[FEATS][CSVTOAgents] [Various Bug Fixes]

new_features_examples/qdrant_agent.py

@@ -3,7 +3,6 @@ import uuid
 from datetime import datetime
 from typing import Dict, List, Optional
 
-import numpy as np
 from qdrant_client import QdrantClient
 from qdrant_client.http import models
 from qdrant_client.http.models import Distance, VectorParams
@@ -18,7 +17,7 @@ class QdrantMemory:
         collection_name: str = "agent_memories",
         vector_size: int = 1536,  # Default size for Claude embeddings
         url: Optional[str] = None,
-        api_key: Optional[str] = None
+        api_key: Optional[str] = None,
     ):
         """Initialize Qdrant memory system.
 
@@ -35,7 +34,9 @@ class QdrantMemory:
         if url and api_key:
             self.client = QdrantClient(url=url, api_key=api_key)
         else:
-            self.client = QdrantClient(":memory:")  # Local in-memory storage
+            self.client = QdrantClient(
+                ":memory:"
+            )  # Local in-memory storage
 
         # Create collection if it doesn't exist
         self._create_collection()
@@ -43,22 +44,23 @@ class QdrantMemory:
     def _create_collection(self):
         """Create the Qdrant collection if it doesn't exist."""
         collections = self.client.get_collections().collections
-        exists = any(col.name == self.collection_name for col in collections)
+        exists = any(
+            col.name == self.collection_name for col in collections
+        )
 
         if not exists:
             self.client.create_collection(
                 collection_name=self.collection_name,
                 vectors_config=VectorParams(
-                    size=self.vector_size,
-                    distance=Distance.COSINE
-                )
+                    size=self.vector_size, distance=Distance.COSINE
+                ),
             )
 
     def add(
         self,
         text: str,
         embedding: List[float],
-        metadata: Optional[Dict] = None
+        metadata: Optional[Dict] = None,
     ) -> str:
         """Add a memory to the store.
 
@@ -75,21 +77,18 @@ class QdrantMemory:
 
         # Add timestamp and generate ID
         memory_id = str(uuid.uuid4())
-        metadata.update({
-            "timestamp": datetime.utcnow().isoformat(),
-            "text": text
-        })
+        metadata.update(
+            {"timestamp": datetime.utcnow().isoformat(), "text": text}
+        )
 
         # Store the point
         self.client.upsert(
             collection_name=self.collection_name,
             points=[
                 models.PointStruct(
-                    id=memory_id,
-                    payload=metadata,
-                    vector=embedding
+                    id=memory_id, payload=metadata, vector=embedding
                 )
-            ]
+            ],
         )
 
         return memory_id
@@ -98,7 +97,7 @@ class QdrantMemory:
         self,
         query_embedding: List[float],
         limit: int = 5,
-        score_threshold: float = 0.7
+        score_threshold: float = 0.7,
     ) -> List[Dict]:
         """Query memories based on vector similarity.
 
@@ -114,7 +113,7 @@ class QdrantMemory:
             collection_name=self.collection_name,
             query_vector=query_embedding,
             limit=limit,
-            score_threshold=score_threshold
+            score_threshold=score_threshold,
         )
 
         memories = []
@@ -129,9 +128,7 @@ class QdrantMemory:
         """Delete a specific memory by ID."""
         self.client.delete(
             collection_name=self.collection_name,
-            points_selector=models.PointIdsList(
-                points=[memory_id]
-            )
+            points_selector=models.PointIdsList(points=[memory_id]),
         )
 
     def clear(self):
@@ -139,6 +136,7 @@ class QdrantMemory:
         self.client.delete_collection(self.collection_name)
         self._create_collection()
 
+
 # # Example usage
 # if __name__ == "__main__":
 #     # Initialize memory
@@ -168,7 +166,9 @@ class QdrantMemory:
 # Initialize memory with optional cloud configuration
 memory = QdrantMemory(
     url=os.getenv("QDRANT_URL"),  # Optional: For cloud deployment
-    api_key=os.getenv("QDRANT_API_KEY")  # Optional: For cloud deployment
+    api_key=os.getenv(
+        "QDRANT_API_KEY"
+    ),  # Optional: For cloud deployment
 )
 
 # Model
@@ -184,4 +184,6 @@ agent = Agent(
 )
 
 # Run a query
-agent.run("What are the components of a startup's stock incentive equity plan?")
+agent.run(
+    "What are the components of a startup's stock incentive equity plan?"
+)

pulsar_swarm.py

@@ -14,39 +14,71 @@ from prometheus_client import Counter, Histogram, start_http_server
 from pydantic import BaseModel, Field, validator
 from tenacity import retry, stop_after_attempt, wait_exponential
 
-from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
 from swarms.structs.agent import Agent
 
 
 # Enhanced metrics
-TASK_COUNTER = Counter('swarm_tasks_total', 'Total number of tasks processed')
-TASK_LATENCY = Histogram('swarm_task_duration_seconds', 'Task processing duration')
-TASK_FAILURES = Counter('swarm_task_failures_total', 'Total number of task failures')
-AGENT_ERRORS = Counter('swarm_agent_errors_total', 'Total number of agent errors')
+TASK_COUNTER = Counter(
+    "swarm_tasks_total", "Total number of tasks processed"
+)
+TASK_LATENCY = Histogram(
+    "swarm_task_duration_seconds", "Task processing duration"
+)
+TASK_FAILURES = Counter(
+    "swarm_task_failures_total", "Total number of task failures"
+)
+AGENT_ERRORS = Counter(
+    "swarm_agent_errors_total", "Total number of agent errors"
+)
 
 # Define types using Literal
 TaskStatus = Literal["pending", "processing", "completed", "failed"]
 TaskPriority = Literal["low", "medium", "high", "critical"]
 
+
 class SecurityConfig(BaseModel):
     """Security configuration for the swarm"""
-    encryption_key: str = Field(..., description="Encryption key for sensitive data")
-    tls_cert_path: Optional[str] = Field(None, description="Path to TLS certificate")
-    tls_key_path: Optional[str] = Field(None, description="Path to TLS private key")
-    auth_token: Optional[str] = Field(None, description="Authentication token")
-    max_message_size: int = Field(default=1048576, description="Maximum message size in bytes")
-    rate_limit: int = Field(default=100, description="Maximum tasks per minute")
-
-    @validator('encryption_key')
+
+    encryption_key: str = Field(
+        ..., description="Encryption key for sensitive data"
+    )
+    tls_cert_path: Optional[str] = Field(
+        None, description="Path to TLS certificate"
+    )
+    tls_key_path: Optional[str] = Field(
+        None, description="Path to TLS private key"
+    )
+    auth_token: Optional[str] = Field(
+        None, description="Authentication token"
+    )
+    max_message_size: int = Field(
+        default=1048576, description="Maximum message size in bytes"
+    )
+    rate_limit: int = Field(
+        default=100, description="Maximum tasks per minute"
+    )
+
+    @validator("encryption_key")
     def validate_encryption_key(cls, v):
         if len(v) < 32:
-            raise ValueError("Encryption key must be at least 32 bytes long")
+            raise ValueError(
+                "Encryption key must be at least 32 bytes long"
+            )
         return v
 
+
 class Task(BaseModel):
     """Enhanced task model with additional metadata and validation"""
-    task_id: str = Field(..., description="Unique identifier for the task")
-    description: str = Field(..., description="Task description or instructions")
+
+    task_id: str = Field(
+        ..., description="Unique identifier for the task"
+    )
+    description: str = Field(
+        ..., description="Task description or instructions"
+    )
     output_type: Literal["string", "json", "file"] = Field("string")
     status: TaskStatus = Field(default="pending")
     priority: TaskPriority = Field(default="medium")
@@ -56,20 +88,19 @@ class Task(BaseModel):
     retry_count: int = Field(default=0)
     metadata: Dict[str, Any] = Field(default_factory=dict)
 
-    @validator('task_id')
+    @validator("task_id")
     def validate_task_id(cls, v):
         if not v.strip():
             raise ValueError("task_id cannot be empty")
         return v
 
     class Config:
-        json_encoders = {
-            datetime: lambda v: v.isoformat()
-        }
+        json_encoders = {datetime: lambda v: v.isoformat()}
 
 
 class TaskResult(BaseModel):
     """Model for task execution results"""
+
     task_id: str
     status: TaskStatus
     result: Any
@@ -121,14 +152,22 @@ class SecurePulsarSwarm:
         self.task_timeout = task_timeout
 
         # Initialize encryption
-        self.cipher_suite = Fernet(security_config.encryption_key.encode())
+        self.cipher_suite = Fernet(
+            security_config.encryption_key.encode()
+        )
 
         # Setup metrics server
         start_http_server(metrics_port)
 
         # Initialize Pulsar client with security settings
         client_config = {
-            "authentication": None if not security_config.auth_token else pulsar.AuthenticationToken(security_config.auth_token),
+            "authentication": (
+                None
+                if not security_config.auth_token
+                else pulsar.AuthenticationToken(
+                    security_config.auth_token
+                )
+            ),
             "operation_timeout_seconds": 30,
             "connection_timeout_seconds": 30,
             "use_tls": bool(security_config.tls_cert_path),
@@ -145,7 +184,9 @@ class SecurePulsarSwarm:
         self.last_execution_time = time.time()
         self.execution_count = 0
 
-        logger.info(f"Secure Pulsar Swarm '{self.name}' initialized with enhanced security features")
+        logger.info(
+            f"Secure Pulsar Swarm '{self.name}' initialized with enhanced security features"
+        )
 
     def _create_producer(self):
         """Create a secure producer with retry logic"""
@@ -155,7 +196,7 @@ class SecurePulsarSwarm:
             compression_type=pulsar.CompressionType.LZ4,
             block_if_queue_full=True,
             batching_enabled=True,
-            batching_max_publish_delay_ms=10
+            batching_max_publish_delay_ms=10,
         )
 
     def _create_consumer(self):
@@ -166,7 +207,7 @@ class SecurePulsarSwarm:
             consumer_type=pulsar.ConsumerType.Shared,
             message_listener=None,
             receiver_queue_size=1000,
-            max_total_receiver_queue_size_across_partitions=50000
+            max_total_receiver_queue_size_across_partitions=50000,
         )
 
     def _encrypt_message(self, data: str) -> bytes:
@@ -177,14 +218,19 @@ class SecurePulsarSwarm:
         """Decrypt message data"""
         return self.cipher_suite.decrypt(data).decode()
 
-    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_exponential(multiplier=1, min=4, max=10),
+    )
     def publish_task(self, task: Task) -> None:
         """Publish a task with enhanced security and reliability"""
         try:
             # Validate message size
             task_data = task.json()
             if len(task_data) > self.security_config.max_message_size:
-                raise ValueError("Task data exceeds maximum message size")
+                raise ValueError(
+                    "Task data exceeds maximum message size"
+                )
 
             # Rate limiting
             current_time = time.time()
@@ -192,7 +238,10 @@ class SecurePulsarSwarm:
                 self.execution_count = 0
                 self.last_execution_time = current_time
 
-            if self.execution_count >= self.security_config.rate_limit:
+            if (
+                self.execution_count
+                >= self.security_config.rate_limit
+            ):
                 raise ValueError("Rate limit exceeded")
 
             # Encrypt and publish
@@ -200,11 +249,15 @@ class SecurePulsarSwarm:
             message_id = self.producer.send(encrypted_data)
 
             self.execution_count += 1
-            logger.info(f"Task {task.task_id} published successfully with message ID {message_id}")
+            logger.info(
+                f"Task {task.task_id} published successfully with message ID {message_id}"
+            )
 
         except Exception as e:
             TASK_FAILURES.inc()
-            logger.error(f"Error publishing task {task.task_id}: {str(e)}")
+            logger.error(
+                f"Error publishing task {task.task_id}: {str(e)}"
+            )
             raise
 
     async def _process_task(self, task: Task) -> TaskResult:
@@ -219,7 +272,9 @@ class SecurePulsarSwarm:
                 self.agents.append(agent)
 
                 # Execute task with timeout
-                future = self.executor.submit(agent.run, task.description)
+                future = self.executor.submit(
+                    agent.run, task.description
+                )
                 result = future.result(timeout=self.task_timeout)
 
                 # Handle different output types
@@ -239,8 +294,9 @@ class SecurePulsarSwarm:
                     task_id=task.task_id,
                     status="completed",
                     result=result,
-                    execution_time=time.time() - task.started_at.timestamp(),
-                    agent_id=agent.agent_name
+                    execution_time=time.time()
+                    - task.started_at.timestamp(),
+                    agent_id=agent.agent_name,
                 )
 
             except TimeoutError:
@@ -253,14 +309,17 @@ class SecurePulsarSwarm:
                     status="failed",
                     result=None,
                     error_message=error_msg,
-                    execution_time=time.time() - task.started_at.timestamp(),
-                    agent_id=agent.agent_name
+                    execution_time=time.time()
+                    - task.started_at.timestamp(),
+                    agent_id=agent.agent_name,
                 )
 
             except Exception as e:
                 TASK_FAILURES.inc()
                 AGENT_ERRORS.inc()
-                error_msg = f"Error processing task {task.task_id}: {str(e)}"
+                error_msg = (
+                    f"Error processing task {task.task_id}: {str(e)}"
+                )
                 logger.error(error_msg)
                 task.status = "failed"
                 return TaskResult(
@@ -268,8 +327,9 @@ class SecurePulsarSwarm:
                     status="failed",
                     result=None,
                     error_message=error_msg,
-                    execution_time=time.time() - task.started_at.timestamp(),
-                    agent_id=agent.agent_name
+                    execution_time=time.time()
+                    - task.started_at.timestamp(),
+                    agent_id=agent.agent_name,
                 )
 
     async def consume_tasks(self):
@@ -281,7 +341,9 @@ class SecurePulsarSwarm:
             try:
                 # Circuit breaker pattern
                 if consecutive_failures >= 5:
-                    logger.warning(f"Circuit breaker triggered. Waiting {backoff_time} seconds")
+                    logger.warning(
+                        f"Circuit breaker triggered. Waiting {backoff_time} seconds"
+                    )
                     await asyncio.sleep(backoff_time)
                     backoff_time = min(backoff_time * 2, 60)
                     continue
@@ -291,7 +353,9 @@ class SecurePulsarSwarm:
 
                 try:
                     # Decrypt and process message
-                    decrypted_data = self._decrypt_message(message.data())
+                    decrypted_data = self._decrypt_message(
+                        message.data()
+                    )
                     task_data = json.loads(decrypted_data)
                     task = Task(**task_data)
 
@@ -306,13 +370,21 @@ class SecurePulsarSwarm:
                     else:
                         if task.retry_count < self.retry_attempts:
                             task.retry_count += 1
-                            await self.consumer.negative_acknowledge(message)
+                            await self.consumer.negative_acknowledge(
+                                message
+                            )
                         else:
-                            await self.consumer.acknowledge_async(message)
-                            logger.error(f"Task {task.task_id} failed after {self.retry_attempts} attempts")
+                            await self.consumer.acknowledge_async(
+                                message
+                            )
+                            logger.error(
+                                f"Task {task.task_id} failed after {self.retry_attempts} attempts"
+                            )
 
                 except Exception as e:
-                    logger.error(f"Error processing message: {str(e)}")
+                    logger.error(
+                        f"Error processing message: {str(e)}"
+                    )
                     await self.consumer.negative_acknowledge(message)
                     consecutive_failures += 1
 
@@ -336,6 +408,7 @@ class SecurePulsarSwarm:
         except Exception as e:
             logger.error(f"Error during cleanup: {str(e)}")
 
+
 if __name__ == "__main__":
     # Example usage with security configuration
     security_config = SecurityConfig(
@@ -344,7 +417,7 @@ if __name__ == "__main__":
         tls_key_path="/path/to/key.pem",
         auth_token="your-auth-token",
         max_message_size=1048576,
-        rate_limit=100
+        rate_limit=100,
     )
 
     # Agent factory function
@@ -383,7 +456,7 @@ if __name__ == "__main__":
         max_workers=5,
         retry_attempts=3,
         task_timeout=300,
-        metrics_port=8000
+        metrics_port=8000,
     ) as swarm:
         # Example task
         task = Task(
@@ -391,7 +464,10 @@ if __name__ == "__main__":
             description="Analyze Q4 financial reports",
             output_type="json",
             priority="high",
-            metadata={"department": "finance", "requester": "john.doe@company.com"}
+            metadata={
+                "department": "finance",
+                "requester": "john.doe@company.com",
+            },
         )
 
         # Run the swarm

swarms/structs/agent.py

@@ -571,9 +571,7 @@ class Agent:
         )
 
         # Telemetry Processor to log agent data
-        threading.Thread(
-            target=log_agent_data(self.to_dict())
-        ).start()
+        log_agent_data(self.to_dict())
 
         if self.llm is None and self.model_name is not None:
             self.llm = self.llm_handling()

swarms/structs/agents.csv

@@ -1,2 +0,0 @@
-agent_name,system_prompt,model_name,max_loops,autosave,dashboard,verbose,dynamic_temperature,saved_state_path,user_name,retry_attempts,context_length,return_step_meta,output_type,streaming
-Financial-Analysis-Agent,You are a financial expert...,gpt-4o-mini,1,True,False,True,True,finance_agent.json,swarms_corp,3,200000,False,string,False

swarms/structs/csv_to_agent.py

@@ -1,7 +1,11 @@
-from typing import List, Dict, Optional, TypedDict, Literal, Union, Any
+from typing import (
+    List,
+    Dict,
+    TypedDict,
+    Any,
+)
 from dataclasses import dataclass
 import csv
-import os
 from pathlib import Path
 import logging
 from enum import Enum
@@ -11,8 +15,10 @@ from swarms import Agent
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 class ModelName(str, Enum):
     """Valid model names for swarms agents"""
+
     GPT4O = "gpt-4o"
     GPT4O_MINI = "gpt-4o-mini"
     GPT4 = "gpt-4"
@@ -30,8 +36,10 @@ class ModelName(str, Enum):
         """Check if model name is valid"""
         return model_name in cls.get_model_names()
 
+
 class AgentConfigDict(TypedDict):
     """TypedDict for agent configuration"""
+
     agent_name: str
     system_prompt: str
     model_name: str  # Using str instead of ModelName for flexibility
@@ -48,9 +56,11 @@ class AgentConfigDict(TypedDict):
     output_type: str
     streaming: bool
 
+
 @dataclass
 class AgentValidationError(Exception):
     """Custom exception for agent validation errors"""
+
     message: str
     field: str
     value: Any
@@ -58,6 +68,7 @@ class AgentValidationError(Exception):
     def __str__(self) -> str:
         return f"Validation error in field '{self.field}': {self.message}. Got value: {self.value}"
 
+
 class AgentValidator:
     """Validates agent configuration data"""
 
@@ -66,46 +77,75 @@ class AgentValidator:
         """Validate and convert agent configuration"""
         try:
             # Validate model name
-            model_name = str(config['model_name'])
+            model_name = str(config["model_name"])
             if not ModelName.is_valid_model(model_name):
                 valid_models = ModelName.get_model_names()
                 raise AgentValidationError(
                     f"Invalid model name. Must be one of: {', '.join(valid_models)}",
                     "model_name",
-                    model_name
+                    model_name,
                 )
 
             # Convert types with error handling
             validated_config: AgentConfigDict = {
-                'agent_name': str(config.get('agent_name', '')),
-                'system_prompt': str(config.get('system_prompt', '')),
-                'model_name': model_name,
-                'max_loops': int(config.get('max_loops', 1)),
-                'autosave': bool(str(config.get('autosave', True)).lower() == 'true'),
-                'dashboard': bool(str(config.get('dashboard', False)).lower() == 'true'),
-                'verbose': bool(str(config.get('verbose', True)).lower() == 'true'),
-                'dynamic_temperature': bool(str(config.get('dynamic_temperature', True)).lower() == 'true'),
-                'saved_state_path': str(config.get('saved_state_path', '')),
-                'user_name': str(config.get('user_name', 'default_user')),
-                'retry_attempts': int(config.get('retry_attempts', 3)),
-                'context_length': int(config.get('context_length', 200000)),
-                'return_step_meta': bool(str(config.get('return_step_meta', False)).lower() == 'true'),
-                'output_type': str(config.get('output_type', 'string')),
-                'streaming': bool(str(config.get('streaming', False)).lower() == 'true')
+                "agent_name": str(config.get("agent_name", "")),
+                "system_prompt": str(config.get("system_prompt", "")),
+                "model_name": model_name,
+                "max_loops": int(config.get("max_loops", 1)),
+                "autosave": bool(
+                    str(config.get("autosave", True)).lower()
+                    == "true"
+                ),
+                "dashboard": bool(
+                    str(config.get("dashboard", False)).lower()
+                    == "true"
+                ),
+                "verbose": bool(
+                    str(config.get("verbose", True)).lower() == "true"
+                ),
+                "dynamic_temperature": bool(
+                    str(
+                        config.get("dynamic_temperature", True)
+                    ).lower()
+                    == "true"
+                ),
+                "saved_state_path": str(
+                    config.get("saved_state_path", "")
+                ),
+                "user_name": str(
+                    config.get("user_name", "default_user")
+                ),
+                "retry_attempts": int(
+                    config.get("retry_attempts", 3)
+                ),
+                "context_length": int(
+                    config.get("context_length", 200000)
+                ),
+                "return_step_meta": bool(
+                    str(config.get("return_step_meta", False)).lower()
+                    == "true"
+                ),
+                "output_type": str(
+                    config.get("output_type", "string")
+                ),
+                "streaming": bool(
+                    str(config.get("streaming", False)).lower()
+                    == "true"
+                ),
             }
 
             return validated_config
 
         except (ValueError, KeyError) as e:
             raise AgentValidationError(
-                str(e),
-                str(e.__class__.__name__),
-                str(config)
+                str(e), str(e.__class__.__name__), str(config)
             )
 
+
 @dataclass
 class AgentCSV:
     """Class to manage agents through CSV with type safety"""
+
     csv_path: Path
 
     def __post_init__(self) -> None:
@@ -117,10 +157,21 @@ class AgentCSV:
     def headers(self) -> List[str]:
         """CSV headers for agent configuration"""
         return [
-            "agent_name", "system_prompt", "model_name", "max_loops",
-            "autosave", "dashboard", "verbose", "dynamic_temperature",
-            "saved_state_path", "user_name", "retry_attempts", "context_length",
-            "return_step_meta", "output_type", "streaming"
+            "agent_name",
+            "system_prompt",
+            "model_name",
+            "max_loops",
+            "autosave",
+            "dashboard",
+            "verbose",
+            "dynamic_temperature",
+            "saved_state_path",
+            "user_name",
+            "retry_attempts",
+            "context_length",
+            "return_step_meta",
+            "output_type",
+            "streaming",
         ]
 
     def create_agent_csv(self, agents: List[Dict[str, Any]]) -> None:
@@ -128,65 +179,96 @@ class AgentCSV:
         validated_agents = []
         for agent in agents:
             try:
-                validated_config = AgentValidator.validate_config(agent)
+                validated_config = AgentValidator.validate_config(
+                    agent
+                )
                 validated_agents.append(validated_config)
             except AgentValidationError as e:
-                logger.error(f"Validation error for agent {agent.get('agent_name', 'unknown')}: {e}")
+                logger.error(
+                    f"Validation error for agent {agent.get('agent_name', 'unknown')}: {e}"
+                )
                 raise
 
-        with open(self.csv_path, 'w', newline='') as f:
+        with open(self.csv_path, "w", newline="") as f:
             writer = csv.DictWriter(f, fieldnames=self.headers)
             writer.writeheader()
             writer.writerows(validated_agents)
 
-        logger.info(f"Created CSV with {len(validated_agents)} agents at {self.csv_path}")
+        logger.info(
+            f"Created CSV with {len(validated_agents)} agents at {self.csv_path}"
+        )
 
     def load_agents(self) -> List[Agent]:
         """Load and create agents from CSV with validation"""
         if not self.csv_path.exists():
-            raise FileNotFoundError(f"CSV file not found at {self.csv_path}")
+            raise FileNotFoundError(
+                f"CSV file not found at {self.csv_path}"
+            )
 
         agents: List[Agent] = []
-        with open(self.csv_path, 'r') as f:
+        with open(self.csv_path, "r") as f:
             reader = csv.DictReader(f)
             for row in reader:
                 try:
-                    validated_config = AgentValidator.validate_config(row)
+                    validated_config = AgentValidator.validate_config(
+                        row
+                    )
 
                     agent = Agent(
-                        agent_name=validated_config['agent_name'],
-                        system_prompt=validated_config['system_prompt'],
-                        model_name=validated_config['model_name'],
-                        max_loops=validated_config['max_loops'],
-                        autosave=validated_config['autosave'],
-                        dashboard=validated_config['dashboard'],
-                        verbose=validated_config['verbose'],
-                        dynamic_temperature_enabled=validated_config['dynamic_temperature'],
-                        saved_state_path=validated_config['saved_state_path'],
-                        user_name=validated_config['user_name'],
-                        retry_attempts=validated_config['retry_attempts'],
-                        context_length=validated_config['context_length'],
-                        return_step_meta=validated_config['return_step_meta'],
-                        output_type=validated_config['output_type'],
-                        streaming_on=validated_config['streaming']
+                        agent_name=validated_config["agent_name"],
+                        system_prompt=validated_config[
+                            "system_prompt"
+                        ],
+                        model_name=validated_config["model_name"],
+                        max_loops=validated_config["max_loops"],
+                        autosave=validated_config["autosave"],
+                        dashboard=validated_config["dashboard"],
+                        verbose=validated_config["verbose"],
+                        dynamic_temperature_enabled=validated_config[
+                            "dynamic_temperature"
+                        ],
+                        saved_state_path=validated_config[
+                            "saved_state_path"
+                        ],
+                        user_name=validated_config["user_name"],
+                        retry_attempts=validated_config[
+                            "retry_attempts"
+                        ],
+                        context_length=validated_config[
+                            "context_length"
+                        ],
+                        return_step_meta=validated_config[
+                            "return_step_meta"
+                        ],
+                        output_type=validated_config["output_type"],
+                        streaming_on=validated_config["streaming"],
                     )
                     agents.append(agent)
                 except AgentValidationError as e:
-                    logger.error(f"Skipping invalid agent configuration: {e}")
+                    logger.error(
+                        f"Skipping invalid agent configuration: {e}"
+                    )
                     continue
 
-        logger.info(f"Loaded {len(agents)} agents from {self.csv_path}")
+        logger.info(
+            f"Loaded {len(agents)} agents from {self.csv_path}"
+        )
         return agents
 
     def add_agent(self, agent_config: Dict[str, Any]) -> None:
         """Add a new validated agent configuration to CSV"""
-        validated_config = AgentValidator.validate_config(agent_config)
+        validated_config = AgentValidator.validate_config(
+            agent_config
+        )
 
-        with open(self.csv_path, 'a', newline='') as f:
+        with open(self.csv_path, "a", newline="") as f:
             writer = csv.DictWriter(f, fieldnames=self.headers)
             writer.writerow(validated_config)
 
-        logger.info(f"Added new agent {validated_config['agent_name']} to {self.csv_path}")
+        logger.info(
+            f"Added new agent {validated_config['agent_name']} to {self.csv_path}"
+        )
+
 
 # Example usage
 if __name__ == "__main__":
@@ -207,7 +289,7 @@ if __name__ == "__main__":
             "context_length": 200000,
             "return_step_meta": False,
             "output_type": "string",
-            "streaming": False
+            "streaming": False,
         }
     ]
 

swarms/structs/multi_agent_orchestrator.py

@@ -11,7 +11,7 @@ Todo:
 import os
 import subprocess
 import uuid
-from datetime import UTC, datetime
+from datetime import datetime
 from typing import List, Literal, Optional
 
 from loguru import logger
@@ -241,7 +241,7 @@ class MultiAgentRouter:
             dict: A dictionary containing the routing result, including the selected agent, reasoning, and response.
         """
         try:
-            start_time = datetime.now(UTC)
+            start_time = datetime.now()
 
             # Get boss decision using function calling
             boss_response = self.function_caller.get_completion(task)
@@ -259,7 +259,7 @@ class MultiAgentRouter:
             final_task = boss_response.modified_task or task
 
             # Execute the task with the selected agent if enabled
-            execution_start = datetime.now(UTC)
+            execution_start = datetime.now()
             agent_response = None
             execution_time = 0
 
@@ -267,20 +267,18 @@ class MultiAgentRouter:
                 # Use the agent's run method directly
                 agent_response = selected_agent.run(final_task)
                 execution_time = (
-                    datetime.now(UTC) - execution_start
+                    datetime.now() - execution_start
                 ).total_seconds()
             else:
                 logger.info(
                     "Task execution skipped (execute_task=False)"
                 )
 
-            total_time = (
-                datetime.now(UTC) - start_time
-            ).total_seconds()
+            total_time = (datetime.now() - start_time).total_seconds()
 
             result = {
                 "id": str(uuid.uuid4()),
-                "timestamp": datetime.now(UTC).isoformat(),
+                "timestamp": datetime.now().isoformat(),
                 "task": {
                     "original": task,
                     "modified": (