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# Agent with Streaming
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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.
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## Installation
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Install the swarms package using pip:
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```bash
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pip install -U swarms
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```
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## Basic Setup
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1. First, set up your environment variables:
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```python
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WORKSPACE_DIR="agent_workspace"
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OPENAI_API_KEY=""
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```
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## Step by Step
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- Install and put your keys in `.env`
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- Turn on streaming in `Agent()` with `streaming_on=True`
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- Optional: If you want to pretty print it, you can do `print_on=True`; if not, it will print normally
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## Code
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```python
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from swarms import Agent
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# Enable real-time streaming
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agent = Agent(
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agent_name="StoryAgent",
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model_name="gpt-4o-mini",
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streaming_on=True, # 🔥 This enables real streaming!
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max_loops=1,
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print_on=True, # By default, it's False for raw streaming!
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)
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# This will now stream in real-time with a beautiful UI!
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response = agent.run("Tell me a detailed story about humanity colonizing the stars")
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print(response)
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```
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## Connect With Us
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If you'd like technical support, join our Discord below and stay updated on our Twitter for new updates!
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| Platform | Link | Description |
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|----------|------|-------------|
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| 📚 Documentation | [docs.swarms.world](https://docs.swarms.world) | Official documentation and guides |
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| 📝 Blog | [Medium](https://medium.com/@kyeg) | Latest updates and technical articles |
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| 💬 Discord | [Join Discord](https://discord.gg/jM3Z6M9uMq) | Live chat and community support |
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| 🐦 Twitter | [@kyegomez](https://twitter.com/kyegomez) | Latest news and announcements |
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| 👥 LinkedIn | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) | Professional network and updates |
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| 📺 YouTube | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) | Tutorials and demos |
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| 🎫 Events | [Sign up here](https://lu.ma/5p2jnc2v) | Join our community events |
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@ -1,81 +0,0 @@
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import json
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from swarms import Agent, SwarmRouter
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# Agent 1: Risk Metrics Calculator
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risk_metrics_agent = Agent(
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agent_name="Risk-Metrics-Calculator",
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agent_description="Calculates key risk metrics like VaR, Sharpe ratio, and volatility",
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system_prompt="""You are a risk metrics specialist. Calculate and explain:
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- Value at Risk (VaR)
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- Sharpe ratio
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- Volatility
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- Maximum drawdown
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- Beta coefficient
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Provide clear, numerical results with brief explanations.""",
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max_loops=1,
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# model_name="gpt-4o-mini",
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random_model_enabled=True,
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dynamic_temperature_enabled=True,
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output_type="str-all-except-first",
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max_tokens=4096,
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)
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# Agent 2: Portfolio Risk Analyzer
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portfolio_risk_agent = Agent(
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agent_name="Portfolio-Risk-Analyzer",
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agent_description="Analyzes portfolio diversification and concentration risk",
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system_prompt="""You are a portfolio risk analyst. Focus on:
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- Portfolio diversification analysis
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- Concentration risk assessment
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- Correlation analysis
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- Sector/asset allocation risk
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- Liquidity risk evaluation
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Provide actionable insights for risk reduction.""",
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max_loops=1,
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# model_name="gpt-4o-mini",
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random_model_enabled=True,
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dynamic_temperature_enabled=True,
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output_type="str-all-except-first",
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max_tokens=4096,
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)
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# Agent 3: Market Risk Monitor
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market_risk_agent = Agent(
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agent_name="Market-Risk-Monitor",
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agent_description="Monitors market conditions and identifies risk factors",
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system_prompt="""You are a market risk monitor. Identify and assess:
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- Market volatility trends
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- Economic risk factors
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- Geopolitical risks
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- Interest rate risks
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- Currency risks
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Provide current risk alerts and trends.""",
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max_loops=1,
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# model_name="gpt-4o-mini",
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random_model_enabled=True,
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dynamic_temperature_enabled=True,
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output_type="str-all-except-first",
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max_tokens=4096,
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)
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swarm = SwarmRouter(
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agents=[
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risk_metrics_agent,
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portfolio_risk_agent,
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],
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max_loops=1,
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swarm_type="MixtureOfAgents",
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output_type="final",
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)
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# swarm.run(
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# "Calculate VaR and Sharpe ratio for a portfolio with 15% annual return and 20% volatility"
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# )
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print(f"Swarm config: {json.dumps(swarm.to_dict(), indent=4)}")
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