diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 50921aa0..a4140bc0 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -260,7 +260,8 @@ nav:
       - Twitter: "swarms_tools/twitter.md"
   - Swarms Cloud API:
     # - Overview: "swarms_cloud/main.md"
-    - Overview: "swarms_cloud/vision.md"
+    # - Overview: "swarms_cloud/vision.md"
+    - Overview: "swarms_cloud/launch.md"
     - Deploying Swarms on Google Cloud Run: "swarms_cloud/cloud_run.md"
     # - Swarms Cloud CLI: "swarms_cloud/cli.md"
     - Swarm APIs:
diff --git a/docs/swarms_cloud/launch.md b/docs/swarms_cloud/launch.md
new file mode 100644
index 00000000..2d53e47f
--- /dev/null
+++ b/docs/swarms_cloud/launch.md
@@ -0,0 +1,369 @@
+# Swarms Cloud API Client Documentation
+
+## Overview
+The Swarms Cloud API Client is a production-grade Python library for interacting with the Swarms Cloud Agent API. It provides a comprehensive interface for managing, executing, and monitoring cloud-based agents.
+
+## Installation
+```bash
+pip install swarms-cloud
+```
+
+## Quick Start
+```python
+from swarms_cloud import SwarmCloudAPI, AgentCreate
+
+# Initialize the client
+client = SwarmCloudAPI(
+    base_url="https://swarmcloud-285321057562.us-central1.run.app",
+    api_key="your_api_key_here"
+)
+
+# Create an agent
+agent_data = AgentCreate(
+    name="TranslateAgent",
+    description="Translates text between languages",
+    code="""
+    def main(request, store):
+        text = request.payload.get('text', '')
+        return f'Translated: {text}'
+    """,
+    requirements="requests==2.25.1",
+    envs="DEBUG=True"
+)
+
+new_agent = client.create_agent(agent_data)
+print(f"Created agent with ID: {new_agent.id}")
+```
+
+## Client Configuration
+
+### Constructor Parameters
+
+| Parameter | Type | Required | Default | Description |
+|-----------|------|----------|----------|-------------|
+| base_url | str | No | https://swarmcloud-285321057562.us-central1.run.app | The base URL of the SwarmCloud API |
+| api_key | str | Yes | None | Your SwarmCloud API key |
+| timeout | float | No | 10.0 | Request timeout in seconds |
+
+## Data Models
+
+### AgentCreate
+Model for creating new agents.
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|----------|-------------|
+| name | str | Yes | - | Name of the agent |
+| description | str | No | None | Description of the agent's purpose |
+| code | str | Yes | - | Python code that defines the agent's behavior |
+| requirements | str | No | None | Python package requirements (pip format) |
+| envs | str | No | None | Environment variables for the agent |
+| autoscaling | bool | No | False | Enable/disable concurrent execution scaling |
+
+### AgentUpdate
+Model for updating existing agents.
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|----------|-------------|
+| name | str | No | None | Updated name of the agent |
+| description | str | No | None | Updated description |
+| code | str | No | None | Updated Python code |
+| requirements | str | No | None | Updated package requirements |
+| autoscaling | bool | No | None | Updated autoscaling setting |
+
+## API Methods
+
+### List Agents
+Retrieve all available agents.
+
+```python
+agents = client.list_agents()
+for agent in agents:
+    print(f"Agent: {agent.name} (ID: {agent.id})")
+```
+
+**Returns**: List[AgentOut]
+
+### Create Agent
+Create a new agent with the specified configuration.
+
+```python
+agent_data = AgentCreate(
+    name="DataProcessor",
+    description="Processes incoming data streams",
+    code="""
+    def main(request, store):
+        data = request.payload.get('data', [])
+        return {'processed': len(data)}
+    """,
+    requirements="pandas==1.4.0\nnumpy==1.21.0",
+    envs="PROCESSING_MODE=fast",
+    autoscaling=True
+)
+
+new_agent = client.create_agent(agent_data)
+```
+
+**Returns**: AgentOut
+
+### Get Agent
+Retrieve details of a specific agent.
+
+```python
+agent = client.get_agent("agent_id_here")
+print(f"Agent details: {agent}")
+```
+
+**Parameters**:
+- agent_id (str): The unique identifier of the agent
+
+**Returns**: AgentOut
+
+### Update Agent
+Update an existing agent's configuration.
+
+```python
+update_data = AgentUpdate(
+    name="UpdatedProcessor",
+    description="Enhanced data processing capabilities",
+    code="def main(request, store):\n    return {'status': 'updated'}"
+)
+
+updated_agent = client.update_agent("agent_id_here", update_data)
+```
+
+**Parameters**:
+- agent_id (str): The unique identifier of the agent
+- update (AgentUpdate): The update data
+
+**Returns**: AgentOut
+
+### Execute Agent
+Manually execute an agent with optional payload data.
+
+```python
+# Execute with payload
+result = client.execute_agent(
+    "agent_id_here",
+    payload={"text": "Hello, World!"}
+)
+
+# Execute without payload
+result = client.execute_agent("agent_id_here")
+```
+
+**Parameters**:
+- agent_id (str): The unique identifier of the agent
+- payload (Optional[Dict[str, Any]]): Execution payload data
+
+**Returns**: Dict[str, Any]
+
+### Get Agent History
+Retrieve the execution history and logs for an agent.
+
+```python
+history = client.get_agent_history("agent_id_here")
+for execution in history.executions:
+    print(f"[{execution.timestamp}] {execution.log}")
+```
+
+**Parameters**:
+- agent_id (str): The unique identifier of the agent
+
+**Returns**: AgentExecutionHistory
+
+### Batch Execute Agents
+Execute multiple agents simultaneously with the same payload.
+
+```python
+# Get list of agents
+agents = client.list_agents()
+
+# Execute batch with payload
+results = client.batch_execute_agents(
+    agents=agents[:3],  # Execute first three agents
+    payload={"data": "test"}
+)
+
+print(f"Batch execution results: {results}")
+```
+
+**Parameters**:
+- agents (List[AgentOut]): List of agents to execute
+- payload (Optional[Dict[str, Any]]): Shared execution payload
+
+**Returns**: List[Any]
+
+### Health Check
+Check the API's health status.
+
+```python
+status = client.health()
+print(f"API Status: {status}")
+```
+
+**Returns**: Dict[str, Any]
+
+## Error Handling
+The client uses exception handling to manage various error scenarios:
+
+```python
+from swarms_cloud import SwarmCloudAPI
+import httpx
+
+try:
+    client = SwarmCloudAPI(api_key="your_api_key_here")
+    agents = client.list_agents()
+except httpx.HTTPError as http_err:
+    print(f"HTTP error occurred: {http_err}")
+except Exception as err:
+    print(f"An unexpected error occurred: {err}")
+finally:
+    client.close()
+```
+
+## Context Manager Support
+The client can be used with Python's context manager:
+
+```python
+with SwarmCloudAPI(api_key="your_api_key_here") as client:
+    status = client.health()
+    print(f"API Status: {status}")
+    # Client automatically closes after the with block
+```
+
+## Best Practices
+
+1. Always close the client when finished:
+```python
+client = SwarmCloudAPI(api_key="your_api_key_here")
+try:
+    # Your code here
+finally:
+    client.close()
+```
+
+2. Use context managers for automatic cleanup:
+```python
+with SwarmCloudAPI(api_key="your_api_key_here") as client:
+    # Your code here
+```
+
+3. Handle errors appropriately:
+```python
+try:
+    result = client.execute_agent("agent_id", payload={"data": "test"})
+except httpx.HTTPError as e:
+    logger.error(f"HTTP error: {e}")
+    # Handle error appropriately
+```
+
+4. Set appropriate timeouts for your use case:
+```python
+client = SwarmCloudAPI(
+    api_key="your_api_key_here",
+    timeout=30.0  # Longer timeout for complex operations
+)
+```
+
+## Complete Example
+Here's a complete example showcasing various features of the client:
+
+```python
+from swarms_cloud import SwarmCloudAPI, AgentCreate, AgentUpdate
+import httpx
+
+def main():
+    with SwarmCloudAPI(api_key="your_api_key_here") as client:
+        # Create an agent
+        agent_data = AgentCreate(
+            name="DataAnalyzer",
+            description="Analyzes incoming data streams",
+            code="""
+            def main(request, store):
+                data = request.payload.get('data', [])
+                return {
+                    'count': len(data),
+                    'summary': 'Data processed successfully'
+                }
+            """,
+            requirements="pandas==1.4.0",
+            autoscaling=True
+        )
+        
+        try:
+            # Create the agent
+            new_agent = client.create_agent(agent_data)
+            print(f"Created agent: {new_agent.name} (ID: {new_agent.id})")
+            
+            # Execute the agent
+            result = client.execute_agent(
+                new_agent.id,
+                payload={"data": [1, 2, 3, 4, 5]}
+            )
+            print(f"Execution result: {result}")
+            
+            # Update the agent
+            update_data = AgentUpdate(
+                description="Enhanced data analysis capabilities"
+            )
+            updated_agent = client.update_agent(new_agent.id, update_data)
+            print(f"Updated agent: {updated_agent.name}")
+            
+            # Get execution history
+            history = client.get_agent_history(new_agent.id)
+            print(f"Execution history: {history}")
+            
+        except httpx.HTTPError as e:
+            print(f"HTTP error occurred: {e}")
+        except Exception as e:
+            print(f"Unexpected error: {e}")
+
+if __name__ == "__main__":
+    main()
+```
+
+## Logging
+The client uses the `loguru` library for logging. You can configure the logging level and format:
+
+```python
+from loguru import logger
+
+# Configure logging
+logger.add("swarmcloud.log", rotation="500 MB")
+
+client = SwarmCloudAPI(api_key="your_api_key_here")
+```
+
+## Performance Considerations
+
+1. **Connection Reuse**: The client reuses HTTP connections by default, improving performance for multiple requests.
+
+2. **Timeout Configuration**: Set appropriate timeouts based on your use case:
+```python
+client = SwarmCloudAPI(
+    api_key="your_api_key_here",
+    timeout=5.0  # Shorter timeout for time-sensitive operations
+)
+```
+
+3. **Batch Operations**: Use batch_execute_agents for multiple agent executions:
+```python
+results = client.batch_execute_agents(
+    agents=agents,
+    payload=shared_payload
+)
+```
+
+## Rate Limiting
+The client respects API rate limits but does not implement retry logic. Implement your own retry mechanism if needed:
+
+```python
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+def execute_with_retry(client, agent_id, payload):
+    return client.execute_agent(agent_id, payload)
+```
+
+## Thread Safety
+The client is not thread-safe by default. For concurrent usage, create separate client instances for each thread or implement appropriate synchronization mechanisms.
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