Deep Research Agent Pipeline

Overview

This workflow defines an autonomous deep research pipeline using OpenAI Deep Research or Anthropic Claude as the research agent. The pipeline handles complex research tasks by breaking them into sub-tasks, executing parallel searches, synthesizing findings, and producing a comprehensive cited report — all with minimal human intervention.

Difficulty

Medium

Tools Required

• Research Agent: OpenAI Deep Research or Anthropic Claude (Opus/Sonnet)
• Web Search: Tavily, Brave Search, or Serper API
• Web Fetch: Document reading and content extraction
• Report Generator: Markdown/PDF output formatting
• Citation Manager: Source tracking and link validation

Workflow Steps

Step 1: Research Planning

Break the research question into sub-topics that can be investigated in parallel.

def plan_research(topic: str, depth: str = "comprehensive") -> list[str]:
    # Use LLM to decompose the topic
    sub_topics = llm.generate(
        f"Break this research topic into 4-6 sub-topics for parallel investigation: {topic}"
    )
    return sub_topics

Step 2: Parallel Source Discovery

Search the web for each sub-topic simultaneously.

import asyncio

async def discover_sources(sub_topics: list[str], max_per_topic: int = 10):
    tasks = [
        web_search(topic, max_results=max_per_topic)
        for topic in sub_topics
    ]
    return await asyncio.gather(*tasks)

Step 3: Content Extraction

Read and extract relevant content from each discovered source.

def extract_content(urls: list[str]) -> list[dict]:
    findings = []
    for url in urls:
        content = web_fetch(url)
        findings.append({
            "url": url,
            "content": content,
            "relevance_score": score_relevance(content, topic),
        })
    return sorted(findings, key=lambda x: x["relevance_score"], reverse=True)

Step 4: Synthesis and Analysis

Combine findings across all sources, identify consensus and conflicts.

def synthesize_findings(all_sources: list[dict], topic: str) -> dict:
    consensus = llm.generate(
        "Identify the consensus view across these sources",
        context=all_sources,
    )
    conflicts = llm.generate(
        "Identify conflicting claims and their sources",
        context=all_sources,
    )
    key_insights = llm.generate(
        "Extract the 5 most important insights",
        context=all_sources,
    )
    return {"consensus": consensus, "conflicts": conflicts, "insights": key_insights}

Step 5: Report Generation

Produce a structured report with citations.

def generate_report(synthesis: dict, topic: str) -> str:
    report = llm.generate(
        f"""Generate a comprehensive research report on: {topic}

Include:
1. Executive Summary
2. Key Findings
3. Detailed Analysis (by sub-topic)
4. Conflicts and Open Questions
5. Sources and Citations

Use the following synthesis: {synthesis}""",
        output_format="markdown",
    )
    return report

Example Usage

# Run research via OpenAI Deep Research API
python deep_research_pipeline.py \
  --topic "AI agent security frameworks comparison 2026" \
  --depth comprehensive \
  --max-sources 50 \
  --output report.md

Pros

• ✅ Autonomous — runs with minimal supervision
• ✅ Parallel execution — sub-topics investigated simultaneously
• ✅ Cited output — every claim traceable to sources
• ✅ Configurable depth — from quick answers to deep dives
• ✅ Extensible — swap in different search/LLM backends

Cons

• ❌ Cost — comprehensive research uses significant tokens
• ❌ Time — deep research takes 15-30 minutes
• ❌ Source quality — depends on web accessibility

When to Use

• Market research and competitive analysis
• Academic literature review
• Investment due diligence
• Technical comparison across frameworks
• Long-form content research

Resources

• OpenAI Deep Research
• Anthropic Claude
• Tavily Search API