....

app.py

@@ -140,8 +140,7 @@ class SlpMultiAgent:
             max_steps=3,  # Allow more reasoning steps
             name="ResearchAgent",
             verbosity_level=0,
-            description="Quick factual research and knowledge lookup.",
-            system_prompt=system_prompt
+            description=system_prompt
         )
         
         solver_agent = CodeAgent(
@@ -151,8 +150,7 @@ class SlpMultiAgent:
             max_steps=2,  # Reduced steps
             name="SolverAgent",
             verbosity_level=0,
-            description="Problem solving, calculations, and logical reasoning.",
-            system_prompt=system_prompt
+            description=system_prompt
         )
 
         manager_agent = CodeAgent(
@@ -160,22 +158,12 @@ class SlpMultiAgent:
             tools=[KnowledgeBaseTool(), WikipediaSearchTool(), DuckDuckGoSearchTool()],  # Pass all retrieval tools
             managed_agents=[research_agent, solver_agent],  # Only 2 agents
             name="ManagerAgent",
-            description=(
-                "Efficient manager for quick problem solving. "
-                "Uses Wikipedia and DuckDuckGo for factual questions. "
-                "IMPORTANT: Only output a single code block in this format: "
-                "<code>\\nfinal_answer(\"your direct, simple answer\")\\n</code> "
-                "Do not use variables or function calls as the answer. "
-                "Do not include any other code, comments, or explanations. "
-                "For example, output: <code>\\nfinal_answer(\"5\")\\n</code> "
-                "NOT: <code>\\nfinal_answer(total_sales_usd)\\n</code>"
-            ),
+            description=system_prompt,
             additional_authorized_imports=["re", "math"],
             planning_interval=1,  # Faster planning
             verbosity_level=0,  # Reduce verbosity
             max_steps=3,  # Further reduced steps to avoid timeouts
-            final_answer_checks=[check_reasoning],
-            system_prompt=system_prompt
+            final_answer_checks=[check_reasoning]
         )
         
         # Create a task for the agent run with retry mechanism for rate limits