Structured outputs

Structured outputs constrain Claude's responses to follow a specific schema, ensuring valid, parseable output for downstream processing. Use JSON outputs (output_format) for structured data responses, or strict tool use (strict: true) for guaranteed schema validation on tool names and inputs.

Why use structured outputs

Without structured outputs, Claude can generate malformed JSON responses or invalid tool inputs that break your applications. Even with careful prompting, you may encounter:

• Parsing errors from invalid JSON syntax
• Missing required fields
• Inconsistent data types
• Schema violations requiring error handling and retries

Structured outputs guarantee schema-compliant responses through constrained decoding:

• Always valid: No more JSON.parse() errors
• Type safe: Guaranteed field types and required fields
• Reliable: No retries needed for schema violations
• Two modes: JSON for tasks like data extraction, and strict tools for situations like complex tools and agentic workflows

Quick start

When to use JSON outputs vs strict tool use

Choose the right mode for your use case:

Why strict tool use matters for agents

Building reliable agentic systems requires guaranteed schema conformance. Invalid tool parameters break your functions and workflows. Claude might return incompatible types ("2" instead of 2) or missing fields, causing runtime errors.

Strict tool use guarantees type-safe parameters:

• Functions receive correctly-typed arguments every time
• No need to validate and retry tool calls
• Production-ready agents that work consistently at scale

For example, suppose a booking system needs passengers: int. Without strict mode, Claude might provide passengers: "two" or passengers: "2". With strict: true, you're guaranteed passengers: 2.

How structured outputs work

Working with JSON outputs in SDKs

The Python and TypeScript SDKs provide helpers that make it easier to work with JSON outputs, including schema transformation, automatic validation, and integration with popular schema libraries.

Using Pydantic and Zod

For Python and TypeScript developers, you can use familiar schema definition tools like Pydantic and Zod instead of writing raw JSON schemas.

from pydantic import BaseModel
from anthropic import Anthropic, transform_schema

class ContactInfo(BaseModel):
    name: str
    email: str
    plan_interest: str
    demo_requested: bool

client = Anthropic()

# With .create() - requires transform_schema()
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    betas=["structured-outputs-2025-11-13"],
    messages=[
        {
            "role": "user",
            "content": "Extract the key information from this email: John Smith ([email protected]) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
        }
    ],
    output_format={
        "type": "json_schema",
        "schema": transform_schema(ContactInfo),
    }
)

print(response.content[0].text)

# With .parse() - can pass Pydantic model directly
response = client.beta.messages.parse(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    betas=["structured-outputs-2025-11-13"],
    messages=[
        {
            "role": "user",
            "content": "Extract the key information from this email: John Smith ([email protected]) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
        }
    ],
    output_format=ContactInfo,
)

print(response.parsed_output)

SDK-specific methods

Python: client.beta.messages.parse() (Recommended)

The parse() method automatically transforms your Pydantic model, validates the response, and returns a parsed_output attribute.

Python: transform_schema() helper

For when you need to manually transform schemas before sending, or when you want to modify a Pydantic-generated schema. Unlike client.beta.messages.parse(), which transforms provided schemas automatically, this gives you the transformed schema so you can further customize it.

How SDK transformation works

Both Python and TypeScript SDKs automatically transform schemas with unsupported features:

1. Remove unsupported constraints (e.g., minimum, maximum, minLength, maxLength)
2. Update descriptions with constraint info (e.g., "Must be at least 100"), when the constraint is not directly supported with structured outputs
3. Add additionalProperties: false to all objects
4. Filter string formats to supported list only
5. Validate responses against your original schema (with all constraints)

This means Claude receives a simplified schema, but your code still enforces all constraints through validation.

Example: A Pydantic field with minimum: 100 becomes a plain integer in the sent schema, but the description is updated to "Must be at least 100", and the SDK validates the response against the original constraint.

Common use cases

Important considerations

Grammar compilation and caching

Structured outputs use constrained sampling with compiled grammar artifacts. This introduces some performance characteristics to be aware of:

• First request latency: The first time you use a specific schema, there will be additional latency while the grammar is compiled
• Automatic caching: Compiled grammars are cached for 24 hours from last use, making subsequent requests much faster
• Cache invalidation: The cache is invalidated if you change:
  • The JSON schema structure
  • The set of tools in your request (when using both structured outputs and tool use)
  • Changing only name or description fields does not invalidate the cache

Prompt modification and token costs

When using structured outputs, Claude automatically receives an additional system prompt explaining the expected output format. This means:

• Your input token count will be slightly higher
• The injected prompt costs you tokens like any other system prompt
• Changing the output_format parameter will invalidate any prompt cache for that conversation thread

JSON Schema limitations

Structured outputs support standard JSON Schema with some limitations. Both JSON outputs and strict tool use share these limitations.

Invalid outputs

While structured outputs guarantee schema compliance in most cases, there are scenarios where the output may not match your schema:

Refusals (stop_reason: "refusal")

Claude maintains its safety and helpfulness properties even when using structured outputs. If Claude refuses a request for safety reasons:

• The response will have stop_reason: "refusal"
• You'll receive a 200 status code
• You'll be billed for the tokens generated
• The output may not match your schema (the refusal takes precedence)

Token limit reached (stop_reason: "max_tokens")

If the response is cut off due to reaching the max_tokens limit:

• The response will have stop_reason: "max_tokens"
• The output may be incomplete and not match your schema
• Retry with a higher max_tokens value to get the complete structured output

Schema validation errors

If your schema uses unsupported features or is too complex, you'll receive a 400 error:

"Too many recursive definitions in schema"

• Cause: Schema has excessive or cyclic recursive definitions
• Solution: Simplify schema structure, reduce nesting depth

"Schema is too complex"

• Cause: Schema exceeds complexity limits
• Solution: Break into smaller schemas, simplify structure, or reduce the number of tools marked as strict: true

For persistent issues with valid schemas, contact support with your schema definition.

Feature compatibility

Works with:

• Batch processing: Process structured outputs at scale with 50% discount
• Token counting: Count tokens without compilation
• Streaming: Stream structured outputs like normal responses
• Combined usage: Use JSON outputs (output_format) and strict tool use (strict: true) together in the same request

Incompatible with:

• Citations: Citations require interleaving citation blocks with text, which conflicts with strict JSON schema constraints. Returns 400 error if citations enabled with output_format.
• Message Prefilling: Incompatible with JSON outputs