Self-reflection enables agents to evaluate and improve their own responses before delivering them.

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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, Task, PraisonAIAgents

# Create an agent with self-reflection
agent = Agent(
    role="Senior Research Analyst",
    goal="Analyze and provide insights on given topics",
    backstory="You are an expert analyst with strong critical thinking skills",
    self_reflect=True  # Enable self-reflection
)

# Create a task
task = Task(
    description="Analyze recent developments in AI",
    expected_output="A detailed analysis report",
    agent=agent
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[agent],
    tasks=[task],
    process="sequential",
    verbose=2
)

# Start execution
agents.start()
4

Start Agents

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

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Understanding Self-Reflection

What is Self-Reflection?

Self-reflection enables agents to:
  • Evaluate their own responses before delivery
  • Check for completeness and accuracy
  • Improve output quality through iteration
  • Ensure task requirements are met
  • Make conscious decisions about their responses

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Features

Quality Assurance

Evaluates and improves response quality through self-review.

Iterative Improvement

Refines responses through multiple reflection cycles.

Task Validation

Ensures all aspects of the task are properly addressed.

Conscious Decision Making

Enables thoughtful evaluation of responses.

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Multi-Agent Self-Reflection

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, Task, PraisonAIAgents

# Create first agent with self-reflection
researcher = Agent(
    role="Senior Research Analyst",
    goal="Research and analyze AI developments",
    backstory="Expert analyst specializing in AI trends and impacts",
    self_reflect=True,
    verbose=True
)

# Create second agent with self-reflection
writer = Agent(
    role="Technical Writer",
    goal="Transform research into clear documentation",
    backstory="Experienced in creating technical content and documentation",
    self_reflect=True,
    verbose=True
)

# Create first task
research_task = Task(
    description="Research and analyze recent AI developments",
    expected_output="Comprehensive analysis of AI trends",
    agent=researcher
)

# Create second task
documentation_task = Task(
    description="Create technical documentation from research findings",
    expected_output="Well-structured technical documentation",
    agent=writer
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[researcher, writer],
    tasks=[research_task, documentation_task],
    process="sequential"
)

# Start execution
agents.start()
4

Start Agents

Type this in your terminal to run your agents:
python app.py

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Configuration Options

# Create an agent with advanced self-reflection configuration
agent = Agent(
    role="Research Analyst",
    goal="Provide comprehensive analysis",
    backstory="Expert analyst with critical thinking skills",
    self_reflect=True,  # Enable self-reflection
    verbose=True,  # Enable detailed logging
    llm="gpt-4o",  # Language model to use
    allow_delegation=True  # Allow task delegation
)

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Troubleshooting

Response Issues

If responses are not meeting expectations:
  • Enable verbose mode for debugging
  • Review agent configuration
  • Check task description clarity

Quality Issues

If output quality is insufficient:
  • Enable verbose mode
  • Review agent role and goal
  • Clarify expected output

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Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

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For optimal results, configure reflection parameters based on your specific use case requirements.