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An agent-based workflow where LLMs act autonomously within a loop, interacting with their environment and receiving feedback to refine their actions and decisions.

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Quick Start

1

Install Package

First, install the PraisonAI Agents package:
pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:
export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:
from praisonaiagents import Agent, Workflow
from praisonaiagents.workflows import route, repeat

# Create monitor agent that analyzes environment state
monitor_agent = Agent(
    name="Monitor",
    role="Environment Monitor",
    goal="Monitor and analyze system state",
    instructions="Analyze the current state. Respond with ONLY 'normal', 'critical', or 'optimal'."
)

# Create action agents for different states
maintenance_agent = Agent(
    name="Maintenance",
    role="System Maintainer",
    goal="Maintain normal system operations",
    instructions="Perform routine maintenance tasks. Report what was done."
)

emergency_agent = Agent(
    name="Emergency",
    role="Emergency Responder",
    goal="Handle critical situations",
    instructions="Address the critical issue immediately. Provide resolution steps."
)

optimization_agent = Agent(
    name="Optimizer",
    role="System Optimizer",
    goal="Optimize system performance",
    instructions="The system is optimal. Suggest further improvements."
)

# Create feedback agent
feedback_agent = Agent(
    name="Feedback",
    role="Feedback Processor",
    goal="Process and learn from feedback",
    instructions="Analyze the action taken and provide feedback for improvement."
)

# Check if optimal state reached
def is_optimal(ctx) -> bool:
    return "optimal" in ctx.previous_result.lower()

# Create autonomous workflow
workflow = Workflow(
    steps=[
        repeat(
            monitor_agent,  # Monitor keeps checking state
            until=is_optimal,
            max_iterations=3
        ),
        route({
            "normal": [maintenance_agent],
            "critical": [emergency_agent],
            "optimal": [optimization_agent]
        }),
        feedback_agent
    ]
)

result = workflow.start("Monitor the AI system health")
print(f"Result: {result['output'][:500]}...")
4

Start Workflow

Type this in your terminal to run your workflow:
python app.py
Requirements
  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
  • Basic understanding of Python

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Understanding Autonomous Workflow

What is Autonomous Workflow?

Autonomous Workflow enables:
  • Continuous environment monitoring
  • Automated decision-making and action execution
  • Real-time feedback processing
  • Self-adapting behavior based on outcomes

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Features

Environment Monitoring

Continuously monitor and analyze environment state.

Adaptive Actions

Execute context-aware actions based on state analysis.

Feedback Processing

Process and learn from action outcomes.

Self-Optimization

Improve performance through continuous learning.

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Configuration Options

# Create a monitor agent
monitor = Agent(
    name="Environment Monitor",
    role="State analyzer",
    goal="Monitor and analyze state",
    tools=[get_environment_state],  # Environment monitoring tools
    verbose=True  # Enable detailed logging
)

# Create an action agent
action = Agent(
    name="Action Executor",
    role="Action performer",
    goal="Execute appropriate actions",
    tools=[perform_action]  # Action execution tools
)

# Create monitoring task
monitor_task = Task(
    name="monitor_environment",
    description="Monitor environment state",
    agent=monitor,
    is_start=True,
    task_type="decision",
    condition={
        "normal": ["execute_action"],
        "critical": ["execute_action"],
        "optimal": "exit"
    }
)

# Create feedback loop task
feedback_task = Task(
    name="process_feedback",
    description="Process and adapt",
    agent=feedback_agent,
    next_tasks=["monitor_environment"],  # Create feedback loop
    context=[monitor_task, action_task]  # Access history
)

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Troubleshooting

Monitoring Issues

If monitoring fails:
  • Check environment access
  • Verify state detection
  • Enable verbose mode for debugging

Adaptation Flow

If adaptation is incorrect:
  • Review feedback processing
  • Check action outcomes
  • Verify learning loop

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Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents
For optimal results, ensure your environment monitoring is reliable and your feedback processing logic is properly configured for effective adaptation.