diff --git a/README.md b/README.md
index 179b5aac..2064c281 100644
--- a/README.md
+++ b/README.md
@@ -634,6 +634,77 @@ Swarm architectures leverage these communication patterns to ensure that agents
 
 
 
+## Swarms API
+
+We recently launched our enterprise-grade Swarms API. This API allows you to create, manage, and execute swarms from your own application.
+
+#### Steps:
+
+1. Create a Swarms API key [HERE](https://swarms.world)
+2. Upload your key to the `.env` file like so: `SWARMS_API_KEY=<your-api-key>`
+3. Use the following code to create and execute a swarm:
+4. Read our docs for more information for deeper customization [HERE](https://docs.swarms.world/en/latest/swarms_cloud/swarms_api/)
+
+
+```python
+import json
+from swarms.structs.swarms_api import (
+    SwarmsAPIClient,
+    SwarmRequest,
+    AgentInput,
+)
+import os
+
+agents = [
+    AgentInput(
+        agent_name="Medical Researcher",
+        description="Conducts medical research and analysis",
+        system_prompt="You are a medical researcher specializing in clinical studies.",
+        max_loops=1,
+        model_name="gpt-4o",
+        role="worker",
+    ),
+    AgentInput(
+        agent_name="Medical Diagnostician",
+        description="Provides medical diagnoses based on symptoms and test results",
+        system_prompt="You are a medical diagnostician with expertise in identifying diseases.",
+        max_loops=1,
+        model_name="gpt-4o",
+        role="worker",
+    ),
+    AgentInput(
+        agent_name="Pharmaceutical Expert",
+        description="Advises on pharmaceutical treatments and drug interactions",
+        system_prompt="You are a pharmaceutical expert knowledgeable about medications and their effects.",
+        max_loops=1,
+        model_name="gpt-4o",
+        role="worker",
+    ),
+]
+
+swarm_request = SwarmRequest(
+    name="Medical Swarm",
+    description="A swarm for medical research and diagnostics",
+    agents=agents,
+    max_loops=1,
+    swarm_type="ConcurrentWorkflow",
+    output_type="str",
+    return_history=True,
+    task="What is the cause of the common cold?",
+)
+
+client = SwarmsAPIClient(
+    api_key=os.getenv("SWARMS_API_KEY"), format_type="json"
+)
+
+response = client.run(swarm_request)
+
+print(json.dumps(response, indent=4))
+
+
+```
+
+
 ### `SequentialWorkflow`
 Sequential Workflow enables you to sequentially execute tasks with `Agent` and then pass the output into the next agent and onwards until you have specified your max loops.
 
diff --git a/swarms_api_client.py b/swarms_api_examples/swarms_api_client.py
similarity index 100%
rename from swarms_api_client.py
rename to swarms_api_examples/swarms_api_client.py
diff --git a/swarms_api_example.py b/swarms_api_examples/swarms_api_example.py
similarity index 89%
rename from swarms_api_example.py
rename to swarms_api_examples/swarms_api_example.py
index c23d9b88..2df3112b 100644
--- a/swarms_api_example.py
+++ b/swarms_api_examples/swarms_api_example.py
@@ -43,7 +43,7 @@ def run_single_swarm():
             },
         ],
         "max_loops": 1,
-        "swarm_type": "HiearchicalSwarm",
+        "swarm_type": "SequentialWorkflow",
         "task": "What are the best etfs and index funds for ai and tech?",
         "output_type": "dict",
         # "return_history": True,
@@ -71,11 +71,11 @@ def get_logs():
 
 
 if __name__ == "__main__":
-    # result = run_single_swarm()
-    # print("Swarm Result:")
-    # print(result)
-
-    logs = get_logs()
-    logs = json.dumps(logs, indent=4)
-    print("Logs:")
-    print(logs)
+    result = run_single_swarm()
+    print("Swarm Result:")
+    print(result)
+
+    # logs = get_logs()
+    # logs = json.dumps(logs, indent=4)
+    # print("Logs:")
+    # print(logs)