Reasoning Agents

Create AI agents with advanced reasoning capabilities for complex problem-solving

Reasoning agents think step-by-step before acting, breaking complex problems into structured solutions.

from praisonaiagents import Agent

agent = Agent(
    name="Reasoner",
    instructions="You are an expert problem solver. Think step by step.",
)

agent.start("Analyse the pros and cons of electric vehicles")

The user poses a complex problem; the agent reasons step-by-step before responding.

Quick Start

1. Simple Usage

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Reasoner",
       instructions="You are an expert problem solver. Think step by step.",
   )
   
   agent.start("Analyse the pros and cons of electric vehicles")
   
2. With Configuration

   Use a reasoning-optimised model for multi-step problems:

   from praisonaiagents import Agent
   
   agent = Agent(
       name="DeepReasoner",
       instructions="You solve complex problems with structured analysis.",
       llm="o1-mini",
   )
   
   agent.start("What strategy should a startup use to enter a competitive market?")
   

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How It Works

Stage	Description
Reason	Agent breaks the problem into sub-questions
Decide	Agent evaluates options with evidence
Act	Agent executes the chosen approach
Output	Agent returns solution with explanation

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Common Patterns

Multi-agent reasoning pipeline

from praisonaiagents import Agent, Task, AgentTeam

analyst = Agent(
    name="Analyst",
    instructions="Analyse problems and identify key issues with evidence.",
)

solver = Agent(
    name="Solver",
    instructions="Design comprehensive solutions based on analysis.",
)

team = AgentTeam(
    agents=[analyst, solver],
    tasks=[
        Task(
            description="Analyse market challenges in the electric vehicle industry",
            expected_output="Detailed analysis with key findings",
            agent=analyst,
        ),
        Task(
            description="Develop strategy based on the analysis",
            expected_output="Action plan with prioritised recommendations",
            agent=solver,
        ),
    ],
)

team.start()

Reasoning with tools

from praisonaiagents import Agent
from praisonaiagents.tools import internet_search

agent = Agent(
    name="ResearchReasoner",
    instructions="Research topics deeply and reason about the findings.",
    tools=[internet_search],
)

agent.start("What are the biggest AI safety concerns in 2025?")

Structured output

from praisonaiagents import Agent
from pydantic import BaseModel

class Analysis(BaseModel):
    summary: str
    pros: list[str]
    cons: list[str]
    recommendation: str

agent = Agent(
    name="StructuredReasoner",
    instructions="Analyse topics and provide structured assessments.",
    output_pydantic=Analysis,
)

result = agent.start("Should we migrate our database to PostgreSQL?")
print(result.recommendation)

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Best Practices

Write clear, specific problem statements

Vague inputs produce vague reasoning. State the problem with context: instead of “help with marketing”, use “suggest three low-budget marketing strategies for a B2B SaaS startup targeting mid-sized logistics companies.”

Use reasoning-optimised models for complex tasks

Models like o1-mini, o3-mini, or claude-3-7-sonnet perform better on multi-step problems. Use standard models for simpler tasks to save cost and latency.

Break problems into sub-tasks with multi-agent pipelines

Complex reasoning benefits from specialisation. Assign analysis to one agent and solution design to another — each agent focuses on its strength without context overload.

Use structured output for decision-making

When reasoning must produce actionable decisions, use output_pydantic to get typed results. This ensures reasoning outputs are machine-readable and integration-ready.

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Related

Planning Mode

Enable agents to plan before executing multi-step tasks

Reasoning Extract

Chain a reasoning model with a smaller agent to extract concise answers