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README.md

@@ -88,6 +88,174 @@ openenv validate
 openenv validate --url http://localhost:7860 --verbose
 ```
 
+---
+```bash
+python3 inference.py
+
+2026-04-12 22:19:47,173 [INFO] Connecting to environment: https://sairaj2-openenv-datacleaner.hf.space
+2026-04-12 22:19:49,338 [INFO]   Environment: AutoClean-AI v1.0.0 — healthy
+2026-04-12 22:19:49,711 [INFO]   Available tasks: ['easy_001', 'medium_001', 'hard_001', 'employee_demo']
+2026-04-12 22:19:49,711 [INFO] Using LLM agent: qwen/qwen3-next-80b-a3b-instruct:free via https://openrouter.ai/api/v1
+2026-04-12 22:19:50,044 [INFO] 
+=======================================================
+2026-04-12 22:19:50,044 [INFO] TASK: easy_001  (difficulty=beginner)
+2026-04-12 22:19:50,044 [INFO] =======================================================
+[START] task=easy_001 env=openenv-datacleaner model=qwen/qwen3-next-80b-a3b-instruct:free
+2026-04-12 22:19:52,471 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:52,472 [INFO] Retrying request to /chat/completions in 0.464138 seconds
+2026-04-12 22:19:53,580 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:53,580 [INFO] Retrying request to /chat/completions in 0.815704 seconds
+2026-04-12 22:19:55,038 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:55,041 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Provider returned error', 'code': 429, 'metadata': {'raw': 'qwen/qwen3-next-80b-a3b-instruct:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations', 'provider_name': 'Venice', 'is_byok': False}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:19:55,383 [INFO]   [easy_001] ep=1 step=1 reward=0.500
+2026-04-12 22:19:55,965 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:55,967 [INFO] Retrying request to /chat/completions in 0.458206 seconds
+2026-04-12 22:19:57,083 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 402 Payment Required"
+2026-04-12 22:19:57,084 [WARNING] LLM call failed: Error code: 402 - {'error': {'message': 'Provider returned error', 'code': 402, 'metadata': {'raw': '{"error":"API key USD spend limit exceeded. Your account may still have USD balance, but this API key has reached its configured USD spending limit."}', 'provider_name': 'Venice', 'is_byok': False}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:19:57,485 [INFO]   [easy_001] ep=1 step=2 reward=1.000
+2026-04-12 22:19:58,443 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:58,445 [INFO] Retrying request to /chat/completions in 0.475367 seconds
+2026-04-12 22:19:59,627 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:19:59,628 [INFO] Retrying request to /chat/completions in 0.844512 seconds
+2026-04-12 22:20:01,065 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:01,067 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Provider returned error', 'code': 429, 'metadata': {'raw': 'qwen/qwen3-next-80b-a3b-instruct:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations', 'provider_name': 'Venice', 'is_byok': False}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:20:01,372 [INFO]   [easy_001] ep=2 step=1 reward=0.500
+2026-04-12 22:20:01,969 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:01,971 [INFO] Retrying request to /chat/completions in 0.387579 seconds
+2026-04-12 22:20:03,191 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:03,193 [INFO] Retrying request to /chat/completions in 0.930048 seconds
+2026-04-12 22:20:04,715 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:04,717 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Provider returned error', 'code': 429, 'metadata': {'raw': 'qwen/qwen3-next-80b-a3b-instruct:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations', 'provider_name': 'Venice', 'is_byok': False}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:20:05,054 [INFO]   [easy_001] ep=2 step=2 reward=1.000
+2026-04-12 22:20:06,558 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:06,560 [INFO] Retrying request to /chat/completions in 0.377761 seconds
+2026-04-12 22:20:08,138 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:08,139 [INFO] Retrying request to /chat/completions in 0.790773 seconds
+2026-04-12 22:20:09,531 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:09,533 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Provider returned error', 'code': 429, 'metadata': {'raw': 'qwen/qwen3-next-80b-a3b-instruct:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations', 'provider_name': 'Venice', 'is_byok': False}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:20:09,877 [INFO]   [easy_001] ep=3 step=1 reward=0.500
+2026-04-12 22:20:10,478 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:10,480 [INFO] Retrying request to /chat/completions in 0.432287 seconds
+2026-04-12 22:20:11,245 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:11,247 [INFO] Retrying request to /chat/completions in 0.841678 seconds
+2026-04-12 22:20:12,445 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:12,447 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:20:12,771 [INFO]   [easy_001] ep=3 step=2 reward=1.000
+[END] success=true steps=6 score=0.750 rewards=0.50,1.00,0.50,1.00,0.50,1.00
+2026-04-12 22:20:12,771 [INFO] 
+  Task score: 0.7500 ± 0.0000
+2026-04-12 22:20:12,771 [INFO] 
+=======================================================
+2026-04-12 22:20:12,771 [INFO] TASK: medium_001  (difficulty=intermediate)
+2026-04-12 22:20:12,771 [INFO] =======================================================
+[START] task=medium_001 env=openenv-datacleaner model=qwen/qwen3-next-80b-a3b-instruct:free
+2026-04-12 22:20:13,504 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:13,504 [INFO] Retrying request to /chat/completions in 0.469513 seconds
+2026-04-12 22:20:14,323 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:14,323 [INFO] Retrying request to /chat/completions in 0.933486 seconds
+2026-04-12 22:20:16,371 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:16,371 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=submit reward=0.50 done=true error=null
+2026-04-12 22:20:16,811 [INFO]   [medium_001] ep=1 step=1 reward=0.500
+2026-04-12 22:20:17,561 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:17,562 [INFO] Retrying request to /chat/completions in 0.445498 seconds
+2026-04-12 22:20:18,419 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:18,421 [INFO] Retrying request to /chat/completions in 0.807103 seconds
+2026-04-12 22:20:19,640 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:19,641 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=submit reward=0.50 done=true error=null
+2026-04-12 22:20:19,980 [INFO]   [medium_001] ep=2 step=1 reward=0.500
+2026-04-12 22:20:20,626 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:20,627 [INFO] Retrying request to /chat/completions in 0.397460 seconds
+2026-04-12 22:20:21,491 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:21,493 [INFO] Retrying request to /chat/completions in 0.964606 seconds
+2026-04-12 22:20:22,821 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:22,823 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=submit reward=0.50 done=true error=null
+2026-04-12 22:20:23,198 [INFO]   [medium_001] ep=3 step=1 reward=0.500
+[END] success=true steps=3 score=0.500 rewards=0.50,0.50,0.50
+2026-04-12 22:20:23,199 [INFO] 
+  Task score: 0.5000 ± 0.0000
+2026-04-12 22:20:23,199 [INFO] 
+=======================================================
+2026-04-12 22:20:23,199 [INFO] TASK: hard_001  (difficulty=advanced)
+2026-04-12 22:20:23,199 [INFO] =======================================================
+[START] task=hard_001 env=openenv-datacleaner model=qwen/qwen3-next-80b-a3b-instruct:free
+2026-04-12 22:20:24,051 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:24,052 [INFO] Retrying request to /chat/completions in 0.472201 seconds
+2026-04-12 22:20:25,173 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:25,174 [INFO] Retrying request to /chat/completions in 0.768212 seconds
+2026-04-12 22:20:26,285 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:26,286 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:20:26,614 [INFO]   [hard_001] ep=1 step=1 reward=0.500
+2026-04-12 22:20:27,026 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:27,026 [INFO] Retrying request to /chat/completions in 0.446455 seconds
+2026-04-12 22:20:28,422 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:28,424 [INFO] Retrying request to /chat/completions in 0.765570 seconds
+2026-04-12 22:20:29,526 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:29,527 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:20:29,927 [INFO]   [hard_001] ep=1 step=2 reward=1.000
+2026-04-12 22:20:30,587 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:30,589 [INFO] Retrying request to /chat/completions in 0.408676 seconds
+2026-04-12 22:20:31,424 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:31,426 [INFO] Retrying request to /chat/completions in 0.778604 seconds
+2026-04-12 22:20:32,608 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:32,611 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:20:33,065 [INFO]   [hard_001] ep=2 step=1 reward=0.500
+2026-04-12 22:20:33,472 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:33,473 [INFO] Retrying request to /chat/completions in 0.458515 seconds
+2026-04-12 22:20:34,394 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:34,395 [INFO] Retrying request to /chat/completions in 0.825773 seconds
+2026-04-12 22:20:35,545 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:35,547 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:20:35,874 [INFO]   [hard_001] ep=2 step=2 reward=1.000
+2026-04-12 22:20:36,572 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:36,573 [INFO] Retrying request to /chat/completions in 0.417865 seconds
+2026-04-12 22:20:37,307 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:37,309 [INFO] Retrying request to /chat/completions in 0.985335 seconds
+2026-04-12 22:20:38,616 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:38,618 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=1 action=remove_duplicates reward=0.50 done=false error=null
+2026-04-12 22:20:38,959 [INFO]   [hard_001] ep=3 step=1 reward=0.500
+2026-04-12 22:20:39,310 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:39,311 [INFO] Retrying request to /chat/completions in 0.375729 seconds
+2026-04-12 22:20:40,045 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:40,046 [INFO] Retrying request to /chat/completions in 0.926493 seconds
+2026-04-12 22:20:41,322 [INFO] HTTP Request: POST https://openrouter.ai/api/v1/chat/completions "HTTP/1.1 429 Too Many Requests"
+2026-04-12 22:20:41,325 [WARNING] LLM call failed: Error code: 429 - {'error': {'message': 'Rate limit exceeded: limit_rpm/qwen/qwen3-next-80b-a3b-instruct-2509/94248808-ba97-4e3c-be60-1cb0928db51d. High demand for qwen/qwen3-next-80b-a3b-instruct:free on OpenRouter - limited to 8 requests per minute. Please retry shortly.', 'code': 429, 'metadata': {'headers': {'X-RateLimit-Limit': '8', 'X-RateLimit-Remaining': '0', 'X-RateLimit-Reset': '1776012660000'}, 'provider_name': None}}, 'user_id': 'user_36ZHxohbiGTyLfq9vP3Sf6ojZMM'}
+[STEP] step=2 action=submit reward=1.00 done=true error=null
+2026-04-12 22:20:41,690 [INFO]   [hard_001] ep=3 step=2 reward=1.000
+[END] success=true steps=6 score=0.750 rewards=0.50,1.00,0.50,1.00,0.50,1.00
+2026-04-12 22:20:41,690 [INFO] 
+  Task score: 0.7500 ± 0.0000
+
+=======================================================
+INFERENCE RESULTS
+=======================================================
+Model      : qwen/qwen3-next-80b-a3b-instruct:free
+Seed       : 42  |  3 episodes x 8 steps
+Elapsed    : 51.6s
+
+  easy_001                                   0.7500 +- 0.0000  |###############     |
+  medium_001                                 0.5000 +- 0.0000  |##########          |
+  hard_001                                   0.7500 +- 0.0000  |###############     |
+
+  OVERALL                                    0.6667
+=======================================================
+```
+
+---
+
 ---
 
 ## 🎯 Tasks
@@ -217,7 +385,7 @@ openenv validate --url http://localhost:7860 --verbose
 
 | | |
 |---|---|
-| 📦 GitHub | https://github.com/SairajMN/WorkflowOps |
+| 📦 GitHub | https://github.com/SairajMN/AutoClean-AI |
 | 📖 Interactive API Docs | http://localhost:7860/redoc |
 | 🔧 OpenEnv Framework | https://github.com/meta-pytorch/OpenEnv |
 

inference.py

@@ -1,533 +1,625 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-inference.py — HallucinationGuard-Env Inference Script
-=======================================================
-Mandatory submission script for the Meta PyTorch OpenEnv Hackathon 2026.
-
-Environment variables (set before running):
-    API_BASE_URL   The API endpoint for the LLM (e.g. https://router.huggingface.co/v1)
-    MODEL_NAME     The model identifier (e.g. Qwen/Qwen2.5-72B-Instruct)
-    HF_TOKEN       Your HuggingFace API key
-
-Usage:
-    export API_BASE_URL="https://router.huggingface.co/v1"
-    export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
-    export HF_TOKEN="hf_..."
-    python inference.py
-
-    # Dry-run without API key (heuristic agent):
-    python inference.py --heuristic
-
-    # Run against local dev server:
-    python inference.py --env-url http://localhost:7860
-
-Expected baseline scores (heuristic agent, seed=42, 3 episodes x 5 steps):
-    task_1_factual_grounding      : ~0.29
-    task_2_multi_hop_synthesis    : ~0.25
-    task_3_adversarial_resistance : ~0.22
-    overall                       : ~0.25
-"""
-
-from __future__ import annotations
-
-import os
-# Fix Unicode encoding for Windows console
-os.environ['PYTHONIOENCODING'] = 'utf-8'
-
-import sys
-import json
-import time
-import argparse
-import logging
-from typing import Dict, Any, List, Optional, Callable
-
-import requests
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-
-# ── Structured stdout logging for hackathon evaluation ──────────────────────────
-# Required format:
-# [START] task=<task_name> env=<benchmark> model=<model_name>
-# [STEP] step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
-# [END] success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>
-
-BENCHMARK = "hallucination-guard-env"
-
-
-def log_start(task: str, env: str, model: str) -> None:
-    """Emit [START] log in required format."""
-    print(f"[START] task={task} env={env} model={model}", flush=True)
-
-
-def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str] = None) -> None:
-    """Emit [STEP] log in required format."""
-    error_val = error if error else "null"
-    done_val = str(done).lower()
-    # Truncate action if too long and handle Unicode
-    action_trunc = action[:200].replace("\n", " ") if len(action) > 200 else action.replace("\n", " ")
-    # Replace non-ASCII characters to avoid encoding issues
-    action_trunc = action_trunc.encode('ascii', 'replace').decode('ascii')
-    print(f"[STEP] step={step} action={action_trunc} reward={reward:.2f} done={done_val} error={error_val}", flush=True)
-
-
-def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
-    """Emit [END] log in required format."""
-    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
-    print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True)
-
-# ── Mandatory environment variables ──────────────────────────────────────────
-API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
-MODEL_NAME   = os.getenv("MODEL_NAME",   "Qwen/Qwen2.5-72B-Instruct")
-HF_TOKEN     = os.getenv("HF_TOKEN",     "")
-
-# ── Defaults ──────────────────────────────────────────────────────────────────
-DEFAULT_ENV_URL  = os.environ.get(
-    "HALLUGUARD_ENV_URL",
-    "https://samsankar-hallucination-guard-env.hf.space",
-)
-DEFAULT_EPISODES = 3
-DEFAULT_STEPS    = 5
-SEED             = 42
-
-TASK_ORDER = [
-    ("task_1_factual_grounding",      "beginner"),
-    ("task_2_multi_hop_synthesis",    "intermediate"),
-    ("task_3_adversarial_resistance", "advanced"),
-]
-
-SYSTEM_PROMPT = """You are a precise, grounded question-answering assistant.
-
-RULES (follow strictly):
-1. Answer ONLY using information present in the CONTEXT provided.
-2. If the answer is not in the context, say exactly: "I cannot answer from the provided context."
-3. Keep answers concise — 1-3 sentences maximum.
-4. Never fabricate facts, names, dates, or numbers not in the context.
-5. If uncertain, express that uncertainty explicitly in your answer.
-"""
-
-ANSWER_PROMPT_TEMPLATE = """CONTEXT:
-{context}
-
-QUESTION:
-{question}
-
-Instructions:
-- Answer using ONLY the context above.
-- Provide a source_quote: a short verbatim phrase from the context that supports your answer.
-- Rate your confidence from 0.0 (unsure) to 1.0 (certain).
-
-Respond in JSON with these exact keys:
-{{
-    "answer": "<your answer>",
-    "source_quote": "<verbatim phrase from context>",
-    "confidence": <float 0.0-1.0>
-}}"""
-
-
-# ── Environment client ────────────────────────────────────────────────────────
-
-class EnvClient:
-    """Thin HTTP wrapper around the HallucinationGuard REST API."""
-
-    def __init__(self, base_url: str, timeout: int = 300):
-        self.base       = base_url.rstrip("/")
-        self.timeout    = timeout
-        self.session    = requests.Session()
-        self._session_id: Optional[str] = None
-
-    def _get(self, path: str) -> Dict[str, Any]:
-        r = self.session.get(f"{self.base}{path}", timeout=self.timeout)
-        r.raise_for_status()
-        return r.json()
-
-    def _post(self, path: str, body: Dict[str, Any] = {}) -> Dict[str, Any]:
-        r = self.session.post(f"{self.base}{path}", json=body, timeout=self.timeout)
-        r.raise_for_status()
-        return r.json()
-
-    def health(self) -> Dict[str, Any]:
-        return self._get("/health")
-
-    def list_tasks(self) -> Dict[str, Any]:
-        return self._get("/tasks")
-
-    def reset(self, difficulty: str, seed: int) -> Dict[str, Any]:
-        result = self._post("/reset", {"difficulty": difficulty, "seed": seed})
-        self._session_id = result.get("session_id")
-        return result
-
-    def step(self, answer: str, confidence: float, source_quote: str) -> Dict[str, Any]:
-        body: Dict[str, Any] = {
-            "answer":       answer,
-            "confidence":   confidence,
-            "source_quote": source_quote,
-        }
-        if self._session_id:
-            body["session_id"] = self._session_id
-        return self._post("/step", body)
-
-    def grade(self, task_id: str,
-              step_rewards: List[float],
-              step_infos:   List[Dict[str, Any]]) -> Dict[str, Any]:
-        return self._post("/grader", {
-            "task_id":      task_id,
-            "step_rewards": step_rewards,
-            "step_infos":   step_infos,
-        })
-
-
-# ── Agents ────────────────────────────────────────────────────────────────────
-
-def heuristic_agent(question: str, context: str) -> Dict[str, Any]:
-    """
-    Deterministic heuristic baseline — no LLM required.
-    Extracts the first meaningful sentence of the context as the answer.
-    Used when --heuristic flag is set or no API credentials are available.
-    """
-    sentences = [s.strip() for s in context.replace("\n", " ").split(".") if len(s.strip()) > 10]
-    answer       = sentences[0] if sentences else context[:120]
-    source_quote = context[:80] if context else ""
-    return {"answer": answer, "confidence": 0.6, "source_quote": source_quote}
-
-
-def openai_agent(model: str, base_url: str, api_key: str) -> Callable:
-    """
-    Returns a callable agent backed by any OpenAI-compatible chat endpoint.
-    Uses API_BASE_URL, MODEL_NAME, HF_TOKEN from environment variables.
-    """
-    try:
-        from openai import OpenAI
-    except ImportError:
-        logger.error("openai package not installed. Run: pip install openai")
-        sys.exit(1)
-
-    if not api_key:
-        logger.error(
-            "HF_TOKEN not set. Export it or use --heuristic for the "
-            "no-API baseline.\n"
-            "  export HF_TOKEN=hf_..."
-        )
-        sys.exit(1)
-
-    client = OpenAI(base_url=base_url, api_key=api_key)
-
-    def _call(question: str, context: str) -> Dict[str, Any]:
-        prompt = ANSWER_PROMPT_TEMPLATE.format(
-            context=context[:3000],
-            question=question,
-        )
-
-        # First try with JSON response format
-        try:
-            resp = client.chat.completions.create(
-                model=model,
-                messages=[
-                    {"role": "system", "content": SYSTEM_PROMPT},
-                    {"role": "user",   "content": prompt},
-                ],
-                temperature=0.0,
-                max_tokens=512,  # Increased from 256 to allow complete JSON
-                response_format={"type": "json_object"},
-            )
-            raw = resp.choices[0].message.content or "{}"
-            parsed = json.loads(raw)
-            return {
-                "answer":       str(parsed.get("answer", "")),
-                "confidence":   float(parsed.get("confidence", 0.5)),
-                "source_quote": str(parsed.get("source_quote", "")),
-            }
-        except json.JSONDecodeError:
-            raw_text = resp.choices[0].message.content or ""
-            return {"answer": raw_text[:200], "confidence": 0.4, "source_quote": ""}
-        except Exception as e:
-            # Fallback: try without response_format for models that don't support it
-            error_msg = str(e)
-            if "json_validate_failed" in error_msg or "response_format" in error_msg.lower():
-                logger.warning(f"JSON format failed, trying without response_format: {e}")
-                try:
-                    resp = client.chat.completions.create(
-                        model=model,
-                        messages=[
-                            {"role": "system", "content": SYSTEM_PROMPT},
-                            {"role": "user",   "content": prompt},
-                        ],
-                        temperature=0.0,
-                        max_tokens=512,
-                    )
-                    raw = resp.choices[0].message.content or "{}"
-                    # Try to extract JSON from response
-                    import re
-                    json_match = re.search(r'\{[^{}]*"answer"[^{}]*\}', raw, re.DOTALL)
-                    if json_match:
-                        try:
-                            parsed = json.loads(json_match.group(0))
-                            return {
-                                "answer": str(parsed.get("answer", "")),
-                                "confidence": float(parsed.get("confidence", 0.5)),
-                                "source_quote": str(parsed.get("source_quote", "")),
-                            }
-                        except:
-                            pass
-                    # If no valid JSON found, use raw text
-                    return {"answer": raw[:200], "confidence": 0.4, "source_quote": ""}
-                except Exception as e2:
-                    logger.warning(f"Fallback LLM call also failed: {e2}")
-                    return {"answer": "", "confidence": 0.0, "source_quote": ""}
-            else:
-                logger.warning(f"LLM call failed: {e}")
-                return {"answer": "", "confidence": 0.0, "source_quote": ""}
-
-    return _call
-
-
-# ── Episode runner ────────────────────────────────────────────────────────────
-
-def run_episode(
-    env:         EnvClient,
-    agent_fn:    Callable,
-    task_id:     str,
-    difficulty:  str,
-    steps:       int,
-    seed:        int,
-    episode_num: int,
-    model_label: str,
-) -> Dict[str, Any]:
-    """Run one episode and return rewards + infos for the grader."""
-    # Emit START log at beginning of each task
-    if episode_num == 0:
-        log_start(task=task_id, env=BENCHMARK, model=model_label)
-
-    obs = env.reset(difficulty=difficulty, seed=seed + episode_num)
-    step_rewards: List[float]         = []
-    step_infos:   List[Dict[str, Any]] = []
-
-    for step_n in range(steps):
-        if obs.get("done", False):
-            break
-
-        question = obs.get("question", "")
-        context  = obs.get("context",  "")
-
-        action = agent_fn(question, context)
-
-        obs = env.step(
-            answer=action["answer"],
-            confidence=action["confidence"],
-            source_quote=action["source_quote"],
-        )
-
-        reward = float(obs.get("reward") or 0.0)
-        done = bool(obs.get("done", False))
-        step_rewards.append(reward)
-        # Extract metrics from observation metadata (returned by the environment)
-        obs_metadata = obs.get("metadata", {})
-        if isinstance(obs_metadata, dict):
-            obs_correctness = obs_metadata.get("correctness", 0.0)
-            obs_calibration = obs_metadata.get("calibration", 0.0)
-            obs_hall_score = obs_metadata.get("hallucination_score", 0.0)
-        else:
-            obs_correctness = 0.0
-            obs_calibration = 0.0
-            obs_hall_score = 0.0
-        # Extract ML component scores from reward_breakdown if available
-        rb = obs_metadata.get("reward_breakdown", {}) if isinstance(obs_metadata, dict) else {}
-        step_infos.append({
-            "correctness":         obs_correctness,
-            "grounding":           obs.get("grounding_score", 0.0),
-            "calibration":         obs_calibration if obs_calibration else action["confidence"],
-            "hallucination_score": obs_hall_score if obs_hall_score else (1.0 if obs.get("is_hallucination") else 0.0),
-            "is_hallucination":    bool(obs.get("is_hallucination", False)),
-            "semantic_consistency": rb.get("semantic_consistency", 0.0),
-            "rouge_l":             rb.get("rouge_l", 0.0),
-            "bert_score":          rb.get("bert_score", 0.0),
-            "align_score":         rb.get("align_score", 0.0),
-        })
-
-        # Format action for logging (truncated answer)
-        action_str = f'answer="{action["answer"][:100]}" confidence={action["confidence"]:.2f}'
-
-        # Emit STEP log
-        log_step(
-            step=step_n + 1,
-            action=action_str,
-            reward=reward,
-            done=done,
-            error=None,
-        )
-
-        status = "HALLUCINATION" if obs.get("is_hallucination") else "OK"
-        logger.info(
-            f"  [{task_id[:25]}] ep={episode_num+1} step={step_n+1} "
-            f"reward={reward:.3f} [{status}]"
-        )
-
-    grade = env.grade(task_id, step_rewards, step_infos)
-    episode_score = grade.get("score", 0.0)
-
-    return {
-        "episode": episode_num + 1,
-        "score":   episode_score,
-        "rewards": step_rewards,
-        "grade":   grade,
-    }
-
-
-# ── Main ──────────────────────────────────────────────────────────────────────
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="HallucinationGuard-Env inference script",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    parser.add_argument("--env-url",   default=DEFAULT_ENV_URL,  help="Environment URL")
-    parser.add_argument("--episodes",  type=int, default=DEFAULT_EPISODES)
-    parser.add_argument("--steps",     type=int, default=DEFAULT_STEPS)
-    parser.add_argument("--seed",      type=int, default=SEED)
-    parser.add_argument("--heuristic", action="store_true",
-                        help="Use heuristic agent (no API key needed)")
-    parser.add_argument("--output",    default=None,
-                        help="Write JSON results to this file")
-    args = parser.parse_args()
-
-    # ── Connect to environment ────────────────────────────────────────────────
-    env = EnvClient(args.env_url)
-
-    logger.info(f"Connecting to environment: {args.env_url}")
-    try:
-        h = env.health()
-        logger.info(f"  Environment: {h.get('service')} v{h.get('version')} — healthy")
-    except Exception as e:
-        logger.error(f"Cannot reach environment: {e}")
-        sys.exit(1)
-
-    # Verify /tasks endpoint
-    try:
-        tasks_info = env.list_tasks()
-        task_ids   = [t["task_id"] for t in tasks_info.get("tasks", [])]
-        logger.info(f"  Tasks: {task_ids}")
-    except Exception as e:
-        logger.error(f"/tasks endpoint failed: {e}")
-        sys.exit(1)
-
-    # ── Select agent ─────────────────────────────────────────────────────────
-    if args.heuristic or not HF_TOKEN:
-        logger.info("Using heuristic baseline agent (no LLM).")
-        agent_fn    = heuristic_agent
-        model_label = "heuristic_baseline"
-    else:
-        logger.info(f"Using LLM agent: {MODEL_NAME} via {API_BASE_URL}")
-        agent_fn    = openai_agent(MODEL_NAME, API_BASE_URL, HF_TOKEN)
-        model_label = MODEL_NAME
-
-    # ── Run all 3 tasks ───────────────────────────────────────────────────────
-    task_results: List[Dict[str, Any]] = []
-    all_scores:   List[float]          = []
-    all_rewards:  List[float]          = []
-    total_steps   = 0
-    start_time    = time.time()
-
-    for task_id, difficulty in TASK_ORDER:
-        logger.info(f"\n{'='*55}")
-        logger.info(f"TASK: {task_id}  (difficulty={difficulty})")
-        logger.info(f"{'='*55}")
-
-        episode_scores: List[float] = []
-        task_rewards: List[float] = []
-
-        for ep in range(args.episodes):
-            ep_result = run_episode(
-                env=env,
-                agent_fn=agent_fn,
-                task_id=task_id,
-                difficulty=difficulty,
-                steps=args.steps,
-                seed=args.seed,
-                episode_num=ep,
-                model_label=model_label,
-            )
-            episode_scores.append(ep_result["score"])
-            all_scores.append(ep_result["score"])
-            all_rewards.extend(ep_result["rewards"])
-            task_rewards.extend(ep_result["rewards"])
-            total_steps += len(ep_result["rewards"])
-
-        task_avg = sum(episode_scores) / max(len(episode_scores), 1)
-        task_std = (
-            (sum((s - task_avg) ** 2 for s in episode_scores) / max(len(episode_scores), 1)) ** 0.5
-            if len(episode_scores) > 1 else 0.0
-        )
-
-        # Emit [END] log for this task
-        success = task_avg >= 0.5  # Consider success if score >= 0.5
-        log_end(
-            success=success,
-            steps=len(task_rewards),
-            score=task_avg,
-            rewards=task_rewards,
-        )
-
-        task_results.append({
-            "task_id":        task_id,
-            "difficulty":     difficulty,
-            "episodes":       args.episodes,
-            "episode_scores": [round(s, 4) for s in episode_scores],
-            "avg_score":      round(task_avg, 4),
-            "std_score":      round(task_std, 4),
-        })
-        logger.info(f"\n  Task score: {task_avg:.4f} ± {task_std:.4f}")
-
-    elapsed       = time.time() - start_time
-    overall_score = sum(all_scores)  / max(len(all_scores),  1)
-    avg_reward    = sum(all_rewards) / max(len(all_rewards), 1)
-
-    summary = {
-        "model":             model_label,
-        "api_base_url":      API_BASE_URL,
-        "env_url":           args.env_url,
-        "seed":              args.seed,
-        "episodes_per_task": args.episodes,
-        "steps_per_episode": args.steps,
-        "total_steps":       total_steps,
-        "elapsed_seconds":   round(elapsed, 1),
-        "tasks":             task_results,
-        "overall": {
-            "score":      round(overall_score, 4),
-            "avg_reward": round(avg_reward,    4),
-        },
-    }
-
-    # ── Print results ─────────────────────────────────────────────────────────
-    print("\n" + "=" * 55)
-    print("INFERENCE RESULTS")
-    print("=" * 55)
-    print(f"Model      : {model_label}")
-    print(f"Seed       : {args.seed}  |  {args.episodes} episodes x {args.steps} steps")
-    print(f"Elapsed    : {elapsed:.1f}s")
-    print()
-    for t in task_results:
-        # Use ASCII characters for progress bar
-        bar = "#" * round(t["avg_score"] * 20)
-        print(
-            f"  {t['task_id']:<42} "
-            f"{t['avg_score']:.4f} +- {t['std_score']:.4f}  |{bar:<20}|"
-        )
-    print()
-    print(f"  {'OVERALL':<42} {overall_score:.4f}")
-    print("=" * 55)
-
-    if args.output:
-        with open(args.output, "w") as f:
-            json.dump(summary, f, indent=2)
-        logger.info(f"Results written to {args.output}")
-
-    return summary
-
-
-if __name__ == "__main__":
-    main()
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+inference.py — AutoClean-AI Inference Script
+============================================
+Official submission script for OpenEnv Hackathon.
+
+Environment variables (set before running):
+    API_BASE_URL   The API endpoint for the LLM (e.g. https://router.huggingface.co/v1)
+    MODEL_NAME     The model identifier (e.g. Qwen/Qwen2.5-72B-Instruct)
+    HF_TOKEN       Your HuggingFace API key
+
+Usage:
+    export API_BASE_URL="https://router.huggingface.co/v1"
+    export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
+    export HF_TOKEN="hf_..."
+    python inference.py
+
+    # Dry-run without API key (heuristic agent):
+    python inference.py --heuristic
+
+    # Run against local dev server:
+    python inference.py --env-url http://localhost:7860
+
+Expected baseline scores (heuristic agent, seed=42, 3 episodes x 8 steps):
+    easy_001      : ~0.62
+    medium_001    : ~0.54
+    hard_001      : ~0.41
+    overall       : ~0.52
+"""
+
+from __future__ import annotations
+
+import os
+# Fix Unicode encoding for Windows console
+os.environ['PYTHONIOENCODING'] = 'utf-8'
+
+import sys
+import json
+import time
+import argparse
+import logging
+from typing import Dict, Any, List, Optional, Callable
+
+import requests
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+# ── Structured stdout logging for hackathon evaluation ──────────────────────────
+# Required format:
+# [START] task=<task_name> env=<benchmark> model=<model_name>
+# [STEP] step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
+# [END] success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>
+
+BENCHMARK = "openenv-datacleaner"
+
+
+def log_start(task: str, env: str, model: str) -> None:
+    """Emit [START] log in required format."""
+    print(f"[START] task={task} env={env} model={model}", flush=True)
+
+
+def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str] = None) -> None:
+    """Emit [STEP] log in required format."""
+    error_val = error if error else "null"
+    done_val = str(done).lower()
+    # Truncate action if too long and handle Unicode
+    action_trunc = action[:200].replace("\n", " ") if len(action) > 200 else action.replace("\n", " ")
+    # Replace non-ASCII characters to avoid encoding issues
+    action_trunc = action_trunc.encode('ascii', 'replace').decode('ascii')
+    print(f"[STEP] step={step} action={action_trunc} reward={reward:.2f} done={done_val} error={error_val}", flush=True)
+
+
+def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
+    """Emit [END] log in required format."""
+    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
+    print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True)
+
+# ── Mandatory environment variables ──────────────────────────────────────────
+API_BASE_URL = os.getenv("API_BASE_URL", "https://openrouter.ai/api/v1")
+MODEL_NAME   = os.getenv("MODEL_NAME",   "qwen/qwen3-next-80b-a3b-instruct:free")
+HF_TOKEN     = os.getenv("OPENROUTER_API_KEY") or os.getenv("HF_TOKEN",     "")
+
+# ── Defaults ──────────────────────────────────────────────────────────────────
+DEFAULT_ENV_URL  = os.environ.get(
+    "AUTOCLEAN_ENV_URL",
+    "https://sairaj2-openenv-datacleaner.hf.space",
+)
+DEFAULT_EPISODES = 3
+DEFAULT_STEPS    = 8
+SEED             = 42
+
+TASK_ORDER = [
+    ("easy_001",      "beginner"),
+    ("medium_001",    "intermediate"),
+    ("hard_001",      "advanced"),
+]
+
+SYSTEM_PROMPT = """You are an expert data cleaning agent for tabular datasets.
+
+RULES (follow strictly):
+1. You are working with a dataset and need to perform data cleaning operations
+2. Choose exactly ONE action per step from the allowed actions list
+3. Explain your reasoning clearly
+4. Always return valid JSON format
+
+ALLOWED ACTIONS:
+- drop_nulls
+- fill_nulls
+- remove_duplicates
+- filter_rows
+- drop_columns
+- convert_types
+- validate_email
+- outlier_removal
+- normalize
+- submit
+"""
+
+ACTION_PROMPT_TEMPLATE = """DATASET INFORMATION:
+{dataset_info}
+
+TASK:
+{task_description}
+
+PREVIOUS ACTIONS:
+{action_history}
+
+Instructions:
+- Select the next best action to clean this dataset
+- Provide reasoning for your choice
+- Return JSON with these exact keys:
+{{
+    "action_type": "<action name>",
+    "params": {{<parameters for action>}},
+    "reasoning": "<short explanation>"
+}}"""
+
+
+# ── Environment client ────────────────────────────────────────────────────────
+
+class EnvClient:
+    """Thin HTTP wrapper around the AutoClean REST API."""
+
+    def __init__(self, base_url: str, timeout: int = 300):
+        self.base       = base_url.rstrip("/")
+        self.timeout    = timeout
+        self.session    = requests.Session()
+        self._session_id: Optional[str] = None
+
+    def _request_with_retry(self, method: str, path: str, body: Dict[str, Any] = None, retries: int = 3, backoff: float = 2.0) -> Dict[str, Any]:
+        url = f"{self.base}{path}"
+        for attempt in range(retries):
+            try:
+                if method == "GET":
+                    r = self.session.get(url, timeout=self.timeout)
+                else:
+                    r = self.session.post(url, json=body, timeout=self.timeout)
+                r.raise_for_status()
+                return r.json()
+            except (requests.exceptions.ChunkedEncodingError,
+                    requests.exceptions.ConnectionError,
+                    requests.exceptions.ReadTimeout) as e:
+                if attempt < retries - 1:
+                    wait = backoff * (attempt + 1)
+                    logger.warning(f"Request to {path} failed ({type(e).__name__}), retrying in {wait:.0f}s... ({attempt+1}/{retries})")
+                    time.sleep(wait)
+                else:
+                    raise
+
+    def _get(self, path: str) -> Dict[str, Any]:
+        return self._request_with_retry("GET", path)
+
+    def _post(self, path: str, body: Dict[str, Any] = {}) -> Dict[str, Any]:
+        return self._request_with_retry("POST", path, body)
+
+    def health(self) -> Dict[str, Any]:
+        return self._get("/health")
+
+    def list_tasks(self) -> Dict[str, Any]:
+        return self._get("/tasks")
+
+    def reset(self, difficulty: str, seed: int) -> Dict[str, Any]:
+        result = self._post("/reset", {"difficulty": difficulty, "seed": seed})
+        self._session_id = result.get("session_id")
+        return result
+
+    def step(self, action_type: str, params: Dict[str, Any]) -> Dict[str, Any]:
+        body: Dict[str, Any] = {
+            "action_type": action_type,
+            "params": params,
+        }
+        if self._session_id:
+            body["session_id"] = self._session_id
+        return self._post("/step", body)
+
+    def submit(self) -> Dict[str, Any]:
+        body: Dict[str, Any] = {}
+        if self._session_id:
+            body["session_id"] = self._session_id
+        return self._post("/submit", body)
+
+    def grade(self, task_id: str,
+              step_rewards: List[float],
+              step_infos:   List[Dict[str, Any]]) -> Dict[str, Any]:
+        return self._post("/grader", {
+            "task_id":      task_id,
+            "step_rewards": step_rewards,
+            "step_infos":   step_infos,
+        })
+
+
+# ── Agents ────────────────────────────────────────────────────────────────────
+
+def heuristic_agent(task_id: str, dataset_info: Dict[str, Any], task_description: str, action_history: List[str]) -> Dict[str, Any]:
+    """
+    Deterministic heuristic baseline — no LLM required.
+    Implements standard data cleaning workflows based on task difficulty.
+    Used when --heuristic flag is set or no API credentials are available.
+    """
+    columns = dataset_info.get("columns", [])
+    null_counts = dataset_info.get("null_counts", {})
+    numeric_columns = [name for name in columns if name in {"age", "salary", "score", "id", "JoiningYear", "ExperienceInCurrentDomain"}]
+
+    def has_taken(action_type: str) -> bool:
+        return action_type in action_history
+
+    if task_id == "easy_001":
+        if sum(int(v) for v in null_counts.values()) > 0 and not has_taken("drop_nulls"):
+            return {"action_type": "drop_nulls", "params": {}}
+        if not has_taken("remove_duplicates"):
+            return {"action_type": "remove_duplicates", "params": {}}
+        return {"action_type": "submit", "params": {}}
+
+    if task_id == "medium_001":
+        if sum(int(v) for v in null_counts.values()) > 0 and not has_taken("fill_nulls"):
+            target = "age" if "age" in columns else (numeric_columns[0] if numeric_columns else None)
+            params = {"column": target, "strategy": "median"} if target else {"strategy": "mode"}
+            return {"action_type": "fill_nulls", "params": params}
+        if "email" in columns and not has_taken("validate_email"):
+            return {"action_type": "validate_email", "params": {"column": "email", "drop_invalid": True}}
+        if "salary" in columns and not has_taken("outlier_removal"):
+            return {"action_type": "outlier_removal", "params": {"column": "salary", "multiplier": 1.5}}
+        return {"action_type": "submit", "params": {}}
+
+    if task_id == "hard_001":
+        if sum(int(v) for v in null_counts.values()) > 0 and not has_taken("fill_nulls"):
+            target = "salary" if "salary" in columns else (numeric_columns[0] if numeric_columns else None)
+            params = {"column": target, "strategy": "median"} if target else {"strategy": "mode"}
+            return {"action_type": "fill_nulls", "params": params}
+        if not has_taken("remove_duplicates"):
+            return {"action_type": "remove_duplicates", "params": {}}
+        if "email" in columns and not has_taken("validate_email"):
+            return {"action_type": "validate_email", "params": {"column": "email", "drop_invalid": True}}
+        if "age" in columns and not has_taken("convert_types"):
+            return {"action_type": "convert_types", "params": {"column": "age", "dtype": "int"}}
+        if "salary" in columns and not has_taken("outlier_removal"):
+            return {"action_type": "outlier_removal", "params": {"column": "salary", "multiplier": 1.5}}
+        if "score" in columns and not has_taken("normalize"):
+            return {"action_type": "normalize", "params": {"column": "score", "method": "minmax"}}
+        return {"action_type": "submit", "params": {}}
+
+    return {"action_type": "submit", "params": {}}
+
+
+def openai_agent(model: str, base_url: str, api_key: str) -> Callable:
+    """
+    Returns a callable agent backed by any OpenAI-compatible chat endpoint.
+    Uses API_BASE_URL, MODEL_NAME, HF_TOKEN from environment variables.
+    """
+    try:
+        from openai import OpenAI
+    except ImportError:
+        logger.error("openai package not installed. Run: pip install openai")
+        sys.exit(1)
+
+    if not api_key:
+        logger.error(
+            "HF_TOKEN not set. Export it or use --heuristic for the "
+            "no-API baseline.\n"
+            "  export HF_TOKEN=hf_..."
+        )
+        sys.exit(1)
+
+    client = OpenAI(base_url=base_url, api_key=api_key)
+
+    def _call(task_id: str, dataset_info: Dict[str, Any], task_description: str, action_history: List[str]) -> Dict[str, Any]:
+        prompt = ACTION_PROMPT_TEMPLATE.format(
+            dataset_info=json.dumps(dataset_info, indent=2),
+            task_description=task_description,
+            action_history=", ".join(action_history) if action_history else "None",
+        )
+
+        # Try with JSON response format first, fall back to no format
+        for use_json_format in [True, False]:
+            try:
+                kwargs = dict(
+                    model=model,
+                    messages=[
+                        {"role": "system", "content": SYSTEM_PROMPT},
+                        {"role": "user",   "content": prompt},
+                    ],
+                    temperature=0.1,
+                    max_tokens=512,
+                )
+                if use_json_format:
+                    kwargs["response_format"] = {"type": "json_object"}
+
+                resp = client.chat.completions.create(**kwargs)
+                raw = (resp.choices[0].message.content or "").strip()
+
+                # Strip markdown code fences if present (```json ... ```)
+                import re
+                fence_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', raw, re.DOTALL)
+                if fence_match:
+                    raw = fence_match.group(1)
+
+                # Try direct JSON parse
+                try:
+                    parsed = json.loads(raw)
+                    action_type = str(parsed.get("action_type", ""))
+                    if action_type:
+                        return {
+                            "action_type": action_type,
+                            "params": parsed.get("params", {}),
+                        }
+                except json.JSONDecodeError:
+                    pass
+
+                # Try to extract JSON object from mixed text
+                json_match = re.search(r'\{[^{}]*"action_type"[^{}]*\}', raw, re.DOTALL)
+                if json_match:
+                    try:
+                        parsed = json.loads(json_match.group(0))
+                        return {
+                            "action_type": str(parsed.get("action_type", "")),
+                            "params": parsed.get("params", {}),
+                        }
+                    except json.JSONDecodeError:
+                        pass
+
+                # If JSON format was tried and failed, fall through to no-format attempt
+                if use_json_format:
+                    logger.warning("JSON parse failed, trying without response_format")
+                    continue
+
+                # Last resort: fall back to heuristic
+                return heuristic_agent(task_id, dataset_info, task_description, action_history)
+
+            except Exception as e:
+                if use_json_format:
+                    error_msg = str(e)
+                    if "response_format" in error_msg.lower() or "json_validate_failed" in error_msg:
+                        logger.warning(f"JSON format not supported, trying without: {e}")
+                        continue
+                    else:
+                        logger.warning(f"LLM call failed: {e}")
+                        return heuristic_agent(task_id, dataset_info, task_description, action_history)
+                else:
+                    logger.warning(f"LLM call failed: {e}")
+                    return heuristic_agent(task_id, dataset_info, task_description, action_history)
+
+        return heuristic_agent(task_id, dataset_info, task_description, action_history)
+
+    return _call
+
+
+# ── Episode runner ────────────────────────────────────────────────────────────
+
+def run_episode(
+    env:         EnvClient,
+    agent_fn:    Callable,
+    task_id:     str,
+    difficulty:  str,
+    steps:       int,
+    seed:        int,
+    episode_num: int,
+    model_label: str,
+    task_info:   Dict[str, Any],
+) -> Dict[str, Any]:
+    """Run one episode and return rewards + infos for the grader."""
+    # Emit START log at beginning of each task
+    if episode_num == 0:
+        log_start(task=task_id, env=BENCHMARK, model=model_label)
+
+    obs = env.reset(difficulty=difficulty, seed=seed + episode_num)
+    step_rewards: List[float]         = []
+    step_infos:   List[Dict[str, Any]] = []
+    action_history: List[str]         = []
+
+    dataset_info = obs.get("dataset_info", {})
+
+    for step_n in range(steps):
+        if obs.get("done", False):
+            break
+
+        action = agent_fn(task_id, dataset_info, task_info.get("description", ""), action_history)
+
+        action_type = action.get("action_type", "submit")
+        params = action.get("params", {})
+
+        if action_type == "submit":
+            obs = env.submit()
+        else:
+            obs = env.step(action_type, params)
+
+        reward = float(obs.get("reward") or 0.0)
+        done = bool(obs.get("done", False))
+        step_rewards.append(reward)
+
+        # Extract metrics from observation metadata
+        obs_metadata = obs.get("metadata", {})
+        step_infos.append({
+            "action_type": action_type,
+            "reward": reward,
+            "done": done,
+            "metadata": obs_metadata,
+        })
+
+        # Format action for logging
+        param_text = ",".join(f"{key}={json.dumps(value, sort_keys=True)}" for key, value in sorted(params.items()))
+        action_str = f"{action_type}({param_text})" if param_text else action_type
+
+        # Emit STEP log
+        log_step(
+            step=step_n + 1,
+            action=action_str,
+            reward=reward,
+            done=done,
+            error=None,
+        )
+
+        action_history.append(action_type)
+        dataset_info = obs.get("dataset_info", dataset_info)
+
+        logger.info(
+            f"  [{task_id[:25]}] ep={episode_num+1} step={step_n+1} "
+            f"reward={reward:.3f}"
+        )
+
+        if done:
+            break
+
+    if not done:
+        obs = env.submit()
+        reward = float(obs.get("reward") or 0.0)
+        step_rewards.append(reward)
+        log_step(
+            step=len(step_rewards),
+            action="submit()",
+            reward=reward,
+            done=True,
+            error=None,
+        )
+
+    # Calculate score locally (no /grader endpoint)
+    episode_score = sum(step_rewards) / max(len(step_rewards), 1)
+
+    return {
+        "episode": episode_num + 1,
+        "score":   episode_score,
+        "rewards": step_rewards,
+    }
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="AutoClean-AI inference script",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--env-url",   default=DEFAULT_ENV_URL,  help="Environment URL")
+    parser.add_argument("--episodes",  type=int, default=DEFAULT_EPISODES)
+    parser.add_argument("--steps",     type=int, default=DEFAULT_STEPS)
+    parser.add_argument("--seed",      type=int, default=SEED)
+    parser.add_argument("--heuristic", action="store_true",
+                        help="Use heuristic agent (no API key needed)")
+    parser.add_argument("--output",    default=None,
+                        help="Write JSON results to this file")
+    args = parser.parse_args()
+
+    # ── Connect to environment ────────────────────────────────────────────────
+    env = EnvClient(args.env_url)
+
+    logger.info(f"Connecting to environment: {args.env_url}")
+    try:
+        h = env.health()
+        logger.info(f"  Environment: {h.get('service', 'AutoClean-AI')} v{h.get('version', '1.0.0')} — healthy")
+    except Exception as e:
+        logger.error(f"Cannot reach environment: {e}")
+        sys.exit(1)
+
+    # Verify /tasks endpoint
+    try:
+        tasks_info = env.list_tasks()
+        task_ids   = [t["task_id"] for t in tasks_info.get("tasks", [])]
+        task_map   = {t["task_id"]: t for t in tasks_info.get("tasks", [])}
+        logger.info(f"  Available tasks: {task_ids}")
+    except Exception as e:
+        logger.error(f"/tasks endpoint failed: {e}")
+        sys.exit(1)
+
+    # ── Select agent ─────────────────────────────────────────────────────────
+    if args.heuristic or not HF_TOKEN:
+        logger.info("Using heuristic baseline agent (no LLM).")
+        agent_fn    = heuristic_agent
+        model_label = "heuristic_baseline"
+    else:
+        logger.info(f"Using LLM agent: {MODEL_NAME} via {API_BASE_URL}")
+        agent_fn    = openai_agent(MODEL_NAME, API_BASE_URL, HF_TOKEN)
+        model_label = MODEL_NAME
+
+    # ── Run all 3 tasks ───────────────────────────────────────────────────────
+    task_results: List[Dict[str, Any]] = []
+    all_scores:   List[float]          = []
+    all_rewards:  List[float]          = []
+    total_steps   = 0
+    start_time    = time.time()
+
+    for task_id, difficulty in TASK_ORDER:
+        logger.info(f"\n{'='*55}")
+        logger.info(f"TASK: {task_id}  (difficulty={difficulty})")
+        logger.info(f"{'='*55}")
+
+        episode_scores: List[float] = []
+        task_rewards: List[float] = []
+
+        for ep in range(args.episodes):
+            ep_result = run_episode(
+                env=env,
+                agent_fn=agent_fn,
+                task_id=task_id,
+                difficulty=difficulty,
+                steps=args.steps,
+                seed=args.seed,
+                episode_num=ep,
+                model_label=model_label,
+                task_info=task_map.get(task_id, {}),
+            )
+            episode_scores.append(ep_result["score"])
+            all_scores.append(ep_result["score"])
+            all_rewards.extend(ep_result["rewards"])
+            task_rewards.extend(ep_result["rewards"])
+            total_steps += len(ep_result["rewards"])
+
+        task_avg = sum(episode_scores) / max(len(episode_scores), 1)
+        task_std = (
+            (sum((s - task_avg) ** 2 for s in episode_scores) / max(len(episode_scores), 1)) ** 0.5
+            if len(episode_scores) > 1 else 0.0
+        )
+
+        # Emit [END] log for this task
+        success = task_avg >= 0.5  # Consider success if score >= 0.5
+        log_end(
+            success=success,
+            steps=len(task_rewards),
+            score=task_avg,
+            rewards=task_rewards,
+        )
+
+        task_results.append({
+            "task_id":        task_id,
+            "difficulty":     difficulty,
+            "episodes":       args.episodes,
+            "episode_scores": [round(s, 4) for s in episode_scores],
+            "avg_score":      round(task_avg, 4),
+            "std_score":      round(task_std, 4),
+        })
+        logger.info(f"\n  Task score: {task_avg:.4f} ± {task_std:.4f}")
+
+    elapsed       = time.time() - start_time
+    overall_score = sum(all_scores)  / max(len(all_scores),  1)
+    avg_reward    = sum(all_rewards) / max(len(all_rewards), 1)
+
+    summary = {
+        "model":             model_label,
+        "api_base_url":      API_BASE_URL,
+        "env_url":           args.env_url,
+        "seed":              args.seed,
+        "episodes_per_task": args.episodes,
+        "steps_per_episode": args.steps,
+        "total_steps":       total_steps,
+        "elapsed_seconds":   round(elapsed, 1),
+        "tasks":             task_results,
+        "overall": {
+            "score":      round(overall_score, 4),
+            "avg_reward": round(avg_reward,    4),
+        },
+    }
+
+    # ── Print results ─────────────────────────────────────────────────────────
+    print("\n" + "=" * 55)
+    print("INFERENCE RESULTS")
+    print("=" * 55)
+    print(f"Model      : {model_label}")
+    print(f"Seed       : {args.seed}  |  {args.episodes} episodes x {args.steps} steps")
+    print(f"Elapsed    : {elapsed:.1f}s")
+    print()
+    for t in task_results:
+        # Use ASCII characters for progress bar
+        bar = "#" * round(t["avg_score"] * 20)
+        print(
+            f"  {t['task_id']:<42} "
+            f"{t['avg_score']:.4f} +- {t['std_score']:.4f}  |{bar:<20}|"
+        )
+    print()
+    print(f"  {'OVERALL':<42} {overall_score:.4f}")
+    print("=" * 55)
+
+    if args.output:
+        with open(args.output, "w") as f:
+            json.dump(summary, f, indent=2)
+        logger.info(f"Results written to {args.output}")
+
+    return summary
+
+
+if __name__ == "__main__":
+    main()