​
Overview

Deep Research is an advanced feature of Parallel’s Task API that enables comprehensive multi-step web exploration, targeted information retrieval, and sophisticated synthesis in a single API call. This powerful capability compresses hours of manual research into minutes, delivering high-quality intelligence at scale. Available exclusively with pro and ultra processor families, Deep Research transforms natural language research queries into comprehensive, structured intelligence reports complete with inline citations and verification. When leveraging pro and ultra processors, Deep Research automatically interprets natural language research intent and produces analyst-grade outputs tailored to your specific query. The system intelligently determines the optimal output structure based on your research objectives, automatically generating appropriate JSON schemas or formatted markdown reports.

​
Key Features

  • Declarative Approach: Users specify what intelligence they need. Our system handles the complex orchestration of all research components and structuring it in a way that makes the most sense given the input and research output.
  • Flexible Output Structure: Users specify either auto or text schema mode, and receive optimally structured JSON output or markdown report.
  • Structured Intelligence with Verification: Parallel delivers structured outputs with granular calibrated verification built into every response.
Long-Running Tasks: Deep Research can take up to 45 minutes to complete. Use webhooks or server-sent events for real-time updates.

​
Creating a Deep Research Task

Deep Research accepts any input schema as input, including plain-text strings. The more specific and detailed your input, the better the research results would be.
Input size restriction: Deep Research is optimized for concise research prompts and is not meant for long context inputs. Keep your input under 15,000 characters for optimal performance and results.
Deep Research supports two output formats to meet different integration needs:

​
Auto Schema

Specifying auto schema mode in the Task API output schema triggers Deep Research and ensures well-structured outputs, without the need to specify a desired output structure. The final schema type will follow a JSONSchema format and will be determined by the processor automatically. Auto schema mode is the default mode when using pro and ultra line of processors. This format is ideal for programmatic processing, data analysis, and integration with other systems. 
from parallel import Parallel

client = Parallel(api_key="PARALLEL_API_KEY")

task_run = client.task_run.create(
    input="Create a comprehensive market research report on the HVAC industry in the USA including an analysis of recent M&A activity and other relevant details.",
    processor="ultra"
)
print(f"Run ID: {task_run.run_id}")

run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output)

​
Text Schema

Specifying text schema mode in the Task API output schema triggers Deep Research with a markdown report output format. The generated result will contain extensive research formatted into a markdown report with in-line citations. This format is perfect for human-readable content as well as LLM ingestion. To provide guidance on the output, use the description field when specifying text schema. This allows users to steer the report generated towards a certain direction like control over the length or the content of the report. 
from parallel import Parallel
from parallel.type import TextSchemaParam

client = Parallel(api_key="PARALLEL_API_KEY")

task_run = client.task_run.create(
    input="Create a comprehensive market research report on the HVAC industry in the USA including an analysis of recent M&A activity and other relevant details.",
    processor="ultra",
    task_spec=TextSchemaParam()
)
print(f"Run ID: {task_run.run_id}")

run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output)

​
Sample Response

Important: The response below shows the final completed result after Deep Research has finished. When you first create a task, you’ll receive an immediate response with "status": "running". You’ll need to poll the task or use webhooks to get the final structured research output shown below.
Below is a shortened sample response using the auto schema. The complete response contained 124 content fields, with 610 total citations for this Task. 
{
  "output": {
    "content": {
      "market_size_and_forecast": {
        "cagr": "6.9%",
        "market_segment": "U.S. HVAC Systems",
        "current_valuation": "USD 29.89 billion (2024)",
        "forecasted_valuation": "USD 54.02 billion",
        "forecast_period": "2025-2033"
      },
      "company_profiles": [
        {
          "company_name": "Carrier Global Corporation",
          "stock_ticker": "CARR",
          "revenue": "$22.5 billion (FY2024)",
          "market_capitalization": "$63.698 billion (July 1, 2025)",
          "market_position": "Global leader in intelligent climate and energy solutions",
          "recent_developments": "Acquisition of Viessmann Climate Solutions for $13 billion"
        },
        {
          "company_name": "Daikin Industries, Ltd.",
          "stock_ticker": "DKILY",
          "revenue": "¥4,752.3 billion (FY2024)",
          "market_position": "Japan's leading HVAC manufacturer and top global player",
          "recent_developments": "Multiple acquisitions to strengthen supply capabilities"
        }
      ],
      "recent_mergers_and_acquisitions": {
        "acquiring_company": "Carrier Ventures",
        "target_company": "ZutaCore",
        "deal_summary": "Strategic investment in liquid cooling systems for data centers",
        "date": "February 2025"
      },
      "growth_opportunities": "Data center cooling, building retrofits, electrification, healthcare applications, and enhanced aftermarket services",
      "market_segmentation_analysis": {
        "dominant_segment": "Residential",
        "dominant_segment_share": "39.8% (in 2024)",
        "fastest_growing_segment": "Commercial",
        "fastest_growing_segment_cagr": "7.4% (from 2025 to 2033)"
      },
      "publicly_traded_hvac_companies": [
        {
          "company_name": "Carrier Global Corporation",
          "stock_ticker": "CARR"
        },
        {
          "company_name": "Daikin Industries, Ltd.",
          "stock_ticker": "DKILY"
        },
        {
          "company_name": "Johnson Controls International plc",
          "stock_ticker": "JCI"
        }
      ]
    },
    "basis": [
      {
        "field": "market_size_and_forecast.current_valuation",
        "reasoning": "Market size data sourced from Grand View Research industry analysis report, which provides comprehensive market valuation for the U.S. HVAC systems market in 2024.",
        "citations": [
          {
            "url": "https://www.grandviewresearch.com/industry-analysis/us-hvac-systems-market",
            "excerpts": [
              "The U.S. HVAC systems market size was estimated at USD 29.89 billion in 2024"
            ],
            "title": "U.S. HVAC Systems Market Size, Share & Trends Analysis Report"
          }
        ],
        "confidence": "high"
      },
      {
        "field": "company_profiles.0.revenue",
        "reasoning": "Carrier Global Corporation's 2024 revenue figures are directly reported in their financial communications and investor relations materials.",
        "citations": [
          {
            "url": "https://monexa.ai/blog/carrier-global-corporation-strategic-climate-pivot-CARR-2025-07-02",
            "excerpts": [
              "Carrier reported **2024 revenues of $22.49 billion**, a modest increase of +1.76% year-over-year"
            ],
            "title": "Carrier Global Corporation: Strategic Climate Pivot"
          }
        ],
        "confidence": "high"
      },
      {
        "field": "recent_mergers_and_acquisitions",
        "reasoning": "Carrier Ventures' strategic investment in ZutaCore represents recent M&A activity focused on next-generation cooling technologies for data centers.",
        "citations": [
          {
            "url": "https://finance.yahoo.com/news/10-biggest-hvac-companies-usa-142547989.html",
            "excerpts": [
              "Strategic investment activity by Carrier Ventures in companies specializing in liquid cooling systems"
            ],
            "title": "10 Biggest HVAC Companies in the USA"
          }
        ],
        "confidence": "medium"
      }
    ],
    "run_id": "trun_646e167d826747e1b4690e58d2b9941e",
    "status": "completed",
    "created_at": "2025-01-30T20:12:18.123456Z",
    "completed_at": "2025-01-30T20:25:41.654321Z",
    "processor": "ultra",
    "warnings": null,
    "error": null,
    "taskgroup_id": null
  }
}
Deep Research returns a response which includes the content and the basis, as with other Task API executions. The key difference is that the basis object in an auto mode output contains Nested FieldBasis.

​
Nested FieldBasis

In text mode, FieldBasis is not nested. It contains a list of citations (with URLs and excerpts) for all sites visited during research. The most relevant citations are included at the base of the report itself, with inline references.
In auto mode, the Basis object maps each output field (including nested fields) with supporting evidence. This ensures that every output, including nested output fields, has citations, excerpts, confidence levels and reasoning. For nested fields, the basis uses dot notation for indexing:
  • key_players.0 for the first item in a key players array
  • industry_overview.growth_cagr for nested object fields
  • market_trends.2.description for nested arrays with objects

​
Example: Market Research Assistant

Here’s how to build a market research tool with Deep Research, showing different approaches for handling the async nature of the Task API: 
from parallel import Parallel

client = Parallel(api_key="PARALLEL_API_KEY")

# Execute research task (handles polling internally)
task_run = client.task_run.create(
    input="Create a comprehensive market research report on the renewable energy storage market in Europe, focusing on battery technologies and policy impacts",
    processor="ultra"
)
print(f"Run ID: {task_run.run_id}")

run_result = clien.task_run.result(task_run.run_id, api_timeout=3600)

print(f"Research completed! Output has {len(run_result.output.basis)} structured fields")
for field in run_result.output.basis[:3]:
    print(f"- {field.field}: {len(field.citations)} citations")