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Structured output lets you constrain LLM responses to valid JSON that follows a specific format. This is useful when you need to parse model output programmatically — for example, extracting entities, generating API payloads, or building data pipelines.

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Overview

Eden AI supports two modes of structured output through the response_format parameter:
ModeDescription
json_objectThe model returns valid JSON. You guide the structure via your system prompt.
json_schemaThe model returns JSON that conforms to a specific JSON Schema you provide.
When using response_format, always include a system or user message that instructs the model to respond in JSON. Some providers require this instruction to be present.

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JSON Object Mode

The simplest approach: set response_format to {"type": "json_object"} and instruct the model to return JSON in your prompt. 
import requests
import json

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-4o",
    "response_format": {"type": "json_object"},
    "messages": [
        {
            "role": "system",
            "content": "You extract contact information. Respond with a JSON object containing: name, email, phone, company. Use null for missing fields."
        },
        {
            "role": "user",
            "content": "John Smith works at Acme Corp. His email is john@acme.com and his phone is 555-0123."
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

# Parse the JSON response
content = result["choices"][0]["message"]["content"]
data = json.loads(content)
print(json.dumps(data, indent=2))
Example response: 
{
  "name": "John Smith",
  "email": "john@acme.com",
  "phone": "555-0123",
  "company": "Acme Corp"
}

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JSON Schema Mode

For stricter control, provide a JSON Schema that the model’s response must conform to. This guarantees the output matches your expected structure. 
import requests
import json

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-4o",
    "response_format": {
        "type": "json_schema",
        "json_schema": {
            "name": "product_review",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "sentiment": {
                        "type": "string",
                        "enum": ["positive", "negative", "neutral"]
                    },
                    "rating": {
                        "type": "integer",
                        "description": "Rating from 1 to 5"
                    },
                    "summary": {
                        "type": "string",
                        "description": "One-sentence summary of the review"
                    },
                    "keywords": {
                        "type": "array",
                        "items": {"type": "string"},
                        "description": "Key topics mentioned"
                    }
                },
                "required": ["sentiment", "rating", "summary", "keywords"],
                "additionalProperties": False
            }
        }
    },
    "messages": [
        {
            "role": "system",
            "content": "Analyze the following product review and return structured data."
        },
        {
            "role": "user",
            "content": "This laptop is amazing! Great battery life, super fast processor, and the screen is gorgeous. Only downside is it's a bit heavy. Definitely worth the price."
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

content = result["choices"][0]["message"]["content"]
data = json.loads(content)
print(json.dumps(data, indent=2))
Example response: 
{
  "sentiment": "positive",
  "rating": 4,
  "summary": "An excellent laptop with great performance and display, though slightly heavy.",
  "keywords": ["battery life", "processor", "screen", "heavy", "price"]
}

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Supported Providers

Providerjson_objectjson_schema
OpenAI (GPT-4o, GPT-4 Turbo)YesYes
Anthropic (Claude 3.5+, Claude 4)YesYes
Google (Gemini 1.5+, Gemini 2.5)YesYes
Provider support for structured output varies. If a provider does not support json_schema, use json_object mode with a detailed system prompt describing the expected format.

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Best Practices

  • Always include JSON instructions in your prompt. Even with response_format set, some providers require the prompt to mention JSON output.
  • Use json_schema for critical parsing. When your downstream code depends on exact field names and types, json_schema mode is more reliable than json_object.
  • Set additionalProperties: false in your schema to prevent the model from adding extra fields.
  • Always parse the response. The content field is still a string — use json.loads() (Python) or JSON.parse() (JavaScript) to convert it to a structured object.
  • Handle parsing errors gracefully. In rare cases, the model may return malformed JSON. Wrap your parsing in a try/catch block.

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Next Steps

LLMs vs Expert Models

When to use structured LLM output vs Expert Model schemas