feat: implement interactive multi-agent system with MCP integration

.replit

@@ -4,6 +4,7 @@ modules = ["python-3.10", "bash"]
 channel = "stable-24_05"
 
 [workflows]
+runButton = "Run Interactive Agents"
 
 [[workflows.workflow]]
 name = "Run MCP Tests"
@@ -13,3 +14,12 @@ mode = "sequential"
 [[workflows.workflow.tasks]]
 task = "shell.exec"
 args = "python -m pytest tests/tools/test_mcp_integration.py -v"
+
+[[workflows.workflow]]
+name = "Run Interactive Agents"
+author = 13983571
+mode = "sequential"
+
+[[workflows.workflow.tasks]]
+task = "shell.exec"
+args = "python -m pytest tests/tools/test_mcp_integration.py::test_interactive_multi_agent_mcp -s"

tests/tools/test_mcp_integration.py

@@ -1,24 +1,75 @@
 
 import pytest
-from swarms.tools.mcp_integration import MCPServerSseParams, mcp_flow
+from swarms.tools.mcp_integration import MCPServerSseParams
+from swarms import Agent
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
 
-def test_mcp_flow():
-    params = MCPServerSseParams(
-        url="http://localhost:6274",
+def test_interactive_multi_agent_mcp():
+    # Configure two MCP servers
+    server_one = MCPServerSseParams(
+        url="http://0.0.0.0:6274",
         headers={"Content-Type": "application/json"}
     )
     
-    function_call = {
-        "tool_name": "test_tool",
-        "args": {"param1": "value1"}
-    }
-    
+    server_two = MCPServerSseParams(
+        url="http://0.0.0.0:6275", 
+        headers={"Content-Type": "application/json"}
+    )
+
+    # Create two agents with different roles
+    finance_agent = Agent(
+        agent_name="Finance-Agent",
+        agent_description="Financial analysis expert",
+        system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+        max_loops=1,
+        mcp_servers=[server_one],
+        interactive=True,
+        streaming_on=True
+    )
+
+    research_agent = Agent(
+        agent_name="Research-Agent", 
+        agent_description="Market research specialist",
+        system_prompt="You are a market research specialist. Analyze market trends and provide insights.",
+        max_loops=1,
+        mcp_servers=[server_two],
+        interactive=True,
+        streaming_on=True
+    )
+
     try:
-        result = mcp_flow(params, function_call)
-        assert isinstance(result, str)
+        # Interactive loop
+        while True:
+            # Get user input for which agent to use
+            print("\nWhich agent would you like to interact with?")
+            print("1. Finance Agent")
+            print("2. Research Agent")
+            print("3. Exit")
+            
+            choice = input("Enter your choice (1-3): ")
+            
+            if choice == "3":
+                break
+                
+            # Get the task from user
+            task = input("\nEnter your task for the agent: ")
+            
+            # Route to appropriate agent
+            if choice == "1":
+                response = finance_agent.run(task)
+                print(f"\nFinance Agent Response:\n{response}")
+            elif choice == "2":
+                response = research_agent.run(task)
+                print(f"\nResearch Agent Response:\n{response}")
+            else:
+                print("Invalid choice, please try again")
+
     except Exception as e:
-        pytest.fail(f"MCP flow failed: {e}")
+        pytest.fail(f"Interactive multi-agent test failed: {e}")
 
 def test_mcp_invalid_params():
     with pytest.raises(Exception):
         mcp_flow(None, {})
+
+if __name__ == "__main__":
+    test_interactive_multi_agent_mcp()