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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.

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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

StageDescription
ReasonAgent breaks the problem into sub-questions
DecideAgent evaluates options with evidence
ActAgent executes the chosen approach
OutputAgent returns solution with explanation

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Common Patterns

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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()

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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?")

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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

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.”
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.
Complex reasoning benefits from specialisation. Assign analysis to one agent and solution design to another — each agent focuses on its strength without context overload.
When reasoning must produce actionable decisions, use output_pydantic to get typed results. This ensures reasoning outputs are machine-readable and integration-ready.

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