swarms/docs/examples/agent_stream.md

Agent with Streaming

The Swarms framework provides powerful real-time streaming capabilities for agents, allowing you to see responses being generated token by token as they're produced by the language model. This creates a more engaging and interactive experience, especially useful for long-form content generation, debugging, or when you want to provide immediate feedback to users.

Installation

Install the swarms package using pip:

pip install -U swarms

Basic Setup

1. First, set up your environment variables:

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY=""

Step by Step

• Install and put your keys in .env

• Turn on streaming in Agent() with streaming_on=True

• Optional: If you want to pretty print it, you can do print_on=True; if not, it will print normally

Code

from swarms import Agent

# Enable real-time streaming
agent = Agent(
    agent_name="StoryAgent",
    model_name="gpt-4o-mini",
    streaming_on=True,  # 🔥 This enables real streaming!
    max_loops=1,
    print_on=True,  # By default, it's False for raw streaming!
)

# This will now stream in real-time with a beautiful UI!
response = agent.run("Tell me a detailed story about humanity colonizing the stars")
print(response)

Streaming with Tools Execution

Swarms also supports real-time streaming while executing tools, providing immediate feedback on both the thinking process and tool execution results:

from swarms import Agent

def get_weather(location: str, units: str = "celsius") -> str:
    """
    Get the current weather for a location.
    
    Args:
        location (str): The city/location to get weather for
        units (str): Temperature units (celsius or fahrenheit)

    Returns:
        str: Weather information
    """
    weather_data = {
        "New York": {"temperature": "22°C", "condition": "sunny", "humidity": "65%"},
        "London": {"temperature": "15°C", "condition": "cloudy", "humidity": "80%"},
        "Tokyo": {"temperature": "28°C", "condition": "rainy", "humidity": "90%"},
    }
    
    location_key = location.title()
    if location_key in weather_data:
        data = weather_data[location_key]
        temp = data["temperature"] 
        if units == "fahrenheit" and "°C" in temp:
            celsius = int(temp.replace("°C", ""))
            fahrenheit = (celsius * 9/5) + 32
            temp = f"{fahrenheit}°F"
        
        return f"Weather in {location}: {temp}, {data['condition']}, humidity: {data['humidity']}"
    else:
        return f"Weather data not available for {location}"

# Create agent with streaming and tool support
agent = Agent(
    model_name="gpt-4o",
    max_loops=1,
    verbose=True,
    streaming_on=True,  # Enable streaming
    print_on=True,      # Enable pretty printing
    tools=[get_weather], # Add tools
)

# This will stream both the reasoning and tool execution results
agent.run("What is the weather in Tokyo? ")

Key Features of Streaming with Tools:

• Real-time tool execution: See tool calls happen as they're invoked
• Streaming responses: Get immediate feedback on the agent's reasoning
• Tool result integration: Watch how tools results are incorporated into the final response
• Interactive debugging: Monitor the complete workflow from thought to action

Best Practices:

1. Set appropriate max_loops: Use max_loops=1 for simple tasks or higher values for complex multi-step operations
2. Enable verbose mode: Use verbose=True to see detailed tool execution logs
3. Use print_on for UI: Enable print_on=True for better visual streaming experience
4. Monitor performance: Streaming with tools may be slower due to real-time processing

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