diff --git a/chat.py b/chat.py
new file mode 100644
index 00000000..134f7f16
--- /dev/null
+++ b/chat.py
@@ -0,0 +1,11 @@
+from swarms import Orchestrator, Worker
+
+# Instantiate the Orchestrator with 10 agents
+orchestrator = Orchestrator(
+    Worker, 
+    agent_list=[Worker]*10, 
+    task_queue=[]
+)
+
+# Agent 1 sends a message to Agent 2
+orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
\ No newline at end of file
diff --git a/swarms/__init__.py b/swarms/__init__.py
index 8c105356..fcd31eb2 100644
--- a/swarms/__init__.py
+++ b/swarms/__init__.py
@@ -4,6 +4,7 @@ print(logo2)
 
 # worker
 import swarms.workers
+from swarms.workers.worker import Worker
 
 #boss
 # from swarms.boss.boss_node import Boss
@@ -16,6 +17,6 @@ import swarms.structs
 
 # swarms
 import swarms.swarms
-
+from swarms.swarms.orchestrate import Orchestrator
 #agents
 import swarms.agents
\ No newline at end of file
diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py
index 6a1cfea3..24c0ae9e 100644
--- a/swarms/agents/__init__.py
+++ b/swarms/agents/__init__.py
@@ -2,7 +2,6 @@
 
 
 ###########
-# #tools
 # from swarms.tools.base import BaseTool, Tool, StructuredTool, ToolWrapper, BaseToolSet, ToolCreator, GlobalToolsCreator, SessionToolsCreator, ToolsFactory
 # from swarms.tools.autogpt import pushd, process_csv, async_load_playwright, run_async, browse_web_page, WebpageQATool, web_search, query_website_tool
 # from swarms.tools.exit_conversation import ExitConversation
@@ -15,7 +14,7 @@
 
 
 #agents
-from swarms.agents.profitpilot import ProfitPilot
+# from swarms.agents.profitpilot import ProfitPilot
 from swarms.agents.aot import AoTAgent
-from swarms.agents.multi_modal_agent import MultiModalVisualAgent
-from swarms.agents.omni_modal_agent import OmniModalAgent
\ No newline at end of file
+# from swarms.agents.multi_modal_agent import MultiModalVisualAgent
+# from swarms.agents.omni_modal_agent import OmniModalAgent
\ No newline at end of file
diff --git a/swarms/swarms/orchestrate.py b/swarms/swarms/orchestrate.py
index fde746b7..083a8f7b 100644
--- a/swarms/swarms/orchestrate.py
+++ b/swarms/swarms/orchestrate.py
@@ -2,8 +2,8 @@ import logging
 import queue
 import threading
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Dict, List
 from enum import Enum
+from typing import Any, Dict, List
 
 import chromadb
 from chromadb.utils import embedding_functions
@@ -71,7 +71,7 @@ class Orchestrator:
     
     ###Usage
     ```
-    from exa import Orchestrator 
+    from swarms import Orchestrator 
     
     # Instantiate the Orchestrator with 10 agents
     orchestrator = Orchestrator(llm, agent_list=[llm]*10, task_queue=[])
diff --git a/swarms/swarms/scable_groupchat.py b/swarms/swarms/scable_groupchat.py
new file mode 100644
index 00000000..9c59882e
--- /dev/null
+++ b/swarms/swarms/scable_groupchat.py
@@ -0,0 +1,213 @@
+import logging
+from enum import Enum
+from typing import Any
+
+from chromadb.utils import embedding_functions
+
+from swarms.workers.worker import Worker
+
+
+class TaskStatus(Enum):
+    QUEUED = 1
+    RUNNING = 2
+    COMPLETED = 3
+    FAILED = 4
+
+class ScalableGroupChat:
+    """
+    The Orchestrator takes in an agent, worker, or boss as input 
+    then handles all the logic for
+    - task creation,
+    - task assignment,
+    - and task compeletion.
+
+    And, the communication for millions of agents to chat with eachother through
+    a vector database that each agent has access to chat with.
+
+    Each LLM agent chats with the orchestrator through a dedicated 
+    communication layer. The orchestrator assigns tasks to each LLM agent,
+    which the agents then complete and return. 
+
+    This setup allows for a high degree of flexibility, scalability, and robustness.
+
+    In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Database
+    for communication. This database could store and manage 
+    the high-dimensional vectors produced by each LLM agent.
+
+    Strengths: This approach would allow for similarity-based lookup and matching of 
+    LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.
+
+    Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance. 
+    It might also require significant computational resources, 
+    depending on the volume of data being handled and the complexity of the vectors. 
+    The handling and transmission of high-dimensional vectors could also pose challenges 
+    in terms of network load.
+
+    # Orchestrator
+    * Takes in an agent class with vector store, 
+    then handles all the communication and scales 
+    up a swarm with number of agents and handles task assignment and task completion
+
+    from swarms import OpenAI, Orchestrator, Swarm
+
+    orchestrated = Orchestrate(OpenAI, nodes=40) #handles all the task assignment and allocation and agent communication using a vectorstore as a universal communication layer and also handlles the task completion logic
+
+    Objective = "Make a business website for a marketing consultancy"
+
+    Swarms = Swarms(orchestrated, auto=True, Objective))
+    ```
+
+    In terms of architecture, the swarm might look something like this:
+
+    ```
+                                            (Orchestrator)
+                                                /        \
+            Tools + Vector DB -- (LLM Agent)---(Communication Layer)       (Communication Layer)---(LLM Agent)-- Tools + Vector DB 
+                /                  |                                           |                 \
+    (Task Assignment)      (Task Completion)                    (Task Assignment)       (Task Completion)
+
+    
+    ###Usage
+    ```
+    from swarms import Orchestrator 
+    
+    # Instantiate the Orchestrator with 10 agents
+    orchestrator = Orchestrator(llm, agent_list=[llm]*10, task_queue=[])
+
+    # Add tasks to the Orchestrator
+    tasks = [{"content": f"Write a short story about a {animal}."} for animal in ["cat", "dog", "bird", "fish", "lion", "tiger", "elephant", "giraffe", "monkey", "zebra"]]
+    orchestrator.assign_tasks(tasks)
+
+    # Run the Orchestrator
+    orchestrator.run()
+
+    # Retrieve the results
+    for task in tasks:
+    print(orchestrator.retrieve_result(id(task)))
+    ```
+    """
+    def __init__(
+        self,
+        worker_count: int = 5,
+        collection_name: str = "swarm",
+        api_key: str = None,
+        model_name: str = None,
+        worker = None
+    ):
+        self.workers = []
+        self.worker_count = worker_count
+
+        # Create a list of Worker instances with unique names
+        for i in range(worker_count):
+            self.workers.append(Worker(openai_api_key="", ai_name=f"Worker-{i}"))
+        
+    def embed(self, input, api_key, model_name):
+        openai = embedding_functions.OpenAIEmbeddingFunction(
+            api_key=api_key,
+            model_name=model_name
+        )
+        embedding = openai(input)
+        return embedding
+                
+    
+    # @abstractmethod
+    def retrieve_results(
+        self, 
+        agent_id: int
+    ) -> Any:
+        """Retrieve results from a specific agent"""
+
+        try:
+            #Query the vector database for documents created by the agents 
+            results = self.collection.query(
+                query_texts=[str(agent_id)], 
+                n_results=10
+            )
+
+            return results
+        except Exception as e:
+            logging.error(f"Failed to retrieve results from agent {agent_id}. Error {e}")
+            raise
+    
+    # @abstractmethod
+    def update_vector_db(self, data) -> None:
+        """Update the vector database"""
+
+        try:
+            self.collection.add(
+                embeddings=[data["vector"]],
+                documents=[str(data["task_id"])],
+                ids=[str(data["task_id"])]
+            )
+
+        except Exception as e:
+            logging.error(f"Failed to update the vector database. Error: {e}")
+            raise
+
+
+    # @abstractmethod
+    def get_vector_db(self):
+        """Retrieve the vector database"""
+        return self.collection
+    
+
+    def append_to_db(
+        self, 
+        result: str
+    ):
+        """append the result of the swarm to a specifici collection in the database"""
+
+        try:
+            self.collection.add(
+                documents=[result],
+                ids=[str(id(result))]
+            )
+
+        except Exception as e:
+            logging.error(f"Failed to append the agent output to database. Error: {e}")
+            raise
+
+    
+    
+    def chat(
+        self,
+        sender_id: int,
+        receiver_id: int,
+        message: str
+    ):
+        """
+        
+        Allows the agents to chat with eachother thrught the vectordatabase
+
+        # Instantiate the Orchestrator with 10 agents
+        orchestrator = Orchestrator(
+            llm, 
+            agent_list=[llm]*10, 
+            task_queue=[]
+        )
+
+        # Agent 1 sends a message to Agent 2
+        orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
+            
+        """
+        if sender_id < 0 or sender_id >= self.worker_count or receiver_id < 0 or receiver_id >= self.worker_count:
+            raise ValueError("Invalid sender or receiver ID")
+        
+        message_vector = self.embed(
+            message,
+            self.api_key,
+            self.model_name
+        )
+
+        #store the mesage in the vector database
+        self.collection.add(
+            embeddings=[message_vector],
+            documents=[message],
+            ids=[f"{sender_id}_to_{receiver_id}"]
+        )
+
+        self.run(
+            objective=f"chat with agent {receiver_id} about {message}"
+        )
+
+