diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 87490930..b887b9b7 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -103,9 +103,14 @@ nav:
   - Usage Examples:
     - Build an Agent: "diy_your_own_agent.md"
     - Build an Agent with tools: "examples/tools_agents.md"
+    # Add an examples with building agents
+    # Add multiple blogs on orchestrating agents with different types of frameworks
   - Why does Swarms Exist?:
     - Why Swarms? Orchestrating Agents for Enterprise Automation: "why.md"
     - Limitations of Individual Agents: "limits_of_individual_agents.md"
+  - References:
+    - Agent Glossary: "swarms/glossary.md"
+    - List of The Best Multi-Agent Papers: "swarms/papers.md"
 - Swarms Cloud API:
   - Overview: "swarms_cloud/main.md"
   - Available Models: "swarms_cloud/available_models.md"
diff --git a/docs/swarms/glossary.md b/docs/swarms/glossary.md
new file mode 100644
index 00000000..cc59af4a
--- /dev/null
+++ b/docs/swarms/glossary.md
@@ -0,0 +1,48 @@
+# Glossary of Terms
+
+**Agent**:  
+An LLM (Large Language Model) equipped with tools and memory, operating with a specific objective in a loop. An agent can perform tasks, interact with other agents, and utilize external tools and memory systems to achieve its goals.
+
+**Swarms**:  
+A group of more than two agents working together and communicating to accomplish a shared objective. Swarms enable complex, collaborative tasks that leverage the strengths of multiple agents.
+
+**Tool**:  
+A Python function that is converted into a function call, allowing agents to perform specific actions or access external resources. Tools enhance the capabilities of agents by providing specialized functionalities.
+
+**Memory System**:  
+A system for managing information retrieval and storage, often implemented as a Retrieval-Augmented Generation (RAG) system or a memory vector database. Memory systems enable agents to recall previous interactions, store new information, and improve decision-making based on historical data.
+
+**LLM (Large Language Model)**:  
+A type of AI model designed to understand and generate human-like text. LLMs, such as GPT-3 or GPT-4, are used as the core computational engine for agents.
+
+**System Prompt**:  
+A predefined prompt that sets the context and instructions for an agent's task. The system prompt guides the agent's behavior and response generation.
+
+**Max Loops**:  
+The maximum number of iterations an agent will perform to complete its task. This parameter helps control the extent of an agent's processing and ensures tasks are completed efficiently.
+
+**Dashboard**:  
+A user interface that provides real-time monitoring and control over the agents and their activities. Dashboards can display agent status, logs, and performance metrics.
+
+**Streaming On**:  
+A setting that enables agents to stream their output incrementally, providing real-time feedback as they process tasks. This feature is useful for monitoring progress and making adjustments on the fly.
+
+**Verbose**:  
+A setting that controls the level of detail in an agent's output and logging. When verbose mode is enabled, the agent provides more detailed information about its operations and decisions.
+
+**Multi-modal**:  
+The capability of an agent to process and integrate multiple types of data, such as text, images, and audio. Multi-modal agents can handle more complex tasks that require diverse inputs.
+
+**Autosave**:  
+A feature that automatically saves the agent's state and progress at regular intervals. Autosave helps prevent data loss and allows for recovery in case of interruptions.
+
+**Flow**:  
+The predefined sequence in which agents in a swarm interact and process tasks. The flow ensures that each agent's output is appropriately passed to the next agent, facilitating coordinated efforts.
+
+**Long Term Memory**:  
+A component of the memory system that retains information over extended periods, enabling agents to recall and utilize past interactions and experiences.
+
+**Output Schema**:  
+A structured format for the output generated by agents, often defined using data models like Pydantic's BaseModel. Output schemas ensure consistency and clarity in the information produced by agents.
+
+By understanding these terms, you can effectively build and orchestrate agents and swarms, leveraging their capabilities to perform complex, collaborative tasks.
\ No newline at end of file
diff --git a/docs/swarms/papers.md b/docs/swarms/papers.md
new file mode 100644
index 00000000..3df45299
--- /dev/null
+++ b/docs/swarms/papers.md
@@ -0,0 +1,3 @@
+# awesome-multi-agent-papers
+
+An awesome list of multi-agent papers that show you various swarm architectures and much more. [Get started](https://github.com/kyegomez/awesome-multi-agent-papers)
\ No newline at end of file
diff --git a/playground/demos/octomology_swarm/swarm_of_gpt4o_agents_for_octomogly.py b/playground/demos/octomology_swarm/api.py
similarity index 61%
rename from playground/demos/octomology_swarm/swarm_of_gpt4o_agents_for_octomogly.py
rename to playground/demos/octomology_swarm/api.py
index 19aead70..acb9fec5 100644
--- a/playground/demos/octomology_swarm/swarm_of_gpt4o_agents_for_octomogly.py
+++ b/playground/demos/octomology_swarm/api.py
@@ -1,16 +1,34 @@
 import os
-
 from dotenv import load_dotenv
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
 
 from swarms import Agent
-from swarms.models import Anthropic
+from swarms.models import OpenAIChat
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 from swarms.structs.rearrange import AgentRearrange
+from fastapi import FastAPI
+from typing import Optional, List, Dict, Any
 
 # Load the environment variables
 load_dotenv()
 
+# LLM
+llm = GPT4VisionAPI(
+    model_name="gpt-4-1106-vision-preview",
+    max_tokens=3000,
+)
+openai = OpenAIChat(
+    openai_api_key=os.getenv("OPENAI_API_KEY"),
+    max_tokens=3000,
+)
+
+
+# Setup the FastAPI app
+app = FastAPI()
+
+
+
 
 def DIAGNOSIS_SYSTEM_PROMPT() -> str:
     return """
@@ -60,86 +78,103 @@ def TREATMENT_PLAN_SYSTEM_PROMPT() -> str:
     """
 
 
-class DiagnosisSchema(BaseModel):
-    image_name: str = Field(
-        ...,
-        title="Image Name",
-        description="The name of the image to be diagnosed",
-    )
-    task: str = Field(
-        ...,
-        title="Task",
-        description="The task to be performed on the image",
-    )
-    diagnosis: str = Field(
-        ..., title="Diagnosis", description="The diagnosis of the image"
+class LLMConfig(BaseModel):
+    model_name: str
+    max_tokens: int
+
+class AgentConfig(BaseModel):
+    agent_name: str
+    system_prompt: str
+    llm: LLMConfig
+    max_loops: int
+    autosave: bool
+    dashboard: bool
+
+class AgentRearrangeConfig(BaseModel):
+    agents: List[AgentConfig]
+    flow: str
+    max_loops: int
+    verbose: bool
+
+
+class AgentRunResult(BaseModel):
+    agent_name: str
+    output: Dict[str, Any]
+    tokens_generated: int
+
+class RunAgentsResponse(BaseModel):
+    results: List[AgentRunResult]
+    total_tokens_generated: int
+    
+class AgentRearrangeResponse(BaseModel):
+    results: List[AgentRunResult]
+    total_tokens_generated: int
+    
+class RunConfig(BaseModel):
+    task: str = Field(..., title="The task to run")
+    flow: str = "D -> T"
+    image: Optional[str] = None  # Optional image path as a string
+    max_loops: Optional[int] = 1
+
+
+@app.get("/v1/health")
+async def health_check():
+    return JSONResponse(content={"status": "healthy"})
+
+@app.get("/v1/models_available")
+async def models_available():
+    available_models = {
+        "models": [
+            {"name": "gpt-4-1106-vision-preview", "type": "vision"},
+            {"name": "openai-chat", "type": "text"}
+        ]
+    }
+    return JSONResponse(content=available_models)
+
+
+@app.get("/v1/swarm/completions")
+async def run_agents(run_config: RunConfig):
+    # Diagnoser agent
+    diagnoser = Agent(
+        # agent_name="Medical Image Diagnostic Agent",
+        agent_name="D",
+        system_prompt=DIAGNOSIS_SYSTEM_PROMPT(),
+        llm=llm,
+        max_loops=1,
+        autosave=True,
+        dashboard=True,
     )
 
 
-class TreatMentSchema(BaseModel):
-    image_name: str = Field(
-        ...,
-        title="Image Name",
-        description="The name of the image to be treated",
-    )
-    task: str = Field(
-        ...,
-        title="Task",
-        description="The task to be performed on the image",
+    # Agent 2 the treatment plan provider
+    treatment_plan_provider = Agent(
+        # agent_name="Medical Treatment Recommendation Agent",
+        agent_name="T",
+        system_prompt=TREATMENT_PLAN_SYSTEM_PROMPT(),
+        llm=openai,
+        max_loops=1,
+        autosave=True,
+        dashboard=True,
     )
-    treatment: str = Field(
-        ..., title="Treatment", description="The treatment of the image"
-    )
-
 
-# LLM
-llm = GPT4VisionAPI(
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-    model_name="gpt-4o",
-    max_tokens=1000,
-)
-
-# Anthropic
-anthropic = Anthropic(
-    anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
-)
-
-# Diagnoser agent
-diagnoser = Agent(
-    # agent_name="Medical Image Diagnostic Agent",
-    agent_name="D",
-    system_prompt=DIAGNOSIS_SYSTEM_PROMPT(),
-    llm=llm,
-    max_loops=1,
-    autosave=True,
-    dashboard=True,
-)
-
-
-# Agent 2 the treatment plan provider
-treatment_plan_provider = Agent(
-    # agent_name="Medical Treatment Recommendation Agent",
-    agent_name="T",
-    system_prompt=TREATMENT_PLAN_SYSTEM_PROMPT(),
-    llm=anthropic,
-    max_loops=1,
-    autosave=True,
-    dashboard=True,
-)
+    # Agent 3 the re-arranger
+    rearranger = AgentRearrange(
+        agents=[diagnoser, treatment_plan_provider],
+        flow=run_config.flow,
+        max_loops=run_config.max_loops,
+        verbose=True,
+    )
 
-# Agent 3 the re-arranger
-rearranger = AgentRearrange(
-    agents=[diagnoser, treatment_plan_provider],
-    flow="D -> T",
-    max_loops=1,
-    verbose=True,
-)
+    # Run the rearranger
+    out = rearranger(
+        run_config.task,
+        image=run_config.image,
+    )
+    
+    return JSONResponse(content=out)
 
-image = "ear_4.jpg"
 
-# Run the rearranger
-out = rearranger(
-    "Diagnose this medical image, it's an ear canal, be precise",
-    image,
-)
-print(out)
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+    
diff --git a/playground/demos/octomology_swarm/ear.png b/playground/demos/octomology_swarm/ear.png
new file mode 100644
index 00000000..c76d4a41
Binary files /dev/null and b/playground/demos/octomology_swarm/ear.png differ
diff --git a/playground/demos/plant_biologist_swarm/swarm_workers_agents.py b/playground/demos/plant_biologist_swarm/swarm_workers_agents.py
index 44590df0..008387c5 100644
--- a/playground/demos/plant_biologist_swarm/swarm_workers_agents.py
+++ b/playground/demos/plant_biologist_swarm/swarm_workers_agents.py
@@ -17,7 +17,7 @@ from playground.demos.plant_biologist_swarm.prompts import (
     treatment_recommender_agent,
 )
 
-from swarms import Agent, GPT4VisionAPI
+from swarms import Agent, Fuyu
 
 
 # Load the OpenAI API key from the .env file
@@ -28,9 +28,9 @@ api_key = os.environ.get("OPENAI_API_KEY")
 
 
 # llm = llm,
-llm = GPT4VisionAPI(
+llm = Fuyu(
     max_tokens=4000,
-    model_name="gpt-4o",
+    openai_api_key=api_key,
 )
 
 # Initialize Diagnoser Agent
diff --git a/playground/examples/example_swarms.md b/playground/examples/example_swarms.md
deleted file mode 100644
index 9b46a70e..00000000
--- a/playground/examples/example_swarms.md
+++ /dev/null
@@ -1,25 +0,0 @@
-hey guys, we out here testing out swarms which is a multi-modal agent 
-framework which potentially makes all the agents work in a single pot 
-for instance take an empty pot and place all the known agents in that 
-pot and output a well structured answer out of it 
-
-that's basically it, we belive that a multi-agent framework beats a single 
-agent framework which is not really rocket science 
-
-ight first we gotta make sure out evn clean, install python3-pip,
-this runs on python3.10 
-
-our current version of swarms==4.1.0
-
-make sure you in a virtual env or conda 
-
-just do 
-	$ python3 -m venv ~/.venv
-	$ source ~/.venv/bin/active
-
-then boom we in a virtual env LFG
-
-now for the best we install swarms
-
-	$ pip3 instll --upgrade swamrs==4.1.0
-
diff --git a/playground/structs/agent_basic_customize.py b/playground/structs/agent/agent_basic_customize.py
similarity index 100%
rename from playground/structs/agent_basic_customize.py
rename to playground/structs/agent/agent_basic_customize.py
diff --git a/playground/structs/agent_with_longterm_memory.py b/playground/structs/agent/agent_with_longterm_memory.py
similarity index 95%
rename from playground/structs/agent_with_longterm_memory.py
rename to playground/structs/agent/agent_with_longterm_memory.py
index 588d6546..c4923e97 100644
--- a/playground/structs/agent_with_longterm_memory.py
+++ b/playground/structs/agent/agent_with_longterm_memory.py
@@ -32,7 +32,7 @@ agent = Agent(
     max_loops=4,
     autosave=True,
     dashboard=True,
-    long_term_memory=ChromaDB(),
+    long_term_memory=chromadb,
 )
 
 # Run the workflow on a task
diff --git a/playground/structs/agent_with_many_tools.py b/playground/structs/agent/agent_with_many_tools.py
similarity index 100%
rename from playground/structs/agent_with_many_tools.py
rename to playground/structs/agent/agent_with_many_tools.py
diff --git a/playground/structs/agent_with_tools_example.py b/playground/structs/agent/agent_with_tools_example.py
similarity index 100%
rename from playground/structs/agent_with_tools_example.py
rename to playground/structs/agent/agent_with_tools_example.py
diff --git a/playground/structs/basic_agent_with_azure_openai.py b/playground/structs/agent/basic_agent_with_azure_openai.py
similarity index 100%
rename from playground/structs/basic_agent_with_azure_openai.py
rename to playground/structs/agent/basic_agent_with_azure_openai.py
diff --git a/playground/structs/custom_model_with_agent.py b/playground/structs/agent/custom_model_with_agent.py
similarity index 100%
rename from playground/structs/custom_model_with_agent.py
rename to playground/structs/agent/custom_model_with_agent.py
diff --git a/playground/structs/easy_example.py b/playground/structs/agent/easy_example.py
similarity index 100%
rename from playground/structs/easy_example.py
rename to playground/structs/agent/easy_example.py
diff --git a/playground/structs/fuyu_flow_example.py b/playground/structs/agent/fuyu_flow_example.py
similarity index 100%
rename from playground/structs/fuyu_flow_example.py
rename to playground/structs/agent/fuyu_flow_example.py
diff --git a/playground/structs/kyle_hackathon.py b/playground/structs/agent/kyle_hackathon.py
similarity index 100%
rename from playground/structs/kyle_hackathon.py
rename to playground/structs/agent/kyle_hackathon.py
diff --git a/playground/structs/multi_modal_flow_example.py b/playground/structs/agent/multi_modal_flow_example.py
similarity index 100%
rename from playground/structs/multi_modal_flow_example.py
rename to playground/structs/agent/multi_modal_flow_example.py
diff --git a/playground/structs/multi_modal_rag_agent.py b/playground/structs/agent/multi_modal_rag_agent.py
similarity index 100%
rename from playground/structs/multi_modal_rag_agent.py
rename to playground/structs/agent/multi_modal_rag_agent.py
diff --git a/playground/structs/tool_agent.py b/playground/structs/agent/tool_agent.py
similarity index 100%
rename from playground/structs/tool_agent.py
rename to playground/structs/agent/tool_agent.py
diff --git a/playground/structs/gui_app_example.py b/playground/structs/gui_app_example.py
deleted file mode 100644
index 99934dd5..00000000
--- a/playground/structs/gui_app_example.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from swarms.structs.hiearchical_swarm import HierarchicalSwarm
-
-# Retrieve your API key from the environment or replace with your actual key
-api_key = "sksdsds"
-
-# Initialize HierarchicalSwarm with your API key
-swarm = HierarchicalSwarm(openai_api_key=api_key)
-
-# Define an objective
-objective = """
-Please make a web GUI for using HTTP API server.
-The name of it is HierarchicalSwarm.
-You can check the server code at ./main.py.
-The server is served on localhost:8000.
-Users should be able to write text input as 'query' and url array as 'files', and check the response.
-Users input form should be delivered in JSON format.
-I want it to have neumorphism-style. Serve it on port 4500.
-
-"""
-
-# Run HierarchicalSwarm
-swarm.run(objective)
diff --git a/playground/structs/hiearchical_swarm_example.py b/playground/structs/hiearchical_swarm_example.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/playground/structs/hierarchical_swarm_example.py b/playground/structs/hierarchical_swarm_example.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/playground/structs/multi_agent_collab_example.py b/playground/structs/multi_agent_collab_example.py
deleted file mode 100644
index 0d0b115f..00000000
--- a/playground/structs/multi_agent_collab_example.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from swarms import DialogueSimulator, Worker
-
-
-def select_next_speaker(step: int, agents) -> int:
-    idx = (step) % len(agents)
-    return idx
-
-
-debate = DialogueSimulator(Worker, select_next_speaker)
-
-debate.run()
diff --git a/playground/structs/agent_rearrange.py b/playground/structs/multi_agent_collaboration/agent_rearrange.py
similarity index 80%
rename from playground/structs/agent_rearrange.py
rename to playground/structs/multi_agent_collaboration/agent_rearrange.py
index d8044e75..8dc75fbf 100644
--- a/playground/structs/agent_rearrange.py
+++ b/playground/structs/multi_agent_collaboration/agent_rearrange.py
@@ -1,4 +1,4 @@
-from swarms import Agent, AgentRearrange, rearrange, Anthropic
+from swarms import Agent, AgentRearrange, OpenAIChat
 
 
 # Initialize the director agent
@@ -6,7 +6,7 @@ from swarms import Agent, AgentRearrange, rearrange, Anthropic
 director = Agent(
     agent_name="Director",
     system_prompt="Directs the tasks for the workers",
-    llm=Anthropic(),
+    llm=OpenAIChat(),
     max_loops=1,
     dashboard=False,
     streaming_on=True,
@@ -22,7 +22,7 @@ director = Agent(
 worker1 = Agent(
     agent_name="Worker1",
     system_prompt="Generates a transcript for a youtube video on what swarms are",
-    llm=Anthropic(),
+    llm=OpenAIChat(),
     max_loops=1,
     dashboard=False,
     streaming_on=True,
@@ -37,7 +37,7 @@ worker1 = Agent(
 worker2 = Agent(
     agent_name="Worker2",
     system_prompt="Summarizes the transcript generated by Worker1",
-    llm=Anthropic(),
+    llm=OpenAIChat(),
     max_loops=1,
     dashboard=False,
     streaming_on=True,
@@ -62,11 +62,11 @@ output = agent_system.run(
 print(output)
 
 
-# Using rearrange function
-output = rearrange(
-    agents,
-    flow,
-    "Create a format to express and communicate swarms of llms in a structured manner for youtube",
-)
+# # Using rearrange function
+# output = rearrange(
+#     agents,
+#     flow,
+#     "Create a format to express and communicate swarms of llms in a structured manner for youtube",
+# )
 
-print(output)
+# print(output)
diff --git a/playground/structs/agent_rearrange_human_in_loop.py b/playground/structs/multi_agent_collaboration/agent_rearrange_human_in_loop.py
similarity index 100%
rename from playground/structs/agent_rearrange_human_in_loop.py
rename to playground/structs/multi_agent_collaboration/agent_rearrange_human_in_loop.py
diff --git a/playground/structs/autoswarm.py b/playground/structs/multi_agent_collaboration/autoswarm.py
similarity index 100%
rename from playground/structs/autoswarm.py
rename to playground/structs/multi_agent_collaboration/autoswarm.py
diff --git a/playground/structs/build_your_own_swarm.py b/playground/structs/multi_agent_collaboration/build_your_own_swarm.py
similarity index 100%
rename from playground/structs/build_your_own_swarm.py
rename to playground/structs/multi_agent_collaboration/build_your_own_swarm.py
diff --git a/playground/structs/company_example.py b/playground/structs/multi_agent_collaboration/company_example.py
similarity index 100%
rename from playground/structs/company_example.py
rename to playground/structs/multi_agent_collaboration/company_example.py
diff --git a/playground/structs/concurrent_workflow_example.py b/playground/structs/multi_agent_collaboration/concurrent_workflow_example.py
similarity index 100%
rename from playground/structs/concurrent_workflow_example.py
rename to playground/structs/multi_agent_collaboration/concurrent_workflow_example.py
diff --git a/playground/structs/debate_example.py b/playground/structs/multi_agent_collaboration/debate_example.py
similarity index 100%
rename from playground/structs/debate_example.py
rename to playground/structs/multi_agent_collaboration/debate_example.py
diff --git a/playground/structs/load_balancer_example.py b/playground/structs/multi_agent_collaboration/load_balancer_example.py
similarity index 100%
rename from playground/structs/load_balancer_example.py
rename to playground/structs/multi_agent_collaboration/load_balancer_example.py
diff --git a/playground/structs/majority_voting.py b/playground/structs/multi_agent_collaboration/majority_voting.py
similarity index 100%
rename from playground/structs/majority_voting.py
rename to playground/structs/multi_agent_collaboration/majority_voting.py
diff --git a/playground/structs/message_pool.py b/playground/structs/multi_agent_collaboration/message_pool.py
similarity index 100%
rename from playground/structs/message_pool.py
rename to playground/structs/multi_agent_collaboration/message_pool.py
diff --git a/playground/structs/message_pool_example.py b/playground/structs/multi_agent_collaboration/message_pool_example.py
similarity index 73%
rename from playground/structs/message_pool_example.py
rename to playground/structs/multi_agent_collaboration/message_pool_example.py
index da815157..7ff75825 100644
--- a/playground/structs/message_pool_example.py
+++ b/playground/structs/multi_agent_collaboration/message_pool_example.py
@@ -2,16 +2,30 @@ from swarms import OpenAIChat
 from swarms.structs.agent import Agent
 from swarms.structs.message_pool import MessagePool
 
+# Create agents
 agent1 = Agent(llm=OpenAIChat(), agent_name="agent1")
 agent2 = Agent(llm=OpenAIChat(), agent_name="agent2")
 agent3 = Agent(llm=OpenAIChat(), agent_name="agent3")
 
+# Create moderator agent
 moderator = Agent(agent_name="moderator")
+
+# Create a list of agents
 agents = [agent1, agent2, agent3]
+
+# Create a message pool with 5 turns
 message_pool = MessagePool(agents=agents, moderator=moderator, turns=5)
+
+# Add messages to the message pool
 message_pool.add(agent=agent1, content="Hello, agent2!", turn=1)
 message_pool.add(agent=agent2, content="Hello, agent1!", turn=1)
 message_pool.add(agent=agent3, content="Hello, agent1!", turn=1)
+
+# Get all messages in the message pool
 message_pool.get_all_messages()
+
+# Get visible messages for agent1 in turn 1
 message_pool.get_visible_messages(agent=agent1, turn=1)
+
+# Get visible messages for agent2 in turn 1
 message_pool.get_visible_messages(agent=agent2, turn=1)
diff --git a/playground/structs/multi_process_workflow.py b/playground/structs/multi_agent_collaboration/multi_process_workflow.py
similarity index 100%
rename from playground/structs/multi_process_workflow.py
rename to playground/structs/multi_agent_collaboration/multi_process_workflow.py
diff --git a/playground/structs/recursive_example.py b/playground/structs/multi_agent_collaboration/recursive_example.py
similarity index 100%
rename from playground/structs/recursive_example.py
rename to playground/structs/multi_agent_collaboration/recursive_example.py
diff --git a/playground/structs/round_robin_example.py b/playground/structs/multi_agent_collaboration/round_robin_example.py
similarity index 100%
rename from playground/structs/round_robin_example.py
rename to playground/structs/multi_agent_collaboration/round_robin_example.py
diff --git a/playground/structs/sequential_workflow_example.py b/playground/structs/multi_agent_collaboration/sequential_workflow_example.py
similarity index 100%
rename from playground/structs/sequential_workflow_example.py
rename to playground/structs/multi_agent_collaboration/sequential_workflow_example.py
diff --git a/playground/structs/sequential_workflow_new.py b/playground/structs/multi_agent_collaboration/sequential_workflow_new.py
similarity index 100%
rename from playground/structs/sequential_workflow_new.py
rename to playground/structs/multi_agent_collaboration/sequential_workflow_new.py
diff --git a/playground/structs/sequential_workflow_with_agents.py b/playground/structs/multi_agent_collaboration/sequential_workflow_with_agents.py
similarity index 100%
rename from playground/structs/sequential_workflow_with_agents.py
rename to playground/structs/multi_agent_collaboration/sequential_workflow_with_agents.py
diff --git a/playground/structs/society_of_agents.py b/playground/structs/multi_agent_collaboration/society_of_agents.py
similarity index 100%
rename from playground/structs/society_of_agents.py
rename to playground/structs/multi_agent_collaboration/society_of_agents.py
diff --git a/playground/structs/swarm_network_example.py b/playground/structs/multi_agent_collaboration/swarm_network_example.py
similarity index 100%
rename from playground/structs/swarm_network_example.py
rename to playground/structs/multi_agent_collaboration/swarm_network_example.py
diff --git a/playground/structs/multi_agent_debate_example.py b/playground/structs/multi_agent_debate_example.py
deleted file mode 100644
index e3ec0d58..00000000
--- a/playground/structs/multi_agent_debate_example.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from swarms.models import OpenAIChat
-from swarms.structs.multi_agent_debate import (
-    MultiAgentDebate,
-    select_speaker,
-)
-from swarms.workers.worker import Worker
-
-llm = OpenAIChat()
-
-worker1 = Worker(
-    llm=llm,
-    ai_name="Bumble Bee",
-    ai_role="Worker in a swarm",
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-worker2 = Worker(
-    llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-worker3 = Worker(
-    llm=llm,
-    ai_name="Megatron",
-    ai_role="Worker in a swarm",
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-
-# init agents
-agents = [worker1, worker2, worker3]
-
-# Initialize multi-agent debate with the selection function
-debate = MultiAgentDebate(agents, select_speaker)
-
-# Run task
-task = (
-    "What were the winning boston marathon times for the past 5 years"
-    " (ending in 2022)? Generate a table of the year, name, country"
-    " of origin, and times."
-)
-results = debate.run(task, max_iters=4)
-
-# Print results
-for result in results:
-    print(f"Agent {result['agent']} responded: {result['response']}")
diff --git a/playground/structs/new_concurrent_workflow.py b/playground/structs/new_concurrent_workflow.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/playground/structs/task_example.py b/playground/structs/task_example.py
index c2ade96a..259d6e73 100644
--- a/playground/structs/task_example.py
+++ b/playground/structs/task_example.py
@@ -1,7 +1,5 @@
 import os
-
 from dotenv import load_dotenv
-
 from swarms.structs import Agent, OpenAIChat, Task
 
 # Load the environment variables
diff --git a/pyproject.toml b/pyproject.toml
index e6bbf86b..cef9d458 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,7 +48,6 @@ Pillow = "10.3.0"
 psutil = "*"
 sentry-sdk = "*"
 python-dotenv = "*"
-opencv-python-headless = "*"
 PyYAML = "*"
 docstring_parser = "0.16"
 
diff --git a/swarm_network_api_on.py b/swarm_network_api_on.py
new file mode 100644
index 00000000..c7efdc72
--- /dev/null
+++ b/swarm_network_api_on.py
@@ -0,0 +1,25 @@
+from swarms import Agent, OpenAIChat, SwarmNetwork
+
+# Create an instance of Agent
+agent1 = Agent(
+    agent_name="Covid-19-Chat",  # Name of the agent
+    agent_description="This agent provides information about COVID-19 symptoms.",  # Description of the agent
+    llm=OpenAIChat(),  # Language model used by the agent
+    max_loops="auto",  # Maximum number of loops the agent can run
+    autosave=True,  # Whether to automatically save the agent's state
+    verbose=True,  # Whether to print verbose output
+    stopping_condition="finish",  # Condition for stopping the agent
+)
+
+agents = [agent1]  # List of agents (add more agents as needed)
+
+swarm_name = "HealthSwarm"  # Name of the swarm
+swarm_description = "A swarm of agents providing health-related information."  # Description of the swarm
+
+# Create an instance of SwarmNetwork with API enabled
+agent_api = SwarmNetwork(
+    swarm_name, swarm_description, agents, api_on=True
+)
+
+# Run the agent API
+agent_api.run()
diff --git a/swarms/models/base_multimodal_model.py b/swarms/models/base_multimodal_model.py
index 0f1d38b1..7a2dc079 100644
--- a/swarms/models/base_multimodal_model.py
+++ b/swarms/models/base_multimodal_model.py
@@ -11,14 +11,6 @@ import requests
 from PIL import Image
 from termcolor import colored
 
-try:
-    import cv2
-except ImportError:
-    print(
-        "Error importing cv2 try installing it with `pip install"
-        " opencv-python`"
-    )
-
 
 class BaseMultiModalModel:
     """
@@ -148,42 +140,6 @@ class BaseMultiModalModel:
         image_pil = Image.open(img)
         return image_pil
 
-    def process_video(
-        self,
-        video_path: str = None,
-        type_img: str = ".jpg",
-        *args,
-        **kwargs,
-    ):
-        """Process a video
-
-        Args:
-            video_path (str, optional): _description_. Defaults to None.
-            type_img (str, optional): _description_. Defaults to ".jpg".
-            *args: _description_.
-            **kwargs: _description_.
-
-        """
-        try:
-            video = cv2.VideoCapture(video_path)
-
-            base64Frames = []
-            while video.isOpened():
-                success, frame = video.read()
-                if not success:
-                    break
-                _, buffer = cv2.imencode(type_img, frame)
-                base64Frames.append(
-                    base64.b64encode(buffer).decode("utf-8")
-                )
-
-            video.release()
-            print(len(base64Frames), "frames read")
-            return base64Frames
-        except Exception as error:
-            print(f"Error processing video {error} try again")
-            raise error
-
     def clear_chat_history(self):
         """Clear the chat history"""
         self.chat_history = []
diff --git a/swarms/models/gpt4_vision_api.py b/swarms/models/gpt4_vision_api.py
index 998ed2ea..9dc3909d 100644
--- a/swarms/models/gpt4_vision_api.py
+++ b/swarms/models/gpt4_vision_api.py
@@ -11,14 +11,6 @@ from termcolor import colored
 from swarms.utils.loguru_logger import logger
 from swarms.models.base_multimodal_model import BaseMultiModalModel
 
-try:
-    import cv2
-except ImportError:
-    print(
-        "OpenCV not installed. Please install OpenCV to use this" " model."
-    )
-    raise ImportError
-
 # Load environment variables
 load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
@@ -222,92 +214,6 @@ class GPT4VisionAPI(BaseMultiModalModel):
         for chunk in content:
             print(chunk)
 
-    def process_video(self, video: str = None):
-        """
-        Process a video into a list of base64 frames
-
-        Parameters
-        ----------
-        video : str
-            The path to the video file
-
-        Returns
-        -------
-        base64_frames : list
-            A list of base64 frames
-
-        Examples
-        --------
-        >>> from swarms.models import GPT4VisionAPI
-        >>> llm = GPT4VisionAPI()
-        >>> video = "video.mp4"
-        >>> base64_frames = llm.process_video(video)
-
-        """
-        video = cv2.VideoCapture(video)
-
-        base64_frames = []
-        while video.isOpened():
-            success, frame = video.read()
-            if not success:
-                break
-            _, buffer = cv2.imencode(".jpg", frame)
-            base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
-
-        video.release()
-        print(len(base64_frames), "frames read.")
-        return base64_frames
-
-    def run_with_video(
-        self,
-        task: str = None,
-        video: str = None,
-        *args,
-        **kwargs,
-    ):
-        prompt = self.video_prompt(self.process_video(video))
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-        payload = {
-            "model": self.model_name,
-            "messages": [
-                {
-                    "role": "system",
-                    "content": [self.system_prompt],
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        (task,),  # task
-                        *map(
-                            lambda x: {"image": x, "resize": 768},
-                            prompt[0::50],
-                        ),
-                    ],
-                },
-            ],
-            "max_tokens": self.max_tokens,
-        }
-        response = requests.post(
-            self.openai_proxy,
-            headers=headers,
-            json=payload,
-        )
-
-        out = response.json()
-        content = out["choices"][0]["message"]["content"]
-
-        if self.streaming_enabled:
-            content = self.stream_response(content)
-
-        if self.beautify:
-            content = colored(content, "cyan")
-            print(content)
-        else:
-            print(content)
-
     def __call__(
         self,
         task: Optional[str] = None,
diff --git a/swarms/models/kosmos_two.py b/swarms/models/kosmos_two.py
index 59f1f98e..ba03bc54 100644
--- a/swarms/models/kosmos_two.py
+++ b/swarms/models/kosmos_two.py
@@ -1,9 +1,4 @@
-import os
-
-import cv2
-import numpy as np
 import requests
-import torch
 from PIL import Image
 from transformers import AutoModelForVision2Seq, AutoProcessor
 
@@ -18,25 +13,12 @@ def is_overlapping(rect1, rect2):
 
 
 class Kosmos(BaseMultiModalModel):
-    """
-    Kosmos model by Yen-Chun Shieh
-
-    Parameters
-    ----------
-    model_name : str
-        Path to the pretrained model
-
-    Examples
-    --------
-    >>> kosmos = Kosmos()
-    >>> kosmos("Hello, my name is", "path/to/image.png")
-
-    """
 
     def __init__(
         self,
         model_name="ydshieh/kosmos-2-patch14-224",
         max_new_tokens: int = 64,
+        verbose: bool = False,
         *args,
         **kwargs,
     ):
@@ -121,201 +103,6 @@ class Kosmos(BaseMultiModalModel):
         task = "<grounding> Describe this image in detail"
         self.run(task, image_url)
 
-    def draw_entity_boxes_on_image(
-        image, entities, show=False, save_path=None
-    ):
-        """_summary_
-        Args:
-            image (_type_): image or image path
-            collect_entity_location (_type_): _description_
-        """
-        if isinstance(image, Image.Image):
-            image_h = image.height
-            image_w = image.width
-            image = np.array(image)[:, :, [2, 1, 0]]
-        elif isinstance(image, str):
-            if os.path.exists(image):
-                pil_img = Image.open(image).convert("RGB")
-                image = np.array(pil_img)[:, :, [2, 1, 0]]
-                image_h = pil_img.height
-                image_w = pil_img.width
-            else:
-                raise ValueError(f"invaild image path, {image}")
-        elif isinstance(image, torch.Tensor):
-            # pdb.set_trace()
-            image_tensor = image.cpu()
-            reverse_norm_mean = torch.tensor(
-                [0.48145466, 0.4578275, 0.40821073]
-            )[:, None, None]
-            reverse_norm_std = torch.tensor(
-                [0.26862954, 0.26130258, 0.27577711]
-            )[:, None, None]
-            image_tensor = (
-                image_tensor * reverse_norm_std + reverse_norm_mean
-            )
-            # pil_img = T.ToPILImage()(image_tensor)
-            image_h = pil_img.height
-            image_w = pil_img.width
-            image = np.array(pil_img)[:, :, [2, 1, 0]]
-        else:
-            raise ValueError(
-                f"invaild image format, {type(image)} for {image}"
-            )
-
-        if len(entities) == 0:
-            return image
-
-        new_image = image.copy()
-        previous_bboxes = []
-        # size of text
-        text_size = 1
-        # thickness of text
-        text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
-        box_line = 3
-        (c_width, text_height), _ = cv2.getTextSize(
-            "F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line
-        )
-        base_height = int(text_height * 0.675)
-        text_offset_original = text_height - base_height
-        text_spaces = 3
-
-        for entity_name, (start, end), bboxes in entities:
-            for x1_norm, y1_norm, x2_norm, y2_norm in bboxes:
-                orig_x1, orig_y1, orig_x2, orig_y2 = (
-                    int(x1_norm * image_w),
-                    int(y1_norm * image_h),
-                    int(x2_norm * image_w),
-                    int(y2_norm * image_h),
-                )
-                # draw bbox
-                # random color
-                color = tuple(np.random.randint(0, 255, size=3).tolist())
-                new_image = cv2.rectangle(
-                    new_image,
-                    (orig_x1, orig_y1),
-                    (orig_x2, orig_y2),
-                    color,
-                    box_line,
-                )
-
-                l_o, r_o = (
-                    box_line // 2 + box_line % 2,
-                    box_line // 2 + box_line % 2 + 1,
-                )
-
-                x1 = orig_x1 - l_o
-                y1 = orig_y1 - l_o
-
-                if (
-                    y1
-                    < text_height + text_offset_original + 2 * text_spaces
-                ):
-                    y1 = (
-                        orig_y1
-                        + r_o
-                        + text_height
-                        + text_offset_original
-                        + 2 * text_spaces
-                    )
-                    x1 = orig_x1 + r_o
-
-                # add text background
-                (text_width, text_height), _ = cv2.getTextSize(
-                    f"  {entity_name}",
-                    cv2.FONT_HERSHEY_COMPLEX,
-                    text_size,
-                    text_line,
-                )
-                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = (
-                    x1,
-                    y1
-                    - (
-                        text_height
-                        + text_offset_original
-                        + 2 * text_spaces
-                    ),
-                    x1 + text_width,
-                    y1,
-                )
-
-                for prev_bbox in previous_bboxes:
-                    while is_overlapping(
-                        (
-                            text_bg_x1,
-                            text_bg_y1,
-                            text_bg_x2,
-                            text_bg_y2,
-                        ),
-                        prev_bbox,
-                    ):
-                        text_bg_y1 += (
-                            text_height
-                            + text_offset_original
-                            + 2 * text_spaces
-                        )
-                        text_bg_y2 += (
-                            text_height
-                            + text_offset_original
-                            + 2 * text_spaces
-                        )
-                        y1 += (
-                            text_height
-                            + text_offset_original
-                            + 2 * text_spaces
-                        )
-
-                        if text_bg_y2 >= image_h:
-                            text_bg_y1 = max(
-                                0,
-                                image_h
-                                - (
-                                    text_height
-                                    + text_offset_original
-                                    + 2 * text_spaces
-                                ),
-                            )
-                            text_bg_y2 = image_h
-                            y1 = image_h
-                            break
-
-                alpha = 0.5
-                for i in range(text_bg_y1, text_bg_y2):
-                    for j in range(text_bg_x1, text_bg_x2):
-                        if i < image_h and j < image_w:
-                            if j < text_bg_x1 + 1.35 * c_width:
-                                # original color
-                                bg_color = color
-                            else:
-                                # white
-                                bg_color = [255, 255, 255]
-                            new_image[i, j] = (
-                                alpha * new_image[i, j]
-                                + (1 - alpha) * np.array(bg_color)
-                            ).astype(np.uint8)
-
-                cv2.putText(
-                    new_image,
-                    f"  {entity_name}",
-                    (x1, y1 - text_offset_original - 1 * text_spaces),
-                    cv2.FONT_HERSHEY_COMPLEX,
-                    text_size,
-                    (0, 0, 0),
-                    text_line,
-                    cv2.LINE_AA,
-                )
-                # previous_locations.append((x1, y1))
-                previous_bboxes.append(
-                    (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)
-                )
-
-        pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
-        if save_path:
-            pil_image.save(save_path)
-        if show:
-            pil_image.show()
-
-        return new_image
-
     def generate_boxees(self, task, image_url):
         image = self.get_image(image_url)
         processed_text, entities = self.process_task(task, image)
diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py
index ead7f2e7..2fb9fc66 100644
--- a/swarms/structs/agent.py
+++ b/swarms/structs/agent.py
@@ -333,6 +333,9 @@ class Agent(BaseStructure):
         self.planning = planning
         self.planning_prompt = planning_prompt
         self.device = device
+        self.custom_planning_prompt = custom_planning_prompt
+        self.rules = rules
+        self.custom_tools_prompt = custom_tools_prompt
 
         # Name
         self.name = agent_name
diff --git a/swarms/structs/agent_base_model.py b/swarms/structs/agent_base_model.py
new file mode 100644
index 00000000..5310abad
--- /dev/null
+++ b/swarms/structs/agent_base_model.py
@@ -0,0 +1,78 @@
+from typing import Any, Callable, Dict, List, Optional
+from pydantic import BaseModel
+
+
+class AgentSchemaBaseModel(BaseModel):
+    id: Optional[str] = None
+    llm: Optional[Any] = None
+    template: Optional[str] = None
+    max_loops: Optional[int] = None
+    stopping_condition: Optional[Callable[[str], bool]] = None
+    loop_interval: Optional[int] = None
+    retry_attempts: Optional[int] = None
+    retry_interval: Optional[int] = None
+    return_history: Optional[bool] = False
+    stopping_token: Optional[str] = None
+    dynamic_loops: Optional[bool] = False
+    interactive: Optional[bool] = False
+    dashboard: Optional[bool] = False
+    agent_name: Optional[str] = None
+    agent_description: Optional[str] = None
+    system_prompt: Optional[str] = None
+    tools: Optional[List[Callable]] = None
+    dynamic_temperature_enabled: Optional[bool] = None
+    sop: Optional[str] = None
+    sop_list: Optional[List[str]] = None
+    saved_state_path: Optional[str] = None
+    autosave: Optional[bool] = True
+    context_length: Optional[int] = None
+    user_name: Optional[str] = None
+    self_healing_enabled: Optional[bool] = None
+    code_interpreter: Optional[bool] = False
+    multi_modal: Optional[bool] = False
+    pdf_path: Optional[str] = None
+    list_of_pdf: Optional[str] = None
+    tokenizer: Optional[Any] = None
+    long_term_memory: Optional[Any] = None
+    preset_stopping_token: Optional[bool] = False
+    traceback: Optional[Any] = None
+    traceback_handlers: Optional[Any] = None
+    streaming_on: Optional[bool] = False
+    docs: Optional[List[str]] = None
+    docs_folder: Optional[str] = None
+    verbose: Optional[bool] = True
+    parser: Optional[Callable] = None
+    best_of_n: Optional[int] = None
+    callback: Optional[Callable] = None
+    metadata: Optional[Dict[str, Any]] = None
+    callbacks: Optional[List[Callable]] = None
+    logger_handler: Optional[Any] = None
+    search_algorithm: Optional[Callable] = None
+    logs_to_filename: Optional[str] = None
+    evaluator: Optional[Callable] = None
+    output_json: Optional[bool] = False
+    stopping_func: Optional[Callable] = None
+    custom_loop_condition: Optional[Callable] = None
+    sentiment_threshold: Optional[float] = None
+    custom_exit_command: Optional[str] = None
+    sentiment_analyzer: Optional[Callable] = None
+    limit_tokens_from_string: Optional[Callable] = None
+    custom_tools_prompt: Optional[Callable] = None
+    tool_schema: Optional[Any] = None
+    output_type: Optional[Any] = None
+    function_calling_type: Optional[str] = None
+    output_cleaner: Optional[Callable] = None
+    function_calling_format_type: Optional[str] = None
+    list_base_models: Optional[List[Any]] = None
+    metadata_output_type: Optional[str] = None
+    state_save_file_type: Optional[str] = None
+    chain_of_thoughts: Optional[bool] = False
+    algorithm_of_thoughts: Optional[bool] = False
+    tree_of_thoughts: Optional[bool] = False
+    tool_choice: Optional[str] = None
+    execute_tool: Optional[bool] = False
+    rules: Optional[str] = None
+    planning: Optional[str] = None
+    planning_prompt: Optional[str] = None
+    device: Optional[str] = None
+    custom_planning_prompt: Optional[str] = None
diff --git a/swarms/structs/loop.py b/swarms/structs/loop.py
new file mode 100644
index 00000000..9c8511ce
--- /dev/null
+++ b/swarms/structs/loop.py
@@ -0,0 +1,11 @@
+"""
+A loop is a sequence of instructions that is continually repeated until a certain condition is met. In the context of agent-based systems, a loop can be used to control the behavior of an agent, such as how many times it interacts with other agents or how long it runs for.
+
+- Code Example:
+
+loop = Loop(
+    PlanGeneratorAgent(),
+    ExecutionAgent(),
+    max_loops=10,
+)
+"""
diff --git a/swarms/structs/swarm_net.py b/swarms/structs/swarm_net.py
index 60e5ec7f..bdb4724d 100644
--- a/swarms/structs/swarm_net.py
+++ b/swarms/structs/swarm_net.py
@@ -1,17 +1,55 @@
+"""
+Todo
+- [ ] Test the new api feature
+- [ ] Add the agent schema for every agent -- following OpenAI assistaants schema
+- [ ] then add the swarm schema for the swarm url: /v1/swarms/{swarm_name}/agents/{agent_id}
+- [ ] Add the agent schema for the agent url: /v1/swarms/{swarm_name}/agents/{agent_id}
+"""
+
 import asyncio
-import logging
+import multiprocessing
 import queue
 import threading
-from typing import List, Optional
+from typing import Dict, List, Optional
 
-# from fastapi import FastAPI
+import tenacity
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
 
 from swarms.structs.agent import Agent
-from swarms.structs.base_structure import BaseStructure
-from swarms.utils.logger import logger  # noqa: F401
+from swarms.structs.base_swarm import BaseSwarm
+from swarms.utils.loguru_logger import logger
+
+
+# Pydantic models
+class TaskRequest(BaseModel):
+    task: str
+
+
+# Pydantic models
+class TaskResponse(BaseModel):
+    result: str
+
 
+class AgentInfo(BaseModel):
+    agent_name: str
+    agent_description: str
 
-class SwarmNetwork(BaseStructure):
+
+class SwarmInfo(BaseModel):
+    swarm_name: str
+    swarm_description: str
+    agents: List[AgentInfo]
+
+
+# Helper function to get the number of workers
+def get_number_of_workers():
+    return multiprocessing.cpu_count()
+
+
+# [TODO] Add the agent schema for every agent -- following OpenAI assistaants schema
+class SwarmNetwork(BaseSwarm):
     """
     SwarmNetwork class
 
@@ -68,15 +106,23 @@ class SwarmNetwork(BaseStructure):
 
     def __init__(
         self,
+        name: str = None,
+        description: str = None,
         agents: List[Agent] = None,
         idle_threshold: float = 0.2,
         busy_threshold: float = 0.7,
         api_enabled: Optional[bool] = False,
         logging_enabled: Optional[bool] = False,
+        api_on: Optional[bool] = False,
+        host: str = "0.0.0.0",
+        port: int = 8000,
+        swarm_callable: Optional[callable] = None,
         *args,
         **kwargs,
     ):
-        super().__init__()
+        super().__init__(*args, **kwargs)
+        self.name = name
+        self.description = description
         self.agents = agents
         self.task_queue = queue.Queue()
         self.idle_threshold = idle_threshold
@@ -84,15 +130,37 @@ class SwarmNetwork(BaseStructure):
         self.lock = threading.Lock()
         self.api_enabled = api_enabled
         self.logging_enabled = logging_enabled
-        self.agent_pool = []
+        self.host = host
+        self.port = port
+        self.swarm_callable = swarm_callable
+
+        # Ensure that the agents list is not empty
+        if not agents:
+            raise ValueError("The agents list cannot be empty")
 
-        logging.basicConfig(level=logging.INFO)
-        self.logger = logging.getLogger(__name__)
+        # Create a dictionary of agents for easy access
+        self.agent_dict = {agent.id: agent for agent in agents}
 
         # For each agent in the pool, run it on it's own thread
         if agents is not None:
             for agent in agents:
-                self.agent_pool.append(agent)
+                self.agents.append(agent)
+
+        # Create the FastAPI instance
+        if api_on is True:
+            logger.info("Creating FastAPI instance")
+            self.app = FastAPI(debug=True, *args, **kwargs)
+
+            self.app.add_middleware(
+                CORSMiddleware,
+                allow_origins=["*"],
+                allow_credentials=True,
+                allow_methods=["*"],
+                allow_headers=["*"],
+            )
+
+            logger.info("Routes set for creation")
+            self._create_routes()
 
     def add_task(self, task):
         """Add task to the task queue
@@ -145,6 +213,98 @@ class SwarmNetwork(BaseStructure):
             )
             raise error
 
+    def _create_routes(self) -> None:
+        """
+        Creates the routes for the API.
+        """
+        # Extensive logginbg
+        logger.info("Creating routes for the API")
+
+        # Routes available
+        logger.info(
+            "Routes available: /v1/swarms, /v1/health, /v1/swarms/{swarm_name}/agents/{agent_id}, /v1/swarms/{swarm_name}/run"
+        )
+
+        @self.app.get("/v1/swarms", response_model=SwarmInfo)
+        async def get_swarms() -> SwarmInfo:
+            try:
+                logger.info("Getting swarm information")
+                return SwarmInfo(
+                    swarm_name=self.swarm_name,
+                    swarm_description=self.swarm_description,
+                    agents=[
+                        AgentInfo(
+                            agent_name=agent.agent_name,
+                            agent_description=agent.agent_description,
+                        )
+                        for agent in self.agents
+                    ],
+                )
+            except Exception as e:
+                logger.error(f"Error getting swarm information: {str(e)}")
+                raise HTTPException(
+                    status_code=500, detail="Internal Server Error"
+                )
+
+        @self.app.get("/v1/health")
+        async def get_health() -> Dict[str, str]:
+            try:
+                logger.info("Checking health status")
+                return {"status": "healthy"}
+            except Exception as e:
+                logger.error(f"Error checking health status: {str(e)}")
+                raise HTTPException(
+                    status_code=500, detail="Internal Server Error"
+                )
+
+        @self.app.get(f"/v1/swarms/{self.swarm_name}/agents/{{agent_id}}")
+        async def get_agent_info(agent_id: str) -> AgentInfo:
+            try:
+                logger.info(f"Getting information for agent {agent_id}")
+                agent = self.agent_dict.get(agent_id)
+                if not agent:
+                    raise HTTPException(
+                        status_code=404, detail="Agent not found"
+                    )
+                return AgentInfo(
+                    agent_name=agent.agent_name,
+                    agent_description=agent.agent_description,
+                )
+            except Exception as e:
+                logger.error(f"Error getting agent information: {str(e)}")
+                raise HTTPException(
+                    status_code=500, detail="Internal Server Error"
+                )
+
+        @self.app.post(
+            f"/v1/swarms/{self.swarm_name}/agents/{{agent_id}}/run",
+            response_model=TaskResponse,
+        )
+        async def run_agent_task(
+            task_request: TaskRequest,
+        ) -> TaskResponse:
+            try:
+                logger.info("Running agent task")
+                # Assuming only one agent in the swarm for this example
+                agent = self.agents[0]
+                logger.info(f"Running agent task: {task_request.task}")
+                result = agent.run(task_request.task)
+                return TaskResponse(result=result)
+            except Exception as e:
+                logger.error(f"Error running agent task: {str(e)}")
+                raise HTTPException(
+                    status_code=500, detail="Internal Server Error"
+                )
+
+    def get_app(self) -> FastAPI:
+        """
+        Returns the FastAPI instance.
+
+        Returns:
+            FastAPI: The FastAPI instance.
+        """
+        return self.app
+
     def run_single_agent(
         self, agent_id, task: Optional[str], *args, **kwargs
     ):
@@ -162,7 +322,7 @@ class SwarmNetwork(BaseStructure):
         """
         self.logger.info(f"Running task {task} on agent {agent_id}")
         try:
-            for agent in self.agent_pool:
+            for agent in self.agents:
                 if agent.id == agent_id:
                     out = agent.run(task, *args, **kwargs)
             return out
@@ -184,8 +344,7 @@ class SwarmNetwork(BaseStructure):
         self.logger.info(f"Running task {task} on all agents")
         try:
             return [
-                agent.run(task, *args, **kwargs)
-                for agent in self.agent_pool
+                agent.run(task, *args, **kwargs) for agent in self.agents
             ]
         except Exception as error:
             logger.error(f"Error running task on agents: {error}")
@@ -196,8 +355,8 @@ class SwarmNetwork(BaseStructure):
         self.logger.info("[Listing all active agents]")
 
         try:
-            # Assuming self.agent_pool is a list of agent objects
-            for agent in self.agent_pool:
+            # Assuming self.agents is a list of agent objects
+            for agent in self.agents:
                 self.logger.info(
                     f"[Agent] [ID: {agent.id}] [Name:"
                     f" {agent.agent_name}] [Description:"
@@ -219,7 +378,7 @@ class SwarmNetwork(BaseStructure):
         self.logger.info(f"Getting agent {agent_id}")
 
         try:
-            for agent in self.agent_pool:
+            for agent in self.agents:
                 if agent.id == agent_id:
                     return agent
             raise ValueError(f"No agent found with ID {agent_id}")
@@ -235,7 +394,7 @@ class SwarmNetwork(BaseStructure):
         """
         self.logger.info(f"Adding agent {agent} to pool")
         try:
-            self.agent_pool.append(agent)
+            self.agents.append(agent)
         except Exception as error:
             print(f"Error adding agent to pool: {error}")
             raise error
@@ -248,9 +407,9 @@ class SwarmNetwork(BaseStructure):
         """
         self.logger.info(f"Removing agent {agent_id} from pool")
         try:
-            for agent in self.agent_pool:
+            for agent in self.agents:
                 if agent.id == agent_id:
-                    self.agent_pool.remove(agent)
+                    self.agents.remove(agent)
                     return
             raise ValueError(f"No agent found with ID {agent_id}")
         except Exception as error:
@@ -281,7 +440,7 @@ class SwarmNetwork(BaseStructure):
         self.logger.info(f"Scaling up agent pool by {num_agents}")
         try:
             for _ in range(num_agents):
-                self.agent_pool.append(Agent())
+                self.agents.append(Agent())
         except Exception as error:
             print(f"Error scaling up agent pool: {error}")
             raise error
@@ -293,32 +452,56 @@ class SwarmNetwork(BaseStructure):
             num_agents (int, optional): _description_. Defaults to 1.
         """
         for _ in range(num_agents):
-            self.agent_pool.pop()
+            self.agents.pop()
+
+    @tenacity.retry(
+        wait=tenacity.wait_fixed(1),
+        stop=tenacity.stop_after_attempt(3),
+        retry=tenacity.retry_if_exception_type(Exception),
+    )
+    def run(self, *args, **kwargs):
+        """run the swarm network"""
+        app = self.get_app()
 
-    # - Create APIs for each agent in the pool (optional) with fastapi
-    def create_apis_for_agents(self):
-        """Create APIs for each agent in the pool (optional) with fastapi
+        try:
+            import uvicorn
 
-        Returns:
-            _type_: _description_
-        """
-        self.apis = []
-        for agent in self.agent_pool:
-            self.api.get(f"/{agent.id}")
+            logger.info(
+                f"Running the swarm network with {len(self.agents)} on {self.host}:{self.port}"
+            )
+            uvicorn.run(
+                app,
+                host=self.host,
+                port=self.port,
+                # workers=get_number_of_workers(),
+                *args,
+                **kwargs,
+            )
 
-            def run_agent(task: str, *args, **kwargs):
-                return agent.run(task, *args, **kwargs)
+            return app
+        except Exception as error:
+            logger.error(f"Error running the swarm network: {error}")
+            raise error
 
-            self.apis.append(self.api)
 
-    def run(self):
-        """run the swarm network"""
-        # Observe all agents in the pool
-        self.logger.info("Starting the SwarmNetwork")
-
-        for agent in self.agent_pool:
-            self.logger.info(f"Starting agent {agent.id}")
-            self.logger.info(
-                f"[Agent][{agent.id}] [Status] [Running] [Awaiting"
-                " Task]"
-            )
+# # # Example usage
+# if __name__ == "__main__":
+
+#     agent1 = Agent(
+#         agent_name="Covid-19-Chat",
+#         agent_description="This agent provides information about COVID-19 symptoms.",
+#         llm=OpenAIChat(),
+#         max_loops="auto",
+#         autosave=True,
+#         verbose=True,
+#         stopping_condition="finish",
+#     )
+
+#     agents = [agent1]  # Add more agents as needed
+#     swarm_name = "HealthSwarm"
+#     swarm_description = (
+#         "A swarm of agents providing health-related information."
+#     )
+
+#     agent_api = SwarmNetwork(swarm_name, swarm_description, agents)
+#     agent_api.run()