diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 6e563af26..c0ad132e3 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -1,6 +1,8 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
 
+# [ ] TODO [pep 458](https://blog.pypi.org/posts/2024-11-14-pypi-now-supports-digital-attestations/)
+
 name: Python package
 
 on:
@@ -16,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4
diff --git a/.gitignore b/.gitignore
index 89b0cdc7f..9f6e25b6b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,7 +8,8 @@ audio/
 video/
 artifacts_three
 dataframe/
-
+.ruff_cache
+.pytest_cache
 static/generated
 runs
 Financial-Analysis-Agent_state.json
diff --git a/README.md b/README.md
index 6469883d8..378b8c54b 100644
--- a/README.md
+++ b/README.md
@@ -131,13 +131,24 @@ The `run` method is the primary entry point for executing tasks with an `Agent`
 
 ```python
 from swarms import Agent
+from swarms.models import OpenAIChat
+
+model = OpenAIChat(
+    openai_api_key="your-api-key",
+    model_name="gpt-4o-mini",
+    temperature=0.1
+)
 
 agent = Agent(
     agent_name="Stock-Analysis-Agent",
-    model_name="gpt-4o-mini",
+    system_prompt="You are a stock market analysis expert. Analyze market trends and provide insights.",
+    llm=model,
     max_loops="auto",
     interactive=True,
     streaming_on=True,
+    verbose=True,
+    autosave=True,
+    saved_state_path="stock_analysis_agent.json"
 )
 
 agent.run("What is the current market trend for tech stocks?")
@@ -150,29 +161,31 @@ The `Agent` class offers a range of settings to tailor its behavior to specific
 | Setting | Description | Default Value |
 | --- | --- | --- |
 | `agent_name` | The name of the agent. | "DefaultAgent" |
-| `system_prompt` | The system prompt to use for the agent. | "Default system prompt." |
-| `llm` | The language model to use for processing tasks. | `OpenAIChat` instance |
+| `system_prompt` | The system prompt to use for the agent. | None |
+| `llm` | The language model to use for processing tasks. | Required |
 | `max_loops` | The maximum number of loops to execute for a task. | 1 |
 | `autosave` | Enables or disables autosaving of the agent's state. | False |
 | `dashboard` | Enables or disables the dashboard for the agent. | False |
 | `verbose` | Controls the verbosity of the agent's output. | False |
+| `streaming_on` | Enables or disables response streaming. | True |
 | `dynamic_temperature_enabled` | Enables or disables dynamic temperature adjustment for the language model. | False |
-| `saved_state_path` | The path to save the agent's state. | "agent_state.json" |
-| `user_name` | The username associated with the agent. | "default_user" |
-| `retry_attempts` | The number of retry attempts for failed tasks. | 1 |
-| `context_length` | The maximum length of the context to consider for tasks. | 200000 |
-| `return_step_meta` | Controls whether to return step metadata in the output. | False |
-| `output_type` | The type of output to return (e.g., "json", "string"). | "string" |
+| `saved_state_path` | The path to save the agent's state. | None |
+| `user_name` | The username associated with the agent. | "User" |
+| `retry_attempts` | The number of retry attempts for failed tasks. | 3 |
+| `context_length` | The maximum length of the context to consider for tasks. | 8192 |
+| `multi_modal` | Enables or disables multimodal support. | False |
+| `code_interpreter` | Enables or disables code execution. | False |
+| `self_healing_enabled` | Enables or disables error recovery. | False |
+| `sentiment_threshold` | The threshold for response evaluation. | 0.7 |
+| `tags` | A list of strings for categorizing the agent. | None |
+| `use_cases` | A list of dictionaries documenting the agent's use cases. | None |
 
 
 ```python
 import os
 from swarms import Agent
-from swarm_models import OpenAIChat
-
-from swarms.prompts.finance_agent_sys_prompt import (
-    FINANCIAL_AGENT_SYS_PROMPT,
-)
+from swarms.models import OpenAIChat
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -180,7 +193,7 @@ load_dotenv()
 # Get the OpenAI API key from the environment variable
 api_key = os.getenv("OPENAI_API_KEY")
 
-# Create an instance of the OpenAIChat class
+# Create model instance
 model = OpenAIChat(
     openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
 )
@@ -194,21 +207,29 @@ agent = Agent(
     autosave=True,
     dashboard=False,
     verbose=True,
+    streaming_on=True,
     dynamic_temperature_enabled=True,
     saved_state_path="finance_agent.json",
     user_name="swarms_corp",
-    retry_attempts=1,
+    retry_attempts=3,
     context_length=200000,
-    return_step_meta=False,
-    output_type="string",
-    streaming_on=False,
+    multi_modal=False,
+    code_interpreter=True,
+    self_healing_enabled=True,
+    sentiment_threshold=0.7,
+    tags=["finance", "analysis"],
+    use_cases=[{"name": "Financial Analysis", "description": "Analyze financial data and provide insights"}]
 )
 
+# Modern method usage
+agent.update_system_prompt("New system prompt")
+agent.update_max_loops(5)
+agent.update_loop_interval(2)
+agent.update_retry_attempts(5)
+print(agent.get_llm_parameters())
 
-agent.run(
-    "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
-)
-
+# Run the agent
+agent.run("Analyze the latest quarterly financial report for Tesla")
 ```
 -----
 ### Integrating RAG with Swarms for Enhanced Long-Term Memory
@@ -231,19 +252,22 @@ graph TD
 ```python
 import os
 
-from swarms_memory import ChromaDB
+from swarms.memory import ChromaDB
 
 # Initialize the ChromaDB client for long-term memory management
 chromadb = ChromaDB(
     metric="cosine",  # Metric for similarity measurement
     output_dir="finance_agent_rag",  # Directory for storing RAG data
-    # docs_folder="artifacts",  # Uncomment and specify the folder containing your documents
+    limit_tokens=1000,  # Maximum tokens per query
+    n_results=1,  # Number of results to retrieve
+    docs_folder=None,  # Optional folder containing documents to add
+    verbose=False  # Enable verbose logging if needed
 )
 ```
 
 **Step 2: Define the Model**
 ```python
-from swarm_models import Anthropic
+from swarms.models import Anthropic
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
@@ -378,7 +402,7 @@ The following is an example of an agent that intakes a pydantic basemodel and ou
 ```python
 from pydantic import BaseModel, Field
 from swarms import Agent
-from swarm_models import Anthropic
+from swarms.models import OpenAIChat
 
 
 # Initialize the schema for the person's information
@@ -410,7 +434,11 @@ agent = Agent(
     ),
     # Set the tool schema to the JSON string -- this is the key difference
     tool_schema=tool_schema,
-    llm=Anthropic(),
+    llm=OpenAIChat(
+        openai_api_key="your-api-key",
+        model_name="gpt-4o",
+        temperature=0.1
+    ),
     max_loops=3,
     autosave=True,
     dashboard=False,
@@ -442,6 +470,7 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
 import os
 from dotenv import load_dotenv
 from swarms import Agent
+from swarms.models import OpenAIChat
 
 from swarm_models import GPT4VisionAPI
 
@@ -450,7 +479,7 @@ load_dotenv()
 
 
 # Initialize the language model
-llm = GPT4VisionAPI(
+llm = OpenAIChat(
     openai_api_key=os.environ.get("OPENAI_API_KEY"),
     max_tokens=500,
 )
@@ -552,7 +581,7 @@ Steps:
 
 For example, here's an example on how to create an agent from griptape.
 
-Here’s how you can create a custom **Griptape** agent that integrates with the **Swarms** framework by inheriting from the `Agent` class in **Swarms** and overriding the `run(task: str) -> str` method.
+Here's how you can create a custom **Griptape** agent that integrates with the **Swarms** framework by inheriting from the `Agent` class in **Swarms** and overriding the `run(task: str) -> str` method.
 
 
 ```python
@@ -694,7 +723,7 @@ In this example, each `Agent` represents a task that is executed sequentially. T
 ```python
 import os
 from swarms import Agent, SequentialWorkflow
-from swarm_models import OpenAIChat
+from swarms.models import OpenAIChat
 
 # model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
 company = "Nvidia"
@@ -911,7 +940,7 @@ The `run` method returns the final output after all agents have processed the in
 from swarms import Agent, AgentRearrange
 
 
-from swarm_models import Anthropic
+from swarm.models import Anthropic
 
 # Initialize the director agent
 
@@ -1288,7 +1317,7 @@ The `run` method returns a dictionary containing the outputs of each agent that
 ```python
 import os
 from swarms import Agent
-from swarm_models import OpenAIChat
+from swarms.models import OpenAIChat
 from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
 
 # Define custom system prompts for each social media platform
@@ -1613,7 +1642,7 @@ The `SwarmRouter` class is a flexible routing system designed to manage differen
 import os
 from dotenv import load_dotenv
 from swarms import Agent
-from swarm_models import OpenAIChat
+from swarms.models import OpenAIChat
 from swarms.structs.swarm_router import SwarmRouter, SwarmType
 
 load_dotenv()
diff --git a/docs/swarms/install/install.md b/docs/swarms/install/install.md
index f69a09bdd..9d52d84e5 100644
--- a/docs/swarms/install/install.md
+++ b/docs/swarms/install/install.md
@@ -127,7 +127,7 @@ Before you begin, ensure you have the following installed:
                 poetry install --extras "desktop"
                 ```
 
-=== "Using Docker"
+=== "Using Docker COMING SOON [DOES NOT WORK YET]"
 
     Docker is an excellent option for creating isolated and reproducible environments, suitable for both development and production.
 
diff --git a/swarms/structs/auto_swarm_builder.py b/swarms/structs/auto_swarm_builder.py
index 8d981dda0..93e542fd4 100644
--- a/swarms/structs/auto_swarm_builder.py
+++ b/swarms/structs/auto_swarm_builder.py
@@ -1,5 +1,3 @@
-from loguru import logger
-
 import os
 from typing import List