Return code: 0
Duration: 181.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("Task analysis complete. Proceeding with imp...
  python_exec: defining function create_asp_program()
  python_exec: defining function solve_blocks_world()
  python_exec: defining function test_grounding()
  python_exec: defining function create_asp_program_optimized()
  python_exec: defining function format_solution()
  python_exec: defining function verify_solution()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully solved the blocks world problem using Answer Set Programming  
(ASP) with the clingo Python API. Here's what was accomplished:                 

                               Solution Overview                                

 • Plan Length: 35 moves (well within the 50-move maximum)                      
 • All Constraints Satisfied:                                                   
    • ✓ Only clear blocks moved                                                 
    • ✓ Weight constraint enforced (heavier blocks cannot be placed on lighter  
      ones)                                                                     
    • ✓ Table limit respected (maximum 6 blocks on table at any time)           
    • ✓ Height limit respected (no tower exceeds height 5)                      
    • ✓ Sequential moves (one per timestep)                                     
    • ✓ Goal configuration achieved                                             

                           Key Implementation Details                           

 1 Sequential Planning Pattern: Used the correct pattern for temporal planning  
   with exactly one move per timestep                                           
 2 State Management: Implemented proper fluents with state exclusivity          
   constraints                                                                  
 3 Frame Axioms: Blocks persist in their positions unless explicitly moved      
 4 Efficient Search: Constrained choices at the source to only generate valid   
   moves                                                                        
 5 Verification: Solution was validated by simulating all moves and checking    
   constraints                                                                  

The final solution transforms the three initial stacks into the three goal      
towers through a carefully orchestrated sequence of 35 moves, respecting all    
physical and structural constraints.                                            

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            7 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        224,492
  Output tokens:       7,035
  Total tokens:        231,527

⏱️  Execution time: 2m 57.8s
==================================================


=== STDERR ===
