feat: add OpenThoughts-TBLite evaluation environment and configuration files

Introduced a new evaluation environment for OpenThoughts-TBLite, including the main evaluation script, configuration YAML, and README documentation. This environment provides a faster alternative to Terminal-Bench 2.0, featuring 100 difficulty-calibrated tasks for terminal agents. The setup allows for easy evaluation and configuration, enhancing the benchmarking capabilities for terminal agents.

environments/benchmarks/tblite/tblite_env.py

@@ -0,0 +1,119 @@
+"""
+OpenThoughts-TBLite Evaluation Environment
+
+A lighter, faster alternative to Terminal-Bench 2.0 for iterating on terminal
+agents. Uses the same evaluation logic as TerminalBench2EvalEnv but defaults
+to the NousResearch/openthoughts-tblite dataset (100 difficulty-calibrated
+tasks vs TB2's 89 harder tasks).
+
+TBLite tasks are a curated subset of TB2 with a difficulty distribution
+designed to give meaningful signal even for smaller models:
+  - Easy (40 tasks):   >= 70% pass rate with Claude Haiku 4.5
+  - Medium (26 tasks): 40-69% pass rate
+  - Hard (26 tasks):   10-39% pass rate
+  - Extreme (8 tasks): < 10% pass rate
+
+Usage:
+    python environments/benchmarks/tblite/tblite_env.py evaluate
+
+    # Filter to specific tasks:
+    python environments/benchmarks/tblite/tblite_env.py evaluate \\
+        --env.task_filter "broken-python,pandas-etl"
+"""
+
+import os
+import sys
+from pathlib import Path
+from typing import List, Tuple
+
+_repo_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_repo_root) not in sys.path:
+    sys.path.insert(0, str(_repo_root))
+
+from pydantic import Field
+
+from atroposlib.envs.base import EvalHandlingEnum
+from atroposlib.envs.server_handling.server_manager import APIServerConfig
+
+from environments.benchmarks.terminalbench_2.terminalbench2_env import (
+    TerminalBench2EvalConfig,
+    TerminalBench2EvalEnv,
+)
+
+
+class TBLiteEvalConfig(TerminalBench2EvalConfig):
+    """Configuration for the OpenThoughts-TBLite evaluation environment.
+
+    Inherits all TB2 config fields. Only the dataset default and task timeout
+    differ -- TBLite tasks are calibrated to be faster.
+    """
+
+    dataset_name: str = Field(
+        default="NousResearch/openthoughts-tblite",
+        description="HuggingFace dataset containing TBLite tasks.",
+    )
+
+    task_timeout: int = Field(
+        default=1200,
+        description="Maximum wall-clock seconds per task. TBLite tasks are "
+        "generally faster than TB2, so 20 minutes is usually sufficient.",
+    )
+
+
+class TBLiteEvalEnv(TerminalBench2EvalEnv):
+    """OpenThoughts-TBLite evaluation environment.
+
+    Inherits all evaluation logic from TerminalBench2EvalEnv (agent loop,
+    test verification, Docker image resolution, metrics, wandb logging).
+    Only the default configuration differs.
+    """
+
+    name = "openthoughts-tblite"
+    env_config_cls = TBLiteEvalConfig
+
+    @classmethod
+    def config_init(cls) -> Tuple[TBLiteEvalConfig, List[APIServerConfig]]:
+        env_config = TBLiteEvalConfig(
+            enabled_toolsets=["terminal", "file"],
+            disabled_toolsets=None,
+            distribution=None,
+
+            max_agent_turns=60,
+            max_token_length=16000,
+            agent_temperature=0.6,
+            system_prompt=None,
+
+            terminal_backend="modal",
+            terminal_timeout=300,
+
+            test_timeout=180,
+
+            # 100 tasks in parallel
+            tool_pool_size=128,
+
+            eval_handling=EvalHandlingEnum.STOP_TRAIN,
+            group_size=1,
+            steps_per_eval=1,
+            total_steps=1,
+
+            tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B",
+            use_wandb=True,
+            wandb_name="openthoughts-tblite",
+            ensure_scores_are_not_same=False,
+        )
+
+        server_configs = [
+            APIServerConfig(
+                base_url="https://openrouter.ai/api/v1",
+                model_name="anthropic/claude-sonnet-4",
+                server_type="openai",
+                api_key=os.getenv("OPENROUTER_API_KEY", ""),
+                health_check=False,
+            )
+        ]
+
+        return env_config, server_configs
+
+
+if __name__ == "__main__":
+    TBLiteEvalEnv.cli()