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The problem: Regular models often rush to answers on complex problems, missing steps or making logical errors. The solution: Enable reasoning=True and watch your model break down the problem, explore multiple approaches, validate results, and deliver thoroughly vetted solutions. The beauty? It works with any model, from GPT-4o to Claude to local models via Ollama. You’re not limited to specialized reasoning models.

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How It Works

Enable reasoning on any agent by setting reasoning=True: 
from agno.agent import Agent
from agno.models.openai import OpenAIChat

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),  # Any model works
    reasoning=True,
)
Behind the scenes, Agno creates a separate reasoning agent instance that uses your same model but with specialized prompting that guides it through a rigorous 6-step reasoning framework:

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The Reasoning Framework

  1. Problem Analysis
    • Restate the task to ensure full comprehension
    • Identify required information and necessary tools
  2. Decompose and Strategize
    • Break down the problem into subtasks
    • Develop multiple distinct approaches
  3. Intent Clarification and Planning
    • Articulate the user’s intent
    • Select the best strategy with clear justification
    • Create a detailed action plan
  4. Execute the Action Plan
    • For each step: document title, action, result, reasoning, next action, and confidence score
    • Call tools as needed to gather information
    • Self-correct if errors are detected
  5. Validation (Mandatory)
    • Cross-verify with alternative approaches
    • Use additional tools to confirm accuracy
    • Reset and revise if validation fails
  6. Final Answer
    • Deliver the thoroughly validated solution
    • Explain how it addresses the original task
The reasoning agent works through these steps iteratively (up to 10 by default), building on previous results, calling tools, and self-correcting until it reaches a confident solution. Once complete, it hands the full reasoning back to your main agent for the final response.

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How It Differs by Model Type

With regular models (gpt-4o, Claude Sonnet, Gemini):
  • Forces structured chain-of-thought through the 6-step framework
  • Creates detailed reasoning steps with confidence scores
  • This is where reasoning agents shine: transforming any model into a reasoning system
With native reasoning models (gpt-5-mini, DeepSeek-R1, o3-mini):
  • Uses the model’s built-in reasoning capabilities
  • Adds a validation pass from your main agent
  • Useful for critical tasks but often unnecessary overhead for simpler problems

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Basic Example

Let’s transform a regular GPT-4o model into a reasoning system:
reasoning_agent.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat

# Transform a regular model into a reasoning system
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning=True,
    markdown=True,
)

reasoning_agent.print_response(
    "Solve the trolley problem. Evaluate multiple ethical frameworks. Include an ASCII diagram of your solution.",
    stream=True,
    show_full_reasoning=True,  # Shows the complete reasoning process
)

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What You’ll See

With show_full_reasoning=True, you’ll see:
  • Each reasoning step with its title, action, and result
  • The agent’s thought process including why it chose each approach
  • Tool calls made during reasoning (if tools are provided)
  • Validation checks performed to verify the solution
  • Confidence scores for each step (0.0–1.0)
  • Self-corrections if the agent detects errors
  • The final polished response from your main agent

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Reasoning with Tools

Here’s where reasoning agents truly excel: combining multi-step reasoning with tool use. The reasoning agent can call tools iteratively, analyze results, and build toward a comprehensive solution.
finance_reasoning.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    instructions=["Use tables to display data"],
    reasoning=True,
    markdown=True,
)

reasoning_agent.print_response(
    "Compare the market performance of NVDA, AMD, and INTC over the past quarter. What are the key drivers?",
    stream=True,
    show_full_reasoning=True,
)
The reasoning agent will:
  1. Break down the task (need stock data for 3 companies)
  2. Use DuckDuckGo to search for current market data
  3. Analyze each company’s performance
  4. Search for news about key drivers
  5. Validate findings across multiple sources
  6. Create a comprehensive comparison with tables
  7. Provide a final answer with clear insights

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Configuration Options

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Display Options

Want to peek under the hood? Control what you see during reasoning: 
agent.print_response(
    "Your question",
    show_full_reasoning=True,  # Display complete reasoning process (default: False)
)

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Capturing Reasoning Events

For building custom UIs or programmatically tracking reasoning progress, you can capture reasoning events (ReasoningStarted, ReasoningStep, ReasoningCompleted) as they happen during streaming. See the Reasoning Reference for event attributes and complete code examples.

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Iteration Control

Adjust how many reasoning steps the agent takes: 
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning=True,
    reasoning_min_steps=2,  # Minimum reasoning steps (default: 1)
    reasoning_max_steps=15,  # Maximum reasoning steps (default: 10)
)
  • reasoning_min_steps: Ensures the agent thinks through at least this many steps before answering
  • reasoning_max_steps: Prevents infinite loops by capping the iteration count

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Custom Reasoning Agent

For advanced use cases, you can provide your own reasoning agent: 
from agno.agent import Agent
from agno.models.openai import OpenAIChat

# Create a custom reasoning agent with specific instructions
custom_reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    instructions=[
        "Focus heavily on mathematical rigor",
        "Always provide step-by-step proofs",
    ],
)

main_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning=True,
    reasoning_agent=custom_reasoning_agent,  # Use your custom agent
)

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Example Use Cases

  • Logical Puzzles
  • Mathematical Proofs
  • Scientific Research
  • Planning & Itineraries
  • Creative Writing
Breaking down complex logic problems:
logical_puzzle.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat

task = (
    "Three missionaries and three cannibals need to cross a river. "
    "They have a boat that can carry up to two people at a time. "
    "If, at any time, the cannibals outnumber the missionaries on either side of the river, the cannibals will eat the missionaries. "
    "How can all six people get across the river safely? Provide a step-by-step solution and show the solution as an ASCII diagram."
)

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning=True,
    markdown=True,
)

reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

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When to Use Reasoning Agents

Use reasoning agents when:
  • Your task requires multiple sequential steps
  • You need the agent to call tools iteratively and build on results
  • You want automated chain-of-thought without manually calling reasoning tools
  • You need self-validation and error correction
  • The problem benefits from exploring multiple approaches before settling on a solution
Consider alternatives when:
  • You’re using a native reasoning model (gpt-5-mini, DeepSeek-R1) for simple tasks: just use the model directly
  • You want explicit control over when the agent thinks vs. acts: use Reasoning Tools instead
  • The task is straightforward and doesn’t require multi-step thinking
Pro tip: Start with reasoning_max_steps=5 for simpler problems to avoid unnecessary overhead. Increase to 10-15 for complex multi-step tasks. Monitor with show_full_reasoning=True to see how many steps your agent actually needs.

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Developer Resources