feat(math): integrate gpt-4o-mini model and fix streaming issues in mcp_execution_flow

attached_assets/Pasted--Math-Agent-System-Initialized-Available-operations-2025-04-20-10-41-14-WARNING-swarms-st-1745145960627.txt

@@ -0,0 +1,50 @@
+
+Math Agent System Initialized
+
+Available operations:
+2025-04-20 10:41:14 | WARNING  | swarms.structs.agent:llm_handling:646 - Model name is not provided, using gpt-4o-mini. You can configure any model from litellm if desired.
+Math Agent: add, multiply, divide
+
+Enter your query (or 'exit' to quit): add 8 and 11
+╭─────────────────────────────────────────────────────── Agent Name: Math Agent [Max Loops: 1] ───────────────────────────────────────────────────────╮
+│ Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f418e20>                                             │
+╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
+2025-04-20 10:44:25 | ERROR    | swarms.structs.agent:_run:1147 - Attempt 1: Error generating response: 'Agent' object has no attribute 'mcp_execution_flow'
+╭─────────────────────────────────────────────────────── Agent Name: Math Agent [Max Loops: 1] ───────────────────────────────────────────────────────╮
+│ Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f3d2950>                                             │
+╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
+2025-04-20 10:44:27 | ERROR    | swarms.structs.agent:_run:1147 - Attempt 2: Error generating response: 'Agent' object has no attribute 'mcp_execution_flow'
+╭─────────────────────────────────────────────────────── Agent Name: Math Agent [Max Loops: 1] ───────────────────────────────────────────────────────╮
+│ Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f3d22c0>                                             │
+╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
+2025-04-20 10:44:29 | ERROR    | swarms.structs.agent:_run:1147 - Attempt 3: Error generating response: 'Agent' object has no attribute 'mcp_execution_flow'
+2025-04-20 10:44:29 | ERROR    | swarms.structs.agent:_run:1160 - Failed to generate a valid response after retry attempts.
+
+Math Agent Response: System: : Your Name: Math Agent 
+
+ Your Description: Specialized agent for mathematical computations 
+
+ You are a specialized math agent that can perform calculations by calling external math service APIs.
+Key responsibilities:
+1. Understand mathematical queries and break them down into basic operations
+2. Use available math tools (add, multiply, divide) appropriately
+3. Provide clear explanations of calculations
+4. Handle errors gracefully if operations fail
+
+Remember to use the available MCP tools for calculations rather than doing them directly.
+
+
+Human:: add 8 and 11
+
+
+Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f418e20>
+
+
+Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f3d2950>
+
+
+Math Agent: <litellm.litellm_core_utils.streaming_handler.CustomStreamWrapper object at 0x7ff74f3d22c0>
+
+
+
+Enter your query (or 'exit' to quit): 

attached_assets/Pasted-Your-mock-Math-MCP-server-is-running-fine-the-errors-are-coming-from-the-Swarms-Agent-side--1745145946200.txt

@@ -0,0 +1,123 @@
+Your mock Math MCP server is running fine — the errors are coming from the
+**Swarms Agent** side:
+
+1. **`mcp_execution_flow` is missing**  
+2. The agent reports **`CustomStreamWrapper …`** instead of the model’s text
+   because `streaming_on=True` but the helper that “unwraps” the stream is not
+   in place yet.
+
+Below is the minimal patch set that makes the smoke test work today.
+Apply the three diffs, reinstall, and re‑run `examples/mcp_example/mcp_client.py`.
+
+---
+
+### ① Add `mcp_execution_flow` to the Agent
+
+Put this anywhere inside **swarms/structs/agent.py** (e.g. just under
+`parse_and_execute_tools`).  
+It converts the LLM‑function‑call JSON into a normal dict, then dispatches it
+to every server you supplied in `mcp_servers`.
+
+```python
+    # --- MCP TOOL EXECUTION -------------------------------------------------
+    def mcp_execution_flow(self, response: str | dict) -> str | None:
+        """
+        Detect an LLM function‑call style response and proxy the call to the
+        configured MCP servers. Returns the tool output as a string so it can
+        be fed back into the conversation.
+        """
+        if not self.mcp_servers:
+            return None
+
+        try:
+            # LLM may give us a JSON string or already‑parsed dict
+            if isinstance(response, str):
+                call_dict = json.loads(response)
+            else:
+                call_dict = response
+
+            if not isinstance(call_dict, dict):
+                return None            # nothing to do
+
+            if "tool_name" not in call_dict and "name" not in call_dict:
+                return None            # not a tool call
+
+            from swarms.tools.mcp_integration import batch_mcp_flow
+            out = batch_mcp_flow(self.mcp_servers, call_dict)
+            return any_to_str(out)
+
+        except Exception as e:
+            logger.error(f"MCP flow failed: {e}")
+            return f"[MCP‑error] {e}"
+```
+
+---
+
+### ② Expose `mcp_servers` on the Agent constructor
+
+Earlier you commented it out; put it back so the attribute exists.
+
+```diff
+-        # mcp_servers: List[MCPServerSseParams] = [],
++        mcp_servers: Optional[List[MCPServerSseParams]] = None,
+         *args, **kwargs,
+     ):
+ ...
+-        # self.mcp_servers = mcp_servers
++        self.mcp_servers = mcp_servers or []
+```
+
+---
+
+### ③ Call the new flow inside `_run`
+
+Replace the tiny block where you already try to use it:
+
+```diff
+-                        if self.tools is not None or hasattr(self, 'mcp_servers'):
++                        if self.tools is not None or self.mcp_servers:
+                             if self.tools:
+                                 out = self.parse_and_execute_tools(response)
+-                            if hasattr(self, 'mcp_servers') and self.mcp_servers:
+-                                out = self.mcp_execution_flow(response)
++                            if self.mcp_servers:
++                                mcp_out = self.mcp_execution_flow(response)
++                                if mcp_out:
++                                    out = mcp_out
+```
+
+---
+
+### ④ (Optionally) turn off streaming for now
+
+Until you implement a stream‑unwrapper, just start the math agent with
+`streaming_on=False`:
+
+```python
+math_agent = Agent(
+    ...
+    streaming_on=False
+)
+```
+
+---
+
+### ⑤ Re‑run the smoke test
+
+```bash
+pip install -U mcp  typing_extensions
+python examples/mcp_example/math_server.py     # leave running
+python examples/mcp_example/mcp_client.py
+```
+
+```
+Enter your query: add 8 and 11
+Math Agent Response: 19
+```
+
+No more attribute errors or `CustomStreamWrapper` objects.  
+You can now iterate on nicer output formatting or re‑enable streaming once you
+unwrap the tokens.
+
+That’s it — your Swarms agent is now able to call MCP tools via the mock
+FastMCP math server.

examples/mcp_example/mcp_client.py

@@ -13,24 +13,23 @@ Key responsibilities:
 
 Remember to use the available MCP tools for calculations rather than doing them directly."""
 
+
 def main():
     # Configure MCP server connection
     math_server = MCPServerSseParams(
         url="http://0.0.0.0:8000/mcp",
         headers={"Content-Type": "application/json"},
         timeout=5.0,
-        sse_read_timeout=30.0
-    )
+        sse_read_timeout=30.0)
 
     # Initialize math agent
     math_agent = Agent(
         agent_name="Math Agent",
         agent_description="Specialized agent for mathematical computations",
         system_prompt=MATH_AGENT_PROMPT,
-        max_loops=auto,
+        max_loops=1,
         mcp_servers=[math_server],
-        streaming_on=True
-    )
+        streaming_on=True)
 
     print("\nMath Agent System Initialized")
     print("\nAvailable operations:")
@@ -46,5 +45,6 @@ def main():
         math_result = math_agent.run(query)
         print("\nMath Agent Response:", math_result)
 
+
 if __name__ == "__main__":
     main()