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Learn how to create AI agents with advanced reasoning capabilities for complex problem-solving.

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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, Agents, Tools

# Create reasoning agent
reasoner = Agent(
    role="Problem Solver",
    goal="Solve complex problems using logical reasoning",
    backstory="Expert in logical analysis and problem-solving",
    tools=[Tools.internet_search],
    
)

# Create task
task = Task(
    description="Analyze and solve a complex business problem",
    expected_output="Detailed solution with reasoning steps",
    agent=reasoner
)

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

# Start execution
result = agents.start()
print(result)
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.
  • Basic understanding of Python

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Understanding Reasoning

What is Reasoning?

Reasoning agents are designed to:
  • Break down complex problems into manageable steps
  • Apply logical analysis to find solutions
  • Explain their thought process and decisions
  • Handle uncertainty and incomplete information

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Features

Problem Decomposition

Break complex problems into smaller, manageable parts.

Logical Analysis

Apply structured thinking to solve problems.

Error Recovery

Handle edge cases and recover from errors.

Explanation

Provide detailed reasoning for decisions.

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Multi-Agent Reasoning

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, Agents, Tools

# Create first agent for analysis
analyst = Agent(
    role="Business Analyst",
    goal="Analyze business problems and identify key issues",
    backstory="Expert in business analysis and problem identification",
    tools=[Tools.internet_search],
    
)

# Create second agent for solution development
solver = Agent(
    role="Solution Architect",
    goal="Develop comprehensive solutions to business problems",
    backstory="Expert in solution design and implementation",
    
)

# Create first task
analysis_task = Task(
    description="Analyze the current market challenges",
    expected_output="Detailed analysis of key issues",
    agent=analyst
)

# Create second task
solution_task = Task(
    description="Develop solutions for identified challenges",
    expected_output="Comprehensive solution strategy",
    agent=solver
)

# Create and start the agents
agents = Agents(
    agents=[analyst, solver],
    tasks=[analysis_task, solution_task],
    process="sequential"
)

# Start execution
result = 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 reasoning configuration
agent = Agent(
    role="Problem Solver",
    goal="Solve complex problems",
    backstory="Expert in problem-solving",
    tools=[Tools.internet_search],
      # Enable detailed logging
    llm="gpt-4o"  # Language model to use
)

# Task with reasoning requirements
task = Task(
    description="Solve complex problem",
    expected_output="Detailed solution",
    agent=agent
)

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Troubleshooting

Reasoning Errors

If reasoning seems incorrect:
  • Check problem description clarity
  • Enable verbose mode for debugging
  • Review agent configuration

Process Flow

If process flow is unclear:
  • Verify task dependencies
  • Check agent roles and goals
  • Review task descriptions

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Next Steps

AutoAgents

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Mini Agents

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For optimal results, ensure your problem descriptions are clear and provide sufficient context for the reasoning agents.