Wide Research - Manus Documentation

What is Wide Research?

Wide Research is Manus’s approach to handling tasks that involve processing many similar items—such as analyzing 100 products, researching 50 companies, or generating 20 pieces of content. Instead of using a single AI agent that processes items sequentially, Wide Research deploys hundreds of independent agents that work in parallel. Each agent receives its own dedicated context and processes one item independently. This architecture solves the context window limitation that causes traditional AI systems to degrade in quality as the number of items increases.

The Context Window Problem

Traditional AI systems, including most chatbots, operate with a fixed context window—a limit on how much information they can actively process at once. When asked to analyze many items sequentially:

Items 1-5: Detailed, thorough analysis with full context available
Items 10-20: Descriptions become shorter as context fills up
Items 30+: Generic summaries and increased errors as earlier context is compressed or lost

This degradation occurs because the AI must keep all previous items in memory while processing new ones. Research shows this “fabrication threshold” typically occurs around 8-10 items for most AI systems.

How Wide Research Works

Wide Research uses a fundamentally different architecture: 1. Task Decomposition: The main agent analyzes your request and breaks it into independent sub-tasks (e.g., “research company #1”, “research company #2”, etc.) 2. Parallel Agent Deployment: Each sub-task is assigned to a dedicated agent with its own fresh context window 3. Independent Processing: Agents work simultaneously, each conducting thorough research without competing for context space 4. Result Synthesis: The main agent collects all completed sub-tasks and assembles them into your requested format (table, report, dataset, etc.) Result: Item #250 receives the same depth of analysis as item #1, because each has its own dedicated agent and full context window.

Quick Start

Simple Request

"Research the top 20 AI researchers and create a table with their
affiliations, research focus, and recent publications"

Detailed Request

"Compare 100 consumer sneaker models. For each, extract: brand, price,
key features, target audience, and customer rating. Organize in a
sortable table."

Creative Request

"Find 20 famous historical figures. Generate professional headshots
for each in a consistent artistic style. Include brief biographies."

Real Examples

Example 1: Researching 250 AI Researchers

"Research the top 250 AI researchers from leading institutions. Create
a comprehensive table with: name, affiliation, research focus, h-index,
notable publications, and contact information."

Output: Complete database with 250 detailed profiles Replay: https://manus.im/share/IXdMjxObbFKbIjUUkBk4EH?replay=1 Why This Works:

No other AI tool can handle this scale
Each researcher gets independent, thorough research
Automatic table generation with all fields filled
Consistent quality from researcher #1 to #250

Example 2: Comparing 100 Sneaker Models

"Analyze 100 consumer sneaker models. Extract brand, price range, key
features, target demographic, and average rating. Create a comparison
table sorted by price."

Output: Comprehensive market research table with 100 products Replay: https://manus.im/share/3zvs5smekSmn4lS14n9QNg?replay=1 Why This Works:

Deep-dive into each product independently
Structured data extraction at scale
Automatic organization and sorting
No quality degradation across 100 items

Example 3: Analyzing AGI Timelines

"Research expert predictions on AGI timelines. Analyze 30+ sources
including research papers, expert interviews, and industry reports.
Create a visualization showing prediction distribution."

Output: Comprehensive analysis with data visualization Replay: https://manus.im/share/GajPnKzrpM4pEbpcrKDmx0?replay=1 Why This Works:

Synthesizes information from dozens of sources
Creates visual representations of findings
Identifies patterns and outliers
Provides evidence-based summary

Example 4: Researching 20 Biographies

"Research 20 influential entrepreneurs. For each, create a detailed
biography covering: early life, career milestones, major achievements,
leadership style, and lasting impact."

Output: 20 comprehensive biographies with consistent structure Replay: https://manus.im/share/ayLBetEJkfSIVuWKo2toPn?replay=1 Why This Works:

Each biography gets thorough, independent research
Consistent structure across all profiles
Deep-dive into multiple sources per person
No shortcuts or generic content

Example 5: Batch Editing LinkedIn Profile Pics

"Download profile pictures from these 50 LinkedIn URLs. Apply
consistent professional editing: remove backgrounds, adjust lighting,
crop to standard dimensions, and save as high-res PNGs."

Output: 50 professionally edited profile pictures Replay: https://manus.im/share/5iT2464ldyvdf1FMxUOCsW?replay=1 Why This Works:

Replaces micro-SaaS tools for batch image processing
Consistent editing applied to all images
Automated download and processing pipeline
Professional results at scale

Example 6: Extract GitHub Prompt Library

"Visit this GitHub awesome-prompts repository. Extract all prompts,
categorize by use case, and create a structured database with: prompt
text, category, intended model, and effectiveness rating."

Output: Structured database of 100+ prompts Replay: https://manus.ai/share/wxTg2q4hV6GN4YY4KnQeFx?replay=1 Why This Works:

Extracts and structures information at scale
Automated categorization and tagging
Creates searchable, organized database
Handles complex web scraping tasks

Use Cases by Category

Category	Example Tasks
Market Research	Compare 100 products, analyze competitor pricing, survey customer reviews
Academic Research	Literature review of 50 papers, analyze research trends, compare methodologies
Competitive Intelligence	Profile 30 competitors, analyze feature sets, track pricing changes
Lead Generation	Research 200 prospects, find contact info, qualify leads
Content Creation	Generate 20 blog outlines, create 50 social posts, write 30 product descriptions
Data Extraction	Scrape 100 websites, extract structured data, compile databases
Creative Production	Generate 20 images, edit 50 photos, create consistent brand assets
Investment Research	Analyze 40 startups, compare 30 funds, research 50 portfolio companie

Why Wide Research vs. Other Tools

Aspect	AI Chatbot	Manus Wide Research
Approach	Single AI helps you	Parallel multi-agent orchestration
Speed	Hours until context saturation	Minutes regardless of scale
Scale	Degrades beyond 8-10 items	Scales to hundreds seamlessly
Quality	Progressive degradation	Uniform quality at any scale
Output	Compressed summaries with detail loss	Complete reports and datasets

When to Use Wide Research

Perfect For:

Competitive intelligence (analyze 50+ competitors)
Market research (compare 100+ products)
Academic research (review 30+ papers)
Lead generation (research 200+ prospects)
Content creation (generate 20+ similar items)
Data extraction (scrape and structure 100+ pages)
Batch processing (edit 50+ images/files)

Not Ideal For:

Single deep-dive analysis (use regular agent mode)
Tasks requiring sequential dependencies
Real-time interactive research
Tasks with fewer than 10 items

Tips for Better Results

Be specific about structure:

✅ “Create a table with columns: name, company, role, email, LinkedIn”
❌ “Research these people”

Specify the scale upfront:

✅ “Analyze all 100 companies in this list”
❌ “Analyze some companies”

Describe desired output format:

✅ “Organize in a sortable spreadsheet with filters”
❌ “Give me the results”

Include evaluation criteria:

✅ “Rate each product on: price, features, reviews, availability”
❌ “Compare these products”

Common Questions

How many items can Wide Research handle?

Tested up to 250 items. Theoretically unlimited, but practical limit depends on task complexity.

How long does it take?

Depends on task complexity and scale. Typically minutes for 50-100 items, regardless of depth.

Can I refine results after?

Yes. Ask for modifications: “Add a column for pricing” or “Re-research items 20-30 with more detail.”

Does it work for non-research tasks?

Yes. Any task that involves processing multiple independent items: image editing, data extraction, content generation, etc.

​What is Wide Research?

​The Context Window Problem

​How Wide Research Works

​Quick Start

​Simple Request

​Detailed Request

​Creative Request

​Real Examples

​Example 1: Researching 250 AI Researchers

​Example 2: Comparing 100 Sneaker Models

​Example 3: Analyzing AGI Timelines

​Example 4: Researching 20 Biographies

​Example 5: Batch Editing LinkedIn Profile Pics

​Example 6: Extract GitHub Prompt Library

​Use Cases by Category

​Why Wide Research vs. Other Tools

​When to Use Wide Research

​Tips for Better Results

​Common Questions

​