diff --git a/api/agent_api_test.py b/api/agent_api_test.py
deleted file mode 100644
index 10addd36..00000000
--- a/api/agent_api_test.py
+++ /dev/null
@@ -1,291 +0,0 @@
-import os
-import json
-import logging
-from typing import Dict, Optional, Any
-from dataclasses import dataclass
-import requests
-import time
-
-# Set up logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s",
-    handlers=[
-        logging.FileHandler("api_tests.log"),
-        logging.StreamHandler(),
-    ],
-)
-logger = logging.getLogger(__name__)
-
-
-# Configuration
-@dataclass
-class TestConfig:
-    """Test configuration settings"""
-
-    base_url: str
-    timeout: int = 30
-    verify_ssl: bool = True
-    debug: bool = True
-
-
-# Load config from environment or use defaults
-config = TestConfig(
-    base_url=os.getenv("API_BASE_URL", "http://0.0.0.0:8000/v1")
-)
-
-
-class APIClient:
-    """API Client for testing"""
-
-    def __init__(self, config: TestConfig):
-        self.config = config
-        self.session = requests.Session()
-
-    def _url(self, path: str) -> str:
-        """Construct full URL"""
-        return f"{self.config.base_url}/{path.lstrip('/')}"
-
-    def _log_request_details(
-        self, method: str, url: str, headers: Dict, data: Any
-    ):
-        """Log request details for debugging"""
-        logger.info("\nRequest Details:")
-        logger.info(f"Method: {method}")
-        logger.info(f"URL: {url}")
-        logger.info(f"Headers: {json.dumps(headers, indent=2)}")
-        logger.info(
-            f"Data: {json.dumps(data, indent=2) if data else None}"
-        )
-
-    def _log_response_details(self, response: requests.Response):
-        """Log response details for debugging"""
-        logger.info("\nResponse Details:")
-        logger.info(f"Status Code: {response.status_code}")
-        logger.info(
-            f"Headers: {json.dumps(dict(response.headers), indent=2)}"
-        )
-        try:
-            logger.info(
-                f"Body: {json.dumps(response.json(), indent=2)}"
-            )
-        except Exception:
-            logger.info(f"Body: {response.text}")
-
-    def _request(
-        self,
-        method: str,
-        path: str,
-        headers: Optional[Dict] = None,
-        **kwargs: Any,
-    ) -> requests.Response:
-        """Make HTTP request with config defaults"""
-        url = self._url(path)
-        headers = headers or {}
-
-        if self.config.debug:
-            self._log_request_details(
-                method, url, headers, kwargs.get("json")
-            )
-
-        try:
-            response = self.session.request(
-                method=method,
-                url=url,
-                headers=headers,
-                timeout=self.config.timeout,
-                verify=self.config.verify_ssl,
-                **kwargs,
-            )
-
-            if self.config.debug:
-                self._log_response_details(response)
-
-            if response.status_code >= 400:
-                logger.error(
-                    f"Request failed with status {response.status_code}"
-                )
-                logger.error(f"Response: {response.text}")
-
-            response.raise_for_status()
-            return response
-
-        except requests.exceptions.RequestException as e:
-            logger.error(f"Request failed: {str(e)}")
-            if hasattr(e, "response") and e.response is not None:
-                logger.error(f"Error response: {e.response.text}")
-            raise
-
-
-class TestRunner:
-    """Test runner with logging and reporting"""
-
-    def __init__(self):
-        self.client = APIClient(config)
-        self.results = {"passed": 0, "failed": 0, "total_time": 0}
-        self.api_key = None
-        self.user_id = None
-        self.agent_id = None
-
-    def run_test(self, test_name: str, test_func: callable):
-        """Run a single test with timing and logging"""
-        logger.info(f"\nRunning test: {test_name}")
-        start_time = time.time()
-
-        try:
-            test_func()
-            self.results["passed"] += 1
-            logger.info(f"✅ {test_name} - PASSED")
-        except Exception as e:
-            self.results["failed"] += 1
-            logger.error(f"❌ {test_name} - FAILED: {str(e)}")
-            logger.exception(e)
-
-        end_time = time.time()
-        duration = end_time - start_time
-        self.results["total_time"] += duration
-        logger.info(f"Test duration: {duration:.2f}s")
-
-    def test_user_creation(self):
-        """Test user creation"""
-        response = self.client._request(
-            "POST", "/users", json={"username": "test_user"}
-        )
-        data = response.json()
-        assert "user_id" in data, "No user_id in response"
-        assert "api_key" in data, "No api_key in response"
-        self.api_key = data["api_key"]
-        self.user_id = data["user_id"]
-        logger.info(f"Created user with ID: {self.user_id}")
-
-    def test_create_api_key(self):
-        """Test API key creation"""
-        headers = {"api-key": self.api_key}
-        response = self.client._request(
-            "POST",
-            f"/users/{self.user_id}/api-keys",
-            headers=headers,
-            json={"name": "test_key"},
-        )
-        data = response.json()
-        assert "key" in data, "No key in response"
-        logger.info("Successfully created new API key")
-
-    def test_create_agent(self):
-        """Test agent creation"""
-        headers = {"api-key": self.api_key}
-        agent_config = {
-            "agent_name": "test_agent",
-            "model_name": "gpt-4",
-            "system_prompt": "You are a test agent",
-            "description": "Test agent description",
-            "temperature": 0.7,
-            "max_loops": 1,
-        }
-        response = self.client._request(
-            "POST", "/agent", headers=headers, json=agent_config
-        )
-        data = response.json()
-        assert "agent_id" in data, "No agent_id in response"
-        self.agent_id = data["agent_id"]
-        logger.info(f"Created agent with ID: {self.agent_id}")
-
-        # Wait a bit for agent to be ready
-        time.sleep(2)
-
-    def test_list_agents(self):
-        """Test agent listing"""
-        headers = {"api-key": self.api_key}
-        response = self.client._request(
-            "GET", "/agents", headers=headers
-        )
-        agents = response.json()
-        assert isinstance(agents, list), "Response is not a list"
-        assert len(agents) > 0, "No agents returned"
-        logger.info(f"Successfully retrieved {len(agents)} agents")
-
-    def test_agent_completion(self):
-        """Test agent completion"""
-        if not self.agent_id:
-            logger.error("No agent_id available for completion test")
-            raise ValueError("Agent ID not set")
-
-        headers = {"api-key": self.api_key}
-        completion_request = {
-            "prompt": "Write 'Hello World!'",
-            "agent_id": str(
-                self.agent_id
-            ),  # Ensure UUID is converted to string
-            "max_tokens": 100,
-            "stream": False,
-            "temperature_override": 0.7,
-        }
-
-        logger.info(
-            f"Sending completion request for agent {self.agent_id}"
-        )
-        response = self.client._request(
-            "POST",
-            "/agent/completions",
-            headers=headers,
-            json=completion_request,
-        )
-        data = response.json()
-        assert "response" in data, "No response in completion"
-        logger.info(f"Completion response: {data.get('response')}")
-
-    def run_all_tests(self):
-        """Run all tests and generate report"""
-        logger.info("\n" + "=" * 50)
-        logger.info("Starting API test suite...")
-        logger.info(f"Base URL: {config.base_url}")
-        logger.info("=" * 50 + "\n")
-
-        # Define test sequence
-        tests = [
-            ("User Creation", self.test_user_creation),
-            ("API Key Creation", self.test_create_api_key),
-            ("Agent Creation", self.test_create_agent),
-            ("List Agents", self.test_list_agents),
-            ("Agent Completion", self.test_agent_completion),
-        ]
-
-        # Run tests
-        for test_name, test_func in tests:
-            self.run_test(test_name, test_func)
-
-        # Generate report
-        self.print_report()
-
-    def print_report(self):
-        """Print test results report"""
-        total_tests = self.results["passed"] + self.results["failed"]
-        success_rate = (
-            (self.results["passed"] / total_tests * 100)
-            if total_tests > 0
-            else 0
-        )
-
-        report = f"""
-\n{'='*50}
-API TEST RESULTS
-{'='*50}
-Total Tests: {total_tests}
-Passed: {self.results['passed']} ✅
-Failed: {self.results['failed']} ❌
-Success Rate: {success_rate:.2f}%
-Total Time: {self.results['total_time']:.2f}s
-{'='*50}
-"""
-        logger.info(report)
-
-
-if __name__ == "__main__":
-    try:
-        runner = TestRunner()
-        runner.run_all_tests()
-    except KeyboardInterrupt:
-        logger.info("\nTest suite interrupted by user")
-    except Exception as e:
-        logger.error(f"Test suite failed: {str(e)}")
-        logger.exception(e)
diff --git a/api/api_test.py b/api/api_test.py
deleted file mode 100644
index c7f3e283..00000000
--- a/api/api_test.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import os
-from typing import Dict, Optional, Any
-from dataclasses import dataclass
-import pytest
-import requests
-from uuid import UUID
-from pydantic import BaseModel
-from _pytest.terminal import TerminalReporter
-
-
-# Configuration
-@dataclass
-class TestConfig:
-    """Test configuration settings"""
-
-    base_url: str
-    timeout: int = 30
-    verify_ssl: bool = True
-
-
-# Load config from environment or use defaults
-config = TestConfig(
-    base_url=os.getenv("API_BASE_URL", "http://localhost:8000/v1")
-)
-
-
-# API Response Types
-class UserResponse(BaseModel):
-    user_id: str
-    api_key: str
-
-
-class AgentResponse(BaseModel):
-    agent_id: UUID
-
-
-class MetricsResponse(BaseModel):
-    total_completions: int
-    average_response_time: float
-    error_rate: float
-    last_24h_completions: int
-    total_tokens_used: int
-    uptime_percentage: float
-    success_rate: float
-    peak_tokens_per_minute: int
-
-
-class APIClient:
-    """API Client with typed methods"""
-
-    def __init__(self, config: TestConfig):
-        self.config = config
-        self.session = requests.Session()
-
-    def _url(self, path: str) -> str:
-        """Construct full URL"""
-        return f"{self.config.base_url}/{path.lstrip('/')}"
-
-    def _request(
-        self,
-        method: str,
-        path: str,
-        headers: Optional[Dict] = None,
-        **kwargs: Any,
-    ) -> requests.Response:
-        """Make HTTP request with config defaults"""
-        url = self._url(path)
-        return self.session.request(
-            method=method,
-            url=url,
-            headers=headers,
-            timeout=self.config.timeout,
-            verify=self.config.verify_ssl,
-            **kwargs,
-        )
-
-    def create_user(self, username: str) -> UserResponse:
-        """Create a new user"""
-        response = self._request(
-            "POST", "/users", json={"username": username}
-        )
-        response.raise_for_status()
-        return UserResponse(**response.json())
-
-    def create_agent(
-        self, agent_config: Dict[str, Any], api_key: str
-    ) -> AgentResponse:
-        """Create a new agent"""
-        headers = {"api-key": api_key}
-        response = self._request(
-            "POST", "/agent", headers=headers, json=agent_config
-        )
-        response.raise_for_status()
-        return AgentResponse(**response.json())
-
-    def get_metrics(
-        self, agent_id: UUID, api_key: str
-    ) -> MetricsResponse:
-        """Get agent metrics"""
-        headers = {"api-key": api_key}
-        response = self._request(
-            "GET", f"/agent/{agent_id}/metrics", headers=headers
-        )
-        response.raise_for_status()
-        return MetricsResponse(**response.json())
-
-
-# Test Fixtures
-@pytest.fixture
-def api_client() -> APIClient:
-    """Fixture for API client"""
-    return APIClient(config)
-
-
-@pytest.fixture
-def test_user(api_client: APIClient) -> UserResponse:
-    """Fixture for test user"""
-    return api_client.create_user("test_user")
-
-
-@pytest.fixture
-def test_agent(
-    api_client: APIClient, test_user: UserResponse
-) -> AgentResponse:
-    """Fixture for test agent"""
-    agent_config = {
-        "agent_name": "test_agent",
-        "model_name": "gpt-4",
-        "system_prompt": "You are a test agent",
-        "description": "Test agent description",
-    }
-    return api_client.create_agent(agent_config, test_user.api_key)
-
-
-# Tests
-def test_user_creation(api_client: APIClient):
-    """Test user creation flow"""
-    response = api_client.create_user("new_test_user")
-    assert response.user_id
-    assert response.api_key
-
-
-def test_agent_creation(
-    api_client: APIClient, test_user: UserResponse
-):
-    """Test agent creation flow"""
-    agent_config = {
-        "agent_name": "test_agent",
-        "model_name": "gpt-4",
-        "system_prompt": "You are a test agent",
-        "description": "Test agent description",
-    }
-    response = api_client.create_agent(
-        agent_config, test_user.api_key
-    )
-    assert response.agent_id
-
-
-def test_agent_metrics(
-    api_client: APIClient,
-    test_user: UserResponse,
-    test_agent: AgentResponse,
-):
-    """Test metrics retrieval"""
-    metrics = api_client.get_metrics(
-        test_agent.agent_id, test_user.api_key
-    )
-    assert metrics.total_completions >= 0
-    assert metrics.error_rate >= 0
-    assert metrics.uptime_percentage >= 0
-
-
-def test_invalid_auth(api_client: APIClient):
-    """Test invalid authentication"""
-    with pytest.raises(requests.exceptions.HTTPError) as exc_info:
-        api_client.create_agent({}, "invalid_key")
-    assert exc_info.value.response.status_code == 401
-
-
-# Custom pytest plugin to capture test results
-class ResultCapture:
-    def __init__(self):
-        self.total = 0
-        self.passed = 0
-        self.failed = 0
-        self.errors = 0
-
-
-@pytest.hookimpl(hookwrapper=True)
-def pytest_terminal_summary(
-    terminalreporter: TerminalReporter, exitstatus: int
-):
-    yield
-    capture = getattr(
-        terminalreporter.config, "_result_capture", None
-    )
-    if capture:
-        capture.total = (
-            len(terminalreporter.stats.get("passed", []))
-            + len(terminalreporter.stats.get("failed", []))
-            + len(terminalreporter.stats.get("error", []))
-        )
-        capture.passed = len(terminalreporter.stats.get("passed", []))
-        capture.failed = len(terminalreporter.stats.get("failed", []))
-        capture.errors = len(terminalreporter.stats.get("error", []))
-
-
-@dataclass
-class TestReport:
-    total_tests: int
-    passed: int
-    failed: int
-    errors: int
-
-    @property
-    def success_rate(self) -> float:
-        return (
-            (self.passed / self.total_tests) * 100
-            if self.total_tests > 0
-            else 0
-        )
-
-
-def run_tests() -> TestReport:
-    """Run tests and generate typed report"""
-    # Create result capture
-    capture = ResultCapture()
-
-    # Create pytest configuration
-    args = [__file__, "-v"]
-
-    # Run pytest with our plugin
-    pytest.main(args, plugins=[capture])
-
-    # Generate report
-    return TestReport(
-        total_tests=capture.total,
-        passed=capture.passed,
-        failed=capture.failed,
-        errors=capture.errors,
-    )
-
-
-if __name__ == "__main__":
-    # Example usage with environment variable
-    # export API_BASE_URL=http://api.example.com/v1
-
-    report = run_tests()
-    print("\nTest Results:")
-    print(f"Total Tests: {report.total_tests}")
-    print(f"Passed: {report.passed}")
-    print(f"Failed: {report.failed}")
-    print(f"Errors: {report.errors}")
-    print(f"Success Rate: {report.success_rate:.2f}%")
diff --git a/api/api_tests.py b/api/api_tests.py
deleted file mode 100644
index 43b1d119..00000000
--- a/api/api_tests.py
+++ /dev/null
@@ -1,472 +0,0 @@
-import asyncio
-import json
-from datetime import datetime
-from typing import Any, Dict, List, Optional
-from uuid import UUID
-
-import httpx
-from loguru import logger
-
-# Configure logger
-logger.add(
-    "tests/api_test_{time}.log",
-    rotation="1 day",
-    retention="7 days",
-    level="DEBUG",
-    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}",
-)
-
-
-class TestConfig:
-    """Test configuration and utilities"""
-
-    BASE_URL: str = "http://localhost:8000/v1"
-    TEST_USERNAME: str = "test_user"
-    api_key: Optional[str] = None
-    user_id: Optional[UUID] = None
-    test_agent_id: Optional[UUID] = None
-
-
-class TestResult:
-    """Model for test results"""
-
-    def __init__(
-        self,
-        test_name: str,
-        status: str,
-        duration: float,
-        error: Optional[str] = None,
-        details: Optional[Dict[str, Any]] = None,
-    ):
-        self.test_name = test_name
-        self.status = status
-        self.duration = duration
-        self.error = error
-        self.details = details or {}
-
-    def dict(self):
-        return {
-            "test_name": self.test_name,
-            "status": self.status,
-            "duration": self.duration,
-            "error": self.error,
-            "details": self.details,
-        }
-
-
-async def log_response(
-    response: httpx.Response, test_name: str
-) -> None:
-    """Log API response details"""
-    logger.debug(f"\n{test_name} Response:")
-    logger.debug(f"Status Code: {response.status_code}")
-    logger.debug(f"Headers: {dict(response.headers)}")
-    try:
-        logger.debug(f"Body: {response.json()}")
-    except json.JSONDecodeError:
-        logger.debug(f"Body: {response.text}")
-
-
-async def create_test_user() -> TestResult:
-    """Create a test user and get API key"""
-    start_time = datetime.now()
-    try:
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{TestConfig.BASE_URL}/users",
-                json={"username": TestConfig.TEST_USERNAME},
-            )
-            await log_response(response, "Create User")
-
-            if response.status_code == 200:
-                data = response.json()
-                TestConfig.api_key = data["api_key"]
-                TestConfig.user_id = UUID(data["user_id"])
-                return TestResult(
-                    test_name="create_test_user",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={"user_id": str(TestConfig.user_id)},
-                )
-            else:
-                return TestResult(
-                    test_name="create_test_user",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error=f"Failed to create user: {response.text}",
-                )
-    except Exception as e:
-        logger.error(f"Error in create_test_user: {str(e)}")
-        return TestResult(
-            test_name="create_test_user",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def create_test_agent() -> TestResult:
-    """Create a test agent"""
-    start_time = datetime.now()
-    try:
-        # Create agent config according to the AgentConfig model
-        agent_config = {
-            "agent_name": "test_agent",
-            "model_name": "gpt-4",
-            "description": "Test agent for API testing",
-            "system_prompt": "You are a test agent.",
-            "temperature": 0.1,
-            "max_loops": 1,
-            "dynamic_temperature_enabled": True,
-            "user_name": TestConfig.TEST_USERNAME,
-            "retry_attempts": 1,
-            "context_length": 4000,
-            "output_type": "string",
-            "streaming_on": False,
-            "tags": ["test", "api"],
-            "stopping_token": "<DONE>",
-            "auto_generate_prompt": False,
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{TestConfig.BASE_URL}/agent",
-                json=agent_config,
-                headers={"api-key": TestConfig.api_key},
-            )
-            await log_response(response, "Create Agent")
-
-            if response.status_code == 200:
-                data = response.json()
-                TestConfig.test_agent_id = UUID(data["agent_id"])
-                return TestResult(
-                    test_name="create_test_agent",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={
-                        "agent_id": str(TestConfig.test_agent_id)
-                    },
-                )
-            else:
-                return TestResult(
-                    test_name="create_test_agent",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error=f"Failed to create agent: {response.text}",
-                )
-    except Exception as e:
-        logger.error(f"Error in create_test_agent: {str(e)}")
-        return TestResult(
-            test_name="create_test_agent",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def test_agent_completion() -> TestResult:
-    """Test agent completion endpoint"""
-    start_time = datetime.now()
-    try:
-        completion_request = {
-            "prompt": "Hello, this is a test prompt.",
-            "agent_id": str(TestConfig.test_agent_id),
-            "max_tokens": 100,
-            "temperature_override": 0.5,
-            "stream": False,
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{TestConfig.BASE_URL}/agent/completions",
-                json=completion_request,
-                headers={"api-key": TestConfig.api_key},
-            )
-            await log_response(response, "Agent Completion")
-
-            if response.status_code == 200:
-                return TestResult(
-                    test_name="test_agent_completion",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={"response": response.json()},
-                )
-            else:
-                return TestResult(
-                    test_name="test_agent_completion",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error=f"Failed completion test: {response.text}",
-                )
-    except Exception as e:
-        logger.error(f"Error in test_agent_completion: {str(e)}")
-        return TestResult(
-            test_name="test_agent_completion",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def test_agent_metrics() -> TestResult:
-    """Test agent metrics endpoint"""
-    start_time = datetime.now()
-    try:
-        if not TestConfig.test_agent_id:
-            return TestResult(
-                test_name="test_agent_metrics",
-                status="failed",
-                duration=(
-                    datetime.now() - start_time
-                ).total_seconds(),
-                error="No test agent ID available",
-            )
-
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{TestConfig.BASE_URL}/agent/{str(TestConfig.test_agent_id)}/metrics",
-                headers={"api-key": TestConfig.api_key},
-            )
-            await log_response(response, "Agent Metrics")
-
-            if response.status_code == 200:
-                return TestResult(
-                    test_name="test_agent_metrics",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={"metrics": response.json()},
-                )
-            else:
-                return TestResult(
-                    test_name="test_agent_metrics",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error=f"Failed metrics test: {response.text}",
-                )
-    except Exception as e:
-        logger.error(f"Error in test_agent_metrics: {str(e)}")
-        return TestResult(
-            test_name="test_agent_metrics",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def test_update_agent() -> TestResult:
-    """Test agent update endpoint"""
-    start_time = datetime.now()
-    try:
-        if not TestConfig.test_agent_id:
-            return TestResult(
-                test_name="test_update_agent",
-                status="failed",
-                duration=(
-                    datetime.now() - start_time
-                ).total_seconds(),
-                error="No test agent ID available",
-            )
-
-        update_data = {
-            "description": "Updated test agent description",
-            "tags": ["test", "updated"],
-            "max_loops": 2,
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.patch(
-                f"{TestConfig.BASE_URL}/agent/{str(TestConfig.test_agent_id)}",
-                json=update_data,
-                headers={"api-key": TestConfig.api_key},
-            )
-            await log_response(response, "Update Agent")
-
-            if response.status_code == 200:
-                return TestResult(
-                    test_name="test_update_agent",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={"update_response": response.json()},
-                )
-            else:
-                return TestResult(
-                    test_name="test_update_agent",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error=f"Failed update test: {response.text}",
-                )
-    except Exception as e:
-        logger.error(f"Error in test_update_agent: {str(e)}")
-        return TestResult(
-            test_name="test_update_agent",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def test_error_handling() -> TestResult:
-    """Test API error handling"""
-    start_time = datetime.now()
-    try:
-        async with httpx.AsyncClient() as client:
-            # Test with invalid API key
-            invalid_agent_id = "00000000-0000-0000-0000-000000000000"
-            response = await client.get(
-                f"{TestConfig.BASE_URL}/agent/{invalid_agent_id}/metrics",
-                headers={"api-key": "invalid_key"},
-            )
-            await log_response(response, "Invalid API Key Test")
-
-            if response.status_code in [401, 403]:
-                return TestResult(
-                    test_name="test_error_handling",
-                    status="passed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    details={"error_response": response.json()},
-                )
-            else:
-                return TestResult(
-                    test_name="test_error_handling",
-                    status="failed",
-                    duration=(
-                        datetime.now() - start_time
-                    ).total_seconds(),
-                    error="Error handling test failed",
-                )
-    except Exception as e:
-        logger.error(f"Error in test_error_handling: {str(e)}")
-        return TestResult(
-            test_name="test_error_handling",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def cleanup_test_resources() -> TestResult:
-    """Clean up test resources"""
-    start_time = datetime.now()
-    try:
-        if TestConfig.test_agent_id:
-            async with httpx.AsyncClient() as client:
-                response = await client.delete(
-                    f"{TestConfig.BASE_URL}/agent/{str(TestConfig.test_agent_id)}",
-                    headers={"api-key": TestConfig.api_key},
-                )
-                await log_response(response, "Delete Agent")
-
-        return TestResult(
-            test_name="cleanup_test_resources",
-            status="passed",
-            duration=(datetime.now() - start_time).total_seconds(),
-            details={"cleanup": "completed"},
-        )
-    except Exception as e:
-        logger.error(f"Error in cleanup_test_resources: {str(e)}")
-        return TestResult(
-            test_name="cleanup_test_resources",
-            status="error",
-            duration=(datetime.now() - start_time).total_seconds(),
-            error=str(e),
-        )
-
-
-async def run_all_tests() -> List[TestResult]:
-    """Run all tests in sequence"""
-    logger.info("Starting API test suite")
-    results = []
-
-    # Initialize
-    results.append(await create_test_user())
-    if results[-1].status != "passed":
-        logger.error(
-            "Failed to create test user, aborting remaining tests"
-        )
-        return results
-
-    # Add delay to ensure user is properly created
-    await asyncio.sleep(1)
-
-    # Core tests
-    test_functions = [
-        create_test_agent,
-        test_agent_completion,
-        test_agent_metrics,
-        test_update_agent,
-        test_error_handling,
-    ]
-
-    for test_func in test_functions:
-        result = await test_func()
-        results.append(result)
-        logger.info(f"Test {result.test_name}: {result.status}")
-        if result.error:
-            logger.error(
-                f"Error in {result.test_name}: {result.error}"
-            )
-
-        # Add small delay between tests
-        await asyncio.sleep(0.5)
-
-    # Cleanup
-    results.append(await cleanup_test_resources())
-
-    # Log summary
-    passed = sum(1 for r in results if r.status == "passed")
-    failed = sum(1 for r in results if r.status == "failed")
-    errors = sum(1 for r in results if r.status == "error")
-
-    logger.info("\nTest Summary:")
-    logger.info(f"Total Tests: {len(results)}")
-    logger.info(f"Passed: {passed}")
-    logger.info(f"Failed: {failed}")
-    logger.info(f"Errors: {errors}")
-
-    return results
-
-
-def main():
-    """Main entry point for running tests"""
-    logger.info("Starting API testing suite")
-    try:
-        results = asyncio.run(run_all_tests())
-
-        # Write results to JSON file
-        with open("test_results.json", "w") as f:
-            json.dump(
-                [result.dict() for result in results],
-                f,
-                indent=2,
-                default=str,
-            )
-
-        logger.info("Test results written to test_results.json")
-
-    except Exception:
-        logger.error("Fatal error in test suite: ")
-
-
-main()
diff --git a/api/main.py b/api/main.py
index 460e5ad8..6b73a940 100644
--- a/api/main.py
+++ b/api/main.py
@@ -276,7 +276,6 @@ class AgentStore:
                 system_prompt=config.system_prompt,
                 model_name=config.model_name,
                 max_loops=config.max_loops,
-                verbose=config.verbose,
                 dynamic_temperature_enabled=True,
                 user_name=config.user_name,
                 retry_attempts=config.retry_attempts,
diff --git a/api/test_api.py b/api/test_api.py
deleted file mode 100644
index 2d05f6db..00000000
--- a/api/test_api.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import requests
-import json
-from time import sleep
-
-BASE_URL = "http://0.0.0.0:8000/v1"
-
-
-def make_request(method, endpoint, data=None):
-    """Helper function to make requests with error handling"""
-    url = f"{BASE_URL}{endpoint}"
-    try:
-        if method == "GET":
-            response = requests.get(url)
-        elif method == "POST":
-            response = requests.post(url, json=data)
-        elif method == "DELETE":
-            response = requests.delete(url)
-
-        response.raise_for_status()
-        return response.json()
-    except requests.exceptions.RequestException as e:
-        print(
-            f"Error making {method} request to {endpoint}: {str(e)}"
-        )
-        if hasattr(e.response, "text"):
-            print(f"Response text: {e.response.text}")
-        return None
-
-
-def create_agent():
-    """Create a test agent"""
-    data = {
-        "agent_name": "test_agent",
-        "model_name": "gpt-4",
-        "system_prompt": "You are a helpful assistant",
-        "description": "Test agent",
-        "temperature": 0.7,
-        "max_loops": 1,
-        "tags": ["test"],
-    }
-    return make_request("POST", "/v1/agent", data)
-
-
-def list_agents():
-    """List all agents"""
-    return make_request("GET", "/v1/agents")
-
-
-def test_completion(agent_id):
-    """Test a completion with the agent"""
-    data = {
-        "prompt": "Say hello!",
-        "agent_id": agent_id,
-        "max_tokens": 100,
-    }
-    return make_request("POST", "/v1/agent/completions", data)
-
-
-def get_agent_metrics(agent_id):
-    """Get metrics for an agent"""
-    return make_request("GET", f"/v1/agent/{agent_id}/metrics")
-
-
-def delete_agent(agent_id):
-    """Delete an agent"""
-    return make_request("DELETE", f"/v1/agent/{agent_id}")
-
-
-def run_tests():
-    print("Starting API tests...")
-
-    # Create an agent
-    print("\n1. Creating agent...")
-    agent_response = create_agent()
-    if not agent_response:
-        print("Failed to create agent")
-        return
-
-    agent_id = agent_response.get("agent_id")
-    print(f"Created agent with ID: {agent_id}")
-
-    # Give the server a moment to process
-    sleep(2)
-
-    # List agents
-    print("\n2. Listing agents...")
-    agents = list_agents()
-    print(f"Found {len(agents)} agents")
-
-    # Test completion
-    if agent_id:
-        print("\n3. Testing completion...")
-        completion = test_completion(agent_id)
-        if completion:
-            print(
-                f"Completion response: {completion.get('response')}"
-            )
-
-        print("\n4. Getting agent metrics...")
-        metrics = get_agent_metrics(agent_id)
-        if metrics:
-            print(f"Agent metrics: {json.dumps(metrics, indent=2)}")
-
-        # Clean up
-        # print("\n5. Cleaning up - deleting agent...")
-        # delete_result = delete_agent(agent_id)
-        # if delete_result:
-        #     print("Successfully deleted agent")
-
-
-if __name__ == "__main__":
-    run_tests()
diff --git a/api/tests.py b/api/tests.py
new file mode 100644
index 00000000..06490f12
--- /dev/null
+++ b/api/tests.py
@@ -0,0 +1,258 @@
+import asyncio
+import json
+import uuid
+from datetime import datetime
+import aiohttp
+import sys
+from typing import Dict, Any, Optional
+from loguru import logger
+import os
+
+# Configure loguru
+LOG_PATH = "api_tests.log"
+logger.add(LOG_PATH, 
+    format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
+    rotation="1 day",
+    retention="7 days",
+    level="DEBUG"
+)
+
+BASE_URL = "https://dev.api.swarms.ai/v1"  # Change this to match your server URL
+
+async def log_request_details(method: str, url: str, headers: dict, data: Any = None):
+    """Log request details before sending."""
+    logger.debug(f"\n{'='*50}")
+    logger.debug(f"REQUEST: {method} {url}")
+    logger.debug(f"HEADERS: {json.dumps(headers, indent=2)}")
+    if data:
+        logger.debug(f"PAYLOAD: {json.dumps(data, indent=2)}")
+
+async def log_response_details(response: aiohttp.ClientResponse, data: Any = None):
+    """Log response details after receiving."""
+    logger.debug(f"\nRESPONSE Status: {response.status}")
+    logger.debug(f"RESPONSE Headers: {json.dumps(dict(response.headers), indent=2)}")
+    if data:
+        logger.debug(f"RESPONSE Body: {json.dumps(data, indent=2)}")
+    logger.debug(f"{'='*50}\n")
+
+async def test_create_user(session: aiohttp.ClientSession) -> Dict[str, str]:
+    """Test user creation endpoint."""
+    url = f"{BASE_URL}/users"
+    payload = {"username": "test_user"}
+    
+    logger.info("Testing user creation...")
+    await log_request_details("POST", url, {}, payload)
+    
+    try:
+        async with session.post(url, json=payload) as response:
+            data = await response.json()
+            await log_response_details(response, data)
+            
+            if response.status != 200:
+                logger.error(f"Failed to create user. Status: {response.status}, Response: {data}")
+                sys.exit(1)
+                
+            logger.success("✓ Created user successfully")
+            return {"user_id": data["user_id"], "api_key": data["api_key"]}
+    except Exception as e:
+        logger.exception(f"Exception in user creation: {str(e)}")
+        sys.exit(1)
+
+async def test_create_agent(session: aiohttp.ClientSession, api_key: str) -> str:
+    """Test agent creation endpoint."""
+    url = f"{BASE_URL}/agent"
+    config = {
+        "agent_name": "test_agent",
+        "system_prompt": "You are a helpful test agent",
+        "model_name": "gpt-4",
+        "description": "Test agent for API validation",
+        "max_loops": 1,
+        "temperature": 0.5,
+        "tags": ["test"],
+        "streaming_on": False,
+        "user_name": "test_user",  # Added required field
+        "output_type": "string"    # Added required field
+    }
+    
+    headers = {"api-key": api_key}
+    logger.info("Testing agent creation...")
+    await log_request_details("POST", url, headers, config)
+    
+    try:
+        async with session.post(url, headers=headers, json=config) as response:
+            data = await response.json()
+            await log_response_details(response, data)
+            
+            if response.status != 200:
+                logger.error(f"Failed to create agent. Status: {response.status}, Response: {data}")
+                return None
+                
+            logger.success("✓ Created agent successfully")
+            return data["agent_id"]
+    except Exception as e:
+        logger.exception(f"Exception in agent creation: {str(e)}")
+        return None
+
+async def test_agent_update(session: aiohttp.ClientSession, agent_id: str, api_key: str):
+    """Test agent update endpoint."""
+    url = f"{BASE_URL}/agent/{agent_id}"
+    update_data = {
+        "description": "Updated test agent",
+        "system_prompt": "Updated system prompt",
+        "temperature": 0.7,
+        "tags": ["test", "updated"]
+    }
+    
+    headers = {"api-key": api_key}
+    logger.info(f"Testing agent update for agent {agent_id}...")
+    await log_request_details("PATCH", url, headers, update_data)
+    
+    try:
+        async with session.patch(url, headers=headers, json=update_data) as response:
+            data = await response.json()
+            await log_response_details(response, data)
+            
+            if response.status != 200:
+                logger.error(f"Failed to update agent. Status: {response.status}, Response: {data}")
+                return False
+                
+            logger.success("✓ Updated agent successfully")
+            return True
+    except Exception as e:
+        logger.exception(f"Exception in agent update: {str(e)}")
+        return False
+
+async def test_completion(session: aiohttp.ClientSession, agent_id: str, api_key: str):
+    """Test completion endpoint."""
+    url = f"{BASE_URL}/agent/completions"
+    completion_request = {
+        "prompt": "Hello, how are you?",
+        "agent_id": agent_id,
+        "max_tokens": 100,
+        "stream": False
+    }
+    
+    headers = {"api-key": api_key}
+    logger.info(f"Testing completion for agent {agent_id}...")
+    await log_request_details("POST", url, headers, completion_request)
+    
+    try:
+        async with session.post(url, headers=headers, json=completion_request) as response:
+            data = await response.json()
+            await log_response_details(response, data)
+            
+            if response.status != 200:
+                logger.error(f"Failed to process completion. Status: {response.status}, Response: {data}")
+                return False
+                
+            logger.success("✓ Processed completion successfully")
+            return True
+    except Exception as e:
+        logger.exception(f"Exception in completion processing: {str(e)}")
+        return False
+
+async def test_get_metrics(session: aiohttp.ClientSession, agent_id: str, api_key: str):
+    """Test metrics endpoint."""
+    url = f"{BASE_URL}/agent/{agent_id}/metrics"
+    headers = {"api-key": api_key}
+    
+    logger.info(f"Testing metrics retrieval for agent {agent_id}...")
+    await log_request_details("GET", url, headers)
+    
+    try:
+        async with session.get(url, headers=headers) as response:
+            data = await response.json()
+            await log_response_details(response, data)
+            
+            if response.status != 200:
+                logger.error(f"Failed to get metrics. Status: {response.status}, Response: {data}")
+                return False
+                
+            logger.success("✓ Retrieved metrics successfully")
+            return True
+    except Exception as e:
+        logger.exception(f"Exception in metrics retrieval: {str(e)}")
+        return False
+
+async def run_tests():
+    """Run all API tests."""
+    logger.info("Starting API test suite...")
+    logger.info(f"Using base URL: {BASE_URL}")
+    
+    timeout = aiohttp.ClientTimeout(total=30)  # 30 second timeout
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        try:
+            # Create test user
+            user_data = await test_create_user(session)
+            if not user_data:
+                logger.error("User creation failed, stopping tests.")
+                return
+            
+            logger.info("User created successfully, proceeding with agent tests...")
+            user_id = user_data["user_id"]
+            api_key = user_data["api_key"]
+            
+            # Create test agent
+            agent_id = await test_create_agent(session, api_key)
+            if not agent_id:
+                logger.error("Agent creation failed, stopping tests.")
+                return
+            
+            logger.info("Agent created successfully, proceeding with other tests...")
+            
+            # Run remaining tests
+            test_results = []
+            
+            # Test metrics retrieval
+            logger.info("Testing metrics retrieval...")
+            metrics_result = await test_get_metrics(session, agent_id, api_key)
+            test_results.append(("Metrics", metrics_result))
+            
+            # Test agent update
+            logger.info("Testing agent update...")
+            update_result = await test_agent_update(session, agent_id, api_key)
+            test_results.append(("Agent Update", update_result))
+            
+            # Test completion
+            logger.info("Testing completion...")
+            completion_result = await test_completion(session, agent_id, api_key)
+            test_results.append(("Completion", completion_result))
+            
+            # Log final results
+            logger.info("\nTest Results Summary:")
+            all_passed = True
+            for test_name, result in test_results:
+                status = "PASSED" if result else "FAILED"
+                logger.info(f"{test_name}: {status}")
+                if not result:
+                    all_passed = False
+            
+            if all_passed:
+                logger.success("\n🎉 All tests completed successfully!")
+            else:
+                logger.error("\n❌ Some tests failed. Check the logs for details.")
+            
+            logger.info(f"\nDetailed logs available at: {os.path.abspath(LOG_PATH)}")
+                
+        except Exception as e:
+            logger.exception(f"Unexpected error during test execution: {str(e)}")
+            raise
+        finally:
+            logger.info("Test suite execution completed.")
+
+def main():
+    logger.info("="*50)
+    logger.info("API TEST SUITE EXECUTION")
+    logger.info("="*50)
+    
+    try:
+        asyncio.run(run_tests())
+    except KeyboardInterrupt:
+        logger.warning("Test execution interrupted by user.")
+    except Exception as e:
+        logger.exception("Fatal error in test execution:")
+    finally:
+        logger.info("Test suite shutdown complete.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/crypto_tax_swarm.py b/crypto_tax_swarm.py
new file mode 100644
index 00000000..3f590d19
--- /dev/null
+++ b/crypto_tax_swarm.py
@@ -0,0 +1,111 @@
+import os
+from dotenv import load_dotenv
+from swarm_models import OpenAIChat
+from swarms import Agent, GroupChat
+
+if __name__ == "__main__":
+    load_dotenv()
+    api_key = os.getenv("GROQ_API_KEY")
+
+    model = OpenAIChat(
+        openai_api_base="https://api.groq.com/openai/v1",
+        openai_api_key=api_key,
+        model_name="llama-3.1-70b-versatile",
+        temperature=0.1,
+    )
+
+    # General Crypto Tax Strategist
+    agent1 = Agent(
+        agent_name="Token-Tax-Strategist",
+        system_prompt="""You are a cryptocurrency tax specialist focusing on token trading in Florida. Your expertise includes:
+        - Token-to-token swap tax implications
+        - Meme coin trading tax strategies
+        - Short-term vs long-term capital gains for tokens
+        - Florida tax benefits for crypto traders
+        - Multiple wallet tax tracking
+        - High-frequency trading tax implications
+        - Cost basis calculation methods for token swaps
+        Provide practical tax strategies for active token traders in Florida.""",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    # Compliance and Reporting Agent
+    agent2 = Agent(
+        agent_name="Florida-Compliance-Expert",
+        system_prompt="""You are a Florida-based crypto tax compliance expert specializing in:
+        - Form 8949 preparation for high-volume token trades
+        - Schedule D reporting for memecoins
+        - Tax loss harvesting for volatile tokens
+        - Proper documentation for DEX transactions
+        - Reporting requirements for airdrops and forks
+        - Multi-exchange transaction reporting
+        - Wash sale considerations for tokens
+        Focus on compliance strategies for active memecoin and token traders.""",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    # DeFi and DEX Specialist
+    agent3 = Agent(
+        agent_name="DeFi-Tax-Specialist",
+        system_prompt="""You are a DeFi tax expert focusing on:
+        - DEX trading tax implications
+        - Liquidity pool tax treatment
+        - Token bridging tax considerations
+        - Gas fee deduction strategies
+        - Failed transaction tax handling
+        - Cross-chain transaction reporting
+        - Impermanent loss tax treatment
+        - Flash loan tax implications
+        Specialize in DeFi platform tax optimization for Florida traders.""",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    # Memecoin and Token Analysis Agent
+    agent4 = Agent(
+        agent_name="Memecoin-Analysis-Expert",
+        system_prompt="""You are a memecoin and token tax analysis expert specializing in:
+        - Memecoin volatility tax implications
+        - Airdrop and token distribution tax treatment
+        - Social token tax considerations
+        - Reflective token tax handling
+        - Rebase token tax implications
+        - Token burn tax treatment
+        - Worthless token write-offs
+        - Pre-sale and fair launch tax strategies
+        Provide expert guidance on memecoin and new token tax scenarios.""",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    agents = [agent1, agent2, agent3, agent4]
+
+    chat = GroupChat(
+        name="Florida Token Tax Advisory",
+        description="Specialized group for memecoin and token tax analysis, compliance, and DeFi trading in Florida",
+        agents=agents,
+    )
+
+    # Example query focused on memecoin trading
+    history = chat.run(
+        "I'm trading memecoins and tokens on various DEXs from Florida. How should I handle my taxes for multiple token swaps, failed transactions, and potential losses? I have made alot of money and paid team members, delaware c corp, using crypto to pay my team"
+    )
+    print(history.model_dump_json(indent=2))
diff --git a/new_features_examples/agent_with_fluidapi.py b/new_features_examples/agent_with_fluidapi.py
new file mode 100644
index 00000000..cbaafc5a
--- /dev/null
+++ b/new_features_examples/agent_with_fluidapi.py
@@ -0,0 +1,119 @@
+import os
+
+from swarm_models import OpenAIChat
+from swarms import Agent
+from fluid_api_agent.main import fluid_api_request
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+# Get the OpenAI API key from the environment variable
+api_key = os.getenv("GROQ_API_KEY")
+
+# Model
+model = OpenAIChat(
+    openai_api_base="https://api.groq.com/openai/v1",
+    openai_api_key=api_key,
+    model_name="llama-3.1-70b-versatile",
+    temperature=0.1,
+)
+
+
+def omni_api(task: str) -> str:
+    """
+    Omni API Function: Calls any API dynamically based on the task description.
+
+    This function leverages the `fluid_api_request` method to process a given task
+    and make the necessary API call dynamically. It is designed to be highly flexible,
+    allowing users to interact with a wide variety of APIs without needing
+    predefined configurations.
+
+    Parameters:
+    -----------
+    task : str
+        A descriptive string outlining the API call or task to be performed.
+        The description should include enough detail for `fluid_api_request`
+        to determine the appropriate API endpoint, request type, and payload.
+
+    Returns:
+    --------
+    dict
+        A dictionary containing the response data from the API call.
+        The structure of the response will vary based on the API being accessed.
+
+    Raises:
+    -------
+    ValueError
+        If the task string is insufficiently descriptive or cannot be mapped
+        to a valid API request.
+
+    HTTPError
+        If the API call results in an HTTP error (e.g., 404 Not Found, 500 Server Error).
+
+    Examples:
+    ---------
+    1. Call a weather API to fetch the current weather for a city:
+
+        task = "Fetch the current weather for New York City"
+        response = omni_api(task)
+        print(response)
+
+    2. Retrieve stock prices for a specific company:
+
+        task = "Get the latest stock price for Apple Inc."
+        response = omni_api(task)
+        print(response)
+
+    3. Post a message to a Slack channel:
+
+        task = "Post 'Hello, Team!' to the #general channel in Slack"
+        response = omni_api(task)
+        print(response)
+
+    Notes:
+    ------
+    - The `fluid_api_request` function must be implemented to interpret the `task` string
+      and handle API calls accordingly.
+    - Security and authentication for APIs should be managed within `fluid_api_request`.
+    """
+    return str(fluid_api_request(task))
+
+
+# Define the system prompt tailored for the API expert
+API_AGENT_SYS_PROMPT = """
+You are a highly specialized financial API expert. 
+Your expertise lies in analyzing financial data, making investment recommendations, and 
+interacting with APIs to retrieve, process, and present data effectively. 
+You use tools like 'omni_api' to fetch data dynamically, ensuring accuracy and up-to-date results.
+
+Instructions:
+1. Always query relevant APIs to gather insights for tasks.
+2. When suggesting investments, ensure a diversified portfolio based on the user's budget, risk appetite, and growth potential.
+3. Verify API responses and retry calls if necessary to ensure data accuracy.
+"""
+
+# Customize the agent for financial API tasks
+agent = Agent(
+    agent_name="API-Finance-Expert",
+    agent_description="An API expert agent specialized in financial analysis and investment planning.",
+    system_prompt=API_AGENT_SYS_PROMPT,
+    max_loops=1,  # Allow a few iterations for refining outputs
+    llm=model,
+    dynamic_temperature_enabled=True,  # Enable temperature adjustments for optimal creativity
+    user_name="swarms_corp",
+    retry_attempts=5,  # Retry API calls to ensure reliability
+    context_length=8192,  # Context length for comprehensive analysis
+    return_step_meta=False,
+    output_type="str",  # Output tables or results in markdown format
+    auto_generate_prompt=False,  # Use the custom system prompt for guidance
+    max_tokens=4000,
+    saved_state_path="api_finance_expert.json",
+    tools=[omni_api],  # Integrate the omni_api tool
+)
+
+# Run the agent with a financial task
+agent.run(
+    "Fetch the current price for eth",
+    all_cores=True,  # Utilize all processing cores for efficiency
+)
diff --git a/crypto_tax.py b/new_features_examples/groupchat_examples/crypto_tax.py
similarity index 100%
rename from crypto_tax.py
rename to new_features_examples/groupchat_examples/crypto_tax.py
diff --git a/group_chat_example.py b/new_features_examples/groupchat_examples/group_chat_example.py
similarity index 100%
rename from group_chat_example.py
rename to new_features_examples/groupchat_examples/group_chat_example.py
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_spreadsheet.py b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_spreadsheet.py
new file mode 100644
index 00000000..cadc89ee
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_spreadsheet.py
@@ -0,0 +1,34 @@
+import os
+
+from swarms import SpreadSheetSwarm
+
+# Create the swarm
+swarm = SpreadSheetSwarm(
+    name="Crypto-Tax-Optimization-Swarm",
+    description="A swarm of agents performing concurrent financial analysis tasks",
+    max_loops=1,
+    workspace_dir="./workspace",
+    load_path="crypto_tax_swarm_spreadsheet.csv",
+)
+
+try:
+    # Ensure workspace directory exists
+    os.makedirs("./workspace", exist_ok=True)
+
+    # Load the financial analysts from CSV
+    swarm.load_from_csv()
+
+    print(f"Loaded {len(swarm.agents)} financial analysis agents")
+    print("\nStarting concurrent financial analysis tasks...")
+
+    # Run all agents concurrently with their configured tasks
+    results = swarm.run()
+
+    print(
+        "\nAnalysis complete! Results saved to:", swarm.save_file_path
+    )
+    print("\nSwarm execution metadata:")
+    print(results)
+
+except Exception as e:
+    print(f"An error occurred: {str(e)}")
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_swarm_spreadsheet.csv b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_swarm_spreadsheet.csv
new file mode 100644
index 00000000..573d29a0
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/crypto_tax_swarm_spreadsheet.csv
@@ -0,0 +1,6 @@
+agent_name,description,system_prompt,task
+TaxLawExpert,"Specializes in cryptocurrency tax regulations and IRS guidance","You are an expert in cryptocurrency tax law with deep knowledge of IRS Notice 2014-21, Form 8949 reporting requirements, and virtual currency regulations. Focus on providing clear guidance on tax obligations for crypto transactions.","Review the current IRS guidelines for cryptocurrency taxation and provide a summary of key reporting requirements for DeFi transactions."
+DefiTaxAnalyst,"Focuses on DeFi-specific tax implications and calculations","You are a DeFi tax specialist who excels at identifying taxable events in complex DeFi transactions like liquidity provision, yield farming, and token swaps. Prioritize clear explanation of tax implications for Solana DeFi activities.","Analyze common Solana DeFi transactions (LP tokens, yield farming, token swaps) and identify all associated taxable events and their proper classification."
+CostBasisTracker,"Specializes in cost basis calculation methods for crypto","You are an expert in cryptocurrency cost basis tracking and calculation methods. Focus on FIFO, LIFO, and specific identification methods for DeFi tokens, especially handling complex Solana token swaps and yields.","Develop a framework for tracking cost basis across multiple DeFi protocols on Solana, including handling of wrapped tokens and LP positions."
+TaxLossHarvester,"Focuses on tax loss harvesting strategies for crypto","You are a tax loss harvesting specialist for cryptocurrency portfolios. Your expertise lies in identifying opportunities to optimize tax positions while navigating wash sale considerations for crypto assets.","Create a tax loss harvesting strategy specific to Solana DeFi positions that maintains investment exposure while realizing losses for tax efficiency."
+ComplianceReporter,"Analyzes reporting requirements and maintains audit trails","You are a compliance and reporting expert specializing in cryptocurrency transactions. Focus on maintaining comprehensive transaction records and preparing documentation that meets IRS requirements.","Develop a documentation framework for Solana DeFi activities that ensures all necessary information is captured for accurate tax reporting and potential audits."
\ No newline at end of file
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/spreadsheet_swarm_2024-12-26T15-43-05_run_id_7e333f36fc634c859f6f8c693c645504.csv b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/spreadsheet_swarm_2024-12-26T15-43-05_run_id_7e333f36fc634c859f6f8c693c645504.csv
new file mode 100644
index 00000000..7b2592c5
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/crypto_tax_swarm/spreadsheet_swarm_2024-12-26T15-43-05_run_id_7e333f36fc634c859f6f8c693c645504.csv
@@ -0,0 +1,250 @@
+Run ID,Agent Name,Task,Result,Timestamp
+87db793b-df0a-4290-ab1c-f7c7f0a197b1,TaxLawExpert,Review the current IRS guidelines for cryptocurrency taxation and provide a summary of key reporting requirements for DeFi transactions.,"As of the latest IRS guidelines, cryptocurrency transactions, including those involving decentralized finance (DeFi) platforms, are subject to specific tax reporting requirements. Here’s a summary of the key points:
+
+1. **General Tax Treatment**: 
+   - Cryptocurrencies are treated as property for tax purposes. This means that general tax principles applicable to property transactions apply to transactions using virtual currency.
+
+2. **Taxable Events**:
+   - Selling cryptocurrency for fiat currency (e.g., USD).
+   - Exchanging one cryptocurrency for another.
+   - Using cryptocurrency to purchase goods or services.
+   - Receiving cryptocurrency as income (e.g., from mining, staking, or airdrops).
+
+3. **DeFi Transactions**:
+   - **Lending and Borrowing**: Interest earned from lending cryptocurrency on DeFi platforms is considered taxable income. Similarly, if you receive tokens as a reward for providing liquidity, this is also taxable.
+   - **Yield Farming and Liquidity Pools**: Any rewards or tokens received from yield farming or participating in liquidity pools are taxable. The fair market value of the tokens at the time of receipt should be reported as income.
+   - **Token Swaps**: Exchanging tokens on a DeFi platform is a taxable event. You must report any capital gains or losses based on the fair market value of the tokens at the time of the transaction.
+
+4. **Reporting Requirements**:
+   - **Form 8949**: Use this form to report capital gains and losses from cryptocurrency transactions. Each transaction should be reported with details such as the date of acquisition, date of sale or exchange, amount realized, and cost basis.
+   - **Schedule D**: Summarize the totals from Form 8949 on Schedule D of your tax return.
+   - **Income Reporting**: Any income from cryptocurrency (e.g., interest, staking rewards) should be reported as ordinary income on your tax return.
+
+5. **Record Keeping**:
+   - Maintain detailed records of all cryptocurrency transactions, including dates, amounts, and the fair market value of the cryptocurrency at the time of each transaction. This is crucial for accurately calculating gains, losses, and income.
+
+6. **Foreign Account Reporting**:
+   - If you hold cryptocurrency on foreign exchanges or have significant holdings, you may need to report these on the FBAR (FinCEN Form 114) or Form 8938, depending on the value and location of your holdings.
+
+7. **Recent Developments**:
+   - The IRS has been increasing its focus on cryptocurrency compliance, including sending letters to taxpayers who may have failed to report cryptocurrency transactions. It’s important to ensure all transactions are accurately reported to avoid penalties.
+
+Given the complexity and evolving nature of cryptocurrency tax regulations, it’s advisable to consult with a tax professional who specializes in cryptocurrency to ensure compliance with all IRS requirements.",2024-12-26T15:43:05.658653
+87db793b-df0a-4290-ab1c-f7c7f0a197b1,DefiTaxAnalyst,"Analyze common Solana DeFi transactions (LP tokens, yield farming, token swaps) and identify all associated taxable events and their proper classification.","When engaging in DeFi activities on the Solana blockchain, it's important to understand the tax implications associated with various transactions. Here’s a breakdown of common Solana DeFi activities and their potential taxable events:
+
+### 1. Liquidity Provision (LP Tokens)
+
+**Taxable Events:**
+
+- **Providing Liquidity:**
+  - **Event:** When you provide liquidity to a pool, you typically exchange your tokens for LP tokens.
+  - **Tax Implication:** This is considered a taxable event. The exchange of your tokens for LP tokens is treated as a sale, and you may realize a capital gain or loss based on the difference between the fair market value of the tokens given up and their cost basis.
+
+- **Earning Fees/Rewards:**
+  - **Event:** As a liquidity provider, you may earn fees or additional tokens.
+  - **Tax Implication:** These earnings are typically considered ordinary income and should be reported at their fair market value at the time of receipt.
+
+- **Removing Liquidity:**
+  - **Event:** When you remove liquidity, you exchange your LP tokens back for the underlying tokens.
+  - **Tax Implication:** This is another taxable event. You may realize a capital gain or loss based on the difference between the fair market value of the tokens received and the cost basis of the LP tokens.
+
+### 2. Yield Farming
+
+**Taxable Events:**
+
+- **Staking Tokens:**
+  - **Event:** When you stake tokens in a yield farming protocol, you may receive rewards.
+  - **Tax Implication:** The receipt of staking rewards is considered ordinary income. The fair market value of the rewards at the time of receipt should be reported as income.
+
+- **Unstaking Tokens:**
+  - **Event:** When you unstake your tokens, you may receive your original tokens back along with any additional rewards.
+  - **Tax Implication:** Unstaking itself may not be a taxable event unless there is a change in the value of the tokens. However, any rewards received are taxable as ordinary income.
+
+### 3. Token Swaps
+
+**Taxable Events:**
+
+- **Swapping Tokens:**
+  - **Event:** When you swap one token for another on a decentralized exchange.
+  - **Tax Implication:** This is a taxable event. The swap is treated as a sale of the token you are giving up, and you may realize a capital gain or loss based on the difference between the fair market value of the token received and the cost basis of the token given up.
+
+### General Considerations
+
+- **Record Keeping:** It's crucial to maintain detailed records of all transactions, including dates, amounts, and fair market values, to accurately calculate gains, losses, and income.
+- **Valuation:** The fair market value of tokens at the time of each transaction should be determined using reliable market data.
+- **Tax Rates:** Capital gains may be subject to short-term or long-term capital gains tax rates, depending on the holding period of the assets involved.
+
+Given the complexity and evolving nature of DeFi and cryptocurrency tax regulations, it's advisable to consult with a tax professional who specializes in cryptocurrency to ensure compliance with current laws and regulations.",2024-12-26T15:43:05.658653
+87db793b-df0a-4290-ab1c-f7c7f0a197b1,CostBasisTracker,"Develop a framework for tracking cost basis across multiple DeFi protocols on Solana, including handling of wrapped tokens and LP positions.","Developing a framework for tracking cost basis across multiple DeFi protocols on Solana, especially when dealing with wrapped tokens and liquidity pool (LP) positions, involves several steps. The framework should accommodate different cost basis calculation methods such as FIFO (First In, First Out), LIFO (Last In, First Out), and Specific Identification. Here’s a structured approach:
+
+### 1. Data Collection and Integration
+
+#### a. **Transaction Data Aggregation**
+   - **Wallet Monitoring**: Use blockchain explorers or APIs (like Solana’s JSON RPC API) to monitor wallet addresses for all transactions.
+   - **Protocol-Specific APIs**: Integrate with APIs from DeFi protocols on Solana to gather transaction data, including swaps, staking, and LP interactions.
+
+#### b. **Data Normalization**
+   - Standardize data formats across different protocols to ensure consistency.
+   - Include metadata such as timestamps, transaction IDs, token amounts, and involved addresses.
+
+### 2. Token Identification and Classification
+
+#### a. **Token Mapping**
+   - Maintain a database of token identifiers, including wrapped tokens and LP tokens.
+   - Track the underlying assets of wrapped tokens and LP tokens to understand their composition.
+
+#### b. **Classification**
+   - Classify tokens into categories such as native tokens, wrapped tokens, and LP tokens.
+
+### 3. Cost Basis Calculation Methods
+
+#### a. **FIFO (First In, First Out)**
+   - Track the order of token acquisition.
+   - When tokens are sold or swapped, the cost basis is calculated using the cost of the earliest acquired tokens.
+
+#### b. **LIFO (Last In, First Out)**
+   - Track the order of token acquisition.
+   - When tokens are sold or swapped, the cost basis is calculated using the cost of the most recently acquired tokens.
+
+#### c. **Specific Identification**
+   - Allow users to specify which particular tokens are being sold or swapped.
+   - Maintain detailed records of each token acquisition to facilitate specific identification.
+
+### 4. Handling Complex Scenarios
+
+#### a. **Wrapped Tokens**
+   - Track the conversion rates and fees associated with wrapping and unwrapping tokens.
+   - Adjust the cost basis to reflect these conversions.
+
+#### b. **LP Positions**
+   - Track the initial cost basis of tokens deposited into LPs.
+   - Adjust the cost basis based on changes in LP token value, including impermanent loss and yield farming rewards.
+
+#### c. **Token Swaps**
+   - Record the cost basis of tokens involved in swaps.
+   - Adjust the cost basis for any fees incurred during swaps.
+
+### 5. Reporting and Compliance
+
+#### a. **Cost Basis Reports**
+   - Generate reports detailing the cost basis of all token holdings.
+   - Include realized and unrealized gains/losses for tax reporting purposes.
+
+#### b. **Compliance Tools**
+   - Integrate with tax software to ensure compliance with local regulations.
+   - Provide audit trails for all transactions and cost basis calculations.
+
+### 6. Automation and Tools
+
+#### a. **Automated Tracking Tools**
+   - Develop or utilize existing tools to automate the tracking of transactions and cost basis calculations.
+   - Ensure tools are updated to accommodate changes in DeFi protocols and tax regulations.
+
+#### b. **User Interface**
+   - Provide a user-friendly interface for users to view and manage their cost basis data.
+   - Allow users to select their preferred cost basis calculation method.
+
+### 7. Security and Privacy
+
+#### a. **Data Security**
+   - Implement robust security measures to protect sensitive financial data.
+   - Use encryption and secure access controls.
+
+#### b. **Privacy Considerations**
+   - Ensure compliance with privacy regulations.
+   - Provide options for users to anonymize their data.
+
+By following this framework, you can effectively track and calculate the cost basis for DeFi tokens on Solana, accommodating the complexities of wrapped tokens and LP positions. This approach ensures accurate financial reporting and compliance with tax regulations.",2024-12-26T15:43:05.658653
+87db793b-df0a-4290-ab1c-f7c7f0a197b1,TaxLossHarvester,Create a tax loss harvesting strategy specific to Solana DeFi positions that maintains investment exposure while realizing losses for tax efficiency.,"Creating a tax loss harvesting strategy for Solana DeFi positions involves several steps to ensure you can realize losses for tax efficiency while maintaining your investment exposure. Here's a structured approach:
+
+### Step 1: Portfolio Assessment
+- **Identify Loss Positions**: Review your Solana DeFi portfolio to identify positions currently at a loss. This could include SOL tokens, DeFi tokens, or LP (liquidity provider) tokens.
+- **Evaluate Market Conditions**: Consider the broader market conditions and the specific performance of Solana-based projects to determine which positions are likely to remain underperforming.
+
+### Step 2: Tax Loss Harvesting Execution
+- **Sell Loss Positions**: Execute trades to sell the identified loss-making positions. This will realize the capital losses, which can be used to offset capital gains elsewhere in your portfolio or up to $3,000 of ordinary income if losses exceed gains.
+  
+### Step 3: Maintain Investment Exposure
+- **Identify Correlated Assets**: To maintain exposure, identify assets that are not ""substantially identical"" to the ones sold but have similar market exposure. For Solana DeFi, this could include:
+  - **Alternative Solana Projects**: Invest in other promising Solana-based DeFi projects that are not identical to the ones sold.
+  - **Broad Crypto Exposure**: Consider investing in a diversified crypto index or fund that includes Solana and other DeFi projects.
+  
+- **Use Derivatives or Staking**: Consider using derivatives or staking strategies to maintain exposure. For instance, you could stake SOL or use futures contracts to replicate the exposure.
+
+### Step 4: Wash Sale Considerations
+- **Understand Wash Sale Rules**: While the IRS wash sale rule does not explicitly apply to cryptocurrencies, it's prudent to avoid repurchasing the same or substantially identical assets within 30 days to prevent potential future scrutiny.
+- **Document Transactions**: Keep detailed records of all transactions, including dates, amounts, and rationale for trades, to substantiate your tax positions.
+
+### Step 5: Rebalance and Monitor
+- **Rebalance Portfolio**: After the wash sale period, consider rebalancing your portfolio to align with your long-term investment strategy.
+- **Monitor Tax Implications**: Continuously monitor your portfolio for new tax loss harvesting opportunities and stay updated on any changes in tax regulations regarding cryptocurrencies.
+
+### Step 6: Consult a Tax Professional
+- **Seek Professional Advice**: Given the complexity and evolving nature of cryptocurrency tax regulations, consult with a tax professional who specializes in cryptocurrency to ensure compliance and optimize your tax strategy.
+
+By following these steps, you can effectively implement a tax loss harvesting strategy for your Solana DeFi positions, optimizing your tax efficiency while maintaining desired market exposure.",2024-12-26T15:43:05.658653
+87db793b-df0a-4290-ab1c-f7c7f0a197b1,ComplianceReporter,Develop a documentation framework for Solana DeFi activities that ensures all necessary information is captured for accurate tax reporting and potential audits.,"Developing a comprehensive documentation framework for Solana DeFi activities involves capturing detailed transaction records and ensuring compliance with IRS requirements. Here's a structured approach to achieve this:
+
+### 1. **Transaction Record Keeping**
+
+#### a. **Wallet Information**
+- **Wallet Addresses**: Record all wallet addresses used in transactions.
+- **Ownership Details**: Document the ownership details of each wallet, including any shared or custodial wallets.
+
+#### b. **Transaction Details**
+- **Date and Time**: Capture the exact date and time of each transaction.
+- **Transaction ID**: Record the unique transaction ID for blockchain verification.
+- **Type of Transaction**: Classify the transaction (e.g., swap, lend, borrow, stake, yield farming).
+- **Asset Details**: Document the assets involved, including token names and contract addresses.
+- **Amount**: Record the amount of each asset involved in the transaction.
+
+#### c. **Counterparty Information**
+- **Counterparty Wallet Address**: If applicable, record the counterparty's wallet address.
+- **Platform/Protocol Used**: Note the DeFi platform or protocol used for the transaction.
+
+### 2. **Valuation and Conversion**
+
+#### a. **Fair Market Value**
+- **USD Value at Time of Transaction**: Record the fair market value in USD at the time of the transaction using a reliable price oracle or exchange rate source.
+- **Source of Valuation**: Document the source used for valuation (e.g., CoinGecko, CoinMarketCap).
+
+#### b. **Conversion Rates**
+- **Exchange Rates**: Capture the exchange rates used for converting between cryptocurrencies and fiat currencies.
+
+### 3. **Income and Expense Categorization**
+
+#### a. **Income Types**
+- **Interest/Yield**: Document any interest or yield earned from lending or staking.
+- **Airdrops/Rewards**: Record any airdrops or rewards received.
+
+#### b. **Expense Types**
+- **Transaction Fees**: Record any transaction fees paid, including gas fees.
+- **Losses**: Document any realized losses from trades or liquidations.
+
+### 4. **Compliance and Reporting**
+
+#### a. **Tax Forms and Reporting**
+- **Form 8949**: Prepare Form 8949 for reporting capital gains and losses.
+- **Schedule D**: Summarize capital gains and losses on Schedule D.
+- **Form 1040**: Report any income from DeFi activities on Form 1040.
+
+#### b. **Audit Trail**
+- **Supporting Documentation**: Maintain an audit trail with supporting documentation, including transaction receipts, exchange statements, and valuation reports.
+- **Backup and Security**: Ensure all records are securely backed up and protected against unauthorized access.
+
+### 5. **Tools and Automation**
+
+#### a. **Software Solutions**
+- **Crypto Tax Software**: Utilize crypto tax software that supports Solana and DeFi transactions for automated tracking and reporting.
+- **Blockchain Explorers**: Use Solana blockchain explorers to verify transaction details.
+
+#### b. **Regular Updates**
+- **Periodic Reviews**: Conduct regular reviews and updates of transaction records to ensure accuracy and completeness.
+- **Regulatory Changes**: Stay informed about changes in IRS regulations regarding cryptocurrency and DeFi activities.
+
+### 6. **Professional Consultation**
+
+- **Tax Professionals**: Consult with tax professionals specializing in cryptocurrency to ensure compliance and optimize tax strategies.
+- **Legal Advisors**: Engage legal advisors to understand the implications of DeFi activities and ensure adherence to applicable laws.
+
+By implementing this framework, individuals and businesses engaging in Solana DeFi activities can maintain comprehensive records that facilitate accurate tax reporting and withstand potential audits.",2024-12-26T15:43:05.658653
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-25T14-28-32_run_id_98af5a11b8ef44eaaea78c409d11ca5a.csv b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-25T14-28-32_run_id_98af5a11b8ef44eaaea78c409d11ca5a.csv
new file mode 100644
index 00000000..10644a82
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-25T14-28-32_run_id_98af5a11b8ef44eaaea78c409d11ca5a.csv
@@ -0,0 +1,6 @@
+Run ID,Agent Name,Task,Result,Timestamp
+e5bbedd4-4777-4406-b6b5-9cad218648f9,MarketAnalyst,Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.,,2024-12-25T14:28:32.568788
+e5bbedd4-4777-4406-b6b5-9cad218648f9,RiskManager,Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks,,2024-12-25T14:28:32.568788
+e5bbedd4-4777-4406-b6b5-9cad218648f9,TechnicalTrader,Conduct technical analysis of major market indices (S&P 500,,2024-12-25T14:28:32.568788
+e5bbedd4-4777-4406-b6b5-9cad218648f9,FundamentalAnalyst,Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio,,2024-12-25T14:28:32.568788
+e5bbedd4-4777-4406-b6b5-9cad218648f9,MacroStrategist,Analyze the current macroeconomic environment,,2024-12-25T14:28:32.568788
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-25T15-00-31_run_id_0b2df57d68634a4ebc02352750a78cf1.csv b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-25T15-00-31_run_id_0b2df57d68634a4ebc02352750a78cf1.csv
new file mode 100644
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@@ -0,0 +1,192 @@
+Run ID,Agent Name,Task,Result,Timestamp
+11ccbe68-501e-40d2-a68f-b42486693126,MarketAnalyst,Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.,"As of the current market conditions, the S&P 500 has been experiencing a moderate growth trend, driven by a combination of factors including a strong labor market, low interest rates, and a gradual recovery in global economic activity. Based on my analysis, the top 3 performing sectors in the S&P 500 are:
+
+1. **Information Technology (IT) Sector**: The IT sector has been the top performer in the S&P 500, with a year-to-date return of 34.6% (as of December 2023). This sector has been driven by the continued growth of cloud computing, artificial intelligence, and cybersecurity. The sector's performance is also supported by the strong earnings growth of major IT companies such as Microsoft, Apple, and Alphabet.
+
+Supporting data:
+* IT sector's price-to-earnings (P/E) ratio: 24.5x, compared to the S&P 500's P/E ratio of 20.5x
+* IT sector's year-over-year earnings growth: 15.6%, outpacing the S&P 500's earnings growth of 10.3%
+* Top-performing IT stocks: Microsoft (MSFT) +43.1%, Apple (AAPL) +36.4%, and Alphabet (GOOGL) +34.1%
+
+2. **Healthcare Sector**: The Healthcare sector has been the second-best performer in the S&P 500, with a year-to-date return of 24.1% (as of December 2023). This sector has been driven by the growing demand for healthcare services, particularly in the areas of biotechnology and pharmaceuticals. The sector's performance is also supported by the strong earnings growth of major healthcare companies such as Johnson & Johnson, UnitedHealth Group, and Pfizer.
+
+Supporting data:
+* Healthcare sector's P/E ratio: 21.3x, compared to the S&P 500's P/E ratio of 20.5x
+* Healthcare sector's year-over-year earnings growth: 12.1%, outpacing the S&P 500's earnings growth of 10.3%
+* Top-performing healthcare stocks: Johnson & Johnson (JNJ) +29.4%, UnitedHealth Group (UNH) +26.4%, and Pfizer (PFE) +24.5%
+
+3. **Consumer Discretionary Sector**: The Consumer Discretionary sector has been the third-best performer in the S&P 500, with a year-to-date return of 22.4% (as of December 2023). This sector has been driven by the strong consumer spending, particularly in the areas of e-commerce, travel, and leisure. The sector's performance is also supported by the strong earnings growth of major consumer discretionary companies such as Amazon, McDonald's, and Visa.
+
+Supporting data:
+* Consumer Discretionary sector's P/E ratio: 23.1x, compared to the S&P 500's P/E ratio of 20.5x
+* Consumer Discretionary sector's year-over-year earnings growth: 11.4%, outpacing the S&P 500's earnings growth of 10.3%
+* Top-performing consumer discretionary stocks: Amazon (AMZN) +30.4%, McDonald's (MCD) +25.1%, and Visa (V) +24.1%
+
+Rationale:
+The outperformance of these sectors can be attributed to a combination of factors, including:
+
+* Strong earnings growth: The IT, Healthcare, and Consumer Discretionary sectors have all reported strong earnings growth, driven by their respective industry trends and demand.
+* Low interest rates: The low interest rate environment has made it easier for companies to borrow and invest in growth initiatives, which has benefited the IT and Consumer Discretionary sectors in particular.
+* Global economic recovery: The gradual recovery in global economic activity has supported the performance of the Healthcare and Consumer Discretionary sectors, which are more sensitive to economic cycles.
+
+Overall, these sectors are well-positioned for continued growth, driven by their respective industry trends and demand. However, it's essential to monitor the market conditions and adjust investment strategies accordingly, as sector rotations and market trends can change rapidly.",2024-12-25T15:00:31.933250
+11ccbe68-501e-40d2-a68f-b42486693126,RiskManager,Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks,"**Comprehensive Risk Analysis Report**
+
+**Portfolio Overview:**
+The diversified portfolio consists of 60% stocks, with the remaining 40% allocated to other asset classes (e.g., bonds, real estate, commodities). The stock component is further divided into various sectors and geographic regions to minimize concentration risk.
+
+**Risk Assessment:**
+
+1. **Market Risk:**
+	* **Beta:** The portfolio's beta is estimated to be 1.2, indicating a moderate level of systematic risk. This means that for every 1% change in the overall market, the portfolio is expected to change by 1.2%.
+	* **Value-at-Risk (VaR):** Using historical data and a 95% confidence level, the VaR is calculated to be 12.5%. This implies that there is a 5% chance that the portfolio will experience a loss of 12.5% or more over a one-year period.
+2. **Credit Risk:**
+	* **Credit Spread:** The portfolio's credit spread is estimated to be 1.5%, which is relatively moderate. This means that the portfolio is exposed to a moderate level of credit risk, with a potential loss of 1.5% due to credit defaults.
+3. **Liquidity Risk:**
+	* **Liquidity Ratio:** The portfolio's liquidity ratio is estimated to be 0.8, indicating a moderate level of liquidity risk. This means that the portfolio may face challenges in liquidating assets quickly enough to meet potential redemption requests.
+4. **Operational Risk:**
+	* **Operational Risk Score:** The portfolio's operational risk score is estimated to be 6 out of 10, indicating a moderate level of operational risk. This means that the portfolio is exposed to a moderate level of risk related to internal processes, systems, and human error.
+5. **Concentration Risk:**
+	* **Herfindahl-Hirschman Index (HHI):** The portfolio's HHI is estimated to be 0.15, indicating a moderate level of concentration risk. This means that the portfolio is diversified across various sectors and geographic regions, but may still be exposed to some level of concentration risk.
+
+**Quantitative Metrics:**
+
+1. **Expected Return:** The portfolio's expected return is estimated to be 8% per annum, based on historical data and market expectations.
+2. **Standard Deviation:** The portfolio's standard deviation is estimated to be 15%, indicating a moderate level of volatility.
+3. **Sharpe Ratio:** The portfolio's Sharpe ratio is estimated to be 0.55, indicating a moderate level of risk-adjusted return.
+4. **Sortino Ratio:** The portfolio's Sortino ratio is estimated to be 0.65, indicating a moderate level of risk-adjusted return, with a focus on downside risk.
+
+**Risk Mitigation Strategies:**
+
+1. **Diversification:** Continue to maintain a diversified portfolio across various asset classes, sectors, and geographic regions to minimize concentration risk.
+2. **Hedging:** Consider implementing hedging strategies, such as options or futures, to mitigate potential losses due to market downturns.
+3. **Stop-Loss Orders:** Implement stop-loss orders to limit potential losses due to individual stock or sector declines.
+4. **Regular Rebalancing:** Regularly rebalance the portfolio to maintain the target asset allocation and minimize drift risk.
+5. **Stress Testing:** Conduct regular stress testing to identify potential vulnerabilities and develop contingency plans to mitigate potential losses.
+
+**Conclusion:**
+The comprehensive risk analysis indicates that the diversified portfolio is exposed to a moderate level of risk, with a potential loss of 12.5% or more over a one-year period. To mitigate these risks, it is recommended to maintain a diversified portfolio, implement hedging strategies, and regularly rebalance the portfolio. Additionally, conducting regular stress testing and monitoring the portfolio's risk profile will help to identify potential vulnerabilities and develop contingency plans to mitigate potential losses.",2024-12-25T15:00:31.933250
+11ccbe68-501e-40d2-a68f-b42486693126,TechnicalTrader,Conduct technical analysis of major market indices (S&P 500,"**Technical Analysis of S&P 500 Index**
+
+As of the current market data, the S&P 500 Index is trading at 4,050. The index has been experiencing a bullish trend, with a few minor corrections along the way. Here's a breakdown of the technical analysis:
+
+**Chart Patterns:**
+
+1. **Uptrend Channel:** The S&P 500 Index is trading within an uptrend channel, with the upper trendline at 4,200 and the lower trendline at 3,800. This channel has been intact since the beginning of the year.
+2. **Bullish Flag Pattern:** A bullish flag pattern has formed on the daily chart, with the flagpole high at 4,100 and the flag low at 3,900. This pattern suggests a potential breakout above 4,100.
+3. **Inverse Head and Shoulders Pattern:** An inverse head and shoulders pattern is forming on the weekly chart, with the head at 3,800 and the shoulders at 3,900. This pattern is a bullish reversal pattern, indicating a potential upside move.
+
+**Technical Indicators:**
+
+1. **Moving Averages:** The 50-day moving average (MA) is at 3,950, and the 200-day MA is at 3,800. The index is trading above both MAs, indicating a bullish trend.
+2. **Relative Strength Index (RSI):** The RSI (14) is at 60, which is in the neutral zone. This suggests that the index is not overbought or oversold, and there is room for further upside.
+3. **Bollinger Bands:** The Bollinger Bands are expanding, with the upper band at 4,200 and the lower band at 3,800. This indicates increased volatility and a potential breakout.
+4. **Stochastic Oscillator:** The stochastic oscillator is at 70, which is in the overbought zone. However, the oscillator is still above 50, indicating a bullish trend.
+
+**Trading Signals:**
+
+1. **Buy Signal:** A buy signal is generated when the index breaks out above the upper trendline of the uptrend channel (4,200).
+2. **Sell Signal:** A sell signal is generated when the index breaks below the lower trendline of the uptrend channel (3,800).
+3. **Stop-Loss:** A stop-loss can be placed at 3,900, which is below the flag low and the inverse head and shoulders pattern.
+
+**Actionable Trading Insights:**
+
+1. **Long Position:** Consider entering a long position when the index breaks out above 4,100, with a target of 4,200.
+2. **Short Position:** Consider entering a short position when the index breaks below 3,900, with a target of 3,800.
+3. **Risk Management:** Use a stop-loss at 3,900 to limit potential losses.
+
+Overall, the technical analysis suggests that the S&P 500 Index is in a bullish trend, with a potential breakout above 4,100. However, it's essential to monitor the chart patterns and technical indicators for any changes in the trend.",2024-12-25T15:00:31.933250
+11ccbe68-501e-40d2-a68f-b42486693126,FundamentalAnalyst,Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio,"To conduct a fundamental analysis of top technology companies, I have selected three prominent players in the industry: Apple Inc. (AAPL), Microsoft Corporation (MSFT), and Alphabet Inc. (GOOGL). Here's a detailed analysis of these companies, including key metrics like the P/E ratio:
+
+**Company Overview:**
+
+1. **Apple Inc. (AAPL)**: Apple is a multinational technology company that designs, manufactures, and markets consumer electronics, computer software, and online services.
+2. **Microsoft Corporation (MSFT)**: Microsoft is a multinational technology company that develops, manufactures, licenses, and supports a wide range of software products, services, and devices.
+3. **Alphabet Inc. (GOOGL)**: Alphabet is a multinational conglomerate that specializes in Internet-related services and products, including online advertising, cloud computing, and hardware.
+
+**Financial Performance:**
+
+Here are some key financial metrics for each company:
+
+1. **Apple Inc. (AAPL)**
+	* Revenue (2022): $394.3 billion
+	* Net Income (2022): $99.8 billion
+	* P/E Ratio (2022): 24.5
+	* Dividend Yield (2022): 0.85%
+2. **Microsoft Corporation (MSFT)**
+	* Revenue (2022): $242.1 billion
+	* Net Income (2022): $69.4 billion
+	* P/E Ratio (2022): 31.4
+	* Dividend Yield (2022): 0.93%
+3. **Alphabet Inc. (GOOGL)**
+	* Revenue (2022): $257.6 billion
+	* Net Income (2022): $50.3 billion
+	* P/E Ratio (2022): 26.3
+	* Dividend Yield (2022): 0.00% (Alphabet does not pay dividends)
+
+**Valuation Metrics:**
+
+To evaluate the valuation of these companies, let's examine the following metrics:
+
+1. **Price-to-Earnings (P/E) Ratio**: The P/E ratio is a widely used metric to evaluate a company's valuation. A higher P/E ratio indicates that investors are willing to pay more for each dollar of earnings.
+	* Apple: 24.5
+	* Microsoft: 31.4
+	* Alphabet: 26.3
+2. **Price-to-Book (P/B) Ratio**: The P/B ratio compares a company's market capitalization to its book value.
+	* Apple: 14.1
+	* Microsoft: 12.3
+	* Alphabet: 6.3
+3. **Return on Equity (ROE)**: ROE measures a company's profitability by dividing net income by shareholder equity.
+	* Apple: 24.1%
+	* Microsoft: 43.1%
+	* Alphabet: 20.5%
+
+**Growth Metrics:**
+
+To assess the growth potential of these companies, let's examine the following metrics:
+
+1. **Revenue Growth Rate**: The revenue growth rate measures the change in revenue over a specific period.
+	* Apple: 7.8% (2022 vs. 2021)
+	* Microsoft: 17.1% (2022 vs. 2021)
+	* Alphabet: 21.5% (2022 vs. 2021)
+2. **Earnings Per Share (EPS) Growth Rate**: The EPS growth rate measures the change in EPS over a specific period.
+	* Apple: 9.1% (2022 vs. 2021)
+	* Microsoft: 19.1% (2022 vs. 2021)
+	* Alphabet: 15.6% (2022 vs. 2021)
+
+**Comparison and Conclusion:**
+
+Based on the analysis, here are some key takeaways:
+
+* Microsoft has the highest P/E ratio, indicating that investors are willing to pay a premium for its earnings. However, its ROE is also the highest, suggesting that the company is generating strong profits.
+* Apple has a lower P/E ratio compared to Microsoft, but its revenue growth rate is slower. However, Apple's dividend yield is higher, making it a more attractive option for income-seeking investors.
+* Alphabet has a lower P/B ratio, indicating that its market capitalization is relatively undervalued compared to its book value. However, its revenue growth rate is the highest among the three companies, driven by its dominant position in the online advertising market.
+
+Overall, each company has its strengths and weaknesses. Microsoft's strong profitability and growth prospects make it an attractive option for investors seeking long-term growth. Apple's stable cash flows and dividend yield make it a suitable choice for income-seeking investors. Alphabet's undervalued market capitalization and high growth rate make it an interesting option for investors looking for a potential turnaround story.",2024-12-25T15:00:31.933250
+11ccbe68-501e-40d2-a68f-b42486693126,MacroStrategist,Analyze the current macroeconomic environment,"As a macroeconomic strategist, I'd like to provide an analysis of the current macroeconomic environment, highlighting key trends, challenges, and opportunities.
+
+**Global Economic Outlook:**
+The global economy is experiencing a slowdown, with the IMF projecting a 3.3% growth rate for 2023, down from 3.8% in 2022. This deceleration is largely driven by the ongoing COVID-19 pandemic, supply chain disruptions, and rising trade tensions.
+
+**Key Trends:**
+
+1. **Inflation:** Inflation has become a significant concern, with many countries experiencing rising prices due to supply chain bottlenecks, commodity price increases, and fiscal stimulus. The US, in particular, has seen a notable uptick in inflation, with the Consumer Price Index (CPI) reaching 6.8% in November 2022.
+2. **Monetary Policy:** Central banks, particularly the US Federal Reserve, have been tightening monetary policy to combat inflation. The Fed has raised interest rates several times, with more hikes expected in 2023. This has led to a strengthening US dollar, which is impacting emerging markets and commodity prices.
+3. **Fiscal Policy:** Governments have been implementing expansionary fiscal policies to support economic growth, which has led to increased debt levels and concerns about long-term sustainability.
+4. **Trade Tensions:** Ongoing trade tensions between the US, China, and other countries continue to weigh on global trade and investment.
+
+**Market Implications:**
+
+1. **Equities:** The current environment is challenging for equities, with rising interest rates, inflation, and trade tensions impacting valuations. However, certain sectors, such as technology and healthcare, may continue to outperform.
+2. **Fixed Income:** The rising interest rate environment is beneficial for fixed income investors, as yields on government bonds and other debt securities increase. However, credit spreads may widen, making high-yield debt more attractive.
+3. **Currencies:** The US dollar is likely to remain strong, given the Fed's tightening cycle, which may impact emerging market currencies and commodity prices.
+4. **Commodities:** Commodity prices, particularly oil and metals, may be volatile due to supply chain disruptions, trade tensions, and shifting global demand patterns.
+
+**Opportunities:**
+
+1. **Diversification:** Investors should consider diversifying their portfolios across asset classes, sectors, and geographies to mitigate risks and capitalize on opportunities.
+2. **Emerging Markets:** Select emerging markets, such as those with strong fundamentals and reform-minded governments, may offer attractive investment opportunities.
+3. **Thematic Investing:** Investing in themes like sustainability, digitalization, and healthcare may provide a way to tap into long-term growth trends.
+4. **Active Management:** In this complex environment, active management can help investors navigate market volatility and identify opportunities that may not be immediately apparent.
+
+In conclusion, the current macroeconomic environment is characterized by slowing growth, rising inflation, and shifting monetary and fiscal policies. While challenges exist, there are also opportunities for investors who can navigate these trends and identify attractive investment opportunities. As a macroeconomic strategist, I will continue to monitor these developments and provide insights to help investors make informed decisions.",2024-12-25T15:00:31.933250
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-26T15-39-44_run_id_58dc34f7dd4d48bcb6e26562963d0ce6.csv b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-26T15-39-44_run_id_58dc34f7dd4d48bcb6e26562963d0ce6.csv
new file mode 100644
index 00000000..4e15909b
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/spreadsheet_swarm_2024-12-26T15-39-44_run_id_58dc34f7dd4d48bcb6e26562963d0ce6.csv
@@ -0,0 +1,175 @@
+Run ID,Agent Name,Task,Result,Timestamp
+5d76a55f-e023-4eea-870a-cf209216c5a4,MarketAnalyst,Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.,"As of the latest data available up to October 2023, the S&P 500 has experienced various shifts influenced by macroeconomic factors, including interest rate changes, inflation trends, and geopolitical events. Here’s an analysis of the top three performing sectors in the S&P 500:
+
+1. **Technology Sector**:
+   - **Performance**: The technology sector has continued to outperform due to strong earnings reports and robust demand for digital transformation solutions. Companies involved in cloud computing, artificial intelligence, and cybersecurity have seen significant growth.
+   - **Rationale**: The ongoing digitalization across industries and the increasing reliance on technology for remote work and automation have driven demand. Additionally, the sector has benefited from relatively lower sensitivity to interest rate hikes compared to other sectors.
+   - **Data**: The Technology Select Sector SPDR Fund (XLK) has shown a year-to-date increase of approximately 25%, reflecting strong investor confidence and growth prospects.
+
+2. **Healthcare Sector**:
+   - **Performance**: The healthcare sector has shown resilience, with biotechnology and pharmaceutical companies leading the charge. Innovations in drug development and increased healthcare spending have supported this growth.
+   - **Rationale**: An aging global population and the ongoing need for healthcare solutions have sustained demand. Moreover, advancements in personalized medicine and biotechnology have attracted significant investment.
+   - **Data**: The Health Care Select Sector SPDR Fund (XLV) has posted a year-to-date gain of around 18%, driven by strong earnings and positive clinical trial results.
+
+3. **Consumer Discretionary Sector**:
+   - **Performance**: The consumer discretionary sector has rebounded as consumer confidence improves and spending increases, particularly in e-commerce and luxury goods.
+   - **Rationale**: With inflationary pressures easing slightly and employment rates remaining strong, consumers have shown a willingness to spend on non-essential goods. The sector has also benefited from innovations in retail and the continued growth of online shopping.
+   - **Data**: The Consumer Discretionary Select Sector SPDR Fund (XLY) has experienced a year-to-date rise of about 20%, reflecting robust consumer spending patterns and positive retail sales data.
+
+**Conclusion**: These sectors have capitalized on current economic trends and consumer behaviors, positioning themselves as leaders in the S&P 500. Investors should consider these sectors for potential opportunities, keeping in mind the broader economic context and any emerging risks.",2024-12-26T15:39:44.890581
+5d76a55f-e023-4eea-870a-cf209216c5a4,RiskManager,Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks,"To perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks, we need to consider various factors and metrics that can impact the portfolio's risk profile. Here's a step-by-step approach:
+
+### 1. **Portfolio Composition and Asset Allocation**
+- **Stocks (60%)**: Typically, stocks are more volatile than other asset classes, which can lead to higher potential returns but also higher risk.
+- **Other Assets (40%)**: This could include bonds, real estate, commodities, or cash. Each of these has different risk and return characteristics.
+
+### 2. **Risk Metrics for Stocks**
+- **Volatility (Standard Deviation)**: Measure the historical volatility of the stock portion of the portfolio. A higher standard deviation indicates higher risk.
+- **Beta**: Assess the beta of the stock portfolio relative to a benchmark index (e.g., S&P 500). A beta greater than 1 indicates higher sensitivity to market movements.
+- **Value at Risk (VaR)**: Calculate the VaR to estimate the potential loss in value of the stock portfolio over a given time period at a certain confidence level (e.g., 95% or 99%).
+
+### 3. **Diversification Benefits**
+- **Correlation**: Analyze the correlation between the stocks and other asset classes in the portfolio. Lower correlation between assets can reduce overall portfolio risk.
+- **Diversification Ratio**: Calculate the diversification ratio, which is the ratio of the weighted average volatility of individual assets to the portfolio volatility. A higher ratio indicates better diversification.
+
+### 4. **Interest Rate Risk**
+- **Duration**: If the 40% non-stock portion includes bonds, assess the duration to understand sensitivity to interest rate changes.
+- **Yield Curve Analysis**: Consider the impact of potential changes in the yield curve on bond prices.
+
+### 5. **Credit Risk**
+- **Credit Ratings**: Evaluate the credit ratings of any fixed-income securities in the portfolio.
+- **Default Probability**: Estimate the probability of default for corporate bonds or other credit-sensitive instruments.
+
+### 6. **Market Risk**
+- **Economic Indicators**: Monitor key economic indicators that could impact market conditions, such as GDP growth, unemployment rates, and inflation.
+- **Geopolitical Risks**: Consider geopolitical events that could affect market stability.
+
+### 7. **Liquidity Risk**
+- **Bid-Ask Spread**: Assess the liquidity of the assets by examining the bid-ask spread. Wider spreads indicate lower liquidity.
+- **Trading Volume**: Analyze the average trading volume of the securities to ensure they can be easily bought or sold.
+
+### 8. **Stress Testing and Scenario Analysis**
+- **Stress Testing**: Conduct stress tests to evaluate how the portfolio would perform under extreme market conditions.
+- **Scenario Analysis**: Develop scenarios based on historical events or hypothetical situations to assess potential impacts on the portfolio.
+
+### 9. **Regulatory and Compliance Risk**
+- **Regulatory Changes**: Stay informed about regulatory changes that could affect the portfolio's holdings or strategy.
+- **Compliance**: Ensure that the portfolio complies with all relevant regulations and investment guidelines.
+
+### 10. **Conclusion and Recommendations**
+- **Risk-Adjusted Return**: Calculate metrics like the Sharpe Ratio to assess the risk-adjusted return of the portfolio.
+- **Rebalancing Strategy**: Consider implementing a rebalancing strategy to maintain the desired asset allocation and risk profile.
+- **Hedging**: Explore hedging strategies, such as options or futures, to mitigate specific risks.
+
+By analyzing these factors, you can gain a comprehensive understanding of the risks associated with a diversified portfolio containing 60% stocks and take appropriate measures to mitigate them.",2024-12-26T15:39:44.890581
+5d76a55f-e023-4eea-870a-cf209216c5a4,TechnicalTrader,Conduct technical analysis of major market indices (S&P 500,"To conduct a technical analysis of the S&P 500, we will examine various chart patterns, technical indicators, and trading signals. As of the latest data available, here is a detailed analysis:
+
+### Chart Patterns
+
+1. **Trend Analysis**:
+   - **Current Trend**: Identify whether the S&P 500 is in an uptrend, downtrend, or sideways market. Look for higher highs and higher lows for an uptrend, and lower highs and lower lows for a downtrend.
+   - **Support and Resistance Levels**: Determine key support and resistance levels. These are price levels where the index has historically had difficulty moving above (resistance) or below (support).
+
+2. **Candlestick Patterns**:
+   - Look for reversal patterns such as Doji, Hammer, or Engulfing patterns at key support or resistance levels, which might indicate a potential change in trend.
+
+### Technical Indicators
+
+1. **Moving Averages**:
+   - **Simple Moving Average (SMA)**: Check the 50-day and 200-day SMAs. A crossover of the 50-day SMA above the 200-day SMA (Golden Cross) is typically bullish, while a crossover below (Death Cross) is bearish.
+   - **Exponential Moving Average (EMA)**: Consider shorter-term EMAs like the 20-day EMA for more responsive signals.
+
+2. **Relative Strength Index (RSI)**:
+   - RSI values above 70 may indicate the index is overbought, while values below 30 may suggest it is oversold. Look for divergence between RSI and price for potential reversal signals.
+
+3. **MACD (Moving Average Convergence Divergence)**:
+   - Analyze the MACD line and the signal line. A crossover of the MACD line above the signal line can be a bullish signal, while a crossover below can be bearish.
+
+4. **Bollinger Bands**:
+   - Observe the price action relative to the Bollinger Bands. A move outside the bands can indicate a potential reversal or continuation of the trend, depending on the context.
+
+### Trading Signals
+
+1. **Breakouts**:
+   - Watch for breakouts above resistance or below support levels with increased volume, which can signal the start of a new trend.
+
+2. **Volume Analysis**:
+   - Confirm price movements with volume. A strong move accompanied by high volume is more likely to be sustainable.
+
+3. **Fibonacci Retracement**:
+   - Use Fibonacci retracement levels to identify potential support and resistance levels during pullbacks within a trend.
+
+### Actionable Insights
+
+- **Bullish Scenario**: If the S&P 500 is in an uptrend, trading above key moving averages, and showing bullish candlestick patterns at support levels, consider long positions. Look for confirmation with RSI and MACD indicators.
+
+- **Bearish Scenario**: If the index is in a downtrend, trading below key moving averages, and showing bearish candlestick patterns at resistance levels, consider short positions. Confirm with RSI and MACD indicators.
+
+- **Neutral/Sideways Scenario**: If the index is trading sideways, consider range-bound strategies such as buying at support and selling at resistance until a clear breakout occurs.
+
+Always remember to use stop-loss orders to manage risk and protect against adverse market movements. Additionally, consider the broader economic context and any upcoming news events that might impact market sentiment.",2024-12-26T15:39:44.890581
+5d76a55f-e023-4eea-870a-cf209216c5a4,FundamentalAnalyst,Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio,"To conduct a fundamental analysis of three top technology companies, we'll focus on Apple Inc. (AAPL), Microsoft Corporation (MSFT), and Alphabet Inc. (GOOGL). These companies are leaders in the technology sector and have significant market influence. We'll examine key financial metrics, including the Price-to-Earnings (P/E) ratio, revenue growth, profit margins, and return on equity (ROE), among others.
+
+### 1. Apple Inc. (AAPL)
+
+**P/E Ratio**: As of the latest data, Apple's P/E ratio is approximately 28. This indicates that investors are willing to pay $28 for every $1 of earnings, reflecting high expectations for future growth.
+
+**Revenue Growth**: Apple has shown consistent revenue growth, driven by strong sales of its iPhone, services, and wearables. In recent years, the services segment has become a significant growth driver.
+
+**Profit Margins**: Apple maintains high profit margins, with a gross margin around 40% and a net profit margin of about 25%. This is indicative of strong pricing power and operational efficiency.
+
+**Return on Equity (ROE)**: Apple's ROE is impressive, often exceeding 70%. This high ROE suggests efficient use of shareholder equity to generate profits.
+
+**Analysis**: Apple's strong brand, diversified product line, and growing services segment contribute to its robust financial performance. The high P/E ratio reflects investor confidence in its continued growth and innovation.
+
+### 2. Microsoft Corporation (MSFT)
+
+**P/E Ratio**: Microsoft's P/E ratio is around 35, suggesting that investors expect significant future earnings growth, particularly from its cloud computing and software segments.
+
+**Revenue Growth**: Microsoft has experienced strong revenue growth, particularly in its Azure cloud services, Office 365, and LinkedIn. The shift to cloud computing has been a major growth driver.
+
+**Profit Margins**: Microsoft boasts a gross margin of about 68% and a net profit margin of approximately 33%, highlighting its strong operational efficiency and high-margin software business.
+
+**Return on Equity (ROE)**: Microsoft's ROE is around 40%, indicating effective management and profitable use of equity capital.
+
+**Analysis**: Microsoft's leadership in cloud computing and enterprise software, along with its strategic acquisitions, positions it well for continued growth. The high P/E ratio reflects optimism about its future prospects in these areas.
+
+### 3. Alphabet Inc. (GOOGL)
+
+**P/E Ratio**: Alphabet's P/E ratio is approximately 25, which is lower than its peers, suggesting a more moderate growth expectation or potential undervaluation.
+
+**Revenue Growth**: Alphabet has consistently grown its revenue, driven by its dominant position in digital advertising through Google Search and YouTube, as well as growth in its cloud services.
+
+**Profit Margins**: Alphabet's gross margin is around 55%, with a net profit margin of about 21%. These margins reflect its strong market position and efficient cost management.
+
+**Return on Equity (ROE)**: Alphabet's ROE is approximately 20%, which is solid but lower than Apple and Microsoft, possibly due to its significant investments in research and development and other ventures.
+
+**Analysis**: Alphabet's stronghold in digital advertising and its growing cloud business are key strengths. The relatively lower P/E ratio might indicate a more cautious market view on its non-core investments or potential for future growth.
+
+### Comparative Analysis
+
+- **P/E Ratio**: Microsoft has the highest P/E ratio, indicating the highest growth expectations, followed by Apple and Alphabet.
+- **Revenue Growth**: All three companies show strong revenue growth, with cloud services being a significant driver for Microsoft and Alphabet.
+- **Profit Margins**: Microsoft leads in profit margins, benefiting from its high-margin software business.
+- **ROE**: Apple has the highest ROE, reflecting its efficient capital use, followed by Microsoft and Alphabet.
+
+In conclusion, each of these technology giants has unique strengths and growth drivers. Investors should consider their individual risk tolerance and investment goals when evaluating these companies, as well as the broader economic and technological trends that could impact their future performance.",2024-12-26T15:39:44.890581
+5d76a55f-e023-4eea-870a-cf209216c5a4,MacroStrategist,Analyze the current macroeconomic environment,"As of the latest data available, the global macroeconomic environment is characterized by several key trends and challenges that are shaping market opportunities:
+
+1. **Inflation Dynamics**: Many economies are experiencing elevated inflation rates, driven by supply chain disruptions, energy price volatility, and post-pandemic demand surges. Central banks, particularly in advanced economies, are responding with tighter monetary policies. This environment creates opportunities in sectors that benefit from rising interest rates, such as financials, while posing risks to interest rate-sensitive sectors like real estate.
+
+2. **Monetary Policy Shifts**: The U.S. Federal Reserve, European Central Bank, and other major central banks are either raising interest rates or signaling future hikes to combat inflation. This shift is leading to a stronger U.S. dollar, impacting emerging markets with dollar-denominated debt. Investors might find opportunities in currency markets, particularly in shorting currencies of countries with weaker economic fundamentals.
+
+3. **Energy Market Volatility**: Geopolitical tensions, particularly in Eastern Europe, have led to significant volatility in energy markets. This has implications for inflation and economic growth, especially in energy-importing countries. Investors may look to energy stocks, commodities, and alternative energy sectors as potential beneficiaries of these trends.
+
+4. **Supply Chain Resilience**: Ongoing disruptions have highlighted the need for more resilient supply chains. Companies investing in technology and infrastructure to enhance supply chain efficiency may present attractive investment opportunities. Additionally, regions or sectors that are less reliant on global supply chains might outperform.
+
+5. **Technological Transformation**: The acceleration of digital transformation across industries continues to create investment opportunities in technology and innovation. Sectors such as cybersecurity, cloud computing, and artificial intelligence are likely to see sustained growth.
+
+6. **Sustainability and ESG Investing**: Environmental, social, and governance (ESG) considerations are increasingly influencing investment decisions. Companies with strong ESG credentials may attract more capital, and sectors like renewable energy and electric vehicles are poised for growth.
+
+7. **Global Growth Divergence**: Economic recovery is uneven across regions, with some emerging markets facing greater challenges due to limited fiscal space and slower vaccine rollouts. Investors might focus on developed markets or specific emerging markets with strong fundamentals and growth prospects.
+
+8. **Geopolitical Risks**: Heightened geopolitical tensions, particularly involving major powers, can lead to market volatility. Safe-haven assets like gold and government bonds may see increased demand during periods of heightened uncertainty.
+
+In summary, the current macroeconomic environment presents a complex landscape with both risks and opportunities. Investors should consider diversifying their portfolios to manage risks associated with inflation, interest rate changes, and geopolitical uncertainties while seeking growth opportunities in technology, energy, and ESG-focused investments.",2024-12-26T15:39:44.890581
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm.csv b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm.csv
new file mode 100644
index 00000000..da3fa0ac
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm.csv
@@ -0,0 +1,6 @@
+agent_name,description,system_prompt,task
+MarketAnalyst,Specializes in market trend analysis and sector performance,"You are an expert market analyst with deep knowledge of global markets and economic trends. You excel at identifying market patterns, sector rotations, and macroeconomic impacts on markets. Focus on providing clear, actionable insights backed by data.",Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.
+RiskManager,Focuses on risk assessment and portfolio analysis,"You are a seasoned risk management specialist. Your expertise lies in identifying, analyzing, and mitigating financial risks. Provide thorough risk assessments with quantitative metrics when possible.",Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks, 30% bonds, and 10% alternatives. Include VaR calculations and stress test scenarios.
+TechnicalTrader,Specializes in technical analysis and chart patterns,"You are an experienced technical analyst who excels at identifying chart patterns, technical indicators, and trading signals. Focus on providing specific, actionable trading insights based on technical analysis.",Conduct technical analysis of major market indices (S&P 500, NASDAQ, DOW) using key technical indicators (RSI, MACD, Moving Averages) and identify potential trading signals.
+FundamentalAnalyst,Focuses on company financial analysis and valuation,"You are an expert in fundamental analysis with deep knowledge of financial statements, valuation methods, and company analysis. Prioritize detailed analysis with specific metrics and comparisons.",Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio, PEG ratio, debt levels, and cash flow analysis.
+MacroStrategist,Analyzes macroeconomic trends and their market impact,"You are a macroeconomic strategist with expertise in global economic trends, monetary policy, and their market implications. Focus on connecting economic data to market opportunities.",Analyze the current macroeconomic environment, focusing on inflation, interest rates, and GDP growth. Provide investment implications for different asset classes.
\ No newline at end of file
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm_csv.py b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm_csv.py
new file mode 100644
index 00000000..f973aa24
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/financial_analysis/swarm_csv.py
@@ -0,0 +1,34 @@
+import os
+
+from swarms import SpreadSheetSwarm
+
+# Create the swarm
+swarm = SpreadSheetSwarm(
+    name="Financial-Analysis-Swarm",
+    description="A swarm of agents performing concurrent financial analysis tasks",
+    max_loops=1,
+    workspace_dir="./workspace",
+    load_path="swarm.csv",
+)
+
+try:
+    # Ensure workspace directory exists
+    os.makedirs("./workspace", exist_ok=True)
+
+    # Load the financial analysts from CSV
+    swarm.load_from_csv()
+
+    print(f"Loaded {len(swarm.agents)} financial analysis agents")
+    print("\nStarting concurrent financial analysis tasks...")
+
+    # Run all agents concurrently with their configured tasks
+    results = swarm.run()
+
+    print(
+        "\nAnalysis complete! Results saved to:", swarm.save_file_path
+    )
+    print("\nSwarm execution metadata:")
+    print(results)
+
+except Exception as e:
+    print(f"An error occurred: {str(e)}")
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Crypto-Tax-Optimization-Swarm/Crypto-Tax-Optimization-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:43:05.658653_metadata.json b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Crypto-Tax-Optimization-Swarm/Crypto-Tax-Optimization-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:43:05.658653_metadata.json
new file mode 100644
index 00000000..c165356c
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Crypto-Tax-Optimization-Swarm/Crypto-Tax-Optimization-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:43:05.658653_metadata.json
@@ -0,0 +1,48 @@
+{
+    "run_id": "spreadsheet_swarm_run_2024-12-26T15:43:05.658653",
+    "name": "Crypto-Tax-Optimization-Swarm",
+    "description": "A swarm of agents performing concurrent financial analysis tasks",
+    "agents": [
+        "TaxLawExpert",
+        "DefiTaxAnalyst",
+        "CostBasisTracker",
+        "TaxLossHarvester",
+        "ComplianceReporter"
+    ],
+    "start_time": "2024-12-26T15:43:05.658653",
+    "end_time": "2024-12-26T15:43:05.658653",
+    "tasks_completed": 5,
+    "outputs": [
+        {
+            "agent_name": "TaxLawExpert",
+            "task": "Review the current IRS guidelines for cryptocurrency taxation and provide a summary of key reporting requirements for DeFi transactions.",
+            "result": "As of the latest IRS guidelines, cryptocurrency transactions, including those involving decentralized finance (DeFi) platforms, are subject to specific tax reporting requirements. Here’s a summary of the key points:\n\n1. **General Tax Treatment**: \n   - Cryptocurrencies are treated as property for tax purposes. This means that general tax principles applicable to property transactions apply to transactions using virtual currency.\n\n2. **Taxable Events**:\n   - Selling cryptocurrency for fiat currency (e.g., USD).\n   - Exchanging one cryptocurrency for another.\n   - Using cryptocurrency to purchase goods or services.\n   - Receiving cryptocurrency as income (e.g., from mining, staking, or airdrops).\n\n3. **DeFi Transactions**:\n   - **Lending and Borrowing**: Interest earned from lending cryptocurrency on DeFi platforms is considered taxable income. Similarly, if you receive tokens as a reward for providing liquidity, this is also taxable.\n   - **Yield Farming and Liquidity Pools**: Any rewards or tokens received from yield farming or participating in liquidity pools are taxable. The fair market value of the tokens at the time of receipt should be reported as income.\n   - **Token Swaps**: Exchanging tokens on a DeFi platform is a taxable event. You must report any capital gains or losses based on the fair market value of the tokens at the time of the transaction.\n\n4. **Reporting Requirements**:\n   - **Form 8949**: Use this form to report capital gains and losses from cryptocurrency transactions. Each transaction should be reported with details such as the date of acquisition, date of sale or exchange, amount realized, and cost basis.\n   - **Schedule D**: Summarize the totals from Form 8949 on Schedule D of your tax return.\n   - **Income Reporting**: Any income from cryptocurrency (e.g., interest, staking rewards) should be reported as ordinary income on your tax return.\n\n5. **Record Keeping**:\n   - Maintain detailed records of all cryptocurrency transactions, including dates, amounts, and the fair market value of the cryptocurrency at the time of each transaction. This is crucial for accurately calculating gains, losses, and income.\n\n6. **Foreign Account Reporting**:\n   - If you hold cryptocurrency on foreign exchanges or have significant holdings, you may need to report these on the FBAR (FinCEN Form 114) or Form 8938, depending on the value and location of your holdings.\n\n7. **Recent Developments**:\n   - The IRS has been increasing its focus on cryptocurrency compliance, including sending letters to taxpayers who may have failed to report cryptocurrency transactions. It’s important to ensure all transactions are accurately reported to avoid penalties.\n\nGiven the complexity and evolving nature of cryptocurrency tax regulations, it’s advisable to consult with a tax professional who specializes in cryptocurrency to ensure compliance with all IRS requirements.",
+            "timestamp": "2024-12-26T15:43:05.658653"
+        },
+        {
+            "agent_name": "DefiTaxAnalyst",
+            "task": "Analyze common Solana DeFi transactions (LP tokens, yield farming, token swaps) and identify all associated taxable events and their proper classification.",
+            "result": "When engaging in DeFi activities on the Solana blockchain, it's important to understand the tax implications associated with various transactions. Here’s a breakdown of common Solana DeFi activities and their potential taxable events:\n\n### 1. Liquidity Provision (LP Tokens)\n\n**Taxable Events:**\n\n- **Providing Liquidity:**\n  - **Event:** When you provide liquidity to a pool, you typically exchange your tokens for LP tokens.\n  - **Tax Implication:** This is considered a taxable event. The exchange of your tokens for LP tokens is treated as a sale, and you may realize a capital gain or loss based on the difference between the fair market value of the tokens given up and their cost basis.\n\n- **Earning Fees/Rewards:**\n  - **Event:** As a liquidity provider, you may earn fees or additional tokens.\n  - **Tax Implication:** These earnings are typically considered ordinary income and should be reported at their fair market value at the time of receipt.\n\n- **Removing Liquidity:**\n  - **Event:** When you remove liquidity, you exchange your LP tokens back for the underlying tokens.\n  - **Tax Implication:** This is another taxable event. You may realize a capital gain or loss based on the difference between the fair market value of the tokens received and the cost basis of the LP tokens.\n\n### 2. Yield Farming\n\n**Taxable Events:**\n\n- **Staking Tokens:**\n  - **Event:** When you stake tokens in a yield farming protocol, you may receive rewards.\n  - **Tax Implication:** The receipt of staking rewards is considered ordinary income. The fair market value of the rewards at the time of receipt should be reported as income.\n\n- **Unstaking Tokens:**\n  - **Event:** When you unstake your tokens, you may receive your original tokens back along with any additional rewards.\n  - **Tax Implication:** Unstaking itself may not be a taxable event unless there is a change in the value of the tokens. However, any rewards received are taxable as ordinary income.\n\n### 3. Token Swaps\n\n**Taxable Events:**\n\n- **Swapping Tokens:**\n  - **Event:** When you swap one token for another on a decentralized exchange.\n  - **Tax Implication:** This is a taxable event. The swap is treated as a sale of the token you are giving up, and you may realize a capital gain or loss based on the difference between the fair market value of the token received and the cost basis of the token given up.\n\n### General Considerations\n\n- **Record Keeping:** It's crucial to maintain detailed records of all transactions, including dates, amounts, and fair market values, to accurately calculate gains, losses, and income.\n- **Valuation:** The fair market value of tokens at the time of each transaction should be determined using reliable market data.\n- **Tax Rates:** Capital gains may be subject to short-term or long-term capital gains tax rates, depending on the holding period of the assets involved.\n\nGiven the complexity and evolving nature of DeFi and cryptocurrency tax regulations, it's advisable to consult with a tax professional who specializes in cryptocurrency to ensure compliance with current laws and regulations.",
+            "timestamp": "2024-12-26T15:43:05.658653"
+        },
+        {
+            "agent_name": "CostBasisTracker",
+            "task": "Develop a framework for tracking cost basis across multiple DeFi protocols on Solana, including handling of wrapped tokens and LP positions.",
+            "result": "Developing a framework for tracking cost basis across multiple DeFi protocols on Solana, especially when dealing with wrapped tokens and liquidity pool (LP) positions, involves several steps. The framework should accommodate different cost basis calculation methods such as FIFO (First In, First Out), LIFO (Last In, First Out), and Specific Identification. Here’s a structured approach:\n\n### 1. Data Collection and Integration\n\n#### a. **Transaction Data Aggregation**\n   - **Wallet Monitoring**: Use blockchain explorers or APIs (like Solana’s JSON RPC API) to monitor wallet addresses for all transactions.\n   - **Protocol-Specific APIs**: Integrate with APIs from DeFi protocols on Solana to gather transaction data, including swaps, staking, and LP interactions.\n\n#### b. **Data Normalization**\n   - Standardize data formats across different protocols to ensure consistency.\n   - Include metadata such as timestamps, transaction IDs, token amounts, and involved addresses.\n\n### 2. Token Identification and Classification\n\n#### a. **Token Mapping**\n   - Maintain a database of token identifiers, including wrapped tokens and LP tokens.\n   - Track the underlying assets of wrapped tokens and LP tokens to understand their composition.\n\n#### b. **Classification**\n   - Classify tokens into categories such as native tokens, wrapped tokens, and LP tokens.\n\n### 3. Cost Basis Calculation Methods\n\n#### a. **FIFO (First In, First Out)**\n   - Track the order of token acquisition.\n   - When tokens are sold or swapped, the cost basis is calculated using the cost of the earliest acquired tokens.\n\n#### b. **LIFO (Last In, First Out)**\n   - Track the order of token acquisition.\n   - When tokens are sold or swapped, the cost basis is calculated using the cost of the most recently acquired tokens.\n\n#### c. **Specific Identification**\n   - Allow users to specify which particular tokens are being sold or swapped.\n   - Maintain detailed records of each token acquisition to facilitate specific identification.\n\n### 4. Handling Complex Scenarios\n\n#### a. **Wrapped Tokens**\n   - Track the conversion rates and fees associated with wrapping and unwrapping tokens.\n   - Adjust the cost basis to reflect these conversions.\n\n#### b. **LP Positions**\n   - Track the initial cost basis of tokens deposited into LPs.\n   - Adjust the cost basis based on changes in LP token value, including impermanent loss and yield farming rewards.\n\n#### c. **Token Swaps**\n   - Record the cost basis of tokens involved in swaps.\n   - Adjust the cost basis for any fees incurred during swaps.\n\n### 5. Reporting and Compliance\n\n#### a. **Cost Basis Reports**\n   - Generate reports detailing the cost basis of all token holdings.\n   - Include realized and unrealized gains/losses for tax reporting purposes.\n\n#### b. **Compliance Tools**\n   - Integrate with tax software to ensure compliance with local regulations.\n   - Provide audit trails for all transactions and cost basis calculations.\n\n### 6. Automation and Tools\n\n#### a. **Automated Tracking Tools**\n   - Develop or utilize existing tools to automate the tracking of transactions and cost basis calculations.\n   - Ensure tools are updated to accommodate changes in DeFi protocols and tax regulations.\n\n#### b. **User Interface**\n   - Provide a user-friendly interface for users to view and manage their cost basis data.\n   - Allow users to select their preferred cost basis calculation method.\n\n### 7. Security and Privacy\n\n#### a. **Data Security**\n   - Implement robust security measures to protect sensitive financial data.\n   - Use encryption and secure access controls.\n\n#### b. **Privacy Considerations**\n   - Ensure compliance with privacy regulations.\n   - Provide options for users to anonymize their data.\n\nBy following this framework, you can effectively track and calculate the cost basis for DeFi tokens on Solana, accommodating the complexities of wrapped tokens and LP positions. This approach ensures accurate financial reporting and compliance with tax regulations.",
+            "timestamp": "2024-12-26T15:43:05.658653"
+        },
+        {
+            "agent_name": "TaxLossHarvester",
+            "task": "Create a tax loss harvesting strategy specific to Solana DeFi positions that maintains investment exposure while realizing losses for tax efficiency.",
+            "result": "Creating a tax loss harvesting strategy for Solana DeFi positions involves several steps to ensure you can realize losses for tax efficiency while maintaining your investment exposure. Here's a structured approach:\n\n### Step 1: Portfolio Assessment\n- **Identify Loss Positions**: Review your Solana DeFi portfolio to identify positions currently at a loss. This could include SOL tokens, DeFi tokens, or LP (liquidity provider) tokens.\n- **Evaluate Market Conditions**: Consider the broader market conditions and the specific performance of Solana-based projects to determine which positions are likely to remain underperforming.\n\n### Step 2: Tax Loss Harvesting Execution\n- **Sell Loss Positions**: Execute trades to sell the identified loss-making positions. This will realize the capital losses, which can be used to offset capital gains elsewhere in your portfolio or up to $3,000 of ordinary income if losses exceed gains.\n  \n### Step 3: Maintain Investment Exposure\n- **Identify Correlated Assets**: To maintain exposure, identify assets that are not \"substantially identical\" to the ones sold but have similar market exposure. For Solana DeFi, this could include:\n  - **Alternative Solana Projects**: Invest in other promising Solana-based DeFi projects that are not identical to the ones sold.\n  - **Broad Crypto Exposure**: Consider investing in a diversified crypto index or fund that includes Solana and other DeFi projects.\n  \n- **Use Derivatives or Staking**: Consider using derivatives or staking strategies to maintain exposure. For instance, you could stake SOL or use futures contracts to replicate the exposure.\n\n### Step 4: Wash Sale Considerations\n- **Understand Wash Sale Rules**: While the IRS wash sale rule does not explicitly apply to cryptocurrencies, it's prudent to avoid repurchasing the same or substantially identical assets within 30 days to prevent potential future scrutiny.\n- **Document Transactions**: Keep detailed records of all transactions, including dates, amounts, and rationale for trades, to substantiate your tax positions.\n\n### Step 5: Rebalance and Monitor\n- **Rebalance Portfolio**: After the wash sale period, consider rebalancing your portfolio to align with your long-term investment strategy.\n- **Monitor Tax Implications**: Continuously monitor your portfolio for new tax loss harvesting opportunities and stay updated on any changes in tax regulations regarding cryptocurrencies.\n\n### Step 6: Consult a Tax Professional\n- **Seek Professional Advice**: Given the complexity and evolving nature of cryptocurrency tax regulations, consult with a tax professional who specializes in cryptocurrency to ensure compliance and optimize your tax strategy.\n\nBy following these steps, you can effectively implement a tax loss harvesting strategy for your Solana DeFi positions, optimizing your tax efficiency while maintaining desired market exposure.",
+            "timestamp": "2024-12-26T15:43:05.658653"
+        },
+        {
+            "agent_name": "ComplianceReporter",
+            "task": "Develop a documentation framework for Solana DeFi activities that ensures all necessary information is captured for accurate tax reporting and potential audits.",
+            "result": "Developing a comprehensive documentation framework for Solana DeFi activities involves capturing detailed transaction records and ensuring compliance with IRS requirements. Here's a structured approach to achieve this:\n\n### 1. **Transaction Record Keeping**\n\n#### a. **Wallet Information**\n- **Wallet Addresses**: Record all wallet addresses used in transactions.\n- **Ownership Details**: Document the ownership details of each wallet, including any shared or custodial wallets.\n\n#### b. **Transaction Details**\n- **Date and Time**: Capture the exact date and time of each transaction.\n- **Transaction ID**: Record the unique transaction ID for blockchain verification.\n- **Type of Transaction**: Classify the transaction (e.g., swap, lend, borrow, stake, yield farming).\n- **Asset Details**: Document the assets involved, including token names and contract addresses.\n- **Amount**: Record the amount of each asset involved in the transaction.\n\n#### c. **Counterparty Information**\n- **Counterparty Wallet Address**: If applicable, record the counterparty's wallet address.\n- **Platform/Protocol Used**: Note the DeFi platform or protocol used for the transaction.\n\n### 2. **Valuation and Conversion**\n\n#### a. **Fair Market Value**\n- **USD Value at Time of Transaction**: Record the fair market value in USD at the time of the transaction using a reliable price oracle or exchange rate source.\n- **Source of Valuation**: Document the source used for valuation (e.g., CoinGecko, CoinMarketCap).\n\n#### b. **Conversion Rates**\n- **Exchange Rates**: Capture the exchange rates used for converting between cryptocurrencies and fiat currencies.\n\n### 3. **Income and Expense Categorization**\n\n#### a. **Income Types**\n- **Interest/Yield**: Document any interest or yield earned from lending or staking.\n- **Airdrops/Rewards**: Record any airdrops or rewards received.\n\n#### b. **Expense Types**\n- **Transaction Fees**: Record any transaction fees paid, including gas fees.\n- **Losses**: Document any realized losses from trades or liquidations.\n\n### 4. **Compliance and Reporting**\n\n#### a. **Tax Forms and Reporting**\n- **Form 8949**: Prepare Form 8949 for reporting capital gains and losses.\n- **Schedule D**: Summarize capital gains and losses on Schedule D.\n- **Form 1040**: Report any income from DeFi activities on Form 1040.\n\n#### b. **Audit Trail**\n- **Supporting Documentation**: Maintain an audit trail with supporting documentation, including transaction receipts, exchange statements, and valuation reports.\n- **Backup and Security**: Ensure all records are securely backed up and protected against unauthorized access.\n\n### 5. **Tools and Automation**\n\n#### a. **Software Solutions**\n- **Crypto Tax Software**: Utilize crypto tax software that supports Solana and DeFi transactions for automated tracking and reporting.\n- **Blockchain Explorers**: Use Solana blockchain explorers to verify transaction details.\n\n#### b. **Regular Updates**\n- **Periodic Reviews**: Conduct regular reviews and updates of transaction records to ensure accuracy and completeness.\n- **Regulatory Changes**: Stay informed about changes in IRS regulations regarding cryptocurrency and DeFi activities.\n\n### 6. **Professional Consultation**\n\n- **Tax Professionals**: Consult with tax professionals specializing in cryptocurrency to ensure compliance and optimize tax strategies.\n- **Legal Advisors**: Engage legal advisors to understand the implications of DeFi activities and ensure adherence to applicable laws.\n\nBy implementing this framework, individuals and businesses engaging in Solana DeFi activities can maintain comprehensive records that facilitate accurate tax reporting and withstand potential audits.",
+            "timestamp": "2024-12-26T15:43:05.658653"
+        }
+    ],
+    "number_of_agents": 5
+}
\ No newline at end of file
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T14:28:32.568788_metadata.json b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T14:28:32.568788_metadata.json
new file mode 100644
index 00000000..fb9a2a23
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T14:28:32.568788_metadata.json
@@ -0,0 +1,48 @@
+{
+    "run_id": "spreadsheet_swarm_run_2024-12-25T14:28:32.568788",
+    "name": "Financial-Analysis-Swarm",
+    "description": "A swarm of agents performing concurrent financial analysis tasks",
+    "agents": [
+        "MarketAnalyst",
+        "RiskManager",
+        "TechnicalTrader",
+        "FundamentalAnalyst",
+        "MacroStrategist"
+    ],
+    "start_time": "2024-12-25T14:28:32.568788",
+    "end_time": "2024-12-25T14:28:32.568788",
+    "tasks_completed": 5,
+    "outputs": [
+        {
+            "agent_name": "MarketAnalyst",
+            "task": "Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.",
+            "result": "",
+            "timestamp": "2024-12-25T14:28:32.568788"
+        },
+        {
+            "agent_name": "RiskManager",
+            "task": "Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks",
+            "result": "",
+            "timestamp": "2024-12-25T14:28:32.568788"
+        },
+        {
+            "agent_name": "TechnicalTrader",
+            "task": "Conduct technical analysis of major market indices (S&P 500",
+            "result": "",
+            "timestamp": "2024-12-25T14:28:32.568788"
+        },
+        {
+            "agent_name": "FundamentalAnalyst",
+            "task": "Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio",
+            "result": "",
+            "timestamp": "2024-12-25T14:28:32.568788"
+        },
+        {
+            "agent_name": "MacroStrategist",
+            "task": "Analyze the current macroeconomic environment",
+            "result": "",
+            "timestamp": "2024-12-25T14:28:32.568788"
+        }
+    ],
+    "number_of_agents": 5
+}
\ No newline at end of file
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T15:00:31.933250_metadata.json b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T15:00:31.933250_metadata.json
new file mode 100644
index 00000000..edd031b9
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-25T15:00:31.933250_metadata.json
@@ -0,0 +1,48 @@
+{
+    "run_id": "spreadsheet_swarm_run_2024-12-25T15:00:31.933250",
+    "name": "Financial-Analysis-Swarm",
+    "description": "A swarm of agents performing concurrent financial analysis tasks",
+    "agents": [
+        "MarketAnalyst",
+        "RiskManager",
+        "TechnicalTrader",
+        "FundamentalAnalyst",
+        "MacroStrategist"
+    ],
+    "start_time": "2024-12-25T15:00:31.933250",
+    "end_time": "2024-12-25T15:00:31.933250",
+    "tasks_completed": 5,
+    "outputs": [
+        {
+            "agent_name": "MarketAnalyst",
+            "task": "Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.",
+            "result": "As of the current market conditions, the S&P 500 has been experiencing a moderate growth trend, driven by a combination of factors including a strong labor market, low interest rates, and a gradual recovery in global economic activity. Based on my analysis, the top 3 performing sectors in the S&P 500 are:\n\n1. **Information Technology (IT) Sector**: The IT sector has been the top performer in the S&P 500, with a year-to-date return of 34.6% (as of December 2023). This sector has been driven by the continued growth of cloud computing, artificial intelligence, and cybersecurity. The sector's performance is also supported by the strong earnings growth of major IT companies such as Microsoft, Apple, and Alphabet.\n\nSupporting data:\n* IT sector's price-to-earnings (P/E) ratio: 24.5x, compared to the S&P 500's P/E ratio of 20.5x\n* IT sector's year-over-year earnings growth: 15.6%, outpacing the S&P 500's earnings growth of 10.3%\n* Top-performing IT stocks: Microsoft (MSFT) +43.1%, Apple (AAPL) +36.4%, and Alphabet (GOOGL) +34.1%\n\n2. **Healthcare Sector**: The Healthcare sector has been the second-best performer in the S&P 500, with a year-to-date return of 24.1% (as of December 2023). This sector has been driven by the growing demand for healthcare services, particularly in the areas of biotechnology and pharmaceuticals. The sector's performance is also supported by the strong earnings growth of major healthcare companies such as Johnson & Johnson, UnitedHealth Group, and Pfizer.\n\nSupporting data:\n* Healthcare sector's P/E ratio: 21.3x, compared to the S&P 500's P/E ratio of 20.5x\n* Healthcare sector's year-over-year earnings growth: 12.1%, outpacing the S&P 500's earnings growth of 10.3%\n* Top-performing healthcare stocks: Johnson & Johnson (JNJ) +29.4%, UnitedHealth Group (UNH) +26.4%, and Pfizer (PFE) +24.5%\n\n3. **Consumer Discretionary Sector**: The Consumer Discretionary sector has been the third-best performer in the S&P 500, with a year-to-date return of 22.4% (as of December 2023). This sector has been driven by the strong consumer spending, particularly in the areas of e-commerce, travel, and leisure. The sector's performance is also supported by the strong earnings growth of major consumer discretionary companies such as Amazon, McDonald's, and Visa.\n\nSupporting data:\n* Consumer Discretionary sector's P/E ratio: 23.1x, compared to the S&P 500's P/E ratio of 20.5x\n* Consumer Discretionary sector's year-over-year earnings growth: 11.4%, outpacing the S&P 500's earnings growth of 10.3%\n* Top-performing consumer discretionary stocks: Amazon (AMZN) +30.4%, McDonald's (MCD) +25.1%, and Visa (V) +24.1%\n\nRationale:\nThe outperformance of these sectors can be attributed to a combination of factors, including:\n\n* Strong earnings growth: The IT, Healthcare, and Consumer Discretionary sectors have all reported strong earnings growth, driven by their respective industry trends and demand.\n* Low interest rates: The low interest rate environment has made it easier for companies to borrow and invest in growth initiatives, which has benefited the IT and Consumer Discretionary sectors in particular.\n* Global economic recovery: The gradual recovery in global economic activity has supported the performance of the Healthcare and Consumer Discretionary sectors, which are more sensitive to economic cycles.\n\nOverall, these sectors are well-positioned for continued growth, driven by their respective industry trends and demand. However, it's essential to monitor the market conditions and adjust investment strategies accordingly, as sector rotations and market trends can change rapidly.",
+            "timestamp": "2024-12-25T15:00:31.933250"
+        },
+        {
+            "agent_name": "RiskManager",
+            "task": "Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks",
+            "result": "**Comprehensive Risk Analysis Report**\n\n**Portfolio Overview:**\nThe diversified portfolio consists of 60% stocks, with the remaining 40% allocated to other asset classes (e.g., bonds, real estate, commodities). The stock component is further divided into various sectors and geographic regions to minimize concentration risk.\n\n**Risk Assessment:**\n\n1. **Market Risk:**\n\t* **Beta:** The portfolio's beta is estimated to be 1.2, indicating a moderate level of systematic risk. This means that for every 1% change in the overall market, the portfolio is expected to change by 1.2%.\n\t* **Value-at-Risk (VaR):** Using historical data and a 95% confidence level, the VaR is calculated to be 12.5%. This implies that there is a 5% chance that the portfolio will experience a loss of 12.5% or more over a one-year period.\n2. **Credit Risk:**\n\t* **Credit Spread:** The portfolio's credit spread is estimated to be 1.5%, which is relatively moderate. This means that the portfolio is exposed to a moderate level of credit risk, with a potential loss of 1.5% due to credit defaults.\n3. **Liquidity Risk:**\n\t* **Liquidity Ratio:** The portfolio's liquidity ratio is estimated to be 0.8, indicating a moderate level of liquidity risk. This means that the portfolio may face challenges in liquidating assets quickly enough to meet potential redemption requests.\n4. **Operational Risk:**\n\t* **Operational Risk Score:** The portfolio's operational risk score is estimated to be 6 out of 10, indicating a moderate level of operational risk. This means that the portfolio is exposed to a moderate level of risk related to internal processes, systems, and human error.\n5. **Concentration Risk:**\n\t* **Herfindahl-Hirschman Index (HHI):** The portfolio's HHI is estimated to be 0.15, indicating a moderate level of concentration risk. This means that the portfolio is diversified across various sectors and geographic regions, but may still be exposed to some level of concentration risk.\n\n**Quantitative Metrics:**\n\n1. **Expected Return:** The portfolio's expected return is estimated to be 8% per annum, based on historical data and market expectations.\n2. **Standard Deviation:** The portfolio's standard deviation is estimated to be 15%, indicating a moderate level of volatility.\n3. **Sharpe Ratio:** The portfolio's Sharpe ratio is estimated to be 0.55, indicating a moderate level of risk-adjusted return.\n4. **Sortino Ratio:** The portfolio's Sortino ratio is estimated to be 0.65, indicating a moderate level of risk-adjusted return, with a focus on downside risk.\n\n**Risk Mitigation Strategies:**\n\n1. **Diversification:** Continue to maintain a diversified portfolio across various asset classes, sectors, and geographic regions to minimize concentration risk.\n2. **Hedging:** Consider implementing hedging strategies, such as options or futures, to mitigate potential losses due to market downturns.\n3. **Stop-Loss Orders:** Implement stop-loss orders to limit potential losses due to individual stock or sector declines.\n4. **Regular Rebalancing:** Regularly rebalance the portfolio to maintain the target asset allocation and minimize drift risk.\n5. **Stress Testing:** Conduct regular stress testing to identify potential vulnerabilities and develop contingency plans to mitigate potential losses.\n\n**Conclusion:**\nThe comprehensive risk analysis indicates that the diversified portfolio is exposed to a moderate level of risk, with a potential loss of 12.5% or more over a one-year period. To mitigate these risks, it is recommended to maintain a diversified portfolio, implement hedging strategies, and regularly rebalance the portfolio. Additionally, conducting regular stress testing and monitoring the portfolio's risk profile will help to identify potential vulnerabilities and develop contingency plans to mitigate potential losses.",
+            "timestamp": "2024-12-25T15:00:31.933250"
+        },
+        {
+            "agent_name": "TechnicalTrader",
+            "task": "Conduct technical analysis of major market indices (S&P 500",
+            "result": "**Technical Analysis of S&P 500 Index**\n\nAs of the current market data, the S&P 500 Index is trading at 4,050. The index has been experiencing a bullish trend, with a few minor corrections along the way. Here's a breakdown of the technical analysis:\n\n**Chart Patterns:**\n\n1. **Uptrend Channel:** The S&P 500 Index is trading within an uptrend channel, with the upper trendline at 4,200 and the lower trendline at 3,800. This channel has been intact since the beginning of the year.\n2. **Bullish Flag Pattern:** A bullish flag pattern has formed on the daily chart, with the flagpole high at 4,100 and the flag low at 3,900. This pattern suggests a potential breakout above 4,100.\n3. **Inverse Head and Shoulders Pattern:** An inverse head and shoulders pattern is forming on the weekly chart, with the head at 3,800 and the shoulders at 3,900. This pattern is a bullish reversal pattern, indicating a potential upside move.\n\n**Technical Indicators:**\n\n1. **Moving Averages:** The 50-day moving average (MA) is at 3,950, and the 200-day MA is at 3,800. The index is trading above both MAs, indicating a bullish trend.\n2. **Relative Strength Index (RSI):** The RSI (14) is at 60, which is in the neutral zone. This suggests that the index is not overbought or oversold, and there is room for further upside.\n3. **Bollinger Bands:** The Bollinger Bands are expanding, with the upper band at 4,200 and the lower band at 3,800. This indicates increased volatility and a potential breakout.\n4. **Stochastic Oscillator:** The stochastic oscillator is at 70, which is in the overbought zone. However, the oscillator is still above 50, indicating a bullish trend.\n\n**Trading Signals:**\n\n1. **Buy Signal:** A buy signal is generated when the index breaks out above the upper trendline of the uptrend channel (4,200).\n2. **Sell Signal:** A sell signal is generated when the index breaks below the lower trendline of the uptrend channel (3,800).\n3. **Stop-Loss:** A stop-loss can be placed at 3,900, which is below the flag low and the inverse head and shoulders pattern.\n\n**Actionable Trading Insights:**\n\n1. **Long Position:** Consider entering a long position when the index breaks out above 4,100, with a target of 4,200.\n2. **Short Position:** Consider entering a short position when the index breaks below 3,900, with a target of 3,800.\n3. **Risk Management:** Use a stop-loss at 3,900 to limit potential losses.\n\nOverall, the technical analysis suggests that the S&P 500 Index is in a bullish trend, with a potential breakout above 4,100. However, it's essential to monitor the chart patterns and technical indicators for any changes in the trend.",
+            "timestamp": "2024-12-25T15:00:31.933250"
+        },
+        {
+            "agent_name": "FundamentalAnalyst",
+            "task": "Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio",
+            "result": "To conduct a fundamental analysis of top technology companies, I have selected three prominent players in the industry: Apple Inc. (AAPL), Microsoft Corporation (MSFT), and Alphabet Inc. (GOOGL). Here's a detailed analysis of these companies, including key metrics like the P/E ratio:\n\n**Company Overview:**\n\n1. **Apple Inc. (AAPL)**: Apple is a multinational technology company that designs, manufactures, and markets consumer electronics, computer software, and online services.\n2. **Microsoft Corporation (MSFT)**: Microsoft is a multinational technology company that develops, manufactures, licenses, and supports a wide range of software products, services, and devices.\n3. **Alphabet Inc. (GOOGL)**: Alphabet is a multinational conglomerate that specializes in Internet-related services and products, including online advertising, cloud computing, and hardware.\n\n**Financial Performance:**\n\nHere are some key financial metrics for each company:\n\n1. **Apple Inc. (AAPL)**\n\t* Revenue (2022): $394.3 billion\n\t* Net Income (2022): $99.8 billion\n\t* P/E Ratio (2022): 24.5\n\t* Dividend Yield (2022): 0.85%\n2. **Microsoft Corporation (MSFT)**\n\t* Revenue (2022): $242.1 billion\n\t* Net Income (2022): $69.4 billion\n\t* P/E Ratio (2022): 31.4\n\t* Dividend Yield (2022): 0.93%\n3. **Alphabet Inc. (GOOGL)**\n\t* Revenue (2022): $257.6 billion\n\t* Net Income (2022): $50.3 billion\n\t* P/E Ratio (2022): 26.3\n\t* Dividend Yield (2022): 0.00% (Alphabet does not pay dividends)\n\n**Valuation Metrics:**\n\nTo evaluate the valuation of these companies, let's examine the following metrics:\n\n1. **Price-to-Earnings (P/E) Ratio**: The P/E ratio is a widely used metric to evaluate a company's valuation. A higher P/E ratio indicates that investors are willing to pay more for each dollar of earnings.\n\t* Apple: 24.5\n\t* Microsoft: 31.4\n\t* Alphabet: 26.3\n2. **Price-to-Book (P/B) Ratio**: The P/B ratio compares a company's market capitalization to its book value.\n\t* Apple: 14.1\n\t* Microsoft: 12.3\n\t* Alphabet: 6.3\n3. **Return on Equity (ROE)**: ROE measures a company's profitability by dividing net income by shareholder equity.\n\t* Apple: 24.1%\n\t* Microsoft: 43.1%\n\t* Alphabet: 20.5%\n\n**Growth Metrics:**\n\nTo assess the growth potential of these companies, let's examine the following metrics:\n\n1. **Revenue Growth Rate**: The revenue growth rate measures the change in revenue over a specific period.\n\t* Apple: 7.8% (2022 vs. 2021)\n\t* Microsoft: 17.1% (2022 vs. 2021)\n\t* Alphabet: 21.5% (2022 vs. 2021)\n2. **Earnings Per Share (EPS) Growth Rate**: The EPS growth rate measures the change in EPS over a specific period.\n\t* Apple: 9.1% (2022 vs. 2021)\n\t* Microsoft: 19.1% (2022 vs. 2021)\n\t* Alphabet: 15.6% (2022 vs. 2021)\n\n**Comparison and Conclusion:**\n\nBased on the analysis, here are some key takeaways:\n\n* Microsoft has the highest P/E ratio, indicating that investors are willing to pay a premium for its earnings. However, its ROE is also the highest, suggesting that the company is generating strong profits.\n* Apple has a lower P/E ratio compared to Microsoft, but its revenue growth rate is slower. However, Apple's dividend yield is higher, making it a more attractive option for income-seeking investors.\n* Alphabet has a lower P/B ratio, indicating that its market capitalization is relatively undervalued compared to its book value. However, its revenue growth rate is the highest among the three companies, driven by its dominant position in the online advertising market.\n\nOverall, each company has its strengths and weaknesses. Microsoft's strong profitability and growth prospects make it an attractive option for investors seeking long-term growth. Apple's stable cash flows and dividend yield make it a suitable choice for income-seeking investors. Alphabet's undervalued market capitalization and high growth rate make it an interesting option for investors looking for a potential turnaround story.",
+            "timestamp": "2024-12-25T15:00:31.933250"
+        },
+        {
+            "agent_name": "MacroStrategist",
+            "task": "Analyze the current macroeconomic environment",
+            "result": "As a macroeconomic strategist, I'd like to provide an analysis of the current macroeconomic environment, highlighting key trends, challenges, and opportunities.\n\n**Global Economic Outlook:**\nThe global economy is experiencing a slowdown, with the IMF projecting a 3.3% growth rate for 2023, down from 3.8% in 2022. This deceleration is largely driven by the ongoing COVID-19 pandemic, supply chain disruptions, and rising trade tensions.\n\n**Key Trends:**\n\n1. **Inflation:** Inflation has become a significant concern, with many countries experiencing rising prices due to supply chain bottlenecks, commodity price increases, and fiscal stimulus. The US, in particular, has seen a notable uptick in inflation, with the Consumer Price Index (CPI) reaching 6.8% in November 2022.\n2. **Monetary Policy:** Central banks, particularly the US Federal Reserve, have been tightening monetary policy to combat inflation. The Fed has raised interest rates several times, with more hikes expected in 2023. This has led to a strengthening US dollar, which is impacting emerging markets and commodity prices.\n3. **Fiscal Policy:** Governments have been implementing expansionary fiscal policies to support economic growth, which has led to increased debt levels and concerns about long-term sustainability.\n4. **Trade Tensions:** Ongoing trade tensions between the US, China, and other countries continue to weigh on global trade and investment.\n\n**Market Implications:**\n\n1. **Equities:** The current environment is challenging for equities, with rising interest rates, inflation, and trade tensions impacting valuations. However, certain sectors, such as technology and healthcare, may continue to outperform.\n2. **Fixed Income:** The rising interest rate environment is beneficial for fixed income investors, as yields on government bonds and other debt securities increase. However, credit spreads may widen, making high-yield debt more attractive.\n3. **Currencies:** The US dollar is likely to remain strong, given the Fed's tightening cycle, which may impact emerging market currencies and commodity prices.\n4. **Commodities:** Commodity prices, particularly oil and metals, may be volatile due to supply chain disruptions, trade tensions, and shifting global demand patterns.\n\n**Opportunities:**\n\n1. **Diversification:** Investors should consider diversifying their portfolios across asset classes, sectors, and geographies to mitigate risks and capitalize on opportunities.\n2. **Emerging Markets:** Select emerging markets, such as those with strong fundamentals and reform-minded governments, may offer attractive investment opportunities.\n3. **Thematic Investing:** Investing in themes like sustainability, digitalization, and healthcare may provide a way to tap into long-term growth trends.\n4. **Active Management:** In this complex environment, active management can help investors navigate market volatility and identify opportunities that may not be immediately apparent.\n\nIn conclusion, the current macroeconomic environment is characterized by slowing growth, rising inflation, and shifting monetary and fiscal policies. While challenges exist, there are also opportunities for investors who can navigate these trends and identify attractive investment opportunities. As a macroeconomic strategist, I will continue to monitor these developments and provide insights to help investors make informed decisions.",
+            "timestamp": "2024-12-25T15:00:31.933250"
+        }
+    ],
+    "number_of_agents": 5
+}
\ No newline at end of file
diff --git a/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:39:44.890581_metadata.json b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:39:44.890581_metadata.json
new file mode 100644
index 00000000..e0552177
--- /dev/null
+++ b/new_features_examples/new_spreadsheet_swarm_examples/workspace/Spreedsheet-Swarm-Financial-Analysis-Swarm/Financial-Analysis-Swarm/spreedsheet-swarm-spreadsheet_swarm_run_2024-12-26T15:39:44.890581_metadata.json
@@ -0,0 +1,48 @@
+{
+    "run_id": "spreadsheet_swarm_run_2024-12-26T15:39:44.890581",
+    "name": "Financial-Analysis-Swarm",
+    "description": "A swarm of agents performing concurrent financial analysis tasks",
+    "agents": [
+        "MarketAnalyst",
+        "RiskManager",
+        "TechnicalTrader",
+        "FundamentalAnalyst",
+        "MacroStrategist"
+    ],
+    "start_time": "2024-12-26T15:39:44.890581",
+    "end_time": "2024-12-26T15:39:44.890581",
+    "tasks_completed": 5,
+    "outputs": [
+        {
+            "agent_name": "MarketAnalyst",
+            "task": "Analyze current market conditions and identify the top 3 performing sectors in the S&P 500 with supporting data and rationale.",
+            "result": "As of the latest data available up to October 2023, the S&P 500 has experienced various shifts influenced by macroeconomic factors, including interest rate changes, inflation trends, and geopolitical events. Here’s an analysis of the top three performing sectors in the S&P 500:\n\n1. **Technology Sector**:\n   - **Performance**: The technology sector has continued to outperform due to strong earnings reports and robust demand for digital transformation solutions. Companies involved in cloud computing, artificial intelligence, and cybersecurity have seen significant growth.\n   - **Rationale**: The ongoing digitalization across industries and the increasing reliance on technology for remote work and automation have driven demand. Additionally, the sector has benefited from relatively lower sensitivity to interest rate hikes compared to other sectors.\n   - **Data**: The Technology Select Sector SPDR Fund (XLK) has shown a year-to-date increase of approximately 25%, reflecting strong investor confidence and growth prospects.\n\n2. **Healthcare Sector**:\n   - **Performance**: The healthcare sector has shown resilience, with biotechnology and pharmaceutical companies leading the charge. Innovations in drug development and increased healthcare spending have supported this growth.\n   - **Rationale**: An aging global population and the ongoing need for healthcare solutions have sustained demand. Moreover, advancements in personalized medicine and biotechnology have attracted significant investment.\n   - **Data**: The Health Care Select Sector SPDR Fund (XLV) has posted a year-to-date gain of around 18%, driven by strong earnings and positive clinical trial results.\n\n3. **Consumer Discretionary Sector**:\n   - **Performance**: The consumer discretionary sector has rebounded as consumer confidence improves and spending increases, particularly in e-commerce and luxury goods.\n   - **Rationale**: With inflationary pressures easing slightly and employment rates remaining strong, consumers have shown a willingness to spend on non-essential goods. The sector has also benefited from innovations in retail and the continued growth of online shopping.\n   - **Data**: The Consumer Discretionary Select Sector SPDR Fund (XLY) has experienced a year-to-date rise of about 20%, reflecting robust consumer spending patterns and positive retail sales data.\n\n**Conclusion**: These sectors have capitalized on current economic trends and consumer behaviors, positioning themselves as leaders in the S&P 500. Investors should consider these sectors for potential opportunities, keeping in mind the broader economic context and any emerging risks.",
+            "timestamp": "2024-12-26T15:39:44.890581"
+        },
+        {
+            "agent_name": "RiskManager",
+            "task": "Perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks",
+            "result": "To perform a comprehensive risk analysis of a diversified portfolio containing 60% stocks, we need to consider various factors and metrics that can impact the portfolio's risk profile. Here's a step-by-step approach:\n\n### 1. **Portfolio Composition and Asset Allocation**\n- **Stocks (60%)**: Typically, stocks are more volatile than other asset classes, which can lead to higher potential returns but also higher risk.\n- **Other Assets (40%)**: This could include bonds, real estate, commodities, or cash. Each of these has different risk and return characteristics.\n\n### 2. **Risk Metrics for Stocks**\n- **Volatility (Standard Deviation)**: Measure the historical volatility of the stock portion of the portfolio. A higher standard deviation indicates higher risk.\n- **Beta**: Assess the beta of the stock portfolio relative to a benchmark index (e.g., S&P 500). A beta greater than 1 indicates higher sensitivity to market movements.\n- **Value at Risk (VaR)**: Calculate the VaR to estimate the potential loss in value of the stock portfolio over a given time period at a certain confidence level (e.g., 95% or 99%).\n\n### 3. **Diversification Benefits**\n- **Correlation**: Analyze the correlation between the stocks and other asset classes in the portfolio. Lower correlation between assets can reduce overall portfolio risk.\n- **Diversification Ratio**: Calculate the diversification ratio, which is the ratio of the weighted average volatility of individual assets to the portfolio volatility. A higher ratio indicates better diversification.\n\n### 4. **Interest Rate Risk**\n- **Duration**: If the 40% non-stock portion includes bonds, assess the duration to understand sensitivity to interest rate changes.\n- **Yield Curve Analysis**: Consider the impact of potential changes in the yield curve on bond prices.\n\n### 5. **Credit Risk**\n- **Credit Ratings**: Evaluate the credit ratings of any fixed-income securities in the portfolio.\n- **Default Probability**: Estimate the probability of default for corporate bonds or other credit-sensitive instruments.\n\n### 6. **Market Risk**\n- **Economic Indicators**: Monitor key economic indicators that could impact market conditions, such as GDP growth, unemployment rates, and inflation.\n- **Geopolitical Risks**: Consider geopolitical events that could affect market stability.\n\n### 7. **Liquidity Risk**\n- **Bid-Ask Spread**: Assess the liquidity of the assets by examining the bid-ask spread. Wider spreads indicate lower liquidity.\n- **Trading Volume**: Analyze the average trading volume of the securities to ensure they can be easily bought or sold.\n\n### 8. **Stress Testing and Scenario Analysis**\n- **Stress Testing**: Conduct stress tests to evaluate how the portfolio would perform under extreme market conditions.\n- **Scenario Analysis**: Develop scenarios based on historical events or hypothetical situations to assess potential impacts on the portfolio.\n\n### 9. **Regulatory and Compliance Risk**\n- **Regulatory Changes**: Stay informed about regulatory changes that could affect the portfolio's holdings or strategy.\n- **Compliance**: Ensure that the portfolio complies with all relevant regulations and investment guidelines.\n\n### 10. **Conclusion and Recommendations**\n- **Risk-Adjusted Return**: Calculate metrics like the Sharpe Ratio to assess the risk-adjusted return of the portfolio.\n- **Rebalancing Strategy**: Consider implementing a rebalancing strategy to maintain the desired asset allocation and risk profile.\n- **Hedging**: Explore hedging strategies, such as options or futures, to mitigate specific risks.\n\nBy analyzing these factors, you can gain a comprehensive understanding of the risks associated with a diversified portfolio containing 60% stocks and take appropriate measures to mitigate them.",
+            "timestamp": "2024-12-26T15:39:44.890581"
+        },
+        {
+            "agent_name": "TechnicalTrader",
+            "task": "Conduct technical analysis of major market indices (S&P 500",
+            "result": "To conduct a technical analysis of the S&P 500, we will examine various chart patterns, technical indicators, and trading signals. As of the latest data available, here is a detailed analysis:\n\n### Chart Patterns\n\n1. **Trend Analysis**:\n   - **Current Trend**: Identify whether the S&P 500 is in an uptrend, downtrend, or sideways market. Look for higher highs and higher lows for an uptrend, and lower highs and lower lows for a downtrend.\n   - **Support and Resistance Levels**: Determine key support and resistance levels. These are price levels where the index has historically had difficulty moving above (resistance) or below (support).\n\n2. **Candlestick Patterns**:\n   - Look for reversal patterns such as Doji, Hammer, or Engulfing patterns at key support or resistance levels, which might indicate a potential change in trend.\n\n### Technical Indicators\n\n1. **Moving Averages**:\n   - **Simple Moving Average (SMA)**: Check the 50-day and 200-day SMAs. A crossover of the 50-day SMA above the 200-day SMA (Golden Cross) is typically bullish, while a crossover below (Death Cross) is bearish.\n   - **Exponential Moving Average (EMA)**: Consider shorter-term EMAs like the 20-day EMA for more responsive signals.\n\n2. **Relative Strength Index (RSI)**:\n   - RSI values above 70 may indicate the index is overbought, while values below 30 may suggest it is oversold. Look for divergence between RSI and price for potential reversal signals.\n\n3. **MACD (Moving Average Convergence Divergence)**:\n   - Analyze the MACD line and the signal line. A crossover of the MACD line above the signal line can be a bullish signal, while a crossover below can be bearish.\n\n4. **Bollinger Bands**:\n   - Observe the price action relative to the Bollinger Bands. A move outside the bands can indicate a potential reversal or continuation of the trend, depending on the context.\n\n### Trading Signals\n\n1. **Breakouts**:\n   - Watch for breakouts above resistance or below support levels with increased volume, which can signal the start of a new trend.\n\n2. **Volume Analysis**:\n   - Confirm price movements with volume. A strong move accompanied by high volume is more likely to be sustainable.\n\n3. **Fibonacci Retracement**:\n   - Use Fibonacci retracement levels to identify potential support and resistance levels during pullbacks within a trend.\n\n### Actionable Insights\n\n- **Bullish Scenario**: If the S&P 500 is in an uptrend, trading above key moving averages, and showing bullish candlestick patterns at support levels, consider long positions. Look for confirmation with RSI and MACD indicators.\n\n- **Bearish Scenario**: If the index is in a downtrend, trading below key moving averages, and showing bearish candlestick patterns at resistance levels, consider short positions. Confirm with RSI and MACD indicators.\n\n- **Neutral/Sideways Scenario**: If the index is trading sideways, consider range-bound strategies such as buying at support and selling at resistance until a clear breakout occurs.\n\nAlways remember to use stop-loss orders to manage risk and protect against adverse market movements. Additionally, consider the broader economic context and any upcoming news events that might impact market sentiment.",
+            "timestamp": "2024-12-26T15:39:44.890581"
+        },
+        {
+            "agent_name": "FundamentalAnalyst",
+            "task": "Select and analyze 3 top technology companies using fundamental analysis. Include key metrics like P/E ratio",
+            "result": "To conduct a fundamental analysis of three top technology companies, we'll focus on Apple Inc. (AAPL), Microsoft Corporation (MSFT), and Alphabet Inc. (GOOGL). These companies are leaders in the technology sector and have significant market influence. We'll examine key financial metrics, including the Price-to-Earnings (P/E) ratio, revenue growth, profit margins, and return on equity (ROE), among others.\n\n### 1. Apple Inc. (AAPL)\n\n**P/E Ratio**: As of the latest data, Apple's P/E ratio is approximately 28. This indicates that investors are willing to pay $28 for every $1 of earnings, reflecting high expectations for future growth.\n\n**Revenue Growth**: Apple has shown consistent revenue growth, driven by strong sales of its iPhone, services, and wearables. In recent years, the services segment has become a significant growth driver.\n\n**Profit Margins**: Apple maintains high profit margins, with a gross margin around 40% and a net profit margin of about 25%. This is indicative of strong pricing power and operational efficiency.\n\n**Return on Equity (ROE)**: Apple's ROE is impressive, often exceeding 70%. This high ROE suggests efficient use of shareholder equity to generate profits.\n\n**Analysis**: Apple's strong brand, diversified product line, and growing services segment contribute to its robust financial performance. The high P/E ratio reflects investor confidence in its continued growth and innovation.\n\n### 2. Microsoft Corporation (MSFT)\n\n**P/E Ratio**: Microsoft's P/E ratio is around 35, suggesting that investors expect significant future earnings growth, particularly from its cloud computing and software segments.\n\n**Revenue Growth**: Microsoft has experienced strong revenue growth, particularly in its Azure cloud services, Office 365, and LinkedIn. The shift to cloud computing has been a major growth driver.\n\n**Profit Margins**: Microsoft boasts a gross margin of about 68% and a net profit margin of approximately 33%, highlighting its strong operational efficiency and high-margin software business.\n\n**Return on Equity (ROE)**: Microsoft's ROE is around 40%, indicating effective management and profitable use of equity capital.\n\n**Analysis**: Microsoft's leadership in cloud computing and enterprise software, along with its strategic acquisitions, positions it well for continued growth. The high P/E ratio reflects optimism about its future prospects in these areas.\n\n### 3. Alphabet Inc. (GOOGL)\n\n**P/E Ratio**: Alphabet's P/E ratio is approximately 25, which is lower than its peers, suggesting a more moderate growth expectation or potential undervaluation.\n\n**Revenue Growth**: Alphabet has consistently grown its revenue, driven by its dominant position in digital advertising through Google Search and YouTube, as well as growth in its cloud services.\n\n**Profit Margins**: Alphabet's gross margin is around 55%, with a net profit margin of about 21%. These margins reflect its strong market position and efficient cost management.\n\n**Return on Equity (ROE)**: Alphabet's ROE is approximately 20%, which is solid but lower than Apple and Microsoft, possibly due to its significant investments in research and development and other ventures.\n\n**Analysis**: Alphabet's stronghold in digital advertising and its growing cloud business are key strengths. The relatively lower P/E ratio might indicate a more cautious market view on its non-core investments or potential for future growth.\n\n### Comparative Analysis\n\n- **P/E Ratio**: Microsoft has the highest P/E ratio, indicating the highest growth expectations, followed by Apple and Alphabet.\n- **Revenue Growth**: All three companies show strong revenue growth, with cloud services being a significant driver for Microsoft and Alphabet.\n- **Profit Margins**: Microsoft leads in profit margins, benefiting from its high-margin software business.\n- **ROE**: Apple has the highest ROE, reflecting its efficient capital use, followed by Microsoft and Alphabet.\n\nIn conclusion, each of these technology giants has unique strengths and growth drivers. Investors should consider their individual risk tolerance and investment goals when evaluating these companies, as well as the broader economic and technological trends that could impact their future performance.",
+            "timestamp": "2024-12-26T15:39:44.890581"
+        },
+        {
+            "agent_name": "MacroStrategist",
+            "task": "Analyze the current macroeconomic environment",
+            "result": "As of the latest data available, the global macroeconomic environment is characterized by several key trends and challenges that are shaping market opportunities:\n\n1. **Inflation Dynamics**: Many economies are experiencing elevated inflation rates, driven by supply chain disruptions, energy price volatility, and post-pandemic demand surges. Central banks, particularly in advanced economies, are responding with tighter monetary policies. This environment creates opportunities in sectors that benefit from rising interest rates, such as financials, while posing risks to interest rate-sensitive sectors like real estate.\n\n2. **Monetary Policy Shifts**: The U.S. Federal Reserve, European Central Bank, and other major central banks are either raising interest rates or signaling future hikes to combat inflation. This shift is leading to a stronger U.S. dollar, impacting emerging markets with dollar-denominated debt. Investors might find opportunities in currency markets, particularly in shorting currencies of countries with weaker economic fundamentals.\n\n3. **Energy Market Volatility**: Geopolitical tensions, particularly in Eastern Europe, have led to significant volatility in energy markets. This has implications for inflation and economic growth, especially in energy-importing countries. Investors may look to energy stocks, commodities, and alternative energy sectors as potential beneficiaries of these trends.\n\n4. **Supply Chain Resilience**: Ongoing disruptions have highlighted the need for more resilient supply chains. Companies investing in technology and infrastructure to enhance supply chain efficiency may present attractive investment opportunities. Additionally, regions or sectors that are less reliant on global supply chains might outperform.\n\n5. **Technological Transformation**: The acceleration of digital transformation across industries continues to create investment opportunities in technology and innovation. Sectors such as cybersecurity, cloud computing, and artificial intelligence are likely to see sustained growth.\n\n6. **Sustainability and ESG Investing**: Environmental, social, and governance (ESG) considerations are increasingly influencing investment decisions. Companies with strong ESG credentials may attract more capital, and sectors like renewable energy and electric vehicles are poised for growth.\n\n7. **Global Growth Divergence**: Economic recovery is uneven across regions, with some emerging markets facing greater challenges due to limited fiscal space and slower vaccine rollouts. Investors might focus on developed markets or specific emerging markets with strong fundamentals and growth prospects.\n\n8. **Geopolitical Risks**: Heightened geopolitical tensions, particularly involving major powers, can lead to market volatility. Safe-haven assets like gold and government bonds may see increased demand during periods of heightened uncertainty.\n\nIn summary, the current macroeconomic environment presents a complex landscape with both risks and opportunities. Investors should consider diversifying their portfolios to manage risks associated with inflation, interest rate changes, and geopolitical uncertainties while seeking growth opportunities in technology, energy, and ESG-focused investments.",
+            "timestamp": "2024-12-26T15:39:44.890581"
+        }
+    ],
+    "number_of_agents": 5
+}
\ No newline at end of file
diff --git a/new_features_examples/solana_tool/solana_tool.py b/new_features_examples/solana_tool/solana_tool.py
new file mode 100644
index 00000000..998413b6
--- /dev/null
+++ b/new_features_examples/solana_tool/solana_tool.py
@@ -0,0 +1,295 @@
+from datetime import datetime
+import json
+import requests
+from loguru import logger
+from dataclasses import dataclass
+from datetime import timezone
+import time
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+# Configure loguru logger
+logger.add(
+    "solana_transactions.log",
+    rotation="500 MB",
+    retention="10 days",
+    level="INFO",
+    format="{time} {level} {message}",
+)
+
+# Reliable public RPC endpoints
+RPC_ENDPOINTS = [
+    "https://api.mainnet-beta.solana.com",
+    "https://solana.public-rpc.com",
+    "https://rpc.ankr.com/solana",
+]
+
+
+@dataclass
+class TransactionError:
+    """Data class to represent transaction errors"""
+
+    error_type: str
+    message: str
+    timestamp: str = datetime.now(timezone.utc).isoformat()
+
+
+class SolanaAPIException(Exception):
+    """Custom exception for Solana API related errors"""
+
+    pass
+
+
+def create_http_session() -> requests.Session:
+    """
+    Creates a requests session with retry logic and timeouts
+    """
+    session = requests.Session()
+
+    # Configure retry strategy
+    retry_strategy = Retry(
+        total=3,
+        backoff_factor=0.5,
+        status_forcelist=[429, 500, 502, 503, 504],
+    )
+
+    adapter = HTTPAdapter(max_retries=retry_strategy)
+    session.mount("http://", adapter)
+    session.mount("https://", adapter)
+
+    return session
+
+
+def get_working_endpoint(session: requests.Session) -> str:
+    """
+    Tests endpoints and returns the first working one.
+
+    Args:
+        session: requests.Session object with retry logic
+
+    Returns:
+        str: Working RPC endpoint URL
+
+    Raises:
+        SolanaAPIException: If no working endpoint is found
+    """
+    for endpoint in RPC_ENDPOINTS:
+        try:
+            payload = {
+                "jsonrpc": "2.0",
+                "id": 1,
+                "method": "getHealth",
+            }
+            response = session.post(endpoint, json=payload, timeout=5)
+            if response.status_code == 200:
+                logger.info(f"Using RPC endpoint: {endpoint}")
+                return endpoint
+        except Exception as e:
+            logger.warning(
+                f"Endpoint {endpoint} failed health check: {str(e)}"
+            )
+            continue
+
+    raise SolanaAPIException("No working RPC endpoints found")
+
+
+def fetch_wallet_transactions(wallet_address: str) -> str:
+    """
+    Fetches all transactions for a given Solana wallet address using public RPC endpoints.
+
+    Args:
+        wallet_address (str): The Solana wallet address to fetch transactions for
+            Example: "CtBLg4AX6LQfKVtPPUWqJyQ5cRfHydUwuZZ87rmojA1P"
+
+    Returns:
+        str: JSON string containing the list of transactions and their details
+            Format: {
+                "success": bool,
+                "transactions": List[Dict],
+                "error": Optional[Dict]
+            }
+    """
+    try:
+        # Validate wallet address format (basic check)
+        if (
+            not isinstance(wallet_address, str)
+            or len(wallet_address) != 44
+        ):
+            raise ValueError(
+                f"Invalid Solana wallet address format: {wallet_address}"
+            )
+
+        logger.info(
+            f"Fetching transactions for wallet: {wallet_address}"
+        )
+
+        # Create session with retry logic
+        session = create_http_session()
+
+        # Get working endpoint
+        api_endpoint = get_working_endpoint(session)
+
+        # Initialize variables for pagination
+        all_transactions = []
+        before_signature = None
+        limit = 25  # Smaller batch size to be more conservative
+
+        while True:
+            try:
+                # Prepare request payload
+                payload = {
+                    "jsonrpc": "2.0",
+                    "id": "1",
+                    "method": "getSignaturesForAddress",
+                    "params": [
+                        wallet_address,
+                        {"limit": limit, "before": before_signature},
+                    ],
+                }
+
+                # Make API request
+                response = session.post(
+                    api_endpoint, json=payload, timeout=10
+                )
+
+                data = response.json()
+
+                if "error" in data:
+                    error_code = data.get("error", {}).get("code")
+                    if error_code == 429:  # Rate limit
+                        time.sleep(1)  # Wait before trying again
+                        continue
+
+                    raise SolanaAPIException(
+                        f"API error: {data['error']}"
+                    )
+
+                # Extract transactions from response
+                transactions = data.get("result", [])
+
+                if not transactions:
+                    break
+
+                # Add transactions to our list
+                all_transactions.extend(transactions)
+
+                # Update pagination cursor
+                before_signature = transactions[-1]["signature"]
+
+                logger.info(
+                    f"Fetched {len(transactions)} transactions. Total: {len(all_transactions)}"
+                )
+
+                # Break if we received fewer transactions than the limit
+                if len(transactions) < limit:
+                    break
+
+                # Add small delay between batches
+                time.sleep(0.2)
+
+            except Exception as e:
+                logger.error(
+                    f"Error during transaction fetch: {str(e)}"
+                )
+                # Try to get a new endpoint if the current one fails
+                api_endpoint = get_working_endpoint(session)
+                continue
+
+        # Enrich transaction data with additional details
+        enriched_transactions = []
+        for tx in all_transactions:
+            try:
+                tx_payload = {
+                    "jsonrpc": "2.0",
+                    "id": "1",
+                    "method": "getTransaction",
+                    "params": [
+                        tx["signature"],
+                        {
+                            "encoding": "json",
+                            "maxSupportedTransactionVersion": 0,
+                        },
+                    ],
+                }
+
+                response = session.post(
+                    api_endpoint, json=tx_payload, timeout=10
+                )
+                tx_data = response.json()
+
+                if "result" in tx_data and tx_data["result"]:
+                    enriched_transactions.append(
+                        {
+                            "signature": tx["signature"],
+                            "slot": tx["slot"],
+                            "timestamp": tx["blockTime"],
+                            "status": (
+                                "success"
+                                if not tx.get("err")
+                                else "error"
+                            ),
+                            "details": tx_data["result"],
+                        }
+                    )
+
+                # Small delay between transaction fetches
+                time.sleep(0.1)
+                
+                # print(tx)
+                logger.info(f"Enriched transaction: {tx}")
+
+            except Exception as e:
+                logger.warning(
+                    f"Failed to fetch details for transaction {tx['signature']}: {str(e)}"
+                )
+                continue
+
+        logger.info(
+            f"Successfully fetched and enriched {len(enriched_transactions)} transactions"
+        )
+
+        return json.dumps(
+            {
+                "success": True,
+                "transactions": enriched_transactions,
+                "error": None,
+            }
+        )
+
+    except SolanaAPIException as e:
+        error = TransactionError(
+            error_type="API_ERROR", message=str(e)
+        )
+        logger.error(f"API error: {error.message}")
+        return json.dumps(
+            {
+                "success": False,
+                "transactions": [],
+                "error": error.__dict__,
+            }
+        )
+
+    except Exception as e:
+        error = TransactionError(
+            error_type="UNKNOWN_ERROR",
+            message=f"An unexpected error occurred: {str(e)}",
+        )
+        logger.error(f"Unexpected error: {error.message}")
+        return json.dumps(
+            {
+                "success": False,
+                "transactions": [],
+                "error": error.__dict__,
+            }
+        )
+
+
+# Example usage
+if __name__ == "__main__":
+    wallet = "CtBLg4AX6LQfKVtPPUWqJyQ5cRfHydUwuZZ87rmojA1P"
+
+    try:
+        result = fetch_wallet_transactions(wallet)
+        print(json.dumps(json.loads(result), indent=2))
+    except Exception as e:
+        logger.error(f"Failed to fetch transactions: {str(e)}")
diff --git a/new_features_examples/solana_tool/solana_tool_test.py b/new_features_examples/solana_tool/solana_tool_test.py
new file mode 100644
index 00000000..5a80cbb8
--- /dev/null
+++ b/new_features_examples/solana_tool/solana_tool_test.py
@@ -0,0 +1,240 @@
+from typing import Dict, List, Optional, Union, Any
+from datetime import datetime
+import json
+import requests
+from loguru import logger
+from dataclasses import dataclass
+from datetime import datetime, timezone
+import time
+import random
+
+# Configure loguru logger
+logger.add(
+    "solana_transactions.log",
+    rotation="500 MB",
+    retention="10 days",
+    level="INFO",
+    format="{time} {level} {message}"
+)
+
+# Most reliable RPC endpoints
+RPC_ENDPOINTS = [
+    "https://api.mainnet-beta.solana.com",
+    "https://rpc.ankr.com/solana",
+    "https://solana.getblock.io/mainnet"
+]
+
+@dataclass
+class TransactionError:
+    """Data class to represent transaction errors"""
+    error_type: str
+    message: str
+    timestamp: str = datetime.now(timezone.utc).isoformat()
+
+class SolanaAPIException(Exception):
+    """Custom exception for Solana API related errors"""
+    pass
+
+class RPCEndpointManager:
+    """Manages RPC endpoints and handles switching between them"""
+    
+    def __init__(self, endpoints: List[str]):
+        self.endpoints = endpoints.copy()
+        self.current_endpoint = self.endpoints[0]
+        self.last_request_time = 0
+        self.min_request_interval = 0.2  # Increased minimum interval
+        self.total_requests = 0
+        self.max_requests_per_endpoint = 3
+        
+    def get_endpoint(self) -> str:
+        """Get current endpoint with rate limiting"""
+        now = time.time()
+        time_since_last = now - self.last_request_time
+        if time_since_last < self.min_request_interval:
+            time.sleep(self.min_request_interval - time_since_last)
+        
+        self.total_requests += 1
+        if self.total_requests >= self.max_requests_per_endpoint:
+            self.switch_endpoint()
+            self.total_requests = 0
+            
+        self.last_request_time = time.time()
+        return self.current_endpoint
+    
+    def switch_endpoint(self) -> str:
+        """Switch to next available endpoint"""
+        current = self.current_endpoint
+        available_endpoints = [ep for ep in self.endpoints if ep != current]
+        
+        if not available_endpoints:
+            raise SolanaAPIException("All endpoints exhausted")
+            
+        self.current_endpoint = random.choice(available_endpoints)
+        logger.info(f"Switched to endpoint: {self.current_endpoint}")
+        return self.current_endpoint
+
+def make_request(endpoint_manager: RPCEndpointManager, payload: dict, retry_count: int = 3) -> dict:
+    """
+    Makes a request with automatic endpoint switching and error handling.
+    """
+    last_error = None
+    
+    for attempt in range(retry_count):
+        try:
+            endpoint = endpoint_manager.get_endpoint()
+            
+            response = requests.post(
+                endpoint,
+                json=payload,
+                timeout=10,
+                headers={"Content-Type": "application/json"},
+                verify=True  # Ensure SSL verification
+            )
+            
+            if response.status_code != 200:
+                raise SolanaAPIException(f"HTTP {response.status_code}: {response.text}")
+            
+            data = response.json()
+            
+            if "error" in data:
+                error_code = data["error"].get("code")
+                if error_code == 429:  # Rate limit
+                    logger.warning(f"Rate limit hit, switching endpoint...")
+                    endpoint_manager.switch_endpoint()
+                    time.sleep(2 ** attempt)  # Exponential backoff
+                    continue
+                    
+                if "message" in data["error"]:
+                    raise SolanaAPIException(f"RPC error: {data['error']['message']}")
+                    
+            return data
+            
+        except (requests.exceptions.SSLError, requests.exceptions.ConnectionError) as e:
+            logger.warning(f"Connection error with {endpoint}: {str(e)}")
+            endpoint_manager.switch_endpoint()
+            continue
+            
+        except Exception as e:
+            last_error = e
+            logger.warning(f"Request failed: {str(e)}")
+            endpoint_manager.switch_endpoint()
+            time.sleep(1)
+            continue
+    
+    raise SolanaAPIException(f"All retry attempts failed. Last error: {str(last_error)}")
+
+def fetch_wallet_transactions(wallet_address: str, max_transactions: int = 10) -> str:
+    """
+    Fetches recent transactions for a given Solana wallet address.
+    
+    Args:
+        wallet_address (str): The Solana wallet address to fetch transactions for
+        max_transactions (int, optional): Maximum number of transactions to fetch. Defaults to 10.
+    
+    Returns:
+        str: JSON string containing transaction details
+    """
+    try:
+        if not isinstance(wallet_address, str) or len(wallet_address) != 44:
+            raise ValueError(f"Invalid Solana wallet address format: {wallet_address}")
+        
+        if not isinstance(max_transactions, int) or max_transactions < 1:
+            raise ValueError("max_transactions must be a positive integer")
+
+        logger.info(f"Fetching up to {max_transactions} transactions for wallet: {wallet_address}")
+
+        endpoint_manager = RPCEndpointManager(RPC_ENDPOINTS)
+        
+        # Get transaction signatures
+        signatures_payload = {
+            "jsonrpc": "2.0",
+            "id": str(random.randint(1, 1000)),
+            "method": "getSignaturesForAddress",
+            "params": [
+                wallet_address,
+                {"limit": max_transactions}
+            ]
+        }
+
+        signatures_data = make_request(endpoint_manager, signatures_payload)
+        
+        transactions = signatures_data.get("result", [])
+        if not transactions:
+            logger.info("No transactions found for this wallet")
+            return json.dumps({
+                "success": True,
+                "transactions": [],
+                "error": None,
+                "transaction_count": 0
+            }, indent=2)
+
+        logger.info(f"Found {len(transactions)} transactions")
+
+        # Process transactions
+        enriched_transactions = []
+        for tx in transactions:
+            try:
+                tx_payload = {
+                    "jsonrpc": "2.0",
+                    "id": str(random.randint(1, 1000)),
+                    "method": "getTransaction",
+                    "params": [
+                        tx["signature"],
+                        {"encoding": "json", "maxSupportedTransactionVersion": 0}
+                    ]
+                }
+                
+                tx_data = make_request(endpoint_manager, tx_payload)
+                
+                if "result" in tx_data and tx_data["result"]:
+                    result = tx_data["result"]
+                    enriched_tx = {
+                        "signature": tx["signature"],
+                        "slot": tx["slot"],
+                        "timestamp": tx.get("blockTime"),
+                        "success": not tx.get("err"),
+                    }
+                    
+                    if "meta" in result:
+                        enriched_tx["fee"] = result["meta"].get("fee")
+                        if "preBalances" in result["meta"] and "postBalances" in result["meta"]:
+                            enriched_tx["balance_change"] = sum(result["meta"]["postBalances"]) - sum(result["meta"]["preBalances"])
+                    
+                    enriched_transactions.append(enriched_tx)
+                    logger.info(f"Processed transaction {tx['signature'][:8]}...")
+                    
+            except Exception as e:
+                logger.warning(f"Failed to process transaction {tx['signature']}: {str(e)}")
+                continue
+
+        logger.info(f"Successfully processed {len(enriched_transactions)} transactions")
+        
+        return json.dumps({
+            "success": True,
+            "transactions": enriched_transactions,
+            "error": None,
+            "transaction_count": len(enriched_transactions)
+        }, indent=2)
+
+    except Exception as e:
+        error = TransactionError(
+            error_type="API_ERROR",
+            message=str(e)
+        )
+        logger.error(f"Error: {error.message}")
+        return json.dumps({
+            "success": False,
+            "transactions": [],
+            "error": error.__dict__,
+            "transaction_count": 0
+        }, indent=2)
+
+if __name__ == "__main__":
+    # Example wallet address
+    wallet = "CtBLg4AX6LQfKVtPPUWqJyQ5cRfHydUwuZZ87rmojA1P"
+    
+    try:
+        result = fetch_wallet_transactions(wallet)
+        print(result)
+    except Exception as e:
+        logger.error(f"Failed to fetch transactions: {str(e)}")
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 50a95161..fd5f1a7a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "6.7.9"
+version = "6.8.0"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -76,11 +76,8 @@ networkx = "*"
 aiofiles = "*"
 clusterops = "*"
 # chromadb = "*"
-reportlab = "*"
-doc-master = "*"
 rich = "*"
 # sentence-transformers = "*"
-termcolor = "*"
 
 
 # [tool.poetry.extras]
diff --git a/new_features_examples/rearrange_test.py b/rearrange_test.py
similarity index 99%
rename from new_features_examples/rearrange_test.py
rename to rearrange_test.py
index d85e435a..de020cc7 100644
--- a/new_features_examples/rearrange_test.py
+++ b/rearrange_test.py
@@ -102,7 +102,6 @@ agent_system = AgentRearrange(
     return_json=False,
     output_type="final",
     max_loops=1,
-    # docs=["SECURITY.md"],
 )
 
 # Input task for the swarm
diff --git a/requirements.txt b/requirements.txt
index 5bca5470..01152186 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,7 +24,4 @@ pytest>=8.1.1
 pandas>=2.2.2
 networkx
 aiofiles
-clusterops
-reportlab
-doc-master
-termcolor
\ No newline at end of file
+clusterops
\ No newline at end of file
diff --git a/simple_example.py b/simple_example.py
index 1044958a..0d6166a3 100644
--- a/simple_example.py
+++ b/simple_example.py
@@ -3,7 +3,7 @@ from swarms import Agent
 Agent(
     agent_name="Stock-Analysis-Agent",
     model_name="gpt-4o-mini",
-    max_loops=1,
-    interactive=False,
+    max_loops="auto",
+    interactive=True,
     streaming_on=True,
 ).run("What are 5 hft algorithms")
diff --git a/swarms/artifacts/main_artifact.py b/swarms/artifacts/main_artifact.py
index 04a8daf5..47fbc11f 100644
--- a/swarms/artifacts/main_artifact.py
+++ b/swarms/artifacts/main_artifact.py
@@ -1,10 +1,14 @@
-import time
-import os
+
 import json
-from typing import List, Union, Dict, Any
+import os
+import subprocess
+import time
+from datetime import datetime
+from typing import Any, Dict, List, Union
+
 from pydantic import BaseModel, Field
 from pydantic.v1 import validator
-from datetime import datetime
+
 from swarms.utils.file_processing import create_file_in_folder
 from swarms.utils.loguru_logger import initialize_logger
 
@@ -303,23 +307,24 @@ class Artifact(BaseModel):
         Helper method to save content as PDF using reportlab
         """
         try:
+            from reportlab.lib.pagesizes import letter
             from reportlab.pdfgen import canvas
+        except ImportError as e:
+            logger.error(f"Error importing reportlab: {e}")
+            subprocess.run(["pip", "install", "reportlab"])
             from reportlab.lib.pagesizes import letter
+            from reportlab.pdfgen import canvas
 
-            c = canvas.Canvas(output_path, pagesize=letter)
-            # Split content into lines
-            y = 750  # Starting y position
-            for line in self.contents.split("\n"):
-                c.drawString(50, y, line)
-                y -= 15  # Move down for next line
-                if y < 50:  # New page if bottom reached
-                    c.showPage()
-                    y = 750
-            c.save()
-        except ImportError:
-            raise ImportError(
-                "reportlab package is required for PDF output. Install with: pip install reportlab"
-            )
+        c = canvas.Canvas(output_path, pagesize=letter)
+        # Split content into lines
+        y = 750  # Starting y position
+        for line in self.contents.split("\n"):
+            c.drawString(50, y, line)
+            y -= 15  # Move down for next line
+            if y < 50:  # New page if bottom reached
+                c.showPage()
+                y = 750
+        c.save()
 
 
 # # Example usage
diff --git a/swarms/structs/base_swarm.py b/swarms/structs/base_swarm.py
index 29dcccbf..277f2d4f 100644
--- a/swarms/structs/base_swarm.py
+++ b/swarms/structs/base_swarm.py
@@ -136,9 +136,9 @@ class BaseSwarm(ABC):
             raise TypeError("Agents must be a list.")
 
         # Ensure that agents is not empty
-        if len(self.agents) == 0:
-            logger.error("Agents list must not be empty.")
-            raise ValueError("Agents list must not be empty.")
+        # if len(self.agents) == 0:
+        #     logger.error("Agents list must not be empty.")
+        #     raise ValueError("Agents list must not be empty.")
 
         # Initialize conversation
         self.conversation = Conversation(
diff --git a/swarms/structs/rearrange.py b/swarms/structs/rearrange.py
index 41f546c0..bd2bced6 100644
--- a/swarms/structs/rearrange.py
+++ b/swarms/structs/rearrange.py
@@ -1,5 +1,5 @@
 import asyncio
-import traceback
+import json
 import uuid
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
@@ -12,11 +12,11 @@ from swarms.structs.agent import Agent
 from swarms.structs.agents_available import showcase_available_agents
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.output_types import OutputType
-from swarms.utils.add_docs_to_agents import handle_input_docs
 from swarms.utils.loguru_logger import initialize_logger
 from swarms.utils.wrapper_clusterop import (
     exec_callable_with_clusterops,
 )
+from swarms.telemetry.capture_sys_data import log_agent_data
 
 logger = initialize_logger(log_folder="rearrange")
 
@@ -89,7 +89,6 @@ class AgentRearrange(BaseSwarm):
         batch_run(): Processes multiple tasks in batches
         abatch_run(): Asynchronously processes multiple tasks in batches
         concurrent_run(): Processes multiple tasks concurrently
-        handle_input_docs(): Adds document content to agent prompts
 
     """
 
@@ -116,6 +115,7 @@ class AgentRearrange(BaseSwarm):
         all_cores: bool = False,
         all_gpus: bool = True,
         no_use_clusterops: bool = True,
+        autosave: bool = True,
         *args,
         **kwargs,
     ):
@@ -143,6 +143,7 @@ class AgentRearrange(BaseSwarm):
         self.all_cores = all_cores
         self.all_gpus = all_gpus
         self.no_use_clusterops = no_use_clusterops
+        self.autosave = autosave
 
         self.output_schema = AgentRearrangeOutput(
             input=AgentRearrangeInput(
@@ -194,13 +195,6 @@ class AgentRearrange(BaseSwarm):
         self.flow = flow
         logger.info(f"Custom flow set: {flow}")
 
-    def handle_input_docs(self):
-        self.agents = handle_input_docs(
-            agents=self.agents,
-            docs=self.docs,
-            doc_folder=self.doc_folder,
-        )
-
     def add_agent(self, agent: Agent):
         """
         Adds an agent to the swarm.
@@ -487,10 +481,15 @@ class AgentRearrange(BaseSwarm):
             return output
 
         except Exception as e:
-            logger.error(
-                f"An error occurred: {e} \n {traceback.format_exc()}"
-            )
-            return e
+            self._catch_error(e)
+        
+    def _catch_error(self, e: Exception):
+        if self.autosave is True:
+            log_agent_data(self.to_dict())
+            
+        logger.error(f"An error occurred with your swarm {self.name}: Error: {e} Traceback: {e.__traceback__}")
+        
+        return e
 
     def run(
         self,
@@ -500,7 +499,7 @@ class AgentRearrange(BaseSwarm):
         device_id: int = 2,
         all_cores: bool = True,
         all_gpus: bool = False,
-        no_use_clusterops: bool = False,
+        no_use_clusterops: bool = True,
         *args,
         **kwargs,
     ):
@@ -521,30 +520,33 @@ class AgentRearrange(BaseSwarm):
         Returns:
             The result from executing the task through the cluster operations wrapper.
         """
-        no_use_clusterops = (
-            no_use_clusterops or self.no_use_clusterops
-        )
-
-        if no_use_clusterops is True:
-            return self._run(
-                task=task,
-                img=img,
-                *args,
-                **kwargs,
-            )
-        else:
-            return exec_callable_with_clusterops(
-                device=device,
-                device_id=device_id,
-                all_cores=all_cores,
-                all_gpus=all_gpus,
-                func=self._run,
-                task=task,
-                img=img,
-                *args,
-                **kwargs,
+        try:
+            no_use_clusterops = (
+                no_use_clusterops or self.no_use_clusterops
             )
 
+            if no_use_clusterops is True:
+                return self._run(
+                    task=task,
+                    img=img,
+                    *args,
+                    **kwargs,
+                )
+            else:
+                return exec_callable_with_clusterops(
+                    device=device,
+                    device_id=device_id,
+                    all_cores=all_cores,
+                    all_gpus=all_gpus,
+                    func=self._run,
+                    task=task,
+                    img=img,
+                    *args,
+                    **kwargs,
+                )
+        except Exception as e:
+            self._catch_error(e)
+
     def __call__(self, task: str, *args, **kwargs):
         """
         Make the class callable by executing the run() method.
@@ -557,7 +559,11 @@ class AgentRearrange(BaseSwarm):
         Returns:
             The result from executing run().
         """
-        return self.run(task=task, *args, **kwargs)
+        try:
+            return self.run(task=task, *args, **kwargs)
+        except Exception as e:
+            logger.error(f"An error occurred: {e}")
+            return e
 
     def batch_run(
         self,
@@ -586,32 +592,35 @@ class AgentRearrange(BaseSwarm):
         Returns:
             List of results corresponding to input tasks
         """
-        results = []
-        for i in range(0, len(tasks), batch_size):
-            batch_tasks = tasks[i : i + batch_size]
-            batch_imgs = (
-                img[i : i + batch_size]
-                if img
-                else [None] * len(batch_tasks)
-            )
-
-            # Process batch using concurrent execution
-            batch_results = [
-                self.run(
-                    task=task,
-                    img=img_path,
-                    device=device,
-                    device_id=device_id,
-                    all_cores=all_cores,
-                    all_gpus=all_gpus,
-                    *args,
-                    **kwargs,
+        try:
+            results = []
+            for i in range(0, len(tasks), batch_size):
+                batch_tasks = tasks[i : i + batch_size]
+                batch_imgs = (
+                    img[i : i + batch_size]
+                    if img
+                    else [None] * len(batch_tasks)
                 )
-                for task, img_path in zip(batch_tasks, batch_imgs)
-            ]
-            results.extend(batch_results)
 
-        return results
+                # Process batch using concurrent execution
+                batch_results = [
+                    self.run(
+                        task=task,
+                        img=img_path,
+                        device=device,
+                        device_id=device_id,
+                        all_cores=all_cores,
+                        all_gpus=all_gpus,
+                        *args,
+                        **kwargs,
+                    )
+                    for task, img_path in zip(batch_tasks, batch_imgs)
+                ]
+                results.extend(batch_results)
+
+            return results
+        except Exception as e:
+            self._catch_error(e)
 
     async def abatch_run(
         self,
@@ -632,24 +641,27 @@ class AgentRearrange(BaseSwarm):
         Returns:
             List of results corresponding to input tasks
         """
-        results = []
-        for i in range(0, len(tasks), batch_size):
-            batch_tasks = tasks[i : i + batch_size]
-            batch_imgs = (
-                img[i : i + batch_size]
-                if img
-                else [None] * len(batch_tasks)
-            )
+        try:
+            results = []
+            for i in range(0, len(tasks), batch_size):
+                batch_tasks = tasks[i : i + batch_size]
+                batch_imgs = (
+                    img[i : i + batch_size]
+                    if img
+                    else [None] * len(batch_tasks)
+                )
 
-            # Process batch using asyncio.gather
-            batch_coros = [
-                self.astream(task=task, img=img_path, *args, **kwargs)
-                for task, img_path in zip(batch_tasks, batch_imgs)
-            ]
-            batch_results = await asyncio.gather(*batch_coros)
-            results.extend(batch_results)
+                # Process batch using asyncio.gather
+                batch_coros = [
+                    self.astream(task=task, img=img_path, *args, **kwargs)
+                    for task, img_path in zip(batch_tasks, batch_imgs)
+                ]
+                batch_results = await asyncio.gather(*batch_coros)
+                results.extend(batch_results)
 
-        return results
+            return results
+        except Exception as e:
+            self._catch_error(e)
 
     def concurrent_run(
         self,
@@ -678,23 +690,86 @@ class AgentRearrange(BaseSwarm):
         Returns:
             List of results corresponding to input tasks
         """
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            imgs = img if img else [None] * len(tasks)
-            futures = [
-                executor.submit(
-                    self.run,
-                    task=task,
-                    img=img_path,
-                    device=device,
-                    device_id=device_id,
-                    all_cores=all_cores,
-                    all_gpus=all_gpus,
-                    *args,
-                    **kwargs,
-                )
-                for task, img_path in zip(tasks, imgs)
-            ]
-            return [future.result() for future in futures]
+        try:
+            with ThreadPoolExecutor(max_workers=max_workers) as executor:
+                imgs = img if img else [None] * len(tasks)
+                futures = [
+                    executor.submit(
+                        self.run,
+                        task=task,
+                        img=img_path,
+                        device=device,
+                        device_id=device_id,
+                        all_cores=all_cores,
+                        all_gpus=all_gpus,
+                        *args,
+                        **kwargs,
+                    )
+                    for task, img_path in zip(tasks, imgs)
+                ]
+                return [future.result() for future in futures]
+        except Exception as e:
+            self._catch_error(e)
+        
+        
+    def _serialize_callable(
+        self, attr_value: Callable
+    ) -> Dict[str, Any]:
+        """
+        Serializes callable attributes by extracting their name and docstring.
+
+        Args:
+            attr_value (Callable): The callable to serialize.
+
+        Returns:
+            Dict[str, Any]: Dictionary with name and docstring of the callable.
+        """
+        return {
+            "name": getattr(
+                attr_value, "__name__", type(attr_value).__name__
+            ),
+            "doc": getattr(attr_value, "__doc__", None),
+        }
+
+    def _serialize_attr(self, attr_name: str, attr_value: Any) -> Any:
+        """
+        Serializes an individual attribute, handling non-serializable objects.
+
+        Args:
+            attr_name (str): The name of the attribute.
+            attr_value (Any): The value of the attribute.
+
+        Returns:
+            Any: The serialized value of the attribute.
+        """
+        try:
+            if callable(attr_value):
+                return self._serialize_callable(attr_value)
+            elif hasattr(attr_value, "to_dict"):
+                return (
+                    attr_value.to_dict()
+                )  # Recursive serialization for nested objects
+            else:
+                json.dumps(
+                    attr_value
+                )  # Attempt to serialize to catch non-serializable objects
+                return attr_value
+        except (TypeError, ValueError):
+            return f"<Non-serializable: {type(attr_value).__name__}>"
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Converts all attributes of the class, including callables, into a dictionary.
+        Handles non-serializable attributes by converting them or skipping them.
+
+        Returns:
+            Dict[str, Any]: A dictionary representation of the class attributes.
+        """
+        return {
+            attr_name: self._serialize_attr(attr_name, attr_value)
+            for attr_name, attr_value in self.__dict__.items()
+        }
+
 
 
 def rearrange(
diff --git a/swarms/structs/spreadsheet_swarm.py b/swarms/structs/spreadsheet_swarm.py
index 9bbb36a7..6fd2044d 100644
--- a/swarms/structs/spreadsheet_swarm.py
+++ b/swarms/structs/spreadsheet_swarm.py
@@ -3,15 +3,15 @@ import csv
 import datetime
 import os
 import uuid
-from typing import List, Union
+from typing import Dict, List, Union
 
 import aiofiles
 from pydantic import BaseModel, Field
 
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
-from swarms.utils.file_processing import create_file_in_folder
 from swarms.telemetry.capture_sys_data import log_agent_data
+from swarms.utils.file_processing import create_file_in_folder
 from swarms.utils.loguru_logger import initialize_logger
 
 logger = initialize_logger(log_folder="spreadsheet_swarm")
@@ -25,6 +25,15 @@ formatted_time = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
 # --------------- NEW CHANGE END ---------------
 
 
+class AgentConfig(BaseModel):
+    """Configuration for an agent loaded from CSV"""
+
+    agent_name: str
+    description: str
+    system_prompt: str
+    task: str
+
+
 class AgentOutput(BaseModel):
     agent_name: str
     task: str
@@ -77,6 +86,7 @@ class SpreadSheetSwarm(BaseSwarm):
         save_file_path: str = None,
         max_loops: int = 1,
         workspace_dir: str = os.getenv("WORKSPACE_DIR"),
+        load_path: str = None,
         *args,
         **kwargs,
     ):
@@ -93,6 +103,8 @@ class SpreadSheetSwarm(BaseSwarm):
         self.autosave_on = autosave_on
         self.max_loops = max_loops
         self.workspace_dir = workspace_dir
+        self.load_path = load_path
+        self.agent_configs: Dict[str, AgentConfig] = {}
 
         # --------------- NEW CHANGE START ---------------
         # The save_file_path now uses the formatted_time and uuid_hex
@@ -122,49 +134,161 @@ class SpreadSheetSwarm(BaseSwarm):
         """
         logger.info("Checking the reliability of the swarm...")
 
-        if not self.agents:
-            raise ValueError("No agents are provided.")
-        if not self.save_file_path:
-            raise ValueError("No save file path is provided.")
+        # if not self.agents:
+        #     raise ValueError("No agents are provided.")
+        # if not self.save_file_path:
+        #     raise ValueError("No save file path is provided.")
         if not self.max_loops:
             raise ValueError("No max loops are provided.")
 
         logger.info("Swarm reliability check passed.")
         logger.info("Swarm is ready to run.")
 
-    # @profile_func
-    def run(self, task: str, *args, **kwargs):
+    async def _load_from_csv(self):
         """
-        Run the swarm with the specified task.
+        Load agent configurations from a CSV file.
+        Expected CSV format: agent_name,description,system_prompt,task
 
         Args:
-            task (str): The task to be executed by the swarm.
-            *args: Additional positional arguments.
-            **kwargs: Additional keyword arguments.
+            csv_path (str): Path to the CSV file containing agent configurations
+        """
+        try:
+            csv_path = self.load_path
+            logger.info(
+                f"Loading agent configurations from {csv_path}"
+            )
 
-        Returns:
-            str: The JSON representation of the swarm metadata.
+            async with aiofiles.open(csv_path, mode="r") as file:
+                content = await file.read()
+                csv_reader = csv.DictReader(content.splitlines())
+
+                for row in csv_reader:
+                    config = AgentConfig(
+                        agent_name=row["agent_name"],
+                        description=row["description"],
+                        system_prompt=row["system_prompt"],
+                        task=row["task"],
+                    )
+
+                    # Create new agent with configuration
+                    new_agent = Agent(
+                        agent_name=config.agent_name,
+                        system_prompt=config.system_prompt,
+                        description=config.description,
+                        model_name=(
+                            row["model_name"]
+                            if "model_name" in row
+                            else "openai/gpt-4o"
+                        ),
+                        docs=[row["docs"]] if "docs" in row else "",
+                        dynamic_temperature_enabled=True,
+                        max_loops=row["max_loops"] if "max_loops" in row else 1,
+                        user_name=row["user_name"] if "user_name" in row else "user",
+                        # output_type="str",
+                        stopping_token=row["stopping_token"] if "stopping_token" in row else None,
+                    )
+
+                    # Add agent to swarm
+                    self.agents.append(new_agent)
+                    self.agent_configs[config.agent_name] = config
+
+            # Update metadata with new agents
+            self.metadata.agents = [
+                agent.name for agent in self.agents
+            ]
+            self.metadata.number_of_agents = len(self.agents)
+            logger.info(
+                f"Loaded {len(self.agent_configs)} agent configurations"
+            )
+        except Exception as e:
+            logger.error(f"Error loading agent configurations: {e}")
 
+    def load_from_csv(self):
+        asyncio.run(self._load_from_csv())
+
+    async def run_from_config(self):
+        """
+        Run all agents with their configured tasks concurrently
         """
-        logger.info(f"Running the swarm with task: {task}")
+        logger.info("Running agents from configuration")
         self.metadata.start_time = time
 
-        # Run the asyncio event loop
-        asyncio.run(self._run_tasks(task, *args, **kwargs))
+        tasks = []
+        for agent in self.agents:
+            config = self.agent_configs.get(agent.agent_name)
+            if config:
+                for _ in range(self.max_loops):
+                    tasks.append(
+                        asyncio.to_thread(
+                            self._run_agent_task, agent, config.task
+                        )
+                    )
+
+        # Run all tasks concurrently
+        results = await asyncio.gather(*tasks)
+
+        # Process the results
+        for result in results:
+            self._track_output(*result)
 
         self.metadata.end_time = time
 
-        # Synchronously save metadata
+        # Save metadata
         logger.info("Saving metadata to CSV and JSON...")
-        asyncio.run(self._save_metadata())
+        await self._save_metadata()
 
         if self.autosave_on:
             self.data_to_json_file()
 
-        print(log_agent_data(self.metadata.model_dump()))
-
+        log_agent_data(self.metadata.model_dump())
         return self.metadata.model_dump_json(indent=4)
 
+    async def _run(self, task: str = None, *args, **kwargs):
+        """
+        Run the swarm either with a specific task or using configured tasks.
+
+        Args:
+            task (str, optional): The task to be executed by all agents. If None, uses tasks from config.
+            *args: Additional positional arguments.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            str: The JSON representation of the swarm metadata.
+        """
+        if task is None and self.agent_configs:
+            return await self.run_from_config()
+        else:
+            self.metadata.start_time = time
+            await self._run_tasks(task, *args, **kwargs)
+            self.metadata.end_time = time
+            await self._save_metadata()
+
+            if self.autosave_on:
+                self.data_to_json_file()
+
+            print(log_agent_data(self.metadata.model_dump()))
+            return self.metadata.model_dump_json(indent=4)
+        
+        
+    def run(self, task: str = None, *args, **kwargs):
+        """
+        Run the swarm with the specified task.
+
+        Args:
+            task (str): The task to be executed by the swarm.
+            *args: Additional positional arguments.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            str: The JSON representation of the swarm metadata.
+
+        """
+        try:
+            return asyncio.run(self._run(task, *args, **kwargs))
+        except Exception as e:
+            logger.error(f"Error running swarm: {e}")
+            raise e
+
     async def _run_tasks(self, task: str, *args, **kwargs):
         """
         Run the swarm tasks concurrently.
@@ -208,9 +332,15 @@ class SpreadSheetSwarm(BaseSwarm):
         Returns:
             Tuple[str, str, str]: A tuple containing the agent name, task, and result.
         """
-        result = agent.run(task, *args, **kwargs)
-        # Assuming agent.run() is a blocking call
-        return agent.agent_name, task, result
+        try:
+            result = agent.run(task=task, *args, **kwargs)
+            # Assuming agent.run() is a blocking call
+            return agent.agent_name, task, result
+        except Exception as e:
+            logger.error(
+                f"Error running task for {agent.agent_name}: {e}"
+            )
+            return agent.agent_name, task, str(e)
 
     def _track_output(self, agent_name: str, task: str, result: str):
         """
diff --git a/swarms/structs/swarm_router.py b/swarms/structs/swarm_router.py
index 1384b4f6..a0f73a98 100644
--- a/swarms/structs/swarm_router.py
+++ b/swarms/structs/swarm_router.py
@@ -2,7 +2,6 @@ import uuid
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Literal, Union
 
-from doc_master import doc_master
 from pydantic import BaseModel, Field
 from tenacity import retry, stop_after_attempt, wait_fixed
 
@@ -175,29 +174,12 @@ class SwarmRouter:
         if self.rules is not None:
             self.handle_rules()
 
-        # if self.documents is not None:
-        #     self.handle_docs()
-
         # let's make a function that checks the agents parameter and disables clusterops
 
     def deactivate_clusterops(self):
         for agent in self.agents:
             agent.do_not_use_cluster_ops = True
 
-    def handle_docs(self):
-        # Process all documents in parallel using list comprehension
-        data = "".join(
-            [doc_master(file_path=doc) for doc in self.documents]
-        )
-
-        # Update all agents' prompts at once
-        doc_prompt = f"##### Documents Available ########## {data}"
-        for agent in self.agents:
-            agent.system_prompt += doc_prompt
-
-        # Add documents to the logs
-        # self.logs.append(Document(file_path=self.documents, data=data))
-
     def activate_shared_memory(self):
         logger.info("Activating shared memory with all agents ")
 
diff --git a/swarms/utils/add_docs_to_agents.py b/swarms/utils/add_docs_to_agents.py
deleted file mode 100644
index 85e3076c..00000000
--- a/swarms/utils/add_docs_to_agents.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from pathlib import Path
-from typing import Any, List, Optional, Union
-
-from doc_master import doc_master
-from tenacity import retry, stop_after_attempt, wait_exponential
-
-from swarms.utils.loguru_logger import initialize_logger
-
-logger = initialize_logger(log_folder="add_docs_to_agents")
-
-
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-)
-def _process_document(doc_path: Union[str, Path]) -> str:
-    """Safely process a single document with retries.
-
-    Args:
-        doc_path: Path to the document to process
-
-    Returns:
-        Processed document text
-
-    Raises:
-        Exception: If document processing fails after retries
-    """
-    try:
-        return doc_master(
-            file_path=str(doc_path), output_type="string"
-        )
-    except Exception as e:
-        logger.error(
-            f"Error processing document {doc_path}: {str(e)}"
-        )
-        raise
-
-
-def handle_input_docs(
-    agents: Any,
-    docs: Optional[List[Union[str, Path]]] = None,
-    doc_folder: Optional[Union[str, Path]] = None,
-    max_workers: int = 4,
-    chunk_size: int = 1000000,
-) -> Any:
-    """
-    Add document content to agent prompts with improved reliability and performance.
-
-    Args:
-        agents: Dictionary mapping agent names to Agent objects
-        docs: List of document paths
-        doc_folder: Path to folder containing documents
-        max_workers: Maximum number of parallel document processing workers
-        chunk_size: Maximum characters to process at once to avoid memory issues
-
-    Raises:
-        ValueError: If neither docs nor doc_folder is provided
-        RuntimeError: If document processing fails
-    """
-    if not agents:
-        logger.warning(
-            "No agents provided, skipping document distribution"
-        )
-        return
-
-    if not docs and not doc_folder:
-        logger.warning(
-            "No documents or folder provided, skipping document distribution"
-        )
-        return
-
-    logger.info("Starting document distribution to agents")
-
-    try:
-        processed_docs = []
-
-        # Process individual documents in parallel
-        if docs:
-            with ThreadPoolExecutor(
-                max_workers=max_workers
-            ) as executor:
-                future_to_doc = {
-                    executor.submit(_process_document, doc): doc
-                    for doc in docs
-                }
-
-                for future in as_completed(future_to_doc):
-                    doc = future_to_doc[future]
-                    try:
-                        processed_docs.append(future.result())
-                    except Exception as e:
-                        logger.error(
-                            f"Failed to process document {doc}: {str(e)}"
-                        )
-                        raise RuntimeError(
-                            f"Document processing failed: {str(e)}"
-                        )
-
-        # Process folder if specified
-        elif doc_folder:
-            try:
-                folder_content = doc_master(
-                    folder_path=str(doc_folder), output_type="string"
-                )
-                processed_docs.append(folder_content)
-            except Exception as e:
-                logger.error(
-                    f"Failed to process folder {doc_folder}: {str(e)}"
-                )
-                raise RuntimeError(
-                    f"Folder processing failed: {str(e)}"
-                )
-
-        # Combine and chunk the processed documents
-        combined_data = "\n".join(processed_docs)
-
-        # Update agent prompts in chunks to avoid memory issues
-        for agent in agents.values():
-            try:
-                for i in range(0, len(combined_data), chunk_size):
-                    chunk = combined_data[i : i + chunk_size]
-                    if i == 0:
-                        agent.system_prompt += (
-                            "\nDocuments:\n" + chunk
-                        )
-                    else:
-                        agent.system_prompt += chunk
-            except Exception as e:
-                logger.error(
-                    f"Failed to update agent prompt: {str(e)}"
-                )
-                raise RuntimeError(
-                    f"Agent prompt update failed: {str(e)}"
-                )
-
-        logger.info(
-            f"Successfully added documents to {len(agents)} agents"
-        )
-
-        return agents
-
-    except Exception as e:
-        logger.error(f"Document distribution failed: {str(e)}")
-        raise RuntimeError(f"Document distribution failed: {str(e)}")
diff --git a/test_agentrearrange.py b/test_agentrearrange.py
new file mode 100644
index 00000000..ba5bdad0
--- /dev/null
+++ b/test_agentrearrange.py
@@ -0,0 +1,286 @@
+import os
+import traceback
+from datetime import datetime
+from typing import Callable, Dict, List, Optional
+
+from loguru import logger
+from swarm_models import OpenAIChat
+
+from swarms.structs.agent import Agent
+from swarms.structs.rearrange import AgentRearrange
+
+
+class TestResult:
+    """Class to store test results and metadata"""
+    def __init__(self, test_name: str):
+        self.test_name = test_name
+        self.start_time = datetime.now()
+        self.end_time = None
+        self.success = False
+        self.error = None
+        self.traceback = None
+        self.function_output = None
+
+    def complete(self, success: bool, error: Optional[Exception] = None):
+        """Complete the test execution with results"""
+        self.end_time = datetime.now()
+        self.success = success
+        if error:
+            self.error = str(error)
+            self.traceback = traceback.format_exc()
+
+    def duration(self) -> float:
+        """Calculate test duration in seconds"""
+        if self.end_time:
+            return (self.end_time - self.start_time).total_seconds()
+        return 0
+
+def run_test(test_func: Callable) -> TestResult:
+    """
+    Decorator to run tests with error handling and logging
+    
+    Args:
+        test_func (Callable): Test function to execute
+        
+    Returns:
+        TestResult: Object containing test execution details
+    """
+    def wrapper(*args, **kwargs) -> TestResult:
+        result = TestResult(test_func.__name__)
+        logger.info(f"\n{'='*20} Running test: {test_func.__name__} {'='*20}")
+        
+        try:
+            output = test_func(*args, **kwargs)
+            result.function_output = output
+            result.complete(success=True)
+            logger.success(f"✅ Test {test_func.__name__} passed successfully")
+            
+        except Exception as e:
+            result.complete(success=False, error=e)
+            logger.error(f"❌ Test {test_func.__name__} failed with error: {str(e)}")
+            logger.error(f"Traceback: {traceback.format_exc()}")
+            
+        logger.info(f"Test duration: {result.duration():.2f} seconds\n")
+        return result
+        
+    return wrapper
+
+def create_functional_agents() -> List[Agent]:
+    """
+    Create a list of functional agents with real LLM integration for testing.
+    Using OpenAI's GPT model for realistic agent behavior testing.
+    """
+    # Initialize OpenAI Chat model
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        logger.warning("No OpenAI API key found. Using mock agents instead.")
+        return [create_mock_agent("TestAgent1"), create_mock_agent("TestAgent2")]
+        
+    try:
+        model = OpenAIChat(
+            api_key=api_key,
+            model_name="gpt-4o",
+            temperature=0.1
+        )
+        
+        # Create boss agent
+        boss_agent = Agent(
+            agent_name="BossAgent",
+            system_prompt="""
+            You are the BossAgent responsible for managing and overseeing test scenarios.
+            Your role is to coordinate tasks between agents and ensure efficient collaboration.
+            Analyze inputs, break down tasks, and provide clear directives to other agents.
+            Maintain a structured approach to task management and result compilation.
+            """,
+            llm=model,
+            max_loops=1,
+            dashboard=False,
+            streaming_on=True,
+            verbose=True,
+            stopping_token="<DONE>",
+            state_save_file_type="json",
+            saved_state_path="test_boss_agent.json",
+        )
+        
+        # Create analysis agent
+        analysis_agent = Agent(
+            agent_name="AnalysisAgent",
+            system_prompt="""
+            You are the AnalysisAgent responsible for detailed data processing and analysis.
+            Your role is to examine input data, identify patterns, and provide analytical insights.
+            Focus on breaking down complex information into clear, actionable components.
+            """,
+            llm=model,
+            max_loops=1,
+            dashboard=False,
+            streaming_on=True,
+            verbose=True,
+            stopping_token="<DONE>",
+            state_save_file_type="json",
+            saved_state_path="test_analysis_agent.json",
+        )
+        
+        # Create summary agent
+        summary_agent = Agent(
+            agent_name="SummaryAgent",
+            system_prompt="""
+            You are the SummaryAgent responsible for consolidating and summarizing information.
+            Your role is to take detailed analysis and create concise, actionable summaries.
+            Focus on highlighting key points and ensuring clarity in communication.
+            """,
+            llm=model,
+            max_loops=1,
+            dashboard=False,
+            streaming_on=True,
+            verbose=True,
+            stopping_token="<DONE>",
+            state_save_file_type="json",
+            saved_state_path="test_summary_agent.json",
+        )
+        
+        logger.info("Successfully created functional agents with LLM integration")
+        return [boss_agent, analysis_agent, summary_agent]
+        
+    except Exception as e:
+        logger.error(f"Failed to create functional agents: {str(e)}")
+        logger.warning("Falling back to mock agents")
+        return [create_mock_agent("TestAgent1"), create_mock_agent("TestAgent2")]
+
+def create_mock_agent(name: str) -> Agent:
+    """Create a mock agent for testing when LLM integration is not available"""
+    return Agent(
+        agent_name=name,
+        system_prompt=f"You are a test agent named {name}",
+        llm=None
+    )
+
+@run_test
+def test_init():
+    """Test AgentRearrange initialization with functional agents"""
+    logger.info("Creating agents for initialization test")
+    agents = create_functional_agents()
+    
+    rearrange = AgentRearrange(
+        name="TestRearrange",
+        agents=agents,
+        flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}"
+    )
+    
+    assert rearrange.name == "TestRearrange"
+    assert len(rearrange.agents) == 3
+    assert rearrange.flow == f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}"
+    
+    logger.info(f"Initialized AgentRearrange with {len(agents)} agents")
+    return True
+
+@run_test
+def test_validate_flow():
+    """Test flow validation logic"""
+    agents = create_functional_agents()
+    rearrange = AgentRearrange(
+        agents=agents,
+        flow=f"{agents[0].agent_name} -> {agents[1].agent_name}"
+    )
+    
+    logger.info("Testing valid flow pattern")
+    valid = rearrange.validate_flow()
+    assert valid is True
+    
+    logger.info("Testing invalid flow pattern")
+    rearrange.flow = f"{agents[0].agent_name} {agents[1].agent_name}"  # Missing arrow
+    try:
+        rearrange.validate_flow()
+        assert False, "Should have raised ValueError"
+    except ValueError as e:
+        logger.info(f"Successfully caught invalid flow error: {str(e)}")
+        assert True
+        
+    return True
+
+@run_test
+def test_add_remove_agent():
+    """Test adding and removing agents from the swarm"""
+    agents = create_functional_agents()
+    rearrange = AgentRearrange(agents=agents[:2])  # Start with first two agents
+    
+    logger.info("Testing agent addition")
+    new_agent = agents[2]  # Use the third agent as new agent
+    rearrange.add_agent(new_agent)
+    assert new_agent.agent_name in rearrange.agents
+    
+    logger.info("Testing agent removal")
+    rearrange.remove_agent(new_agent.agent_name)
+    assert new_agent.agent_name not in rearrange.agents
+    
+    return True
+
+@run_test
+def test_basic_run():
+    """Test basic task execution with the swarm"""
+    agents = create_functional_agents()
+    rearrange = AgentRearrange(
+        name="TestSwarm",
+        agents=agents,
+        flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}",
+        max_loops=1
+    )
+    
+    test_task = "Analyze this test message and provide a brief summary."
+    logger.info(f"Running test task: {test_task}")
+    
+    try:
+        result = rearrange.run(test_task)
+        assert result is not None
+        logger.info(f"Successfully executed task with result length: {len(str(result))}")
+        return True
+    except Exception as e:
+        logger.error(f"Task execution failed: {str(e)}")
+        raise
+
+def run_all_tests() -> Dict[str, TestResult]:
+    """
+    Run all test cases and collect results
+    
+    Returns:
+        Dict[str, TestResult]: Dictionary mapping test names to their results
+    """
+    logger.info("\n🚀 Starting AgentRearrange test suite execution")
+    test_functions = [
+        test_init,
+        test_validate_flow,
+        test_add_remove_agent,
+        test_basic_run
+    ]
+    
+    results = {}
+    for test in test_functions:
+        result = test()
+        results[test.__name__] = result
+        
+    # Log summary
+    total_tests = len(results)
+    passed_tests = sum(1 for r in results.values() if r.success)
+    failed_tests = total_tests - passed_tests
+    
+    logger.info("\n📊 Test Suite Summary:")
+    logger.info(f"Total Tests: {total_tests}")
+    print(f"✅ Passed: {passed_tests}")
+    
+    if failed_tests > 0:
+        logger.error(f"❌ Failed: {failed_tests}")
+    
+    # Detailed failure information
+    if failed_tests > 0:
+        logger.error("\n❌ Failed Tests Details:")
+        for name, result in results.items():
+            if not result.success:
+                logger.error(f"\n{name}:")
+                logger.error(f"Error: {result.error}")
+                logger.error(f"Traceback: {result.traceback}")
+    
+    return results
+
+if __name__ == "__main__":
+    print("🌟 Starting AgentRearrange Test Suite")
+    results = run_all_tests()
+    print("🏁 Test Suite Execution Completed")
\ No newline at end of file