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Structured Output
OpenAI
from typing import Literal
from pydantic import BaseModel, Field
from openai import OpenAI
LLM_MODEL = "gpt-5.4"
client = OpenAI()
class TicketAnalysis(BaseModel):
category: Literal["billing", "technical", "account", "product"]
priority: Literal["low", "medium", "high", "critical"]
sentiment: Literal["positive", "neutral", "negative"]
requires_escalation: bool
summary: str = Field(description="One-sentence summary of the issue")
suggested_tags: list[str] = Field(description="1-4 short labels for routing")
# text_format enforces the Pydantic schema on the LLM response
response = client.responses.parse(
model=LLM_MODEL,
text_format=TicketAnalysis,
input=[{
"role": "user",
"content": """\
Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'""",
}],
)
print(response.output_parsed)
# category='billing' priority='high' sentiment='negative' requires_escalation=True
# summary='Customer was double-charged for Pro subscription and hasn't received support.'
# suggested_tags=['billing', 'double-charge', 'escalation', 'churn-risk'] 
Anthropic
from typing import Literal
from pydantic import BaseModel, Field
import anthropic
LLM_MODEL = "claude-opus-4-6"
client = anthropic.Anthropic()
class TicketAnalysis(BaseModel):
category: Literal["billing", "technical", "account", "product"]
priority: Literal["low", "medium", "high", "critical"]
sentiment: Literal["positive", "neutral", "negative"]
requires_escalation: bool
summary: str = Field(description="One-sentence summary of the issue")
suggested_tags: list[str] = Field(description="1-4 short labels for routing")
# output_format enforces the Pydantic schema on the LLM response
response = client.messages.parse(
model=LLM_MODEL,
max_tokens=1024,
output_format=TicketAnalysis,
messages=[{
"role": "user",
"content": """\
Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'""",
}],
)
print(response.parsed_output)
# category='billing' priority='high' sentiment='negative' requires_escalation=True
# summary='Customer was double-charged for Pro subscription and hasn't received support.'
# suggested_tags=['billing', 'double-charge', 'escalation', 'churn-risk'] 
Gemini
from typing import Literal
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
LLM_MODEL = "gemini-pro-latest"
client = genai.Client()
class TicketAnalysis(BaseModel):
category: Literal["billing", "technical", "account", "product"]
priority: Literal["low", "medium", "high", "critical"]
sentiment: Literal["positive", "neutral", "negative"]
requires_escalation: bool
summary: str = Field(description="One-sentence summary of the issue")
suggested_tags: list[str] = Field(description="1-4 short labels for routing")
# response_schema accepts a Pydantic class; response_mime_type forces JSON output
config = types.GenerateContentConfig(
response_mime_type="application/json",
response_schema=TicketAnalysis,
)
response = client.models.generate_content(
model=LLM_MODEL,
config=config,
contents="""\
Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'""",
)
print(response.parsed)
# category='billing' priority='high' sentiment='negative' requires_escalation=True
# summary='Customer was double-charged for Pro subscription and hasn't received support.'
# suggested_tags=['billing', 'double-charge', 'escalation', 'churn-risk'] 
Pydantic AI
from typing import Literal
from pydantic import BaseModel, Field
from pydantic_ai import Agent
LLM_MODEL = "openai:gpt-5.4"
class TicketAnalysis(BaseModel):
category: Literal["billing", "technical", "account", "product"]
priority: Literal["low", "medium", "high", "critical"]
sentiment: Literal["positive", "neutral", "negative"]
requires_escalation: bool
summary: str = Field(description="One-sentence summary of the issue")
suggested_tags: list[str] = Field(description="1-4 short labels for routing")
# output_type constrains the agent to return a validated TicketAnalysis
agent = Agent(LLM_MODEL, output_type=TicketAnalysis)
result = agent.run_sync("""\
Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'""")
print(result.output)
# category='billing' priority='high' sentiment='negative' requires_escalation=True
# summary='Customer was double-charged for Pro subscription and hasn't received support.'
# suggested_tags=['billing', 'double-charge', 'escalation', 'churn-risk'] 
LangGraph
from typing import Literal
from langchain.agents import create_agent
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
LLM_MODEL = "gpt-5.4"
model = ChatOpenAI(model=LLM_MODEL)
class TicketAnalysis(BaseModel):
category: Literal["billing", "technical", "account", "product"]
priority: Literal["low", "medium", "high", "critical"]
sentiment: Literal["positive", "neutral", "negative"]
requires_escalation: bool
summary: str = Field(description="One-sentence summary of the issue")
suggested_tags: list[str] = Field(description="1-4 short labels for routing")
# response_format wires structured output through the agent API
agent = create_agent(model, tools=[], response_format=TicketAnalysis)
result = agent.invoke({
"messages": [("user", """\
Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'""")]
})
print(result["structured_response"])
# category='billing' priority='high' sentiment='negative' requires_escalation=True
# summary='Customer was double-charged for Pro subscription and hasn't received support.'
# suggested_tags=['billing', 'double-charge', 'escalation', 'churn-risk'] 
AI SDK
import { generateText, Output } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
const LLM_MODEL = "gpt-5.4";
const ticketAnalysis = z.object({
category: z.enum(["billing", "technical", "account", "product"]),
priority: z.enum(["low", "medium", "high", "critical"]),
sentiment: z.enum(["positive", "neutral", "negative"]),
requires_escalation: z.boolean(),
summary: z.string().describe("One-sentence summary of the issue"),
suggested_tags: z.array(z.string()).describe("1-4 short labels for routing"),
});
// Output.object() constrains the response to match the Zod schema
const { output } = await generateText({
model: openai(LLM_MODEL),
output: Output.object({ schema: ticketAnalysis }),
prompt: `Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'`,
});
console.log(output);
// { category: 'billing', priority: 'high', sentiment: 'negative',
// requires_escalation: true, summary: 'Customer was double-charged...',
// suggested_tags: ['billing', 'double-charge', 'escalation', 'churn-risk'] } 
Mastra
import { Agent } from "@mastra/core/agent";
import { z } from "zod";
// model specified as "provider/model-name" string
const LLM_MODEL = "openai/gpt-5.4";
const ticketAnalysis = z.object({
category: z.enum(["billing", "technical", "account", "product"]),
priority: z.enum(["low", "medium", "high", "critical"]),
sentiment: z.enum(["positive", "neutral", "negative"]),
requires_escalation: z.boolean(),
summary: z.string().describe("One-sentence summary of the issue"),
suggested_tags: z.array(z.string()).describe("1-4 short labels for routing"),
});
const agent = new Agent({
name: "ticket-analyzer",
instructions: "You are a helpful assistant.",
model: LLM_MODEL,
});
// structuredOutput constrains the response to match the Zod schema
const result = await agent.generate(
`Analyze this support ticket:
'I've been charged twice for my Pro subscription this month.
I contacted support 3 days ago and haven't heard back.
If this isn't resolved by Friday I'm switching to a competitor.'`,
{ structuredOutput: { schema: ticketAnalysis } },
);
console.log(result.object);
// { category: 'billing', priority: 'high', sentiment: 'negative',
// requires_escalation: true, summary: 'Customer was double-charged...',
// suggested_tags: ['billing', 'double-charge', 'escalation', 'churn-risk'] }Tool as Output
OpenAI
import json
from openai import OpenAI
LLM_MODEL = "gpt-5.4"
client = OpenAI()
ARTICLE = """\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149."""
# instead of text_format, use a tool call to extract data —
# the LLM "calls" the function, and we capture the arguments
tools = [{
"type": "function",
"name": "extract_recall",
"description": "Extract product recall information from an article.",
"parameters": {
"type": "object",
"properties": {
"product": {"type": "string"},
"company": {"type": "string"},
"units_affected": {"type": "integer"},
"hazard": {"type": "string", "description": "Primary hazard"},
"injuries_reported": {"type": "integer"},
"fatalities": {"type": "boolean"},
"retailers": {
"type": "array",
"items": {"type": "string"},
"description": "Stores that sold the product",
},
},
"required": [
"product", "company", "units_affected", "hazard",
"injuries_reported", "fatalities", "retailers",
],
},
}]
response = client.responses.create(
model=LLM_MODEL,
tools=tools,
input=[{
"role": "user",
"content": "Extract recall data from this article:\n\n" + ARTICLE,
}],
)
tool_call = next(i for i in response.output if i.type == "function_call")
print(json.loads(tool_call.arguments))
# {'product': 'SmartHeat Pro', 'company': 'BrightHome Inc.', 'units_affected': 142000,
# 'hazard': 'fire and burn', 'injuries_reported': 2, 'fatalities': False,
# 'retailers': ['HomeBase', 'WarmthPlus', 'Amazon.com']} Anthropic
import anthropic
LLM_MODEL = "claude-opus-4-6"
client = anthropic.Anthropic()
ARTICLE = """\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149."""
# instead of output_format, use a tool call to extract data —
# the LLM "calls" the function, and we capture the arguments
tools = [{
"name": "extract_recall",
"description": "Extract product recall information from an article.",
"input_schema": {
"type": "object",
"properties": {
"product": {"type": "string"},
"company": {"type": "string"},
"units_affected": {"type": "integer"},
"hazard": {"type": "string", "description": "Primary hazard"},
"injuries_reported": {"type": "integer"},
"fatalities": {"type": "boolean"},
"retailers": {
"type": "array",
"items": {"type": "string"},
"description": "Stores that sold the product",
},
},
"required": [
"product", "company", "units_affected", "hazard",
"injuries_reported", "fatalities", "retailers",
],
},
}]
response = client.messages.create(
model=LLM_MODEL,
max_tokens=1024,
tools=tools,
messages=[{
"role": "user",
"content": "Extract recall data from this article:\n\n" + ARTICLE,
}],
)
# block.input is already a dict — no json.loads needed
tool_block = next(b for b in response.content if b.type == "tool_use")
print(tool_block.input)
# {'product': 'SmartHeat Pro', 'company': 'BrightHome Inc.', 'units_affected': 142000,
# 'hazard': 'fire and burn', 'injuries_reported': 2, 'fatalities': False,
# 'retailers': ['HomeBase', 'WarmthPlus', 'Amazon.com']} Gemini
from google import genai
from google.genai import types
LLM_MODEL = "gemini-pro-latest"
client = genai.Client()
ARTICLE = """\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149."""
# instead of response_schema, use a tool call to extract data —
# the LLM "calls" the function, and we capture the arguments
tools = [types.Tool(function_declarations=[{
"name": "extract_recall",
"description": "Extract product recall information from an article.",
"parameters": {
"type": "object",
"properties": {
"product": {"type": "string"},
"company": {"type": "string"},
"units_affected": {"type": "integer"},
"hazard": {"type": "string", "description": "Primary hazard"},
"injuries_reported": {"type": "integer"},
"fatalities": {"type": "boolean"},
"retailers": {
"type": "array",
"items": {"type": "string"},
"description": "Stores that sold the product",
},
},
"required": [
"product", "company", "units_affected", "hazard",
"injuries_reported", "fatalities", "retailers",
],
},
}])]
config = types.GenerateContentConfig(tools=tools)
response = client.models.generate_content(
model=LLM_MODEL,
config=config,
contents="Extract recall data from this article:\n\n" + ARTICLE,
)
tool_call = response.function_calls[0]
print(dict(tool_call.args))
# {'product': 'SmartHeat Pro', 'company': 'BrightHome Inc.', 'units_affected': 142000,
# 'hazard': 'fire and burn', 'injuries_reported': 2, 'fatalities': False,
# 'retailers': ['HomeBase', 'WarmthPlus', 'Amazon.com']} Pydantic AI
from pydantic_ai import Agent
LLM_MODEL = "openai:gpt-5.4"
agent = Agent(LLM_MODEL)
ARTICLE = """\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149."""
# instead of output_type, use a tool call to extract data —
# the LLM "calls" the function, and we capture the arguments
@agent.tool_plain
def extract_recall(
product: str, company: str, units_affected: int,
hazard: str, injuries_reported: int, fatalities: bool,
retailers: list[str],
) -> str:
"""Extract product recall information from an article."""
print({
"product": product, "company": company,
"units_affected": units_affected, "hazard": hazard,
"injuries_reported": injuries_reported, "fatalities": fatalities,
"retailers": retailers,
})
return "Extracted."
result = agent.run_sync(
"Extract recall data from this article:\n\n" + ARTICLE
)
# {'product': 'SmartHeat Pro', 'company': 'BrightHome Inc.', 'units_affected': 142000,
# 'hazard': 'fire and burn', 'injuries_reported': 2, 'fatalities': False,
# 'retailers': ['HomeBase', 'WarmthPlus', 'Amazon.com']} LangGraph
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
LLM_MODEL = "gpt-5.4"
model = ChatOpenAI(model=LLM_MODEL)
ARTICLE = """\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149."""
# instead of with_structured_output(), use a tool call to extract data —
# the LLM "calls" the tool, and we capture the arguments
class RecallData(BaseModel):
"""Extract product recall information from an article."""
product: str
company: str
units_affected: int
hazard: str = Field(description="Primary hazard")
injuries_reported: int
fatalities: bool
retailers: list[str] = Field(description="Stores that sold the product")
# tool-as-output needs the tool call arguments without executing the tool,
# so this example binds the schema on the chat model instead of using an agent
model_with_tools = model.bind_tools([RecallData])
response = model_with_tools.invoke(
"Extract recall data from this article:\n\n" + ARTICLE
)
print(response.tool_calls[0]["args"])
# {'product': 'SmartHeat Pro', 'company': 'BrightHome Inc.', 'units_affected': 142000,
# 'hazard': 'fire and burn', 'injuries_reported': 2, 'fatalities': False,
# 'retailers': ['HomeBase', 'WarmthPlus', 'Amazon.com']} AI SDK
import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
const LLM_MODEL = "gpt-5.4";
const ARTICLE = `\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149.`;
// instead of Output.object(), use a tool call to extract data —
// the LLM "calls" the function, and we capture the arguments
const extractRecall = tool({
description: "Extract product recall information from an article.",
inputSchema: z.object({
product: z.string(),
company: z.string(),
units_affected: z.number().int(),
hazard: z.string().describe("Primary hazard"),
injuries_reported: z.number().int(),
fatalities: z.boolean(),
retailers: z.array(z.string()).describe("Stores that sold the product"),
}),
});
// toolChoice forces the model to call extractRecall
const { toolCalls } = await generateText({
model: openai(LLM_MODEL),
tools: { extractRecall },
toolChoice: { type: "tool", toolName: "extractRecall" },
prompt: "Extract recall data from this article:\n\n" + ARTICLE,
});
console.log(toolCalls[0].input);
// { product: 'SmartHeat Pro', company: 'BrightHome Inc.', units_affected: 142000,
// hazard: 'fire and burn', injuries_reported: 2, fatalities: false,
// retailers: ['HomeBase', 'WarmthPlus', 'Amazon.com'] } Mastra
import { Agent } from "@mastra/core/agent";
import { createTool } from "@mastra/core/tools";
import { z } from "zod";
// model specified as "provider/model-name" string
const LLM_MODEL = "openai/gpt-5.4";
const ARTICLE = `\
BrightHome Inc. of Austin, Texas, has recalled 142,000 SmartHeat Pro
space heaters due to fire and burn hazards. The company received 23
reports of overheating, including 4 fires and 2 minor burn injuries.
No deaths have been reported. The heaters were sold at HomeBase,
WarmthPlus, and Amazon.com from September 2023 through February 2024
for between $89 and $149.`;
// instead of structuredOutput, use a tool call to extract data —
// the LLM "calls" the function, and we capture the arguments
const extractRecall = createTool({
id: "extract-recall",
description: "Extract product recall information from an article.",
inputSchema: z.object({
product: z.string(),
company: z.string(),
units_affected: z.number().int(),
hazard: z.string().describe("Primary hazard"),
injuries_reported: z.number().int(),
fatalities: z.boolean(),
retailers: z.array(z.string()).describe("Stores that sold the product"),
}),
execute: async (args) => {
console.log(args);
return "Extracted.";
},
});
const agent = new Agent({
name: "extractor",
instructions: "You are a helpful assistant.",
model: LLM_MODEL,
tools: { extractRecall },
});
const result = await agent.generate(
"Extract recall data from this article:\n\n" + ARTICLE,
);
// { product: 'SmartHeat Pro', company: 'BrightHome Inc.', units_affected: 142000,
// hazard: 'fire and burn', injuries_reported: 2, fatalities: false,
// retailers: ['HomeBase', 'WarmthPlus', 'Amazon.com'] }