Return code: 0
Duration: 89.5s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: vertices = {
  python_exec: defining function generate_asp_facts()
  python_exec: asp_program = """
  python_exec: defining function solve_clique_problem()
  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 completed the constrained maximum clique problem using Answer 
Set Programming with the clingo Python API.                                     

                               Solution Overview                                

The ASP model finds the maximum-sized clique of 6 vertices [2, 5, 8, 11, 14, 17]
that satisfies all constraints:                                                 

✅ Clique Property: All 15 pairs of vertices are connected by edges             
✅ Type Diversity: 2 alpha + 2 beta + 2 gamma (max 2 per type)                  
✅ Weight Limit: Total weight = 99 ≤ 100                                        

                                ASP Model Design                                

The solution uses:                                                              

 1 Choice rule: { in_clique(V) } :- vertex(V). to select vertices               
 2 Clique constraint: Forbids selecting non-adjacent vertices                   
 3 Type diversity constraint: Uses #count aggregate to limit vertices per type  
 4 Weight constraint: Uses #sum aggregate to enforce weight limit               
 5 Optimization: #maximize to find the largest clique                           

The code is clean, well-structured, and produces the required JSON output format
with all requested fields.                                                      

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

🔧 Tool Usage:
  python_exec            6 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        107,768
  Output tokens:       4,919
  Total tokens:        112,687

⏱️  Execution time: 1m 27.7s
==================================================


=== STDERR ===
