> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Self Reflection AI Agents
> Self-reflection enables agents to evaluate and improve their own responses before delivering them.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
In[In] --> Agent[AI Agent]
Agent --> Reflect[Self Reflection]
Reflect --> Agent
Agent --> Out[Out]
style In fill:#8B0000,color:#fff
style Agent fill:#2E8B57,color:#fff
style Reflect fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
Self-reflection enables agents to evaluate and improve their own responses before delivering them.
## Quick Start
First, install the PraisonAI Agents package:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents
```
Set your OpenAI API key as an environment variable in your terminal:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export OPENAI_API_KEY=your_api_key_here
```
Create a new file `app.py` with the basic setup:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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()
```
Type this in your terminal to run your agents:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
python app.py
```
Install the PraisonAI package:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonai
```
Set your OpenAI API key as an environment variable in your terminal:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export OPENAI_API_KEY=your_api_key_here
```
Create a new file `agents.yaml` with the basic setup:
```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```
Type this in your terminal to run your agents:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```
**Requirements**
* Python 3.10 or higher
* OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).
## Understanding 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
Evaluates and improves response quality through self-review.
Refines responses through multiple reflection cycles.
Ensures all aspects of the task are properly addressed.
Enables thoughtful evaluation of responses.
## Multi-Agent Self-Reflection
First, install the PraisonAI Agents package:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents
```
Set your OpenAI API key as an environment variable in your terminal:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export OPENAI_API_KEY=your_api_key_here
```
Create a new file `app.py` with the basic setup:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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()
```
Type this in your terminal to run your agents:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
python app.py
```
Install the PraisonAI package:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonai duckduckgo_search
```
Set your OpenAI API key as an environment variable in your terminal:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export OPENAI_API_KEY=your_api_key_here
```
Create a new file `agents.yaml` with the basic setup:
```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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.
```
Type this in your terminal to run your agents:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```
### Configuration Options
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
If responses are not meeting expectations:
* Enable verbose mode for debugging
* Review agent configuration
* Check task description clarity
If output quality is insufficient:
* Enable verbose mode
* Review agent role and goal
* Clarify expected output
## Next Steps
Learn about automatically created and managed AI agents
Explore lightweight, focused AI agents
For optimal results, configure reflection parameters based on your specific use case requirements.