refactored tests for llm council to have 5 tests and added requests to requirements.txt

requirements.txt

@@ -19,10 +19,11 @@ pytest
 networkx
 aiofiles
 httpx
+requests
 # vllm>=0.2.0
 aiohttp
 mcp
 numpy
 schedule
 uvloop; sys_platform == 'linux' or sys_platform == 'darwin' # linux or macos only
-winloop; sys_platform == 'win32' # windows only
+winloop; sys_platform == 'win32' # windows only

test_llm_council.py

@@ -0,0 +1,241 @@
+"""
+Test file for LLM Council functionality.
+
+Tests core functionalities of the LLM Council including:
+- Initialization (default and custom)
+- Running queries
+- Batch processing
+- Output formatting
+"""
+
+import pytest
+from loguru import logger
+from dotenv import load_dotenv
+from swarms.structs.llm_council import LLMCouncil
+from swarms.structs.agent import Agent
+
+load_dotenv()
+
+
+def test_llm_council_default_initialization():
+    """Test LLM Council initialization with default council members."""
+    try:
+        logger.info("Testing LLM Council default initialization...")
+        
+        council = LLMCouncil(
+            verbose=False,
+            output_type="dict-all-except-first"
+        )
+        
+        assert council is not None, "Council should be initialized"
+        assert council.name == "LLM Council", "Default name should be 'LLM Council'"
+        assert len(council.council_members) > 0, "Should have council members"
+        assert council.chairman is not None, "Chairman should be initialized"
+        assert council.conversation is not None, "Conversation should be initialized"
+        
+        logger.info(f"✓ Council initialized with {len(council.council_members)} members")
+        logger.info("✓ Default initialization test passed")
+        
+    except Exception as e:
+        logger.error(f"✗ Default initialization test failed: {e}")
+        raise
+
+
+def test_llm_council_custom_initialization():
+    """Test LLM Council initialization with custom council members."""
+    try:
+        logger.info("Testing LLM Council custom initialization...")
+        
+        # Create custom council members with simpler models
+        custom_members = [
+            Agent(
+                agent_name="TestAgent1",
+                agent_description="First test agent",
+                system_prompt="You are a helpful test agent.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+            Agent(
+                agent_name="TestAgent2",
+                agent_description="Second test agent",
+                system_prompt="You are a helpful test agent.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+        ]
+        
+        council = LLMCouncil(
+            name="Custom Council",
+            council_members=custom_members,
+            chairman_model="gpt-4o-mini",
+            verbose=False,
+            output_type="string"
+        )
+        
+        assert council is not None, "Council should be initialized"
+        assert council.name == "Custom Council", "Name should match custom value"
+        assert len(council.council_members) == 2, "Should have 2 custom members"
+        assert council.council_members[0].agent_name == "TestAgent1", "First member should match"
+        assert council.council_members[1].agent_name == "TestAgent2", "Second member should match"
+        assert council.output_type == "string", "Output type should be 'string'"
+        
+        logger.info("✓ Custom initialization test passed")
+        
+    except Exception as e:
+        logger.error(f"✗ Custom initialization test failed: {e}")
+        raise
+
+
+def test_llm_council_run():
+    """Test LLM Council run method with a simple query."""
+    try:
+        logger.info("Testing LLM Council run method...")
+        
+        # Use simpler models for testing
+        custom_members = [
+            Agent(
+                agent_name="TestAgent1",
+                agent_description="First test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+            Agent(
+                agent_name="TestAgent2",
+                agent_description="Second test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+        ]
+        
+        council = LLMCouncil(
+            council_members=custom_members,
+            chairman_model="gpt-4o-mini",
+            verbose=False,
+            output_type="dict-all-except-first"
+        )
+        
+        query = "What is 2 + 2? Provide a brief answer."
+        result = council.run(query)
+        
+        assert result is not None, "Result should not be None"
+        assert council.conversation is not None, "Conversation should exist"
+        assert len(council.conversation.messages) > 0, "Conversation should have messages"
+        
+        logger.info("✓ Run method test passed")
+        
+    except Exception as e:
+        logger.error(f"✗ Run method test failed: {e}")
+        raise
+
+
+def test_llm_council_batched_run():
+    """Test LLM Council batched_run method with multiple tasks."""
+    try:
+        logger.info("Testing LLM Council batched_run method...")
+        
+        # Use simpler models for testing
+        custom_members = [
+            Agent(
+                agent_name="TestAgent1",
+                agent_description="First test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+            Agent(
+                agent_name="TestAgent2",
+                agent_description="Second test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+        ]
+        
+        council = LLMCouncil(
+            council_members=custom_members,
+            chairman_model="gpt-4o-mini",
+            verbose=False,
+            output_type="dict-all-except-first"
+        )
+        
+        tasks = [
+            "What is 1 + 1?",
+            "What is 3 + 3?",
+        ]
+        
+        results = council.batched_run(tasks)
+        
+        assert results is not None, "Results should not be None"
+        assert len(results) == len(tasks), f"Should have {len(tasks)} results"
+        assert all(result is not None for result in results), "All results should not be None"
+        
+        logger.info(f"✓ Batched run test passed with {len(results)} results")
+        
+    except Exception as e:
+        logger.error(f"✗ Batched run test failed: {e}")
+        raise
+
+
+def test_llm_council_output_types():
+    """Test LLM Council with different output types."""
+    try:
+        logger.info("Testing LLM Council with different output types...")
+        
+        # Use simpler models for testing
+        custom_members = [
+            Agent(
+                agent_name="TestAgent1",
+                agent_description="First test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+            Agent(
+                agent_name="TestAgent2",
+                agent_description="Second test agent",
+                system_prompt="You are a helpful test agent. Provide concise answers.",
+                model_name="gpt-4o-mini",
+                max_loops=1,
+                verbose=False,
+            ),
+        ]
+        
+        output_types = ["string", "dict-all-except-first", "final"]
+        
+        for output_type in output_types:
+            logger.info(f"Testing output type: {output_type}")
+            
+            council = LLMCouncil(
+                council_members=custom_members,
+                chairman_model="gpt-4o-mini",
+                verbose=False,
+                output_type=output_type
+            )
+            
+            query = "What is 5 + 5? Provide a brief answer."
+            result = council.run(query)
+            
+            assert result is not None, f"Result should not be None for output type {output_type}"
+            assert council.output_type == output_type, f"Output type should be {output_type}"
+            
+            logger.info(f"✓ Output type '{output_type}' test passed")
+        
+        logger.info("✓ All output types test passed")
+        
+    except Exception as e:
+        logger.error(f"✗ Output types test failed: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
+