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

Explore lightweight, focused AI agents

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

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