A workflow where a reasoning agent breaks down complex problems into steps, followed by a smaller agent that processes these steps to provide concise answers.

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Prerequisites

1

Install Package

Install required packages:
pip install "praisonaiagents[llm]"
praisonaiagents[llm] includes all necessary dependencies for reasoning agents
2

Set API Key

Configure your API key:
export OPENAI_API_KEY=your_api_key_here
3

Create File

Create a new file called app.py and add the following code:
4

Run Application

Execute the script:
python app.py

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Code

from praisonaiagents import Agent, Task, PraisonAIAgents

reasoning_agent = Agent(
    role="Helpful Assistant", 
    reasoning_steps=True, 
    llm="deepseek/deepseek-reasoner"
)

small_agent = Agent(
    role="Helpful Assistant", 
    llm="gpt-3.5-turbo"
)

reasoning_task = Task(
    description="How many r's in the word 'Strawberry'?", 
    agent=reasoning_agent
)

small_task = Task(
    description="With the provided reasoning tell me how many r's in the word 'Strawberry'?", 
    agent=small_agent
)

agents = PraisonAIAgents(
    agents=[reasoning_agent, small_agent],
    tasks=[reasoning_task, small_task]
)

agents.start()

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Features

Step-by-Step Reasoning

Break down complex problems into logical steps.

Multi-Agent Collaboration

Combine reasoning and processing agents.

Flexible Models

Use different models for reasoning and processing.

Task Chaining

Connect reasoning output to processing tasks.

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Understanding Reasoning Agents

What are Reasoning Agents?

Reasoning agents enable:
  • Detailed step-by-step analysis
  • Logical problem decomposition
  • Clear reasoning paths
  • Enhanced decision making

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Troubleshooting

Reasoning Issues

If reasoning steps aren’t clear:
  • Check reasoning_steps parameter
  • Verify model compatibility
  • Review task descriptions

Agent Communication

If agents aren’t coordinating:
  • Ensure task order is correct
  • Check task dependencies
  • Verify agent configurations
The reasoning agent uses a specialized model (deepseek-reasoner) optimized for step-by-step analysis, while the small agent can use a more general-purpose model for processing the reasoning output.