You’ve heard of AI ‘Deep Research’ tools…now Manus is launching ‘Wide Research’ that spins up 100+ agents to scour the web for you

Chinese AI startup Manus, which made headlines earlier this year for its approach to a multi-agent orchestration platform for consumers and “pro”-sumers (professionals wanting to run work operations), is back with an interesting new use of its technology.

While many other major rival AI providers such as OpenAI, Google, and xAI that have launched “Deep Research” or “Deep Researcher” AI agents that conduct minutes or hours of extensive, in-depth web research and write well-cited, thorough reports on behalf of users, Manus is taking a different approach.

The company just announced “Wide Research,” a new experimental feature that enables users to execute large-scale, high-volume tasks by leveraging the power of parallelized AI agents — even more than 100 at a single time, all focused on completing a single task (or series of sub-tasks laddering up said overarching goal).

Manus was previously reported to be using Anthropic Claude and Alibaba Qwen models to power its platform.

In a video posted on the official X account, Manus co-founder and Chief Scientist Yichao ‘Peak’ Ji shows a demo of using Wide Research to compare 100 sneakers.

To complete the task, Manus Wide Research nearly instantly spins up 100 concurrent subagents — each assigned to analyze one shoe’s design, pricing, and availability.

The result is a sortable matrix delivered in both spreadsheet and webpage formats within minutes.

The company suggests Wide Research isn’t limited to data analysis. It can also be used for creative tasks like design exploration.

In one scenario, Manus agents simultaneously generated poster designs across 50 distinct visual styles, returning polished assets in a downloadable ZIP file.

According to Manus, this flexibility stems from the system-level approach to parallel processing and agent-to-agent communication.

In the video, Peak explains that Wide Research is the first application of an optimized virtualization and agent architecture capable of scaling compute power 100 times beyond initial offerings.

The feature is designed to activate automatically during tasks that require wide-scale analysis, with no manual toggles or configurations required.

Wide Research is available starting today for users on Manus Pro plan and will gradually become accessible to those on the Plus and Basic plans. As of now, subscription pricing for Manus is structured as follows per month.

• Free – $0/month Includes 300 daily refresh credits, access to Chat mode, 1 concurrent task, and 1 scheduled task.
• Basic – $19/month Adds 1,900 monthly credits (+1,900 bonus during limited offer), 2 concurrent and 2 scheduled tasks, access to advanced models in Agent mode, image/video/slides generation, and exclusive data sources.
• Plus – $39/month Increases to 3 concurrent and 3 scheduled tasks, 3,900 monthly credits (+3,900 bonus), and includes all Basic features.
• Pro – $199/month Offers 10 concurrent and 10 scheduled tasks, 19,900 credits (+19,900 bonus), early access to beta features, a Manus T-shirt, and the full feature set including advanced agent tools and content generation.

There’s also a 17% discount on these prices for users who wish to pay up-front annually.

The launch builds on the infrastructure introduced with Manus earlier this year, which the company describes as not just an AI agent, but a personal cloud computing platform.

Each Manus session runs on a dedicated virtual machine, giving users access to orchestrated cloud compute through natural language — a setup the company sees as key to enabling true general-purpose AI workflows.

With Wide Research, Manus users can delegate research or creative exploration across dozens or even hundreds of subagents.

Unlike traditional multi-agent systems with predefined roles (such as manager, coder, or designer), each subagent within Wide Research is a fully capable, fully featured Manus instance — not a specialized one for a specific role — operating independently and able to take on any general task.

This architectural decision, the company says, opens the door to flexible, scalable task handling unconstrained by rigid templates.

The implication seems to be that running all these agents in parallel is faster and will result in a better and more varied set of work products beyond research reports, as opposed to the single “Deep Research” agents other AI providers have shown or fielded.

But while Manus promotes Wide Research as a breakthrough in agent parallelism, the company does not provide direct evidence that spawning dozens or hundreds of subagents is more effective than having a single, high-capacity agent handle tasks sequentially.

The release does not include performance benchmarks, comparisons, or technical explanations to justify the trade-offs of this approach — such as increased resource usage, coordination complexity, or potential inefficiencies. It also lacks details on how subagents collaborate, how results are merged, or whether the system offers measurable advantages in speed, accuracy, or cost.

As a result, while the feature showcases architectural ambition, its practical benefits over simpler methods remain unproven based on the information provided.

While Manus’s implementation of Wide Research is positioned as an advancement in general AI agent systems, the broader ecosystem has seen mixed results with similar subagent approaches.

For example, on Reddit, self-described users of Claude’s Code have raised concerns about its subagents being slow, consuming large volumes of tokens, and offering limited visibility into execution.

Common pain points include lack of coordination protocols between agents, difficulties in debugging, and erratic performance during high-load periods.

These challenges don’t necessarily reflect on Manus’s implementation, but they highlight the complexity of developing robust multi-agent frameworks.

Manus acknowledges that Wide Research is still experimental and may come with some limitations as development continues.

With the rollout of Wide Research, Manus deepens its commitment to redefining how users interact with AI agents at scale.

As other platforms wrestle with the technical challenges of subagent coordination and reliability, Manus’s approach may serve as a test case for whether generalized agent instances — rather than narrowly scoped modules — can deliver on the vision of seamless, multi-threaded AI collaboration.

The company hints at broader ambitions, suggesting that the infrastructure behind Wide Research lays the groundwork for future offerings. Users and industry watchers alike will be paying close attention to whether this new wave of agent architecture can live up to its potential — or whether the challenges seen elsewhere in the AI space will eventually catch up.