Update agent.py and add project structure documentation

project_structure.txt

@@ -0,0 +1,137 @@
+Swarms Project Structure
+=======================
+
+1. Core Package (swarms/)
+-----------------------
+├── agents/
+│   ├── agent_judge.py
+│   ├── agent_print.py
+│   ├── ape_agent.py
+│   ├── auto_generate_swarm_config.py
+│   ├── consistency_agent.py
+│   ├── create_agents_from_yaml.py
+│   ├── flexion_agent.py
+│   ├── gkp_agent.py
+│   ├── i_agent.py
+│   ├── openai_assistant.py
+│   ├── reasoning_agents.py
+│   ├── reasoning_duo.py
+│   ├── tool_agent.py
+│   └── __init__.py
+
+├── prompts/
+│   ├── accountant_swarm_prompts.py
+│   ├── agent_judge_prompt.py
+│   ├── agent_prompt.py
+│   ├── agent_prompts.py
+│   ├── agent_system_prompts.py
+│   ├── autoswarm.py
+│   ├── finance_agent_prompt.py
+│   ├── finance_agent_sys_prompt.py
+│   ├── math_agent_prompt.py
+│   ├── security_team.py
+│   ├── support_agent_prompt.py
+│   └── many more prompt files...
+
+├── structs/
+│   ├── agent.py
+│   ├── agents_available.py
+│   ├── agent_registry.py
+│   ├── agent_roles.py
+│   ├── agent_router.py
+│   ├── async_workflow.py
+│   ├── base_structure.py
+│   ├── base_swarm.py
+│   ├── base_workflow.py
+│   ├── concurrent_workflow.py
+│   ├── deep_research_swarm.py
+│   ├── graph_swarm.py
+│   ├── groupchat.py
+│   ├── majority_voting.py
+│   ├── matrix_swarm.py
+│   ├── mixture_of_agents.py
+│   ├── sequential_workflow.py
+│   └── many more structure files...
+
+├── tools/
+│   ├── base_tool.py
+│   ├── mcp_integration.py
+│   ├── tool_registry.py
+│   ├── tool_utils.py
+│   └── many more tool files...
+
+├── utils/
+│   ├── any_to_str.py
+│   ├── file_processing.py
+│   ├── formatter.py
+│   ├── loguru_logger.py
+│   ├── pdf_to_text.py
+│   └── many more utility files...
+
+├── schemas/
+│   ├── agent_input_schema.py
+│   ├── agent_step_schemas.py
+│   ├── base_schemas.py
+│   └── __init__.py
+
+├── telemetry/
+│   ├── bootup.py
+│   ├── main.py
+│   └── __init__.py
+
+├── client/
+│   ├── main.py
+│   └── __init__.py
+
+└── cli/
+    ├── create_agent.py
+    ├── main.py
+    ├── onboarding_process.py
+    └── __init__.py
+
+2. Examples (examples/)
+---------------------
+├── advanced_market_analysis/
+├── crypto/
+├── document_processing/
+├── forest_swarm_examples/
+├── groupchat_examples/
+├── healthcare/
+├── mcp_example/
+├── sequential_workflow/
+├── tools_examples/
+└── many more example directories...
+
+3. Documentation (docs/)
+----------------------
+├── swarms/
+├── swarms_cloud/
+├── swarms_memory/
+├── swarms_platform/
+├── swarms_tools/
+└── many more documentation sections...
+
+4. Tests (tests/)
+---------------
+├── agents/
+├── artifacts/
+├── prompts/
+├── structs/
+├── utils/
+└── many more test directories...
+
+5. Configuration Files
+--------------------
+├── pyproject.toml
+├── requirements.txt
+├── poetry.lock
+├── Dockerfile
+└── .env.example
+
+6. Main Project Files
+-------------------
+├── CODE_OF_CONDUCT.md
+├── CONTRIBUTING.md
+├── LICENSE
+├── README.md
+└── SECURITY.md 

swarms/structs/agent.py

@@ -845,9 +845,7 @@ class Agent:
                 # Use a default temperature
                 self.llm.temperature = 0.5
         except Exception as error:
-            logger.error(
+            logger.error(f"Error dynamically changing temperature: {error}")
-                f"Error dynamically changing temperature: {error}"
-            )
 
     def print_dashboard(self):
         """Print dashboard"""
@@ -1873,7 +1871,7 @@ class Agent:
         self.retry_interval = retry_interval
 
     def reset(self):
-        """Reset the agent"""Reset the agent"""
+        """Reset the agent"""
         self.short_memory = None
 
     def ingest_docs(self, docs: List[str], *args, **kwargs):
@@ -2642,45 +2640,98 @@ class Agent:
                 )
 
     def parse_llm_output(self, response: Any) -> str:
-        """Parse and standardize the output from the LLM.
+        """Parse the LLM output to a string."""
+        if isinstance(response, str):
+            return response
+        elif isinstance(response, dict):
+            return json.dumps(response)
+        elif isinstance(response, list):
+            return json.dumps(response)
+        else:
+            return str(response)
+
+    def mcp_execution_flow(self, response: str) -> str:
+        """Forward tool calls to MCP servers with support for various input formats.
         
         Args:
-            response (Any): The response from the LLM in any format
+            response (str): The response from the LLM containing tool calls or natural language.
             
         Returns:
-            str: Standardized string output
+            str: The result of executing the tool calls with preserved formatting.
-
-        Raises:
-            ValueError: If the response format is unexpected and cannot be handled
         """
         try:
-            # Handle dictionary responses
+            # Try to parse as JSON first
-            if isinstance(response, dict):
+            try:
-                if "choices" in response:
+                tool_calls = json.loads(response)
-                    return response["choices"][0]["message"][
+                is_json = True
-                        "content"
+            except json.JSONDecodeError:
-                    ]
+                # If not JSON, treat as natural language
-                return json.dumps(
+                tool_calls = [response]
-                    response
+                is_json = False
-                )  # Convert other dicts to string
+                
-
+            # Execute tool calls against MCP servers
-            # Handle string responses
+            results = []
-            elif isinstance(response, str):
+            errors = []
-                return response
+            
+            # Handle both single tool call and array of tool calls
+            if isinstance(tool_calls, dict):
+                tool_calls = [tool_calls]
+                
+            for tool_call in tool_calls:
+                try:
+                    # Execute the tool call against all MCP servers
+                    result = batch_mcp_flow(self.mcp_servers, tool_call)
+                    if result:
+                        results.extend(result)
+                        # Add successful result to memory with context
+                        self.short_memory.add(
+                            role="assistant",
+                            content=f"Tool execution result: {result}"
+                        )
+                    else:
+                        error_msg = "No result from tool execution"
+                        errors.append(error_msg)
+                        self.short_memory.add(
+                            role="error",
+                            content=error_msg
+                        )
                         
-            # Handle list responses (from check_llm_outputs)
+                except Exception as e:
-            elif isinstance(response, list):
+                    error_msg = f"Error executing tool call: {str(e)}"
-                return "\n".join(response)
+                    errors.append(error_msg)
+                    logger.error(error_msg)
+                    self.short_memory.add(
+                        role="error",
+                        content=error_msg
+                    )
             
-            # Handle any other type by converting to string
+            # Format the final response
+            if results:
+                if len(results) == 1:
+                    # For single results, return as is to preserve formatting
+                    return results[0]
                 else:
-                return str(response)
+                    # For multiple results, combine with context
+                    formatted_results = []
+                    for i, result in enumerate(results, 1):
+                        formatted_results.append(f"Result {i}: {result}")
+                    return "\n".join(formatted_results)
+            elif errors:
+                if len(errors) == 1:
+                    return errors[0]
+                else:
+                    return "Multiple errors occurred:\n" + "\n".join(f"- {err}" for err in errors)
+            else:
+                return "No results or errors returned"
             
         except Exception as e:
-            logger.error(f"Error parsing LLM output: {e}")
+            error_msg = f"Error in MCP execution flow: {str(e)}"
-            raise ValueError(
+            logger.error(error_msg)
-                f"Failed to parse LLM output: {type(response)}"
+            self.short_memory.add(
+                role="error",
+                content=error_msg
             )
+            return error_msg
 
     def sentiment_and_evaluator(self, response: str):
         if self.evaluator:
@@ -2711,16 +2762,3 @@ class Agent:
                 role="Output Cleaner",
                 content=response,
             )
-    def mcp_execution_flow(self, response: str) -> str:
-        """
-        Forward the JSON tool-call coming from the LLM to all MCP servers
-        listed in self.mcp_servers.
-        """
-        try:
-            payload = json.loads(response)  # {"tool_name": ...}
-            results = batch_mcp_flow(self.mcp_servers, payload)
-            # batch_mcp_flow already blocks, so results is a list[str]
-            return any_to_str(results[0] if len(results) == 1 else results)
-        except Exception as err:
-            logger.error(f"MCP flow failed: {err}")
-            return f"[MCP-error] {err}"