get("splits"):
+            console.print(f"  Splits: {info['splits']}")
+        if info.get("subsets"):
+            console.print(f"  Subsets: {info['subsets']}")
+        if info.get("requires"):
+            console.print(f"  Requires: {info['requires']}")
+        console.print()
+
+
 @cli.group()
 def state():
     """Manage saved benchmark state (resume/reset)."""

classic/direct_benchmark/direct_benchmark/adapters/__init__.py

@@ -0,0 +1,57 @@
+"""Benchmark adapter registry and factory."""
+
+from typing import Optional
+
+# Registry of benchmark adapters
+_ADAPTER_REGISTRY: dict[str, type["BenchmarkAdapter"]] = {}  # noqa: F821
+
+
+def register_adapter(name: str):
+    """Decorator to register a benchmark adapter.
+
+    Usage:
+        @register_adapter("gaia")
+        class GAIAAdapter(BenchmarkAdapter):
+            ...
+    """
+
+    def decorator(cls: type["BenchmarkAdapter"]) -> type["BenchmarkAdapter"]:
+        _ADAPTER_REGISTRY[name.lower()] = cls
+        return cls
+
+    return decorator
+
+
+def get_adapter(name: str) -> Optional[type["BenchmarkAdapter"]]:
+    """Get an adapter class by name.
+
+    Args:
+        name: The benchmark name (e.g., "gaia", "swe-bench", "agent-bench").
+
+    Returns:
+        The adapter class, or None if not found.
+    """
+    return _ADAPTER_REGISTRY.get(name.lower())
+
+
+def list_adapters() -> list[str]:
+    """List all registered adapter names."""
+    return list(_ADAPTER_REGISTRY.keys())
+
+
+# Import adapters to trigger registration
+# These imports are at the bottom to avoid circular imports
+from .agent_bench import AgentBenchAdapter  # noqa: E402, F401
+from .base import BenchmarkAdapter  # noqa: E402, F401
+from .gaia import GAIAAdapter  # noqa: E402, F401
+from .swe_bench import SWEBenchAdapter  # noqa: E402, F401
+
+__all__ = [
+    "BenchmarkAdapter",
+    "GAIAAdapter",
+    "SWEBenchAdapter",
+    "AgentBenchAdapter",
+    "register_adapter",
+    "get_adapter",
+    "list_adapters",
+]

classic/direct_benchmark/direct_benchmark/adapters/agent_bench.py

@@ -0,0 +1,684 @@
+"""AgentBench adapter.
+
+AgentBench evaluates LLMs as agents across diverse real-world environments:
+Operating System, Database, Knowledge Graph, Card Game, Lateral Thinking,
+Web Shopping, Web Browsing, and ALFWorld.
+
+GitHub: https://github.com/THUDM/AgentBench
+Paper: https://arxiv.org/abs/2308.03688
+
+Requires:
+    - Docker (for OS environment)
+    - Database drivers (for DB environment)
+    - Environment-specific dependencies
+"""
+
+import json
+import subprocess
+from pathlib import Path
+from typing import Any, Iterator, Optional
+
+from ..models import Challenge, ChallengeResult
+from . import register_adapter
+from .base import BenchmarkAdapter
+
+
+@register_adapter("agent-bench")
+class AgentBenchAdapter(BenchmarkAdapter):
+    """Adapter for the AgentBench benchmark.
+
+    AgentBench includes 8 distinct environments:
+        - os: Operating system tasks in Docker sandbox
+        - db: Database query tasks (SQLite/PostgreSQL)
+        - kg: Knowledge graph reasoning
+        - card_game: Card game strategy (24-point, etc.)
+        - ltp: Lateral thinking puzzles
+        - web_shopping: WebShop navigation
+        - web_browsing: Real web navigation
+        - alfworld: Embodied agent tasks
+
+    Start with: os, db, kg, card_game, ltp (minimal infrastructure)
+
+    Usage:
+        adapter = AgentBenchAdapter(subset="os")
+        for challenge in adapter.load_challenges():
+            # Run challenge...
+    """
+
+    name = "agent-bench"
+    description = "AgentBench - Multi-Environment Agent Benchmark"
+
+    GITHUB_REPO = "THUDM/AgentBench"
+
+    # Environment definitions with requirements
+    # Directory names match the actual AgentBench repo structure
+    ENVIRONMENTS: dict[str, dict[str, Any]] = {
+        "os_interaction": {
+            "name": "Operating System",
+            "difficulty": "medium",
+            "requires": ["docker"],
+            "timeout_multiplier": 1.5,
+        },
+        "dbbench": {
+            "name": "Database",
+            "difficulty": "easy",
+            "requires": [],
+            "timeout_multiplier": 1.0,
+        },
+        "knowledgegraph": {
+            "name": "Knowledge Graph",
+            "difficulty": "medium",
+            "requires": [],
+            "timeout_multiplier": 1.0,
+        },
+        "lateralthinkingpuzzle": {
+            "name": "Lateral Thinking Puzzle",
+            "difficulty": "hard",
+            "requires": [],
+            "timeout_multiplier": 1.0,
+        },
+        "mind2web": {
+            "name": "Mind2Web (Web Browsing)",
+            "difficulty": "hard",
+            "requires": ["playwright"],
+            "timeout_multiplier": 3.0,
+        },
+        "alfworld": {
+            "name": "ALFWorld",
+            "difficulty": "hard",
+            "requires": ["alfworld_server"],
+            "timeout_multiplier": 2.0,
+        },
+        "avalon": {
+            "name": "Avalon (Game)",
+            "difficulty": "medium",
+            "requires": [],
+            "timeout_multiplier": 1.5,
+        },
+    }
+
+    def __init__(
+        self,
+        cache_dir: Optional[Path] = None,
+        split: str = "test",
+        subset: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        """Initialize the AgentBench adapter.
+
+        Args:
+            cache_dir: Directory to cache the dataset.
+            split: Dataset split - "dev" or "test".
+            subset: Environment to use (os, db, kg, card_game, ltp, etc.).
+            limit: Maximum number of challenges to load.
+        """
+        super().__init__(cache_dir, split, subset, limit)
+        self._tasks: dict[str, list[dict[str, Any]]] = {}
+        self._repo_path: Optional[Path] = None
+
+    def setup(self) -> None:
+        """Clone/update AgentBench repository and load tasks."""
+        self._repo_path = self.cache_dir / "agent_bench" / "repo"
+
+        # Clone or update repository
+        if self._repo_path.exists():
+            self._update_repo()
+        else:
+            self._clone_repo()
+
+        # Load tasks from repository
+        self._load_tasks()
+
+        # Check environment requirements
+        if self.subset:
+            self._check_requirements(self.subset)
+
+        self._is_setup = True
+
+    def _clone_repo(self) -> None:
+        """Clone the AgentBench repository."""
+        assert self._repo_path is not None  # Set in setup()
+        self._repo_path.parent.mkdir(parents=True, exist_ok=True)
+
+        result = subprocess.run(
+            [
+                "git",
+                "clone",
+                "--depth",
+                "1",
+                f"https://github.com/{self.GITHUB_REPO}.git",
+                str(self._repo_path),
+            ],
+            capture_output=True,
+            timeout=300,
+        )
+
+        if result.returncode != 0:
+            raise RuntimeError(
+                f"Failed to clone AgentBench repository: {result.stderr.decode()}"
+            )
+
+    def _update_repo(self) -> None:
+        """Update the AgentBench repository."""
+        assert self._repo_path is not None  # Set in setup()
+        result = subprocess.run(
+            ["git", "pull", "--rebase"],
+            cwd=str(self._repo_path),
+            capture_output=True,
+            timeout=60,
+        )
+
+        if result.returncode != 0:
+            # Pull failed, try fresh clone
+            import shutil
+
+            shutil.rmtree(self._repo_path)
+            self._clone_repo()
+
+    def _load_tasks(self) -> None:
+        """Load tasks from the repository data files."""
+        if self._repo_path is None:
+            return
+
+        data_dir = self._repo_path / "data"
+
+        if not data_dir.exists():
+            # Try alternative locations
+            for alt_path in ["thudm_data", "tasks", "benchmarks"]:
+                alt_dir = self._repo_path / alt_path
+                if alt_dir.exists():
+                    data_dir = alt_dir
+                    break
+
+        # Load tasks for each environment
+        for env_name in self.ENVIRONMENTS:
+            env_dir = data_dir / env_name
+            if not env_dir.exists():
+                continue
+
+            self._tasks[env_name] = []
+
+            # Try JSON file first
+            tasks_file = env_dir / f"{self.split}.json"
+            if not tasks_file.exists():
+                tasks_file = env_dir / "tasks.json"
+
+            if tasks_file.exists():
+                with open(tasks_file) as f:
+                    self._tasks[env_name] = json.load(f)
+                continue
+
+            # Try JSONL file (AgentBench format)
+            jsonl_file = env_dir / f"{self.split}.jsonl"
+            if not jsonl_file.exists():
+                jsonl_file = env_dir / "standard.jsonl"
+
+            if jsonl_file.exists():
+                with open(jsonl_file) as f:
+                    for line in f:
+                        line = line.strip()
+                        if line:
+                            self._tasks[env_name].append(json.loads(line))
+                continue
+
+            # Try to load from individual task files
+            for task_file in env_dir.glob("*.json"):
+                if task_file.stem not in ("config", "metadata"):
+                    with open(task_file) as f:
+                        data = json.load(f)
+                        if isinstance(data, list):
+                            self._tasks[env_name].extend(data)
+                        else:
+                            self._tasks[env_name].append(data)
+
+    def _check_requirements(self, environment: str) -> None:
+        """Check if required dependencies are available."""
+        env_config = self.ENVIRONMENTS.get(environment, {})
+        requires = env_config.get("requires", [])
+
+        for req in requires:
+            if req == "docker":
+                self._check_docker()
+            elif req == "playwright":
+                self._check_playwright()
+            # Other requirements can be checked as needed
+
+    def _check_docker(self) -> None:
+        """Verify Docker is available."""
+        try:
+            result = subprocess.run(
+                ["docker", "info"],
+                capture_output=True,
+                timeout=10,
+            )
+            if result.returncode != 0:
+                raise RuntimeError("Docker is not running")
+        except FileNotFoundError:
+            raise RuntimeError(
+                "Docker is required for the OS environment. " "Install Docker first."
+            )
+
+    def _check_playwright(self) -> None:
+        """Verify Playwright is available."""
+        try:
+            import playwright  # noqa: F401
+        except ImportError:
+            raise RuntimeError(
+                "Playwright is required for web_browsing environment. "
+                "Install with: pip install playwright && playwright install"
+            )
+
+    def load_challenges(self) -> Iterator[Challenge]:
+        """Load challenges from the AgentBench dataset.
+
+        Yields:
+            Challenge objects for each AgentBench task.
+        """
+        self.ensure_setup()
+
+        environments = [self.subset] if self.subset else list(self.ENVIRONMENTS.keys())
+
+        count = 0
+        for env_name in environments:
+            if env_name not in self._tasks:
+                continue
+
+            for idx, task in enumerate(self._tasks[env_name]):
+                # Apply limit
+                if self.limit and count >= self.limit:
+                    return
+
+                challenge = self._convert_to_challenge(env_name, idx, task)
+                yield challenge
+                count += 1
+
+    def _convert_to_challenge(
+        self, environment: str, idx: int, task: dict[str, Any]
+    ) -> Challenge:
+        """Convert an AgentBench task to a Challenge."""
+        env_config = self.ENVIRONMENTS[environment]
+
+        # Extract task details (format varies by environment)
+        task_id = task.get("id", task.get("task_id", f"{environment}_{idx}"))
+        description = task.get(
+            "description",
+            task.get("task", task.get("instruction", task.get("question", ""))),
+        )
+
+        # Build task string based on environment
+        task_str = self._format_task(environment, description, task)
+
+        # Get difficulty
+        difficulty = task.get("difficulty", env_config["difficulty"])
+
+        # Calculate timeout
+        base_timeout = 300
+        multiplier = env_config["timeout_multiplier"]
+        cutoff = int(base_timeout * multiplier)
+
+        # Ground truth - extract expected answer based on environment format
+        expected_answer = self._extract_expected_answer(environment, task)
+        ground_truth: dict[str, Any] = {
+            "eval": {"type": f"agent_bench_{environment}"},
+            "environment": environment,
+            "expected": expected_answer,
+            "task_data": task,
+        }
+
+        # Create artifacts directory
+        artifacts_dir = self.cache_dir / "agent_bench" / "artifacts" / task_id
+        artifacts_dir.mkdir(parents=True, exist_ok=True)
+
+        return Challenge(
+            name=f"AgentBench_{task_id}",
+            task=task_str,
+            category=["agent-bench", f"agent-bench_{environment}"],
+            difficulty=difficulty,
+            cutoff=cutoff,
+            ground_truth=ground_truth,
+            artifacts_dir=artifacts_dir,
+            source_path=artifacts_dir / "task.json",
+        )
+
+    def _extract_expected_answer(self, environment: str, task: dict[str, Any]) -> str:
+        """Extract the expected answer from task based on environment format."""
+        # dbbench format: answer in "label" field (list)
+        if environment == "dbbench":
+            label = task.get("label", [])
+            if isinstance(label, list) and label:
+                return str(label[0])
+            return str(label) if label else ""
+
+        # knowledgegraph format: answer in "answer" array with entity_name
+        if environment == "knowledgegraph":
+            answers = task.get("answer", [])
+            if isinstance(answers, list) and answers:
+                first = answers[0]
+                if isinstance(first, dict):
+                    return first.get("entity_name", str(first))
+                return str(first)
+            return ""
+
+        # lateralthinkingpuzzle format
+        if environment == "lateralthinkingpuzzle":
+            return task.get("answer", task.get("solution", ""))
+
+        # Default: try common answer fields
+        for key in ["answer", "expected", "gold", "label", "solution"]:
+            val = task.get(key)
+            if val:
+                if isinstance(val, list):
+                    return str(val[0]) if val else ""
+                return str(val)
+        return ""
+
+    def _format_task(
+        self, environment: str, description: str, task: dict[str, Any]
+    ) -> str:
+        """Format the task description based on environment."""
+        if environment == "os":
+            return (
+                f"Operating System Task\n"
+                f"=====================\n\n"
+                f"{description}\n\n"
+                f"You have access to a Linux command line. Execute commands "
+                f"to complete the task. Save your final answer to 'answer.txt'."
+            )
+
+        elif environment in ("db", "dbbench"):
+            # Extract table information from the task
+            table_info = task.get("table", {})
+            table_name = table_info.get("table_name", "data_table")
+            columns_info = table_info.get("table_info", {}).get("columns", [])
+            rows = table_info.get("table_info", {}).get("rows", [])
+
+            # Format columns
+            col_names = [col.get("name", "") for col in columns_info]
+
+            # Build table display
+            table_str_parts = [
+                f"Table: {table_name}",
+                f"Columns: {', '.join(col_names)}",
+            ]
+            table_str_parts.append("\nData (first 20 rows):")
+            for i, row in enumerate(rows[:20]):
+                row_str = " | ".join(str(cell) for cell in row)
+                table_str_parts.append(f"  {i+1}. {row_str}")
+            if len(rows) > 20:
+                table_str_parts.append(f"  ... ({len(rows) - 20} more rows)")
+
+            table_str = "\n".join(table_str_parts)
+
+            return (
+                f"Database Query Task\n"
+                f"==================\n\n"
+                f"Question: {description}\n\n"
+                f"{table_str}\n\n"
+                f"Analyze the table data above and answer the question. "
+                f"Use the 'finish' command with your answer, or save your answer "
+                f"to 'answer.txt'. Provide only the answer value, not an explanation."
+            )
+
+        elif environment == "kg":
+            kg_info = task.get("kg_info", "")
+            return (
+                f"Knowledge Graph Task\n"
+                f"====================\n\n"
+                f"{description}\n\n"
+                f"Knowledge Graph Information:\n{kg_info}\n\n"
+                f"Reason over the knowledge graph to answer. "
+                f"Save your answer to 'answer.txt'."
+            )
+
+        elif environment == "card_game":
+            numbers = task.get("numbers", [])
+            return (
+                f"Card Game Task (24-point)\n"
+                f"========================\n\n"
+                f"Numbers: {numbers}\n\n"
+                f"Use +, -, *, / and parentheses to make exactly 24. "
+                f"Each number must be used exactly once.\n\n"
+                f"Save your expression to 'answer.txt'."
+            )
+
+        elif environment == "ltp":
+            return (
+                f"Lateral Thinking Puzzle\n"
+                f"======================\n\n"
+                f"{description}\n\n"
+                f"Ask yes/no questions to figure out the answer. "
+                f"Save your final solution to 'answer.txt'."
+            )
+
+        elif environment in ("web_shopping", "web_browsing"):
+            return (
+                f"Web Task ({environment.replace('_', ' ').title()})\n"
+                f"{'=' * 40}\n\n"
+                f"{description}\n\n"
+                f"Navigate the web to complete the task. "
+                f"Save your final answer to 'answer.txt'."
+            )
+
+        elif environment == "alfworld":
+            return (
+                f"ALFWorld Task\n"
+                f"=============\n\n"
+                f"{description}\n\n"
+                f"Navigate and interact with the environment to complete the task. "
+                f"Use available actions to achieve the goal."
+            )
+
+        else:
+            return description
+
+    def evaluate(
+        self,
+        result: ChallengeResult,
+        challenge: Challenge,
+        workspace_dir: Path,
+    ) -> ChallengeResult:
+        """Evaluate using environment-specific logic."""
+        ground = challenge.ground_truth
+        environment = ground["environment"]
+
+        # Extract answer from agent output
+        agent_answer = self._extract_answer(result, environment)
+
+        if not agent_answer:
+            result.success = False
+            result.score = 0.0
+            result.error_message = "No answer found in agent output"
+            return result
+
+        # Environment-specific evaluation
+        expected = ground.get("expected", "")
+
+        if environment == "card_game":
+            eval_result = self._evaluate_card_game(agent_answer, ground["task_data"])
+        elif environment in ("db", "dbbench"):
+            eval_result = self._evaluate_db(agent_answer, expected, ground["task_data"])
+        elif environment in (
+            "os",
+            "os_interaction",
+            "kg",
+            "knowledgegraph",
+            "ltp",
+            "lateralthinkingpuzzle",
+        ):
+            eval_result = self._evaluate_string_match(agent_answer, expected)
+        else:
+            # Default string matching
+            eval_result = self._evaluate_string_match(agent_answer, expected)
+
+        result.success = eval_result["success"]
+        result.score = eval_result["score"]
+        if eval_result.get("error"):
+            result.error_message = eval_result["error"]
+
+        return result
+
+    def _extract_answer(self, result: ChallengeResult, environment: str) -> str:
+        """Extract answer from agent output."""
+        # Look for answer.txt
+        for filename, content in result.output_files.items():
+            if "answer" in filename.lower():
+                return content.strip()
+
+        # Environment-specific extraction
+        if environment in ("db", "dbbench"):
+            for filename, content in result.output_files.items():
+                if filename.endswith(".sql"):
+                    return content.strip()
+
+        # Check if agent used finish command with an answer
+        if result.steps:
+            last_step = result.steps[-1]
+            if last_step.tool_name == "finish":
+                reason = last_step.tool_args.get("reason", "").strip()
+                # Try to extract the actual answer from the finish reason
+                # Often the answer is embedded in the reason
+                if reason:
+                    return reason
+
+        # Look for potential answer in any text file output
+        for filename, content in result.output_files.items():
+            if filename.endswith(".txt") and content.strip():
+                return content.strip()
+
+        return ""
+
+    def _evaluate_card_game(
+        self, answer: str, task_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Evaluate 24-point card game answer."""
+        # Store numbers for potential future use in full validation
+        _numbers = task_data.get("numbers", [])  # noqa: F841
+
+        try:
+            # Check that the expression evaluates to 24
+            # and uses all numbers exactly once
+            expr = answer.strip()
+
+            # Safety check - only allow math operations
+            allowed_chars = set("0123456789+-*/() .")
+            if not all(c in allowed_chars for c in expr):
+                return {
+                    "success": False,
+                    "score": 0.0,
+                    "error": "Invalid characters in expression",
+                }
+
+            # Evaluate the expression
+            result = eval(expr)
+
+            if abs(result - 24) < 0.0001:
+                # Check that all numbers are used exactly once
+                # (simplified check - full implementation would parse the expression)
+                return {"success": True, "score": 1.0, "error": None}
+            else:
+                return {
+                    "success": False,
+                    "score": 0.0,
+                    "error": f"Expression evaluates to {result}, not 24",
+                }
+
+        except Exception as e:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": f"Failed to evaluate expression: {str(e)}",
+            }
+
+    def _evaluate_db(
+        self, query: str, expected: str, task_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Evaluate SQL query answer."""
+        # For now, use string matching on the result
+        # Full implementation would execute query and compare results
+        return self._evaluate_string_match(query, expected)
+
+    def _evaluate_string_match(self, actual: str, expected: str) -> dict[str, Any]:
+        """Strict normalized string matching."""
+        actual_norm = actual.lower().strip()
+        expected_norm = expected.lower().strip()
+
+        # If no expected answer, fail (can't evaluate)
+        if not expected_norm:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": "No expected answer to compare against",
+            }
+
+        # If no actual answer, fail
+        if not actual_norm:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": f"No answer provided, expected '{expected}'",
+            }
+
+        # Exact match (after normalization)
+        if actual_norm == expected_norm:
+            return {"success": True, "score": 1.0, "error": None}
+
+        # Check if expected is contained in actual (for verbose answers)
+        if expected_norm in actual_norm:
+            return {"success": True, "score": 0.9, "error": None}
+
+        return {
+            "success": False,
+            "score": 0.0,
+            "error": f"Expected '{expected}', got '{actual}'",
+        }
+
+    def provision_environment(self, challenge: Challenge) -> dict[str, Any]:
+        """Set up environment-specific resources."""
+        ground = challenge.ground_truth
+        environment = ground["environment"]
+
+        env_config: dict[str, Any] = {
+            "environment": environment,
+        }
+
+        if environment == "os":
+            # Would spin up Docker container here
+            env_config["docker_image"] = "ubuntu:22.04"
+
+        elif environment == "db":
+            # Set up SQLite database
+            task_data = ground["task_data"]
+            db_setup = task_data.get("db_setup", "")
+            env_config["db_type"] = "sqlite"
+            env_config["db_setup"] = db_setup
+
+        return env_config
+
+    def get_challenge_count(self) -> Optional[int]:
+        """Get the number of challenges."""
+        self.ensure_setup()
+
+        if self.subset:
+            tasks = self._tasks.get(self.subset, [])
+            count = len(tasks)
+        else:
+            count = sum(len(tasks) for tasks in self._tasks.values())
+
+        if self.limit:
+            count = min(count, self.limit)
+
+        return count
+
+    def get_metadata(self) -> dict[str, Any]:
+        """Get AgentBench metadata."""
+        metadata = super().get_metadata()
+        metadata.update(
+            {
+                "repository": f"https://github.com/{self.GITHUB_REPO}",
+                "environments": list(self.ENVIRONMENTS.keys()),
+                "easy_environments": ["db", "card_game", "ltp"],
+                "medium_environments": ["os", "kg", "web_shopping"],
+                "hard_environments": ["web_browsing", "alfworld"],
+            }
+        )
+        return metadata

classic/direct_benchmark/direct_benchmark/adapters/base.py

@@ -0,0 +1,135 @@
+"""Base class for benchmark adapters."""
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Any, Iterator, Optional
+
+from ..models import Challenge, ChallengeResult
+
+
+class BenchmarkAdapter(ABC):
+    """Abstract base class for external benchmark adapters.
+
+    Adapters translate external benchmark formats into the Challenge model
+    used by the direct_benchmark harness.
+
+    Subclasses must implement:
+        - setup(): One-time initialization (download datasets, etc.)
+        - load_challenges(): Yield Challenge objects from the benchmark
+        - evaluate(): Custom evaluation logic for the benchmark
+
+    Optionally override:
+        - provision_environment(): Set up runtime environment for challenges
+        - cleanup(): Clean up resources after benchmark run
+    """
+
+    # Override in subclasses
+    name: str = "base"
+    description: str = "Base benchmark adapter"
+
+    def __init__(
+        self,
+        cache_dir: Optional[Path] = None,
+        split: str = "validation",
+        subset: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        """Initialize the adapter.
+
+        Args:
+            cache_dir: Directory to cache downloaded datasets.
+            split: Dataset split to use (train/validation/test).
+            subset: Optional subset filter (e.g., difficulty level, repo name).
+            limit: Maximum number of challenges to load.
+        """
+        self.cache_dir = cache_dir or Path.home() / ".cache" / "autogpt_benchmarks"
+        self.split = split
+        self.subset = subset
+        self.limit = limit
+        self._is_setup = False
+
+    @abstractmethod
+    def setup(self) -> None:
+        """Perform one-time setup (download datasets, authenticate, etc.).
+
+        This method is called before load_challenges() if not already setup.
+        Should be idempotent - safe to call multiple times.
+        """
+        pass
+
+    @abstractmethod
+    def load_challenges(self) -> Iterator[Challenge]:
+        """Load and yield challenges from the external benchmark.
+
+        Yields:
+            Challenge objects translated from the external format.
+        """
+        pass
+
+    @abstractmethod
+    def evaluate(
+        self,
+        result: ChallengeResult,
+        challenge: Challenge,
+        workspace_dir: Path,
+    ) -> ChallengeResult:
+        """Evaluate a challenge result using benchmark-specific logic.
+
+        Args:
+            result: The result from running the challenge.
+            challenge: The challenge that was run.
+            workspace_dir: Directory containing the agent's output.
+
+        Returns:
+            Updated ChallengeResult with success/score populated.
+        """
+        pass
+
+    def provision_environment(self, challenge: Challenge) -> dict[str, Any]:
+        """Set up runtime environment for a challenge.
+
+        Override this for benchmarks that need Docker containers,
+        database setup, etc.
+
+        Args:
+            challenge: The challenge to provision for.
+
+        Returns:
+            Environment configuration dict (passed to runner).
+        """
+        return {}
+
+    def cleanup(self) -> None:
+        """Clean up resources after benchmark run.
+
+        Override this to stop containers, close connections, etc.
+        """
+        pass
+
+    def ensure_setup(self) -> None:
+        """Ensure setup() has been called."""
+        if not self._is_setup:
+            self.setup()
+            self._is_setup = True
+
+    def get_challenge_count(self) -> Optional[int]:
+        """Get the total number of challenges without loading them.
+
+        Returns:
+            Number of challenges, or None if unknown without loading.
+        """
+        return None
+
+    def get_metadata(self) -> dict[str, Any]:
+        """Get metadata about this benchmark.
+
+        Returns:
+            Dict with benchmark metadata (name, description, splits, etc.).
+        """
+        return {
+            "name": self.name,
+            "description": self.description,
+            "split": self.split,
+            "subset": self.subset,
+            "limit": self.limit,
+        }

classic/direct_benchmark/direct_benchmark/adapters/gaia.py

@@ -0,0 +1,318 @@
+"""GAIA benchmark adapter.
+
+GAIA (General AI Assistant Benchmark) evaluates AI assistants on real-world tasks
+requiring reasoning, tool use, and web browsing.
+
+Dataset: https://huggingface.co/datasets/gaia-benchmark/GAIA
+Leaderboard: https://huggingface.co/spaces/gaia-benchmark/leaderboard
+
+Requires:
+    - Hugging Face account with access to the gated dataset
+    - HUGGING_FACE_HUB_TOKEN environment variable set
+    - datasets and huggingface-hub packages
+"""
+
+import os
+import re
+import string
+from pathlib import Path
+from typing import Any, Iterator, Optional
+
+from ..models import Challenge, ChallengeResult
+from . import register_adapter
+from .base import BenchmarkAdapter
+
+
+def _normalize_answer(answer: str) -> str:
+    """Normalize answer for comparison (GAIA-style normalization).
+
+    - Lowercase
+    - Remove articles (a, an, the)
+    - Remove punctuation
+    - Collapse whitespace
+    """
+    # Lowercase
+    answer = answer.lower()
+
+    # Remove articles
+    answer = re.sub(r"\b(a|an|the)\b", " ", answer)
+
+    # Remove punctuation
+    answer = answer.translate(str.maketrans("", "", string.punctuation))
+
+    # Collapse whitespace
+    answer = " ".join(answer.split())
+
+    return answer.strip()
+
+
+@register_adapter("gaia")
+class GAIAAdapter(BenchmarkAdapter):
+    """Adapter for the GAIA benchmark.
+
+    GAIA provides real-world tasks at three difficulty levels:
+        - Level 1: Simple tasks (single tool, straightforward reasoning)
+        - Level 2: Moderate tasks (multiple tools, multi-step reasoning)
+        - Level 3: Complex tasks (complex reasoning, tool chaining)
+
+    Usage:
+        adapter = GAIAAdapter(split="validation", subset="1")
+        for challenge in adapter.load_challenges():
+            # Run challenge...
+    """
+
+    name = "gaia"
+    description = "GAIA - General AI Assistant Benchmark"
+
+    HF_DATASET = "gaia-benchmark/GAIA"
+
+    def __init__(
+        self,
+        cache_dir: Optional[Path] = None,
+        split: str = "validation",
+        subset: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        """Initialize the GAIA adapter.
+
+        Args:
+            cache_dir: Directory to cache the dataset.
+            split: Dataset split - "validation" (has answers) or "test" (leaderboard).
+            subset: Difficulty level filter - "1", "2", or "3".
+            limit: Maximum number of challenges to load.
+        """
+        super().__init__(cache_dir, split, subset, limit)
+        self._dataset = None
+        self._file_cache: dict[str, Path] = {}
+
+    def setup(self) -> None:
+        """Download and cache the GAIA dataset from Hugging Face."""
+        try:
+            from datasets import load_dataset
+        except ImportError:
+            raise ImportError(
+                "GAIA adapter requires the 'datasets' package. "
+                "Install with: pip install datasets huggingface-hub"
+            )
+
+        # Check for HF token
+        token = os.environ.get("HUGGING_FACE_HUB_TOKEN") or os.environ.get("HF_TOKEN")
+        if not token:
+            raise ValueError(
+                "GAIA dataset requires authentication. "
+                "Set HUGGING_FACE_HUB_TOKEN or HF_TOKEN environment variable. "
+                "Get your token at https://huggingface.co/settings/tokens"
+            )
+
+        # Load dataset
+        self._dataset = load_dataset(
+            self.HF_DATASET,
+            split=self.split,
+            token=token,
+            cache_dir=str(self.cache_dir / "gaia"),
+        )
+
+        # Download any associated files
+        self._setup_file_cache()
+
+        self._is_setup = True
+
+    def _setup_file_cache(self) -> None:
+        """Cache file attachments from the dataset."""
+        if self._dataset is None:
+            return
+
+        file_dir = self.cache_dir / "gaia" / "files"
+        file_dir.mkdir(parents=True, exist_ok=True)
+
+        for item in self._dataset:
+            if item.get("file_name") and item.get("file_path"):
+                # The dataset includes file contents inline
+                file_name = item["file_name"]
+                self._file_cache[item["task_id"]] = file_dir / file_name
+
+    def load_challenges(self) -> Iterator[Challenge]:
+        """Load challenges from the GAIA dataset.
+
+        Yields:
+            Challenge objects for each GAIA task.
+        """
+        self.ensure_setup()
+
+        if self._dataset is None:
+            return
+
+        count = 0
+        for item in self._dataset:
+            # Apply subset filter (difficulty level)
+            if self.subset and str(item.get("Level")) != self.subset:
+                continue
+
+            # Apply limit
+            if self.limit and count >= self.limit:
+                break
+
+            challenge = self._convert_to_challenge(item)
+            yield challenge
+            count += 1
+
+    def _convert_to_challenge(self, item: dict[str, Any]) -> Challenge:
+        """Convert a GAIA dataset item to a Challenge."""
+        task_id = item["task_id"]
+        question = item["Question"]
+        level = item.get("Level", 1)
+        final_answer = item.get("Final answer", "")
+        file_name = item.get("file_name", "")
+
+        # Build task description
+        task = question
+        if file_name:
+            task = f"{question}\n\nA file has been provided: {file_name}"
+
+        # Map GAIA levels to difficulty
+        difficulty_map = {1: "easy", 2: "medium", 3: "hard"}
+        difficulty = difficulty_map.get(level, "unknown")
+
+        # Cutoff based on difficulty
+        cutoff_map = {1: 180, 2: 300, 3: 600}
+        cutoff = cutoff_map.get(level, 300)
+
+        # Ground truth for evaluation
+        ground_truth: dict[str, Any] = {
+            "answer": final_answer,
+            "eval": {"type": "gaia_match"},
+        }
+
+        # Create artifacts directory for any files
+        artifacts_dir = self.cache_dir / "gaia" / "artifacts" / task_id
+        artifacts_dir.mkdir(parents=True, exist_ok=True)
+
+        # Copy file if present
+        if task_id in self._file_cache:
+            src = self._file_cache[task_id]
+            if src.exists():
+                import shutil
+
+                shutil.copy2(src, artifacts_dir / src.name)
+
+        return Challenge(
+            name=f"GAIA_{task_id}",
+            task=task,
+            category=["gaia", f"gaia_level_{level}"],
+            difficulty=difficulty,
+            cutoff=cutoff,
+            ground_truth=ground_truth,
+            artifacts_dir=artifacts_dir,
+            source_path=artifacts_dir / "data.json",
+        )
+
+    def evaluate(
+        self,
+        result: ChallengeResult,
+        challenge: Challenge,
+        workspace_dir: Path,
+    ) -> ChallengeResult:
+        """Evaluate using GAIA-style normalized string matching.
+
+        GAIA uses exact string matching after normalization:
+        - Lowercase
+        - Remove articles (a, an, the)
+        - Remove punctuation
+        - Collapse whitespace
+        """
+        ground = challenge.ground_truth
+        expected = ground.get("answer", "")
+
+        if not expected:
+            # Test split has no answers - can't evaluate locally
+            result.success = False
+            result.score = 0.0
+            result.error_message = (
+                "No ground truth (test split - submit to leaderboard)"
+            )
+            return result
+
+        # Get the agent's answer from output
+        agent_answer = self._extract_answer(result)
+
+        if not agent_answer:
+            result.success = False
+            result.score = 0.0
+            result.error_message = "No answer found in agent output"
+            return result
+
+        # Normalize both answers
+        normalized_expected = _normalize_answer(expected)
+        normalized_actual = _normalize_answer(agent_answer)
+
+        # Exact match after normalization
+        if normalized_expected == normalized_actual:
+            result.success = True
+            result.score = 1.0
+        else:
+            result.success = False
+            result.score = 0.0
+            result.error_message = (
+                f"Answer mismatch: expected '{expected}', got '{agent_answer}'"
+            )
+
+        return result
+
+    def _extract_answer(self, result: ChallengeResult) -> str:
+        """Extract the final answer from the agent's output.
+
+        Looks for:
+        1. Content in answer.txt file
+        2. Final step result
+        3. Any file with "answer" in the name
+        """
+        # Check for answer.txt
+        for filename, content in result.output_files.items():
+            if "answer" in filename.lower():
+                return content.strip()
+
+        # Check final step result
+        if result.steps:
+            last_step = result.steps[-1]
+            if last_step.tool_name == "finish":
+                # Try to extract answer from finish arguments
+                reason = last_step.tool_args.get("reason", "")
+                return reason.strip()
+
+        return ""
+
+    def get_challenge_count(self) -> Optional[int]:
+        """Get the number of challenges in the dataset."""
+        self.ensure_setup()
+        if self._dataset is None:
+            return None
+
+        count = len(self._dataset)
+
+        # Apply subset filter
+        if self.subset:
+            count = sum(
+                1 for item in self._dataset if str(item.get("Level")) == self.subset
+            )
+
+        # Apply limit
+        if self.limit:
+            count = min(count, self.limit)
+
+        return count
+
+    def get_metadata(self) -> dict[str, Any]:
+        """Get GAIA benchmark metadata."""
+        metadata = super().get_metadata()
+        metadata.update(
+            {
+                "dataset": self.HF_DATASET,
+                "levels": ["1", "2", "3"],
+                "splits": ["validation", "test"],
+                "requires_auth": True,
+                "leaderboard": (
+                    "https://huggingface.co/spaces/gaia-benchmark/leaderboard"
+                ),
+            }
+        )
+        return metadata

classic/direct_benchmark/direct_benchmark/adapters/swe_bench.py

@@ -0,0 +1,458 @@
+"""SWE-bench adapter.
+
+SWE-bench evaluates AI models on real-world GitHub issues from popular Python
+repositories, requiring models to generate patches that fix the issues.
+
+GitHub: https://github.com/SWE-bench/SWE-bench
+Dataset: https://huggingface.co/datasets/princeton-nlp/SWE-bench
+
+Requires:
+    - Docker Engine (for containerized evaluation)
+    - swebench package (pip install swebench)
+    - ~120GB disk space for full dataset
+    - OR Modal for cloud-based evaluation
+"""
+
+import json
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Any, Iterator, Optional
+
+from ..models import Challenge, ChallengeResult
+from . import register_adapter
+from .base import BenchmarkAdapter
+
+
+@register_adapter("swe-bench")
+class SWEBenchAdapter(BenchmarkAdapter):
+    """Adapter for the SWE-bench benchmark.
+
+    SWE-bench provides 2,294 real GitHub issues from 12 Python repositories.
+    Models must generate patches that fix the issues, evaluated by running
+    the repository's test suite.
+
+    Subsets:
+        - "full": All 2,294 instances
+        - "lite": 300 curated instances
+        - "verified": 500 human-validated solvable instances
+
+    Usage:
+        adapter = SWEBenchAdapter(subset="lite")
+        for challenge in adapter.load_challenges():
+            # Run challenge...
+    """
+
+    name = "swe-bench"
+    description = "SWE-bench - Software Engineering Benchmark"
+
+    HF_DATASET = "princeton-nlp/SWE-bench"
+    HF_LITE = "princeton-nlp/SWE-bench_Lite"
+    HF_VERIFIED = "princeton-nlp/SWE-bench_Verified"
+
+    # Repository-specific timeout multipliers
+    REPO_TIMEOUTS: dict[str, float] = {
+        "django/django": 1.5,
+        "matplotlib/matplotlib": 2.0,
+        "scikit-learn/scikit-learn": 1.5,
+        "sympy/sympy": 1.2,
+    }
+
+    def __init__(
+        self,
+        cache_dir: Optional[Path] = None,
+        split: str = "test",
+        subset: Optional[str] = None,
+        limit: Optional[int] = None,
+        use_modal: bool = False,
+    ):
+        """Initialize the SWE-bench adapter.
+
+        Args:
+            cache_dir: Directory to cache the dataset.
+            split: Dataset split - "dev" or "test".
+            subset: Subset to use - "full", "lite", "verified", or a repo name.
+            limit: Maximum number of challenges to load.
+            use_modal: Use Modal for cloud-based evaluation instead of local Docker.
+        """
+        super().__init__(cache_dir, split, subset, limit)
+        self._dataset = None
+        self._use_modal = use_modal
+
+    def setup(self) -> None:
+        """Download and cache the SWE-bench dataset."""
+        try:
+            from datasets import load_dataset
+        except ImportError:
+            raise ImportError(
+                "SWE-bench adapter requires the 'datasets' package. "
+                "Install with: pip install datasets"
+            )
+
+        # Select dataset based on subset
+        if self.subset == "lite":
+            dataset_name = self.HF_LITE
+        elif self.subset == "verified":
+            dataset_name = self.HF_VERIFIED
+        else:
+            dataset_name = self.HF_DATASET
+
+        # Load dataset
+        self._dataset = load_dataset(
+            dataset_name,
+            split=self.split,
+            cache_dir=str(self.cache_dir / "swe_bench"),
+        )
+
+        # Check for Docker if not using Modal
+        if not self._use_modal:
+            self._check_docker()
+
+        self._is_setup = True
+
+    def _check_docker(self) -> None:
+        """Verify Docker is available for evaluation."""
+        try:
+            result = subprocess.run(
+                ["docker", "info"],
+                capture_output=True,
+                timeout=10,
+            )
+            if result.returncode != 0:
+                raise RuntimeError("Docker is not running")
+        except FileNotFoundError:
+            raise RuntimeError(
+                "Docker is required for SWE-bench evaluation. "
+                "Install Docker or use use_modal=True for cloud evaluation."
+            )
+        except subprocess.TimeoutExpired:
+            raise RuntimeError("Docker is not responding")
+
+    def load_challenges(self) -> Iterator[Challenge]:
+        """Load challenges from the SWE-bench dataset.
+
+        Yields:
+            Challenge objects for each SWE-bench instance.
+        """
+        self.ensure_setup()
+
+        if self._dataset is None:
+            return
+
+        count = 0
+        for item in self._dataset:
+            # Apply repo filter (if subset is a repo name)
+            if self.subset and self.subset not in ("full", "lite", "verified"):
+                if item.get("repo") != self.subset:
+                    continue
+
+            # Apply limit
+            if self.limit and count >= self.limit:
+                break
+
+            challenge = self._convert_to_challenge(item)
+            yield challenge
+            count += 1
+
+    def _convert_to_challenge(self, item: dict[str, Any]) -> Challenge:
+        """Convert a SWE-bench dataset item to a Challenge."""
+        instance_id = item["instance_id"]
+        repo = item.get("repo", "unknown")
+        problem_statement = item.get("problem_statement", "")
+        base_commit = item.get("base_commit", "")
+        hints_text = item.get("hints_text", "")
+
+        # Build comprehensive task description
+        task_parts = [
+            f"Repository: {repo}",
+            f"Base commit: {base_commit}",
+            "",
+            "Problem Statement:",
+            problem_statement,
+        ]
+
+        if hints_text:
+            task_parts.extend(["", "Hints:", hints_text])
+
+        task_parts.extend(
+            [
+                "",
+                "Your task: Generate a patch file (in unified diff format) that "
+                "fixes the issue described above. The patch should be saved to "
+                "'patch.diff' in your workspace.",
+            ]
+        )
+
+        task = "\n".join(task_parts)
+
+        # Determine difficulty based on repo complexity
+        difficulty_map = {
+            "astropy/astropy": "hard",
+            "django/django": "medium",
+            "flask/flask": "easy",
+            "matplotlib/matplotlib": "hard",
+            "pallets/flask": "easy",
+            "psf/requests": "easy",
+            "pydata/xarray": "medium",
+            "pylint-dev/pylint": "medium",
+            "pytest-dev/pytest": "medium",
+            "scikit-learn/scikit-learn": "hard",
+            "sphinx-doc/sphinx": "medium",
+            "sympy/sympy": "hard",
+        }
+        difficulty = difficulty_map.get(repo, "medium")
+
+        # Calculate timeout with repo-specific multiplier
+        base_timeout = 600
+        multiplier = self.REPO_TIMEOUTS.get(repo, 1.0)
+        cutoff = int(base_timeout * multiplier)
+
+        # Ground truth includes the gold patch for reference
+        gold_patch = item.get("patch", "")
+        test_patch = item.get("test_patch", "")
+
+        ground_truth: dict[str, Any] = {
+            "eval": {"type": "swe_bench"},
+            "instance_id": instance_id,
+            "repo": repo,
+            "base_commit": base_commit,
+            "gold_patch": gold_patch,
+            "test_patch": test_patch,
+            "pass_to_pass": item.get("PASS_TO_PASS", ""),
+            "fail_to_pass": item.get("FAIL_TO_PASS", ""),
+        }
+
+        # Create artifacts directory
+        artifacts_dir = self.cache_dir / "swe_bench" / "artifacts" / instance_id
+        artifacts_dir.mkdir(parents=True, exist_ok=True)
+
+        # Save problem context for reference
+        context_file = artifacts_dir / "context.json"
+        with open(context_file, "w") as f:
+            json.dump(
+                {
+                    "instance_id": instance_id,
+                    "repo": repo,
+                    "base_commit": base_commit,
+                    "problem_statement": problem_statement,
+                },
+                f,
+                indent=2,
+            )
+
+        return Challenge(
+            name=f"SWE_{instance_id}",
+            task=task,
+            category=["swe-bench", f"swe-bench_{repo.replace('/', '_')}"],
+            difficulty=difficulty,
+            cutoff=cutoff,
+            ground_truth=ground_truth,
+            artifacts_dir=artifacts_dir,
+            source_path=context_file,
+        )
+
+    def evaluate(
+        self,
+        result: ChallengeResult,
+        challenge: Challenge,
+        workspace_dir: Path,
+    ) -> ChallengeResult:
+        """Evaluate using SWE-bench's Docker-based test harness.
+
+        The agent's patch is applied to the repository in a Docker container,
+        and the test suite is run to verify the fix.
+        """
+        ground = challenge.ground_truth
+
+        # Get the generated patch
+        patch_content = self._extract_patch(result)
+
+        if not patch_content:
+            result.success = False
+            result.score = 0.0
+            result.error_message = "No patch.diff found in agent output"
+            return result
+
+        # Run evaluation
+        if self._use_modal:
+            eval_result = self._evaluate_with_modal(ground, patch_content)
+        else:
+            eval_result = self._evaluate_with_docker(ground, patch_content)
+
+        result.success = eval_result["success"]
+        result.score = eval_result["score"]
+        if eval_result.get("error"):
+            result.error_message = eval_result["error"]
+
+        return result
+
+    def _extract_patch(self, result: ChallengeResult) -> str:
+        """Extract the patch from the agent's output."""
+        # Look for patch.diff file
+        for filename, content in result.output_files.items():
+            if filename.endswith("patch.diff") or filename.endswith(".patch"):
+                return content
+
+        # Look for diff content in any output file
+        for filename, content in result.output_files.items():
+            if content.strip().startswith("diff --git") or content.strip().startswith(
+                "---"
+            ):
+                return content
+
+        return ""
+
+    def _evaluate_with_docker(
+        self, ground: dict[str, Any], patch: str
+    ) -> dict[str, Any]:
+        """Run evaluation using local Docker."""
+        try:
+            # Try to import swebench harness
+            from swebench.harness.run_evaluation import run_evaluation
+        except ImportError:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": (
+                    "swebench package not installed. "
+                    "Install with: pip install swebench"
+                ),
+            }
+
+        instance_id = ground["instance_id"]
+        # These are available for future use in more sophisticated evaluation
+        _repo = ground["repo"]  # noqa: F841
+        _base_commit = ground["base_commit"]  # noqa: F841
+
+        # Write patch to temp file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".patch", delete=False) as f:
+            f.write(patch)
+            patch_file = f.name
+
+        # Initialize predictions_file path before try block
+        predictions_file = Path(patch_file).with_suffix(".json")
+
+        try:
+            # Create predictions file for swebench
+            predictions = [
+                {
+                    "instance_id": instance_id,
+                    "model_name_or_path": "autogpt",
+                    "model_patch": patch,
+                }
+            ]
+
+            with open(predictions_file, "w") as f:
+                json.dump(predictions, f)
+
+            # Run evaluation
+            results = run_evaluation(
+                predictions_path=str(predictions_file),
+                swe_bench_tasks=self.HF_DATASET,
+                log_dir=str(self.cache_dir / "swe_bench" / "logs"),
+                testbed=str(self.cache_dir / "swe_bench" / "testbed"),
+                skip_existing=False,
+                timeout=1800,
+                verbose=False,
+            )
+
+            # Check results
+            if instance_id in results:
+                instance_result = results[instance_id]
+                resolved = instance_result.get("resolved", False)
+                return {
+                    "success": resolved,
+                    "score": 1.0 if resolved else 0.0,
+                    "error": None if resolved else "Tests did not pass",
+                }
+            else:
+                return {
+                    "success": False,
+                    "score": 0.0,
+                    "error": "Evaluation did not produce results",
+                }
+
+        except Exception as e:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": f"Evaluation failed: {str(e)}",
+            }
+
+        finally:
+            # Cleanup temp files
+            Path(patch_file).unlink(missing_ok=True)
+            predictions_file.unlink(missing_ok=True)
+
+    def _evaluate_with_modal(
+        self, ground: dict[str, Any], patch: str
+    ) -> dict[str, Any]:
+        """Run evaluation using Modal cloud infrastructure."""
+        try:
+            import modal  # noqa: F401
+        except ImportError:
+            return {
+                "success": False,
+                "score": 0.0,
+                "error": (
+                    "Modal package not installed. " "Install with: pip install modal"
+                ),
+            }
+
+        # Modal evaluation requires environment setup
+        # This is a simplified interface - full implementation would use
+        # modal's SWE-bench harness
+        return {
+            "success": False,
+            "score": 0.0,
+            "error": (
+                "Modal evaluation not yet implemented. "
+                "Use local Docker evaluation or submit to SWE-bench leaderboard."
+            ),
+        }
+
+    def provision_environment(self, challenge: Challenge) -> dict[str, Any]:
+        """Provide repository context for the challenge."""
+        ground = challenge.ground_truth
+        return {
+            "repo": ground.get("repo"),
+            "base_commit": ground.get("base_commit"),
+            "clone_url": f"https://github.com/{ground.get('repo')}.git",
+        }
+
+    def get_challenge_count(self) -> Optional[int]:
+        """Get the number of challenges in the dataset."""
+        self.ensure_setup()
+        if self._dataset is None:
+            return None
+
+        count = len(self._dataset)
+
+        # Apply repo filter
+        if self.subset and self.subset not in ("full", "lite", "verified"):
+            count = sum(1 for item in self._dataset if item.get("repo") == self.subset)
+
+        # Apply limit
+        if self.limit:
+            count = min(count, self.limit)
+
+        return count
+
+    def get_metadata(self) -> dict[str, Any]:
+        """Get SWE-bench metadata."""
+        metadata = super().get_metadata()
+        metadata.update(
+            {
+                "datasets": {
+                    "full": self.HF_DATASET,
+                    "lite": self.HF_LITE,
+                    "verified": self.HF_VERIFIED,
+                },
+                "subsets": ["full", "lite", "verified"]
+                + list(self.REPO_TIMEOUTS.keys()),
+                "splits": ["dev", "test"],
+                "requires_docker": not self._use_modal,
+                "leaderboard": "https://www.swebench.com/",
+            }
+        )
+        return metadata

classic/direct_benchmark/direct_benchmark/harness.py

@@ -3,7 +3,8 @@
 import asyncio
 import re
 from datetime import datetime
-from typing import Union
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional, Union
 
 from rich.live import Live
 
@@ -14,16 +15,55 @@ from .report import ReportGenerator
 from .state import StateManager
 from .ui import BenchmarkUI, JsonUI, QuietUI, console
 
+if TYPE_CHECKING:
+    from .adapters.base import BenchmarkAdapter
+
 
 class BenchmarkHarness:
     """Main benchmark harness orchestrator."""
 
     def __init__(self, config: HarnessConfig):
         self.config = config
-        self.loader = ChallengeLoader(config.challenges_dir)
         self.reporter = ReportGenerator(config.reports_dir)
         self.state_manager = StateManager(config.reports_dir)
 
+        # Initialize challenge source (adapter or loader)
+        self.adapter: Optional["BenchmarkAdapter"] = None
+        self.loader: Optional[ChallengeLoader] = None
+
+        if config.external_benchmark:
+            self._init_adapter()
+        else:
+            self.loader = ChallengeLoader(config.challenges_dir)
+
+    def _init_adapter(self) -> None:
+        """Initialize external benchmark adapter."""
+        from .adapters import get_adapter
+
+        assert self.config.external_benchmark is not None
+        adapter_cls = get_adapter(self.config.external_benchmark)
+        if adapter_cls is None:
+            from .adapters import list_adapters
+
+            available = list_adapters()
+            raise ValueError(
+                f"Unknown benchmark: {self.config.external_benchmark}. "
+                f"Available: {available}"
+            )
+
+        # Determine cache directory
+        cache_dir = self.config.benchmark_cache_dir
+        if cache_dir is None:
+            cache_dir = Path.home() / ".cache" / "autogpt_benchmarks"
+
+        # Create adapter instance
+        self.adapter = adapter_cls(
+            cache_dir=cache_dir,
+            split=self.config.benchmark_split,
+            subset=self.config.benchmark_subset,
+            limit=self.config.benchmark_limit,
+        )
+
     async def run(
         self,
         ui_mode: str = "default",
@@ -115,17 +155,40 @@ class BenchmarkHarness:
             strategy_names, model_names, self.config.attempts
         )
 
-        # Load challenges
-        challenges = list(
-            self.loader.load_all(
-                categories=self.config.categories,
-                skip_categories=self.config.skip_categories,
-                names=self.config.test_names,
-                maintain=self.config.maintain,
-                improve=self.config.improve,
-                explore=self.config.explore,
+        # Load challenges (from adapter or local loader)
+        if self.adapter:
+            # External benchmark - load via adapter
+            if ui_mode != "json":
+                subset_str = (
+                    f", subset={self.config.benchmark_subset}"
+                    if self.config.benchmark_subset
+                    else ""
+                )
+                limit_str = (
+                    f", limit={self.config.benchmark_limit}"
+                    if self.config.benchmark_limit
+                    else ""
+                )
+                console.print(
+                    f"[cyan]Loading {self.config.external_benchmark} benchmark "
+                    f"(split={self.config.benchmark_split}{subset_str}{limit_str})"
+                    f"...[/cyan]"
+                )
+            assert self.adapter is not None
+            challenges = list(self.adapter.load_challenges())
+        else:
+            # Local challenges - load via ChallengeLoader
+            assert self.loader is not None
+            challenges = list(
+                self.loader.load_all(
+                    categories=self.config.categories,
+                    skip_categories=self.config.skip_categories,
+                    names=self.config.test_names,
+                    maintain=self.config.maintain,
+                    improve=self.config.improve,
+                    explore=self.config.explore,
+                )
             )
-        )
 
         if not challenges:
             console.print("[red]No challenges found matching filters[/red]")
@@ -194,6 +257,7 @@ class BenchmarkHarness:
             attempts=self.config.attempts,
             no_cutoff=self.config.no_cutoff,
             skip_fn=should_skip,
+            adapter=self.adapter,
         )
 
         # Ensure workspace exists

classic/direct_benchmark/direct_benchmark/models.py

@@ -205,7 +205,7 @@ class BenchmarkConfig(BaseModel):
     strategy: StrategyName
     model: ModelConfig
     max_steps: int = 50
-    timeout_seconds: int = 300
+    timeout_seconds: int = 900
 
     @property
     def config_name(self) -> str:
@@ -246,6 +246,13 @@ class HarnessConfig(BaseModel):
     reset_models: Optional[list[str]] = None  # Reset specific models
     reset_challenges: Optional[list[str]] = None  # Reset specific challenges
 
+    # External benchmark options
+    external_benchmark: Optional[str] = None  # gaia, swe-bench, agent-bench
+    benchmark_split: str = "validation"  # train, validation, test
+    benchmark_subset: Optional[str] = None  # Difficulty level, repo name, etc.
+    benchmark_limit: Optional[int] = None  # Max challenges to load
+    benchmark_cache_dir: Optional[Path] = None  # Cache directory for downloads
+
     model_config = {"arbitrary_types_allowed": True}
 
 

classic/direct_benchmark/direct_benchmark/parallel.py

@@ -2,12 +2,15 @@
 
 import asyncio
 from pathlib import Path
-from typing import AsyncIterator, Callable, Optional
+from typing import TYPE_CHECKING, AsyncIterator, Callable, Optional
 
 from .evaluator import Evaluator
 from .models import BenchmarkConfig, Challenge, ChallengeResult, ExecutionProgress
 from .runner import AgentRunner, StepCallback
 
+if TYPE_CHECKING:
+    from .adapters.base import BenchmarkAdapter
+
 # Type for skip predicate: (config_name, challenge_name, attempt) -> bool
 SkipPredicate = Callable[[str, str, int], bool]
 
@@ -23,6 +26,7 @@ class ParallelExecutor:
         attempts: int = 1,
         no_cutoff: bool = False,
         skip_fn: Optional[SkipPredicate] = None,
+        adapter: Optional["BenchmarkAdapter"] = None,
     ):
         self.max_parallel = max_parallel
         self.on_progress = on_progress
@@ -30,6 +34,7 @@ class ParallelExecutor:
         self.attempts = attempts
         self.no_cutoff = no_cutoff
         self.skip_fn = skip_fn
+        self.adapter = adapter
         self._semaphore = asyncio.Semaphore(max_parallel)
         self._evaluator = Evaluator()
 
@@ -121,8 +126,11 @@ class ParallelExecutor:
             )
             result = await runner.run_challenge(challenge, attempt=attempt)
 
-            # Evaluate result
-            result = self._evaluator.evaluate(result, challenge)
+            # Evaluate result - use adapter if available, otherwise standard evaluator
+            if self.adapter is not None:
+                result = self.adapter.evaluate(result, challenge, workspace_root)
+            else:
+                result = self._evaluator.evaluate(result, challenge)
 
             # Notify completion
             if self.on_progress:

classic/direct_benchmark/direct_benchmark/runner.py

@@ -220,6 +220,14 @@ class AgentRunner:
                 "service",  # Block service commands
             ]
 
+        # Disable clipboard commands for benchmarks - they add overhead without value
+        app_config.disabled_commands = [
+            "clipboard_copy",
+            "clipboard_paste",
+            "clipboard_list",
+            "clipboard_clear",
+        ]
+
         self._agent = agent
         self._llm_provider = llm_provider
         return agent
@@ -244,14 +252,28 @@ class AgentRunner:
                 # Propose next action
                 proposal = await agent.propose_action()
 
-                # Check for finish command
+                # Get cumulative cost from LLM provider
+                if self._llm_provider:
+                    cumulative_cost = self._llm_provider.get_incurred_cost()
+
+                # Check for finish command - record it and return
                 if proposal.use_tool.name == "finish":
+                    steps.append(
+                        StepResult(
+                            step_num=step_num + 1,
+                            tool_name=proposal.use_tool.name,
+                            tool_args=proposal.use_tool.arguments,
+                            result="Agent finished",
+                            is_error=False,
+                            cumulative_cost=cumulative_cost,
+                        )
+                    )
                     return True
 
                 # Execute the action
                 result = await agent.execute(proposal)
 
-                # Get cumulative cost from LLM provider
+                # Update cost after execution
                 if self._llm_provider:
                     cumulative_cost = self._llm_provider.get_incurred_cost()
 

classic/poetry.lock

@@ -6,7 +6,7 @@ version = "2.6.1"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
     {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
@@ -18,7 +18,7 @@ version = "3.13.3"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "aiohttp-3.13.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d5a372fd5afd301b3a89582817fdcdb6c34124787c70dbcc616f259013e7eef7"},
     {file = "aiohttp-3.13.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:147e422fd1223005c22b4fe080f5d93ced44460f5f9c105406b753612b587821"},
@@ -175,7 +175,7 @@ version = "1.4.0"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"},
     {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"},
@@ -247,7 +247,7 @@ version = "25.4.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373"},
     {file = "attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11"},
@@ -1197,7 +1197,7 @@ version = "2026.1.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c"},
     {file = "certifi-2026.1.4.tar.gz", hash = "sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120"},
@@ -1319,7 +1319,7 @@ version = "3.4.4"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "charset_normalizer-3.4.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e824f1492727fa856dd6eda4f7cee25f8518a12f3c4a56a74e8095695089cf6d"},
     {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4bd5d4137d500351a30687c2d3971758aac9a19208fc110ccb9d7188fbe709e8"},
@@ -1523,12 +1523,12 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {dev = "platform_system == \"Windows\" or sys_platform == \"win32\""}
+markers = {benchmarks = "platform_system == \"Windows\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\""}
 
 [[package]]
 name = "coloredlogs"
@@ -1874,6 +1874,51 @@ files = [
     {file = "cymem-2.0.13.tar.gz", hash = "sha256:1c91a92ae8c7104275ac26bd4d29b08ccd3e7faff5893d3858cb6fadf1bc1588"},
 ]
 
+[[package]]
+name = "datasets"
+version = "2.18.0"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = ">=3.8.0"
+groups = ["benchmarks"]
+files = [
+    {file = "datasets-2.18.0-py3-none-any.whl", hash = "sha256:f1bbf0e2896917a914de01cbd37075b14deea3837af87ad0d9f697388ccaeb50"},
+    {file = "datasets-2.18.0.tar.gz", hash = "sha256:cdf8b8c6abf7316377ba4f49f9589a4c74556d6b481afd0abd2284f3d69185cb"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+dill = ">=0.3.0,<0.3.9"
+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
+huggingface-hub = ">=0.19.4"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=12.0.0"
+pyarrow-hotfix = "*"
+pyyaml = ">=5.1"
+requests = ">=2.19.0"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0)"]
+audio = ["librosa", "soundfile (>=0.12.1)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0) ; sys_platform != \"win32\" and python_version < \"3.10\"", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1) ; sys_platform != \"darwin\" or platform_machine != \"arm64\"", "tensorflow (>=2.3,!=2.6.0,!=2.6.1) ; sys_platform != \"darwin\" or platform_machine != \"arm64\"", "tensorflow-macos ; sys_platform == \"darwin\" and platform_machine == \"arm64\"", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1) ; sys_platform != \"darwin\" or platform_machine != \"arm64\"", "tensorflow-macos ; sys_platform == \"darwin\" and platform_machine == \"arm64\"", "torch", "transformers"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
+metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
+quality = ["ruff (>=0.3.0)"]
+s3 = ["s3fs"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1) ; sys_platform != \"darwin\" or platform_machine != \"arm64\"", "tensorflow-macos ; sys_platform == \"darwin\" and platform_machine == \"arm64\""]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0) ; sys_platform != \"win32\" and python_version < \"3.10\"", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1) ; sys_platform != \"darwin\" or platform_machine != \"arm64\"", "tensorflow-macos ; sys_platform == \"darwin\" and platform_machine == \"arm64\"", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+torch = ["torch"]
+vision = ["Pillow (>=6.2.1)"]
+
 [[package]]
 name = "dateparser"
 version = "1.2.2"
@@ -1930,6 +1975,22 @@ files = [
     {file = "demjson3-3.0.6.tar.gz", hash = "sha256:37c83b0c6eb08d25defc88df0a2a4875d58a7809a9650bd6eee7afd8053cdbac"},
 ]
 
+[[package]]
+name = "dill"
+version = "0.3.8"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["benchmarks"]
+files = [
+    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
+
 [[package]]
 name = "distlib"
 version = "0.4.0"
@@ -2181,7 +2242,7 @@ version = "3.20.3"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.10"
-groups = ["main", "build", "dev"]
+groups = ["main", "benchmarks", "build", "dev"]
 files = [
     {file = "filelock-3.20.3-py3-none-any.whl", hash = "sha256:4b0dda527ee31078689fc205ec4f1c1bf7d56cf88b6dc9426c4f230e46c2dce1"},
     {file = "filelock-3.20.3.tar.gz", hash = "sha256:18c57ee915c7ec61cff0ecf7f0f869936c7c30191bb0cf406f1341778d0834e1"},
@@ -2222,7 +2283,7 @@ version = "1.8.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b37f6d31b3dcea7deb5e9696e529a6aa4a898adc33db82da12e4c60a7c4d2011"},
     {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef2b7b394f208233e471abc541cc6991f907ffd47dc72584acee3147899d6565"},
@@ -2358,25 +2419,27 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2026.1.0"
+version = "2024.2.0"
 description = "File-system specification"
 optional = false
-python-versions = ">=3.10"
-groups = ["main"]
+python-versions = ">=3.8"
+groups = ["main", "benchmarks"]
 files = [
-    {file = "fsspec-2026.1.0-py3-none-any.whl", hash = "sha256:cb76aa913c2285a3b49bdd5fc55b1d7c708d7208126b60f2eb8194fe1b4cbdcc"},
-    {file = "fsspec-2026.1.0.tar.gz", hash = "sha256:e987cb0496a0d81bba3a9d1cee62922fb395e7d4c3b575e57f547953334fe07b"},
+    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
+    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
 ]
 
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-dev = ["pre-commit", "ruff (>=0.5)"]
-doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+devel = ["pytest", "pytest-cov"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs (>2024.2.0)", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs (>2024.2.0)", "smbprotocol", "tqdm"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
 gcs = ["gcsfs"]
 git = ["pygit2"]
@@ -2391,9 +2454,6 @@ s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
-test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
-test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
-test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "backports-zstd ; python_version < \"3.14\"", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr"]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -2977,42 +3037,6 @@ files = [
 hpack = ">=4.1,<5"
 hyperframe = ">=6.1,<7"
 
-[[package]]
-name = "hf-xet"
-version = "1.2.0"
-description = "Fast transfer of large files with the Hugging Face Hub."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
-files = [
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-    {file = "hf_xet-1.2.0.tar.gz", hash = "sha256:a8c27070ca547293b6890c4bf389f713f80e8c478631432962bb7f4bc0bd7d7f"},
-]
-
-[package.extras]
-tests = ["pytest"]
-
 [[package]]
 name = "hpack"
 version = "4.1.0"
@@ -3170,39 +3194,36 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "1.3.2"
+version = "0.20.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
-python-versions = ">=3.9.0"
-groups = ["main"]
+python-versions = ">=3.8.0"
+groups = ["main", "benchmarks"]
 files = [
-    {file = "huggingface_hub-1.3.2-py3-none-any.whl", hash = "sha256:b552b9562a5532102a041fa31a6966bb9de95138fc7aa578bb3703198c25d1b6"},
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+    {file = "huggingface_hub-0.20.3-py3-none-any.whl", hash = "sha256:d988ae4f00d3e307b0c80c6a05ca6dbb7edba8bba3079f74cda7d9c2e562a7b6"},
+    {file = "huggingface_hub-0.20.3.tar.gz", hash = "sha256:94e7f8e074475fbc67d6a71957b678e1b4a74ff1b64a644fd6cbb83da962d05d"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = ">=2023.5.0"
-hf-xet = {version = ">=1.2.0,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
-httpx = ">=0.23.0,<1"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
-shellingham = "*"
+requests = "*"
 tqdm = ">=4.42.1"
-typer-slim = "*"
-typing-extensions = ">=4.1.0"
+typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["Jinja2", "Pillow", "authlib (>=1.3.2)", "fastapi", "fastapi", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "numpy", "pytest (>=8.4.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-dev = ["Jinja2", "Pillow", "authlib (>=1.3.2)", "fastapi", "fastapi", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "numpy", "pytest (>=8.4.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0) ; python_version == \"3.8\"", "pydantic (>1.1,<3.0) ; python_version > \"3.8\"", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0) ; python_version == \"3.8\"", "pydantic (>1.1,<3.0) ; python_version > \"3.8\"", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-hf-xet = ["hf-xet (>=1.2.0,<2.0.0)"]
-mcp = ["mcp (>=1.8.0)"]
-oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
-quality = ["libcst (>=1.4.0)", "mypy (==1.15.0)", "ruff (>=0.9.0)", "ty"]
-testing = ["Jinja2", "Pillow", "authlib (>=1.3.2)", "fastapi", "fastapi", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.4.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["safetensors[torch]", "torch"]
-typing = ["types-PyYAML", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+inference = ["aiohttp", "pydantic (>1.1,<2.0) ; python_version == \"3.8\"", "pydantic (>1.1,<3.0) ; python_version > \"3.8\""]
+quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
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 name = "humanfriendly"
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 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
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+
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+
 [[package]]
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 version = "1.0.12"
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 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "benchmarks"]
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 optional = false
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 version = "0.17.2.4"
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 description = "Accelerated property cache"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
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+python-versions = ">=3.5"
+groups = ["benchmarks"]
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 [[package]]
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 version = "0.6.2"
@@ -6254,7 +6468,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main"]
+groups = ["main", "benchmarks"]
 files = [
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@@ -6315,7 +6529,7 @@ version = "2025.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
-groups = ["main"]
+groups = ["main", "benchmarks"]
 files = [
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@@ -6358,7 +6572,7 @@ version = "6.0.3"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
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 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
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 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main"]
+groups = ["main", "benchmarks"]
 files = [
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@@ -7739,7 +7953,7 @@ version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "benchmarks"]
 files = [
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 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
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 files = [
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 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
-groups = ["main"]
-markers = "platform_system == \"Windows\""
+groups = ["main", "benchmarks"]
 files = [
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 ]
+markers = {main = "platform_system == \"Windows\""}
 
 [[package]]
 name = "tzlocal"
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 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
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 [package.dependencies]
 h11 = ">=0.16.0,<1"
 
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+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["benchmarks"]
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 [[package]]
 name = "yarl"
 version = "1.22.0"
 description = "Yet another URL library"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main", "benchmarks", "dev"]
 files = [
     {file = "yarl-1.22.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c7bd6683587567e5a49ee6e336e0612bec8329be1b7d4c8af5687dcdeb67ee1e"},
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@@ -8774,4 +9138,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.12"
-content-hash = "9d632f4341ef2e49aa81978032caf9605fd258aff8176263ccb1766b0a1ef4b1"
+content-hash = "f8040ae4c1cc04a87df47a0176c09cc4d9da81e2a51e3428d07d93d5c2e9724f"

classic/pyproject.toml

@@ -142,6 +142,19 @@ optional = true
 cx-freeze = { git = "https://github.com/ntindle/cx_Freeze.git", rev = "main" }
 
 
+[tool.poetry.group.benchmarks]
+optional = true
+
+[tool.poetry.group.benchmarks.dependencies]
+# External benchmark adapters
+datasets = "^2.14"
+huggingface-hub = "^0.20"
+# SWE-bench evaluation (optional - requires Docker)
+# swebench = "^2.0"  # Install separately if needed
+# Modal for cloud evaluation (optional)
+# modal = "^0.70"  # Install separately if needed
+
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"