Structured outputs for Anthropic Claude models on Amazon Bedrock

Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available.

Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure. You no longer need custom parsing, retry logic, or post-processing.

With structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always match your application’s data model.

Structured outputs use constrained decoding to enforce your JSON schema during token generation, not after the fact. The model doesn’t produce tokens that would violate your schema. This changes output formatting from probabilistic to deterministic.

Two complementary mechanisms are now available on both Amazon Bedrock endpoints:

Each response is a valid JSON, so you no longer encounter JSON.parse() errors or parsing exceptions. Responses are also type safe: field types are enforced, and required fields are always present. Because outputs always conform to your schema, you don’t need retries to handle schema violations, making your integration more reliable. Structured outputs also constrains responses to only the fields your schema defines, which reduces output token count and can lower inference costs for high-volume workloads. These properties make structured outputs production ready, so you can deploy at scale without additional schema-validation code.

Structured outputs works with batch inference, so you can process large volumes of requests asynchronously while still receiving schema-compliant outputs. On the bedrock-runtime endpoint, it also works with cross-Region inference, so you can use it across AWS Regions without additional setup. If you need responses in real time, you can stream structured responses as typed events.

When you make a request with a JSON schema, here’s what happens behind the scenes:

For performance, the first request with a new schema might take longer because Amazon Bedrock compiles a grammar artifact before generation begins. After that initial compilation, subsequent requests using the same schema run with minimal overhead because the compiled grammar is cached. This cache is scoped to your account and lasts for 24 hours from first access. Changing the structure of your JSON schema invalidates the cache and triggers a fresh compilation, while changing only the name or description fields leaves the cached grammar intact.

The InvokeModel API takes the Anthropic-native request body, so the schema goes in output_config.format as a JSON object rather than a JSON string. The following example shows how to extract the structured lead data from a customer email using boto3 with an Anthropic Claude model:

import boto3 import json REGION = "us-east-1" # Choose your preferred region bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name=REGION) # Define the JSON schema for structured output schema = { "type": "object", "properties": { "name": {"type": "string", "description": "Customer name"}, "email": {"type": "string", "description": "Customer email address"}, "plan_interest": {"type": "string", "description": "Product plan of interest"}, "demo_requested": {"type": "boolean", "description": "Whether a demo was requested"} }, "required": ["name", "email", "plan_interest", "demo_requested"], "additionalProperties": False } # Make a request with structured outputs via the InvokeModel API # (reusing the schema defined for the Converse example) response = bedrock_runtime.invoke_model( modelId="global.anthropic.claude-sonnet-5-5", body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 512, "messages": [ {"role": "user", "content": ( "Paulo Santos (paulo@example.com) is interested in our Enterprise plan " "and wants to schedule a demo for next Tuesday at 2pm." )} ], "output_config": {"format": {"type": "json_schema", "schema": schema}} }) ) body = json.loads(response["body"].read()) # Check the stop reason before parsing if body["stop_reason"] == "end_turn": result_text = next(b["text"] for b in body["content"] if b["type"] == "text") print(json.dumps(json.loads(result_text), indent=2)) elif body["stop_reason"] == "max_tokens": print("Response truncated: increase max_tokens and retry.") JSON

{ "name": "Paulo Santos", "email": "paulo@example.com", "plan_interest": "Enterprise", "demo_requested": true } The response conforms to your schema. No additional validation is required.

For additional API usage examples, including the Messages API, see the accompanying sample notebook.

Amazon Bedrock Mantle is designed for agentic applications with server-side tools, background inference, and stateful conversations. With strict tool use on the bedrock-mantle endpoint, you can now guarantee that tool parameters match your input schema exactly. The following example shows how to define a tool with strict schema enforcement:

Import json from anthropic import AnthropicBedrockMantle # Reusing the client created in the Messages API example client = AnthropicBedrockMantle(aws_region="us-east-1") message = client.messages.create( model="anthropic.claude-sonnet-5", max_tokens=512, messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}], tools=[ { "name": "get_weather", "description": "Get the current weather for a specified location", "strict": True, "input_schema": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g., San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit" } }, "required": ["location", "unit"], "additionalProperties": False } } ] ) #Tool inputs conform to the schema for block in message.content: if block.type == "tool_use": print(f"Tool: {block.name}") print(f"Input: {json.dumps(block.input, indent=2)}") Setting strict: true constrains the model’s tool input to match your schema exactly. The location field is always a string, and the unit field is always either celsius or fahrenheit. The model can’t add fields outside your schema, so the tool input contains only what your schema specifies.

This is especially valuable for agentic use cases on Amazon Bedrock Mantle, where models orchestrate multi-step workflows with server-side tools like web search and code interpreter.

Structured outputs reduce error-handling complexity the most in three common patterns:

Claude Haiku 5.5 reliably handles quick and repetitive workloads such as summaries, compactions, database queries, and classification requests. The following example shows how to classify and route incoming data using the Invoke API and Haiku 5.5:

import boto3 import json REGION = "us-east-1" # Choose your preferred region bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name=REGION) # Classification schema: enums restrict output to a fixed set of labels schema = { "type": "object", "properties": { "category": { "type": "string", "enum": ["billing", "technical", "account", "sales", "other"], "description": "Primary ticket category" }, "priority": { "type": "string", "enum": ["low", "medium", "high", "urgent"], "description": "Urgency based on business impact" }, "sentiment": { "type": "string", "enum": ["positive", "neutral", "negative"], "description": "Customer sentiment" }, "escalate": {"type": "boolean", "description": "Whether a human should review immediately"}, "reason": {"type": "string", "description": "One-sentence justification for the classification"} }, "required": ["category", "priority", "sentiment", "escalate", "reason"], "additionalProperties": False } ticket = ( "Our production API calls have been failing with 500 errors since this morning. " "We're losing orders every minute. This is the second outage this month!" ) # Make a request with structured outputs via the InvokeModel API response = bedrock_runtime.invoke_model( modelId="global.anthropic.claude-haiku-5-5", body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 512, "system": "You are a support ticket classifier.

Classify the ticket accurately.", "messages": [ {"role": "user", "content": f"Classify this support ticket:\n\n{ticket}"} ], "output_config": {"format": {"type": "json_schema", "schema": schema}} }) ) body = json.loads(response["body"].read()) # Check the stop reason before parsing if body["stop_reason"] == "end_turn": result_text = next(b["text"] for b in body["content"] if b["type"] == "text") result = json.loads(result_text) print(json.dumps(result, indent=2)) # Route the ticket based on the guaranteed-valid labels if result["escalate"]: print(f"Escalating to on-call: {result['category']} / {result['priority']}") elif body["stop_reason"] == "max_tokens": print("Response truncated: increase max_tokens and retry.") Requirements and best practices To use structured outputs effectively on Amazon Bedrock, follow these guidelines:

For additional best practices and schema design recommendations, see Structured outputs in the Amazon Bedrock User Guide.

These keywords are accepted by the API but are not enforced during generation. A request that includes them returns a successful response, and the model can still return values that violate them. Validate these constraints in your application rather than relying on the schema to enforce them.

Structured outputs for Anthropic Claude models on Amazon Bedrock provides the reliability that production AI applications require. It is available now on both the bedrock-runtime and bedrock-mantle endpoints. By combining JSON Schema output format and strict tool use, you can build data pipelines that need no additional validation. You can also create reliable agentic workflows and production AI applications without custom parsing or retry logic.

Structured outputs for Claude on Amazon Bedrock is now generally available. To learn more, see the Amazon Bedrock documentation and explore our sample notebook.

What workflows could validated, schema-compliant JSON improve in your organization? Share your feedback and questions in the comments.

Anshika is a Generative AI Data Scientist at AWS, focused on Amazon Bedrock. She helps enterprise customers accelerate their generative AI and agentic AI adoption by guiding them through building and implementing solutions on Amazon Bedrock. She also works with ISVs and startups to enable them with the latest Anthropic services on Amazon Bedrock.

Dani is a Senior Specialist Solutions Architect for Generative AI at AWS, working on go-to-market for Anthropic on Amazon Bedrock. He helps enterprises across the world design and deploy generative AI solutions using Anthropic’s models and capabilities on Amazon Bedrock to build scalable, production-ready applications.

Alfredo is a Senior Specialist Solutions Architect for Generative AI at AWS, focusing on Anthropic models go-to-market on Amazon Bedrock. He works with Financial Services customers to design and scale generative AI solutions across distributed systems and turn generative AI experiments into production workloads. Outside of work, he is passionate about family and endurance sports.

Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with Amazon Bedrock.

Sofian is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate about empowering diverse talents to drive global impact and achieve their career aspirations.

Ayan is a Principal Partner Solutions Architect and AI Tech Lead at AWS, serving as the Worldwide Tech Lead for Anthropic at AWS. He works at the intersection of cloud architecture and Artificial Intelligence, helping organizations adopt and scale Anthropic’s technologies on AWS.