output_schemas.py
"""
Task API — Output Schema Types
==============================
The Task API supports 4 output schema formats.
This cookbook demonstrates each type.
Output Schema Types:
1. Auto — Parallel determines structure
2. JSON Schema — Enforce specific fields
3. String — Natural language description
4. Text — Markdown report with citations
Prerequisites:
- pip install parallel-web
- export PARALLEL_API_KEY=<your-api-key>
"""
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.parallel import ParallelTools
# =============================================================================
# 1. AUTO SCHEMA
# =============================================================================
# Let Parallel determine the best output structure.
# Good for exploratory research where you don't know the format upfront.
# NOTE: Auto schema requires "pro" processor or higher.
auto_tools = ParallelTools(
enable_search=False,
enable_extract=False,
enable_task=True,
default_processor="pro", # Auto schema requires pro+
default_output_schema={"type": "auto"},
)
auto_agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=[auto_tools],
markdown=True,
)
# =============================================================================
# 2. JSON SCHEMA
# =============================================================================
# Enforce specific fields with types.
# Best for data enrichment and structured extraction.
json_tools = ParallelTools(
enable_search=False,
enable_extract=False,
enable_task=True,
default_output_schema={
"type": "json",
"json_schema": {
"type": "object",
"properties": {
"company_name": {"type": "string"},
"founding_year": {"type": "string"},
"total_funding": {"type": "string"},
"valuation": {"type": "string"},
"key_investors": {
"type": "array",
"items": {"type": "string"},
},
},
"required": ["company_name"],
},
},
)
json_agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=[json_tools],
markdown=True,
)
# =============================================================================
# 3. STRING SCHEMA
# =============================================================================
# Natural language description of expected output.
# Simpler than JSON Schema, more flexible.
string_tools = ParallelTools(
enable_search=False,
enable_extract=False,
enable_task=True,
default_output_schema="Return the company name, founding year, total funding raised, current valuation, and list of major investors",
)
string_agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=[string_tools],
markdown=True,
)
# =============================================================================
# 4. TEXT SCHEMA
# =============================================================================
# Markdown report with embedded citations.
# Best for long-form research reports.
text_tools = ParallelTools(
enable_search=False,
enable_extract=False,
enable_task=True,
default_output_schema={"type": "text"},
)
text_agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=[text_tools],
markdown=True,
)
# =============================================================================
# RUN
# =============================================================================
if __name__ == "__main__":
# Using JSON schema for structured company data
json_agent.print_response(
"Research Anthropic: funding history and key investors.",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai parallel-web
3
Export your API keys
export OPENAI_API_KEY="your_openai_api_key_here"
export PARALLEL_API_KEY="your_parallel_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
$Env:PARALLEL_API_KEY="your_parallel_api_key_here"
4
Run the example
Save the code above as
output_schemas.py, then run:python output_schemas.py