Output Model

Set output_model to generate the final answer with a second model after the primary model handles the run.

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools

agent = Agent(
    model=OpenAIResponses(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    output_model=OpenAIResponses(id="gpt-5.2"),
    output_model_prompt="Write a concise report using the supplied research.",
)

agent.print_response("Summarize the top AI stories on Hacker News")

How It Works

The primary model processes the request and handles tool calls.
Agno removes the primary model’s final assistant message from the run history.
output_model generates a replacement response from the remaining history, including the user request and tool results.
If parser_model is configured, it parses that replacement into output_schema.

output_model receives the run history with the primary final response removed. Use parser_model when the next model must transform the generated content into a Pydantic object.

Choose a Pipeline

Goal	Configuration	Final call order
Return the primary model’s response	`model`	Primary model
Generate the final response with another model	`model` and `output_model`	Primary, then output
Convert the response into a schema	`model`, `parser_model`, and `output_schema`	Primary, then parser
Generate a replacement and structure it	`model`, `output_model`, `parser_model`, and `output_schema`	Primary, output, then parser

Each secondary model adds a model call to the run.

Parameters

Parameter	Description
`model`	Primary model for the run, including reasoning and tool calls
`output_model`	Model that generates a replacement final response from the run history
`output_model_prompt`	System prompt for `output_model`
`output_schema`	Pydantic model or JSON schema for structured output
`parser_model`	Model that converts the preceding response into `output_schema`
`parser_model_prompt`	System prompt for `parser_model`

parser_model requires output_schema. Agno logs a warning and skips parsing when no schema is set.

Control the Output Model

output_model_prompt replaces the existing system message for the output-model call. Agno inserts it when the run history has no system message.

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools

agent = Agent(
    model=OpenAIResponses(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    output_model=OpenAIResponses(id="gpt-5.2"),
    output_model_prompt=(
        "Return an executive summary with three findings and one recommendation."
    ),
)

agent.print_response("Research recent developments in AI agents")

Parse into a Schema

The parser model receives the preceding model’s content as its user message.

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from pydantic import BaseModel, Field


class ArticleSummary(BaseModel):
    title: str
    key_points: list[str] = Field(description="Three to five main points")
    sentiment: str = Field(description="positive, negative, or neutral")


agent = Agent(
    model=OpenAIResponses(id="gpt-5-mini"),
    output_schema=ArticleSummary,
    parser_model=OpenAIResponses(id="gpt-5.2"),
    parser_model_prompt="Extract only facts present in the supplied response.",
)

response = agent.run("Summarize recent changes to Python packaging")
summary: ArticleSummary = response.content
print(summary.key_points)

Agno supplies a default structured-output instruction when parser_model_prompt is unset. Set a custom prompt for extraction rules such as date formats, item limits, or field-specific constraints.

Combine Output and Parser Models

output_model runs before parser_model. The parser therefore structures the output model’s replacement response.

agent = Agent(
    model=OpenAIResponses(id="gpt-5-mini"),
    output_model=OpenAIResponses(id="gpt-5.2"),
    output_model_prompt="Write a concise factual summary.",
    parser_model=OpenAIResponses(id="gpt-5.2"),
    parser_model_prompt="Map the summary to ArticleSummary.",
    output_schema=ArticleSummary,
)

Get Started

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Additional Resources

Output Model

How It Works

Choose a Pipeline

Parameters

Control the Output Model

Parse into a Schema

Combine Output and Parser Models

​How It Works

​Choose a Pipeline

​Parameters

​Control the Output Model

​Parse into a Schema

​Combine Output and Parser Models

​Related

How It Works

Choose a Pipeline

Parameters

Control the Output Model

Parse into a Schema

Combine Output and Parser Models

Related