swarm-deploy 0.0.4
Swarm-Deploy - TGSC
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

# Swarms Deploy 🚀

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Production-grade API deployment framework for Swarms AI workflows. Easily deploy, scale, and manage your swarm-based applications with enterprise features.

## Features ✨

- 🔥 Fast API-based deployment framework
- 🤖 Support for synchronous and asynchronous swarm execution
- 🔄 Built-in load balancing and scaling
- 📊 Real-time monitoring and logging
- 🛡️ Enterprise-grade error handling
- 🎯 Priority-based task execution
- 📦 Simple deployment and configuration
- 🔌 Extensible plugin architecture

## Installation 📦

```bash
pip install -U swarms-deploy
```

## Quick Start 🚀

```python
import os
from dotenv import load_dotenv
from swarms import Agent, SequentialWorkflow
from swarm_models import OpenAIChat
from swarm_deploy import SwarmDeploy

load_dotenv()

# Get the OpenAI API key from the environment variable
api_key = os.getenv("GROQ_API_KEY")

# Model
model = OpenAIChat(
    openai_api_base="https://api.groq.com/openai/v1",
    openai_api_key=api_key,
    model_name="llama-3.1-70b-versatile",
    temperature=0.1,
)


# Initialize specialized agents
data_extractor_agent = Agent(
    agent_name="Data-Extractor",
    system_prompt=None,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="data_extractor_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

summarizer_agent = Agent(
    agent_name="Document-Summarizer",
    system_prompt=None,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="summarizer_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

financial_analyst_agent = Agent(
    agent_name="Financial-Analyst",
    system_prompt=None,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="financial_analyst_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

market_analyst_agent = Agent(
    agent_name="Market-Analyst",
    system_prompt=None,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="market_analyst_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

operational_analyst_agent = Agent(
    agent_name="Operational-Analyst",
    system_prompt=None,
    llm=model,
    max_loops=1,
    autosave=True,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="operational_analyst_agent.json",
    user_name="pe_firm",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
)

# Initialize the SwarmRouter
router = SequentialWorkflow(
    name="pe-document-analysis-swarm",
    description="Analyze documents for private equity due diligence and investment decision-making",
    max_loops=1,
    agents=[
        data_extractor_agent,
        summarizer_agent,
        financial_analyst_agent,
        market_analyst_agent,
        operational_analyst_agent,
    ],
    output_type="all",
)

# Advanced usage with configuration
swarm = SwarmDeploy(
    router,
    max_workers=4,
    # cache_backend="redis"
)
swarm.start(
    host="0.0.0.0",
    port=8000,
    workers=4,
)

```

## Advanced Usage 🔧

### Configuration Options

```python
swarm = SwarmDeploy(
    workflow,
    max_workers=4,
    cache_backend="redis",
    ssl_config={
        "keyfile": "path/to/key.pem",
        "certfile": "path/to/cert.pem"
    }
)
```



## API Reference 📚

### SwarmInput Model

```python
class SwarmInput(BaseModel):
    task: str          # Task description
    img: Optional[str] # Optional image input
    priority: int      # Task priority (0-10)
```

### API Endpoints

- **POST** `/v1/swarms/completions/{callable_name}`
  - Execute a task with the specified swarm
  - Returns: SwarmOutput or SwarmBatchOutput

### Example Request

```bash
curl -X POST "http://localhost:8000/v1/swarms/completions/document-analysis" \
     -H "Content-Type: application/json" \
     -d '{"task": "Analyze financial report", "priority": 5}'
```

## Monitoring and Logging 📊

SwarmDeploy provides built-in monitoring capabilities:

- Real-time task execution stats
- Error tracking and reporting
- Performance metrics
- Task history and audit logs

## Error Handling 🛡️

The system includes comprehensive error handling:

```python
try:
    result = await swarm.run(task)
except Exception as e:
    error_output = SwarmOutput(
        id=str(uuid.uuid4()),
        status="error",
        execution_time=time.time() - start_time,
        result=None,
        error=str(e)
    )
```

## Best Practices 🎯

1. Always set appropriate task priorities
2. Implement proper error handling
3. Use clustering for high-availability
4. Monitor system performance
5. Regular maintenance and updates

## Contributing 🤝

Contributions are welcome! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.

## Support 💬

- Email: kye@swarms.world
- Discord: [Join our community](https://swarms.world/swarms)
- Documentation: [https://docs.swarms.world](https://docs.swarms.world)

## License 📄

MIT License - see the [LICENSE](LICENSE) file for details.

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Powered by [swarms.ai](https://swarms.ai) 🚀

For enterprise support and custom solutions, contact kye@swarms.world