| """ | |
| AgentDebuggerEnv — Pydantic Data Models | |
| ======================================== | |
| All models are Pydantic v2 BaseModel subclasses with exact field names | |
| required by the OpenEnv spec and hackathon validation pipeline. | |
| """ | |
| from pydantic import BaseModel | |
| from typing import List, Dict, Optional | |
| class FixAttempt(BaseModel): | |
| attempt_number: int # 1-indexed attempt number this episode | |
| code_submitted: str # The full code the agent submitted for this attempt | |
| hypothesis: str # Agent's stated hypothesis about the bug before this attempt | |
| execution_output: str # Full stdout + stderr from running the test suite | |
| tests_passed: int # Number of tests that passed after this fix | |
| tests_total: int # Total number of tests in the suite | |
| execution_time_ms: int # How long the sandbox took to run (milliseconds) | |
| timed_out: bool # Whether this attempt hit the 10-second sandbox timeout | |
| class Observation(BaseModel): | |
| # Task context — fixed for the episode | |
| task_id: str # "easy" | "medium" | "hard" | |
| task_description: str # Plain English description of what the code is supposed to do | |
| buggy_code: str # The original broken code (shown once at reset, always available) | |
| test_suite: str # The full test suite code | |
| initial_error_output: str # Output of running the test suite against the buggy code at reset() | |
| # Dynamic state — changes each step | |
| current_code: str # The most recent version of the code | |
| current_error_output: str # Output of running tests against current_code | |
| tests_passed: int # Tests passing on current_code | |
| tests_total: int # Total tests in suite | |
| previous_attempts: List[FixAttempt] # Full history of all fix attempts this episode | |
| # Budget tracking | |
| attempts_remaining: int # How many more fix submissions are allowed | |
| max_attempts: int # Total attempt budget for this task | |
| # Step tracking | |
| step_number: int # Current step number (increments on every action) | |
| max_steps: int # Total step budget (includes both fix and query actions) | |
| done: bool # Whether the episode has ended | |
| # Scoring signal (shown to agent for learning) | |
| score_estimate: float # Running estimate of current grader score (0.0–1.0) | |
| hint_used: bool # Whether the agent has used their one hint this episode | |
| class Action(BaseModel): | |
| action_type: str # "submit_fix" | "query_context" | "give_up" | |
| # ── submit_fix ── | |
| fixed_code: Optional[str] = None | |
| hypothesis: Optional[str] = None | |
| # ── query_context ── | |
| query_type: Optional[str] = None | |
| query_target: Optional[str] = None | |
| # ── give_up ── | |
| final_diagnosis: Optional[str] = None | |
| class Reward(BaseModel): | |
| step_reward: float # Reward for THIS step only. Range: -1.0 to +1.0 | |
| cumulative_reward: float # Sum of all step_rewards this episode | |
| grader_score: float # 0.0 during episode. Set ONLY on terminal step (done=True). | |
| breakdown: Dict[str, float] # Itemized components | |