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# Introducing the Parallel Chat API

Starting today, the Parallel Chat API is generally available in beta - bringing real-time web research to interactive AI applications using our rapidly growing web index.

Tags:Product Release
Reading time: 1 min
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.

The Chat API returns OpenAI ChatCompletions compatible streaming text and JSON with the low latency your interactive apps need. It is built with the same obsession for price-performance that we’ve demonstrated with the Task API[Task API]($https://parallel.ai/blog/parallel-task-api).

### Python Code Snippet 
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from openai import OpenAI

client = OpenAI(
    api_key="PARALLEL_API_KEY",  # Your Parallel API key
    base_url="https://beta.parallel.ai"  # Parallel's API beta endpoint
)

response = client.chat.completions.create(
    model="speed", # Parallel model name
    messages=[
        {"role": "user", "content": "What does Parallel Web Systems do?"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "reasoning_schema",
            "schema": {
                "type": "object",
                "properties": {
                    "reasoning": {
                        "type": "string",
                        "description": "Think step by step to arrive at the answer",
                    },
                    "answer": {
                        "type": "string",
                        "description": "The direct answer to the question",
                    },
                    "citations": {
                        "type": "array",
                        "items": {"type": "string"},
                        "description": "Sources cited to support the answer",
                    },
                },
            },
        },
    },
)

print(response.choices[0].message.content)``` 
from openai import OpenAI
 
client = OpenAI(
    api_key="PARALLEL_API_KEY",  # Your Parallel API key
    base_url="https://beta.parallel.ai"  # Parallel's API beta endpoint
)
 
response = client.chat.completions.create(
    model="speed", # Parallel model name
    messages=[
        {"role": "user", "content": "What does Parallel Web Systems do?"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "reasoning_schema",
            "schema": {
                "type": "object",
                "properties": {
                    "reasoning": {
                        "type": "string",
                        "description": "Think step by step to arrive at the answer",
                    },
                    "answer": {
                        "type": "string",
                        "description": "The direct answer to the question",
                    },
                    "citations": {
                        "type": "array",
                        "items": {"type": "string"},
                        "description": "Sources cited to support the answer",
                    },
                },
            },
        },
    },
)
 
print(response.choices[0].message.content)
```

Last year, we started building our own web crawler and index, recognizing that AIs need native infrastructure to query and reason over the web. The Chat API answers instantly using our index, while the Task API combines our index with real-time web crawling for maximum accuracy and the freshest data. Both APIs return comprehensive citations so you can always verify responses.

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features

##
**Start building.**

Get started in our Developer Platform[Developer Platform]($https://platform.parallel.ai/play/chat) or dive into the documentation[documentation]($https://docs.parallel.ai/chat-api/chat-quickstart).

Parallel avatar

By Parallel

May 30, 2025

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