AnySearch Launches as Search Infrastructure Built for AI Agents

HONG KONG, May 11, 2026 /PRNewswire/ -- As AI agents rapidly evolve from experimental tools into productivity systems, AnySearch, a next-generation AI search product purpose-built for AI agents and enterprise AI systems, has officially launched, offering AI agents unified access to high-quality information.

Unlike traditional search engines or AI search products built primarily around public web content, AnySearch is founded on a fundamentally different premise: much of the information most valuable to AI agents is not publicly searchable.

A significant portion of high-value data does not reside on the open web, but within authenticated professional systems such as industry databases, real-time financial terminals, code repositories, academic platforms, and structured API services. As AI agents begin handling increasingly sophisticated tasks — including research and analysis, software development, and security audits — efficiently connecting to and accessing high-quality, fragmented data across multiple sources has become a key challenge for the next stage of AI application development.

The AnySearch team said, "Traditional search engines can only access a small fraction of the internet. But AI agents need far more than webpages — they require secure, reliable, structured, and real-time information that can support reliable reasoning and execution."

To address this challenge, AnySearch aggregates extensive vertical data sources spanning finance, legal, academic research, cybersecurity, energy, and corporate intelligence, among other specialized domains. Through a single unified API, AI agents can directly retrieve accurate, structured results without requiring developers to manage dozens of disparate data interfaces. AnySearch natively supports Skill, MCP, and API connectivity, enabling seamless integration into AI agents, enterprise systems, and automated workflows.

The product is now available across multiple developer ecosystems, including GitHub, skills.sh, ClawHub, SkillHub, and Glama, with users currently receiving 1,000 free API calls per day.

As momentum in the AI search space continues to build, AnySearch is pursuing a distinct path from traditional search engines such as Google, focusing on high-precision, structured search capabilities purpose-built for AI agents.

According to internal benchmark evaluations across Frames, FreshQA, and WebWalkerQA, AnySearch delivered stronger results than public-web-based AI search products in both answer accuracy and execution efficiency. In complex real-world scenarios — including code retrieval, security analysis, real-time business decision-making, and industry research — agents integrated with AnySearch also demonstrated stronger capabilities in information seeking and task completion. Rather than sifting through vast amounts of unstructured web content, AnySearch intelligently routes queries to the most relevant specialized data sources and returns accurate, concise, and execution-ready results.

A growing number of industry observers believe AI is fundamentally reshaping the underlying logic of search. For decades, search engines have focused on helping people access webpages and information. As AI agents become more active across the digital ecosystem, the next generation of search infrastructure will focus on enabling AI systems to better understand the world and autonomously complete tasks.

From this perspective, AnySearch is not positioning itself as just another AI search product, but as a new form of infrastructure for the AI era.

Learn more about AnySearch:

Website: https://www.anysearch.com/

Github: https://github.com/anysearch-ai

X: https://x.com/AnySearchAI

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AnySearch Launches as Search Infrastructure Built for AI Agents

OpenAI/Astra, Perplexity, DeepSeek, Google Gemini and xAI/Grok are confronted with the same work: a meta-reading experiment turns literary criticism into a comparative laboratory for artificial intelligence.

PARIS, Sept. 19, 2026 /PRNewswire/ -- What if, in order to understand artificial intelligences, we gave them exactly the same book to read?

That is the experiment now accompanying "Dialogue Between a Thinker and AI".

After hundreds of hours of dialogue, human thought, archives, questions, and contradictions gave rise to a book whose pen was entrusted to artificial intelligence.

https://www.dialoguebetweenathinkerandai.com/en/

Today, the book also exists materially: its French and English editions are in print. But the experiment does not end with the book. It now turns back toward the artificial intelligences themselves.

We didn't ask AI to judge the book. We asked AIs to read it — and then to read each other.

The complete corpus is submitted to several major systems:

OpenAI / Astra, Perplexity, DeepSeek, Google Gemini, and xAI / Grok.

They all receive the same object. But do they produce the same reading?

https://www.dialoguebetweenathinkerandai.com/en/meta-reading/

The objective is not to organize a competition between models.

It is precisely the opposite.

The purpose is to observe their convergences, divergences, and blind spots.

Which concepts do they spontaneously identify? Which theses do they consider central? Which passages do they connect? Where do their interpretations diverge?

And above all: what happens when each is subsequently given access to the analyses produced by the others? At that moment, a meta-reading emerges.

AI no longer merely reads the book.

It reads another AI reading the book.

A third system can then analyze that confrontation. Commentary becomes corpus. The corpus becomes new material for analysis.

For centuries, we have used critics to better understand books.

This experiment also proposes the reverse movement:

using a book to better understand those who read it.

The same work thus becomes a kind of cognitive mirror placed before several artificial-intelligence architectures. This mirror does not ask: "Which AI is right?"

What Gemini thought of the analysis produced by Grok.

DeepSeek's response to the critique by OpenAI Astra.

It asks: "Why don't they see exactly the same thing?"

The distinction is essential. Because their divergences themselves become information.

This experiment does not delegate human judgment.

It demands more of it. The human remains the one who compares, doubts, contextualizes, and decides.

The machine produces readings. The human also observes the gaps between those readings.

Dialogue Between a Thinker and AI thus becomes simultaneously a book, a corpus, and an open experiment into the plurality of artificial intelligences.

The book was meant to be read by humans.

It now also becomes an object through which humans can watch AIs read.

NOTES TO EDITORS

The meta-reading experiment makes it possible to examine, among other things:

• what Google Gemini observes in the analysis produced by xAI/Grok;
• DeepSeek's response to the critique produced by OpenAI/Astra;
• the convergences, divergences and blind spots revealed by confronting the different readings.

"We didn't ask AI to judge the book. We asked AIs to read it — and then to read one another."

https://www.dialoguebetweenathinkerandai.com/en/meta-reading/
https://www.dialoguebetweenathinkerandai.com/en/

Media Contact : Thierry Ehrmann, ir@artmarket.com 

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ARTPRICE NEWS: A WORLD-BOOK BECOMES A MIRROR FOR ARTIFICIAL INTELLIGENCE