Most AI tools fail at scale. Manus doesn’t.

See the full picture fast. Hundreds of AI agents research in parallel to deliver actionable insights in minutes.

Why Manus excels at research tasks?

See why Wide Research outperforms manual methods and standard AI chatbots.

Feature

Manual Research
AI Chatbot
Manus Wide Research
Approach
Human-driven, linear execution
Single AI helps you
Parallel multi-agent orchestration
Speed
Days to weeks per analysis cycle
Hours until context saturation
Minutes regardless of scale
Scale
Bounded by cognitive and temporal limits
Degrades beyond 8-10 items due to context window saturation
Scales to hundreds seamlessly
Quality
Subject to human variability and fatigue
Progressive degradation with increased hallucination risk
Uniform quality at any scale
Output
Unstructured notes and source links
Compressed summaries with detail loss
Complete reports and datasets

The context overload problem

Icon of a declining bar chart with a red arrow, symbolizing how too much context causes AI performance to drop.
Too much context causes AI to fail

Ask a chatbot to analyze 50 companies. The first 5 get detailed write-ups. By #20, descriptions get suspiciously brief. By #50, you're getting generic filler.

Icon of a brain with a question mark, indicating why context overload happens.
Why it happens

Traditional AI has a fixed "memory." As it processes more items, previous context fills up the space. Less room = less quality.

Icon of a green check mark, showing that Wide Research solves the problem.
How Wide Research fixes it

Every item gets its own dedicated agent. Item #1 and item #100 receive identical attention. No memory constraints. No quality degradation.

The context overload problem

Icon of a declining bar chart with a red arrow, symbolizing how too much context causes AI performance to drop.
Too much context causes AI to fail

Ask a chatbot to analyze 50 companies. The first 5 get detailed write-ups. By #20, descriptions get suspiciously brief. By #50, you're getting generic filler.

Icon of a brain with a question mark, indicating why context overload happens.
Why it happens

Traditional AI has a fixed "memory." As it processes more items, previous context fills up the space. Less room = less quality.

Icon of a green check mark, showing that Wide Research solves the problem.
How Wide Research fixes it

Every item gets its own dedicated agent. Item #1 and item #100 receive identical attention. No memory constraints. No quality degradation.

What makes Wide Research different

Not just faster—fundamentally different

Large screenshot of the Manus computer interface showing multiple subtasks running in parallel.
True parallel processing

Each sub-agent runs independently with full capabilities: its own VM, tools, and internet access.

Fresh context for every item

Traditional AI accumulates context. Wide Research gives each item a clean slate. The result? Consistent, thorough analysis at any scale.

Centralized orchestration

Main agent distributes tasks and collects results. Sub-agents never talk to each other. This prevents context pollution and reduces hallucinations.

Full-featured sub-agents

Each sub-agent is a complete Manus instance. Not a simplified worker—a fully autonomous agent that can research, code, analyze, and create.

What makes Wide Research different

Not just faster—fundamentally different

Large screenshot of the Manus computer interface showing multiple subtasks running in parallel.
True parallel processing

Each sub-agent runs independently with full capabilities: its own VM, tools, and internet access.

Fresh context for every item

Traditional AI accumulates context. Wide Research gives each item a clean slate. The result? Consistent, thorough analysis at any scale.

Centralized orchestration

Main agent distributes tasks and collects results. Sub-agents never talk to each other. This prevents context pollution and reduces hallucinations.

Full-featured sub-agents

Each sub-agent is a complete Manus instance. Not a simplified worker—a fully autonomous agent that can research, code, analyze, and create.

How it works

Your personal supercomputing cluster, accessible through simple conversation

Step 1

Task breakdown

Main agent breaks your request into hundreds of independent sub-tasks

Step 2

Parallel execution

Each sub-task gets its own dedicated agent with fresh context

Step 3

Autonomous processing

Sub-agents independently research, analyze, and create

Step 4

Bringing It All Together

Main agent gathers all results and synthesizes the final report

Example prompts

Copy and try these in Manus

Example prompts

Copy and try these in Manus

Create a detailed comparison of the top 30 project management tools
Create a detailed comparison of the top 30 project management tools
Analyze the social media strategy of 25 leading DTC brands
Analyze the social media strategy of 25 leading DTC brands
Research 100 potential investors for a Series A fintech startup
Research 100 potential investors for a Series A fintech startup
Compare the curriculum of the top 50 MBA programs worldwide
Compare the curriculum of the top 50 MBA programs worldwide
Generate 100 product ideas for sustainable home goods
Generate 100 product ideas for sustainable home goods

Frequently asked questions

How is this different from asking ChatGPT to research 50 items?

How many agents can I deploy?

What tasks work best with Wide Research?

Will item #100 get the same quality as item #1?

Is this available on all plans?

Frequently asked questions

How is this different from asking ChatGPT to research 50 items?

How many agents can I deploy?

What tasks work best with Wide Research?

Will item #100 get the same quality as item #1?

Is this available on all plans?

Share your Wide Research use cases

Earn 20,000 credits when featured

Step 1

Use it

Apply Wide Research to real projects.

Image of a button labeled “Wide Research”.

Step 2

Share it

Post with #ManusWideResearch

Image containing social‑media icons such as X, Instagram, TikTok and LinkedIn, showing how to share your use case.

Step 3

Earn it

Get 20,000 credits when featured.

Image showing “+20,000” with a reward icon, symbolizing the credits earned when featured.

Share your Wide Research use cases

Earn 20,000 credits when featured

Step 1

Use it

Apply Wide Research to real projects.

Image of a button labeled “Wide Research”.

Step 2

Share it

Post with #ManusWideResearch

Image containing social‑media icons such as X, Instagram, TikTok and LinkedIn, showing how to share your use case.

Step 3

Earn it

Get 20,000 credits when featured.

Image showing “+20,000” with a reward icon, symbolizing the credits earned when featured.

Ready to scale your research?

Stop hitting context limits. Start deploying agent clusters.

Ready to scale your research?

Stop hitting context limits. Start deploying agent clusters.