1037 API Reference Bd3cf235

Parallel Web Systems API Quick Reference

Full Documentation: https://docs.parallel.ai API Key: https://platform.parallel.ai Python SDK: pip install parallel-web Environment Variable: PARALLEL_API_KEY

Search API (Beta)

Endpoint: POST https://api.parallel.ai/v1beta/search Header: parallel-beta: search-extract-2025-10-10

Request

{
  "objective": "Natural language search goal (max 5000 chars)",
  "search_queries": ["keyword query 1", "keyword query 2"],
  "max_results": 10,
  "excerpts": {
    "max_chars_per_result": 10000,
    "max_chars_total": 50000
  },
  "source_policy": {
    "allow_domains": ["example.com"],
    "deny_domains": ["spam.com"],
    "after_date": "2024-01-01"
  }
}

Response

{
  "search_id": "search_...",
  "results": [
    {
      "url": "https://...",
      "title": "Page Title",
      "publish_date": "2025-01-15",
      "excerpts": ["Relevant content..."]
    }
  ]
}

Python SDK

from parallel import Parallel
client = Parallel(api_key="...")
result = client.beta.search(
    objective="...",
    search_queries=["..."],
    max_results=10,
    excerpts={"max_chars_per_result": 10000},
)

Cost: $5 per 1,000 requests (default 10 results each) Rate Limit: 600 requests/minute

Extract API (Beta)

Endpoint: POST https://api.parallel.ai/v1beta/extract Header: parallel-beta: search-extract-2025-10-10

Request

{
  "urls": ["https://example.com/page"],
  "objective": "What to focus on",
  "excerpts": true,
  "full_content": false
}

Response

{
  "extract_id": "extract_...",
  "results": [
    {
      "url": "https://...",
      "title": "Page Title",
      "excerpts": ["Focused content..."],
      "full_content": null
    }
  ],
  "errors": []
}

Python SDK

result = client.beta.extract(
    urls=["https://..."],
    objective="...",
    excerpts=True,
    full_content=False,
)

Cost: $1 per 1,000 URLs Rate Limit: 600 requests/minute

Task API (Deep Research)

Endpoint: POST https://api.parallel.ai/v1/tasks/runs

Create Task Run

{
  "input": "Research question (max 15,000 chars)",
  "processor": "pro-fast",
  "task_spec": {
    "output_schema": {
      "type": "text"
    }
  }
}

Response (immediate)

{
  "run_id": "trun_...",
  "status": "queued"
}

Get Result (blocking)

Endpoint: GET https://api.parallel.ai/v1/tasks/runs/{run_id}/result

Python SDK

# Text output (markdown report with citations)
from parallel.types import TaskSpecParam
task_run = client.task_run.create(
    input="Research question",
    processor="pro-fast",
    task_spec=TaskSpecParam(output_schema={"type": "text"}),
)
result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(result.output.content)

# Auto-schema output (structured JSON)
task_run = client.task_run.create(
    input="Research question",
    processor="pro-fast",
)
result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(result.output.content)  # structured dict
print(result.output.basis)    # citations per field

Processors

Standard (non-fast) processors have the same cost but higher latency and freshest data.

Chat API (Beta)

Endpoint: POST https://api.parallel.ai/chat/completions Compatible with OpenAI SDK.

Models

Python SDK (OpenAI-compatible)

from openai import OpenAI
client = OpenAI(
    api_key="PARALLEL_API_KEY",
    base_url="https://api.parallel.ai",
)
response = client.chat.completions.create(
    model="speed",
    messages=[{"role": "user", "content": "What is Parallel Web Systems?"}],
)

Rate Limits

Source Policy

Control which sources are used in searches:

{
  "source_policy": {
    "allow_domains": ["nature.com", "science.org"],
    "deny_domains": ["unreliable-source.com"],
    "after_date": "2024-01-01"
  }
}

Works with Search API and can be used to focus results on specific authoritative domains.