Self Reflection AI Agents

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

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

Quick Start

Code

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, AgentTeam
   
   # 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",
       reflection=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 = AgentTeam(
       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
   

No Code

1. Install Package

   Install the PraisonAI package:

   pip install praisonai
   
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 agents.yaml with the basic setup:

   framework: praisonai
   process: sequential
   topic: create movie script about cat in mars
   agents:  # Canonical: use 'agents' instead of 'roles'
     scriptwriter:
       instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert in dialogue and script structure, translating concepts into
         scripts.
       goal: Write a movie script about a cat in Mars
       role: Scriptwriter
       self_reflect: true
       min_iterations: 1
       max_iterations: 2
       tasks:
         scriptwriting_task:
           description: Turn the story concept into a production-ready movie script,
             including dialogue and scene details.
           expected_output: Final movie script with dialogue and scene details.
       tools:
       - search_tool
   
4. Start Agents

   Type this in your terminal to run your agents:

   praisonai agents.yaml
   

Requirements

• Python 3.10 or higher
• OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.

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

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.

Multi-Agent Self-Reflection

Code

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, AgentTeam
   
   # 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",
       reflection=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",
       reflection=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 = AgentTeam(
       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
   

No Code

1. Install Package

   Install the PraisonAI package:

   pip install praisonai duckduckgo_search
   
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 agents.yaml with the basic setup:

   framework: praisonai
   process: sequential
   topic: create technical documentation about AI trends
   agents:  # Canonical: use 'agents' instead of 'roles'
     researcher:
       instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert analyst specializing in AI trends and their implications.
       goal: Research and analyze current AI developments
       role: Senior Research Analyst
       self_reflect: true
       min_iterations: 1
       max_iterations: 2
       tasks:
         research_task:
           description: Research and analyze recent developments in AI technology.
           expected_output: Comprehensive analysis of current AI trends.
       tools:
       - duckduckgo
   
     writer:
       instructions:  # Canonical: use 'instructions' instead of 'backstory' Experienced technical writer skilled in creating clear documentation.
       goal: Transform research into accessible documentation
       role: Technical Writer
       tasks:
         documentation_task:
           description: Create technical documentation from research findings.
           expected_output: Well-structured technical documentation.
   
4. Start Agents

   Type this in your terminal to run your agents:

   praisonai agents.yaml
   

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",
    reflection=True,  # Enable self-reflection
      # Enable detailed logging
    llm="gpt-4o",  # Language model to use
    handoffs=[]  # Add agents for delegation
)

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

Next Steps

AutoAgents

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Mini Agents

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