Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

Anni Wang

2,932 subscribers

13,028 views • 6 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

22 Comments

Anni Wang6 months ago

Read about our blog post here:

Anni Wang6 months ago

Check out our docs for details:

Scott Scowden6 months ago

@CloudflareDev Would be nice if we could change the embedding model after creation. Are there any plans to implement that?

Anni Wang6 months ago

@CloudflareDev yes! cc: @G4brym @NelsonJSDuarte 👀

Scott Scowden6 months ago

@CloudflareDev @G4brym @NelsonJSDuarte You guys are awesome always being willing to listen to ideas and respond. Thank you

Sunder6 months ago

@eastdakota @CloudflareDev I really wish you guys would also support web search. That would be the real alpha

Anni Wang6 months ago

@eastdakota @CloudflareDev You mean like Google / Bing Search across the web?

Sunder6 months ago

@eastdakota @CloudflareDev yupe. right now , we have to use browser rendering and then use llm. would love to see ai search -with parameters for domains, output variables and then receive results from cf

anti-commie-ve6 months ago

@CloudflareDev It would be amazing to upload a bunch of links of articles from my company blog and the agent can search quickly what my clients ask for. Or maybe fetch them from any api.

Anni Wang6 months ago

@CloudflareDev We have the ability to add a website you own as a data source to ai search:

anti-commie-ve6 months ago

@CloudflareDev Beautiful.

Ghoshan6 months ago

@CloudflareDev Nothing new tho. That’s how AI search worked for atleast 4 months now

Anni Wang6 months ago

@CloudflareDev we added a couple of things!

Matt6 months ago

@eastdakota @CloudflareDev is this QMD but on cloud?

Luis Rudge6 months ago

@CloudflareDev d1 support wen

Anni Wang6 months ago

@CloudflareDev what’s your use case?

Luis Rudge6 months ago

@CloudflareDev Ai search pointing to my database with my always fresh content

ThomAub6 months ago

@CloudflareDev BM25 with backed by D1??

The Architect6 months ago

this is amazing. cloudflare is continuing to dominate in the AI cloud infra space. we currently leverage cloudflare to help us host our 100% free, private, and open source tools. as search is clearly becoming a default primitive for agents. we been inspired to create the a similar from the local/ private side with BM25 + RAG. EZ-Search is perfect for people who want strong retrieval for code, docs, and images without any setup while being 100% local, private, and open source. excited to test out the new ai search to see how it compares

Samuel Zeller6 months ago

@CloudflareDev Gonna implement this right now for ! Brilliant

Elmoshir6 months ago

@eastdakota @CloudflareDev Fumadocs integration to auto update the dataset on docs changes?

Anni Wang6 months ago

@eastdakota @CloudflareDev We have an API to trigger a sync on the datasource. Could you use with webhook?

Related Videos

Today, we’re launching the Parallel Search API, the most accurate web search for AI agents, built using our proprietary web index and retrieval infrastructure. Traditional search ranks URLs for humans to click. AI search needs something different: the right tokens in their context window. Parallel’s Search API is built from the ground up for AI agents, resulting in higher accuracy, lower costs, and lower latency for end-to-end agentic workflows.

Today, we’re launching the Parallel Search API, the most accurate web search for AI agents, built using our proprietary web index and retrieval infrastructure. Traditional search ranks URLs for humans to click. AI search needs something different: the right tokens in their context window. Parallel’s Search API is built from the ground up for AI agents, resulting in higher accuracy, lower costs, and lower latency for end-to-end agentic workflows.

Parallel Web Systems

670,433 views • 11 months ago

AI Agents can now search and register domains on your behalf! The Beta Registrar API provides commands to search, check, and register domains. All available through the Cloudflare MCP.

AI Agents can now search and register domains on your behalf! The Beta Registrar API provides commands to search, check, and register domains. All available through the Cloudflare MCP.

Cloudflare Developers

23,016 views • 6 months ago

This is the google of AI agents If you're building with AI agents, you need it Search across 164+ agent skills > Filter > Search > Copy install

This is the google of AI agents If you're building with AI agents, you need it Search across 164+ agent skills > Filter > Search > Copy install

0xMarioNawfal - The Roundtable Show

89,688 views • 8 months ago

Today we're launching skill search for Mastra agents! Give your agents dozens of skills, let it search and load on-the-fly:

Today we're launching skill search for Mastra agents! Give your agents dozens of skills, let it search and load on-the-fly:

Sam Bhagwat

21,909 views • 2 months ago

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,308 views • 1 year ago

TinyFish has made web Search and Fetch features FREE for all developers and AI agents, with generous rate limits! > Search: Low-latency search responses for agentic use > Fetch: Real browser render, clean markdown, or JSON outputs > Works with Claude Code, OpenClaw, Cursor, Codex, n8n, Dify, and more

TinyFish has made web Search and Fetch features FREE for all developers and AI agents, with generous rate limits! > Search: Low-latency search responses for agentic use > Fetch: Real browser render, clean markdown, or JSON outputs > Works with Claude Code, OpenClaw, Cursor, Codex, n8n, Dify, and more

🚨 AI News | TestingCatalog

32,989 views • 5 months ago

1.2 million samples. BM25, Embeddings and Hybrid search. Tutorial and code comes tomorrow! Stay tuned!

1.2 million samples. BM25, Embeddings and Hybrid search. Tutorial and code comes tomorrow! Stay tuned!

abhishek

12,266 views • 11 months ago

Search inside an agent workflow is different from search in a browser. A human can scan links, skip noise, and decide what is worth trusting. An agent needs cleaner context up front. We tested AnySearch inside a real agent workflow to see how structured, source-backed search changes the next reasoning step.

Search inside an agent workflow is different from search in a browser. A human can scan links, skip noise, and decide what is worth trusting. An agent needs cleaner context up front. We tested AnySearch inside a real agent workflow to see how structured, source-backed search changes the next reasoning step.

GitHub Projects Community

11,839 views • 3 months ago

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

144,343 views • 2 years ago

Week 3 of YC. We just made semantic search serverless. Until now, semantic search meant embedding every document, every row and every chunk up front, and paying for it before anyone ran a single search. We flipped that. With Polygres (YC F26) you connect your data, choose what's searchable, and search it by meaning. There's no giant embedding job first. You pay for tokens only when you actually search. It runs on our brand new model that matches embedding-model recall in our (early, tbc) internal benchmarks, without neural ingestion. We believe semantic search should work like serverless compute. Acess rolling out to Polygres users (link below) or dm me.

dale

22,063 views • 27 days ago

Introducing Upstash Search - the fastest way to add search to your app 🎉 ◆ Plug-and-play AI search ◆ 100% serverless, zero infra to manage ◆ Works with massive datasets Ship high-quality search in minutes 👇

Introducing Upstash Search - the fastest way to add search to your app 🎉 ◆ Plug-and-play AI search ◆ 100% serverless, zero infra to manage ◆ Works with massive datasets Ship high-quality search in minutes 👇

Upstash

71,100 views • 1 year ago

Nimble is now an OpenAI plugin for ChatGPT and Codex, giving OpenAI models deeper context from the web. With Nimble, ChatGPT and Codex can: - Discover more complete web context by combining web search with computer use to navigate and paginate websites, capturing data that doesn’t show up in search results - Improve web search efficiency with self-learning agents that optimize web retrieval methodology after every run - Govern web retrieval with source controls, confidence scores, and evidence for every data point Available now in the Plugins tab. Learn more here:

Nimble is now an OpenAI plugin for ChatGPT and Codex, giving OpenAI models deeper context from the web. With Nimble, ChatGPT and Codex can: - Discover more complete web context by combining web search with computer use to navigate and paginate websites, capturing data that doesn’t show up in search results - Improve web search efficiency with self-learning agents that optimize web retrieval methodology after every run - Govern web retrieval with source controls, confidence scores, and evidence for every data point Available now in the Plugins tab. Learn more here:

Nimble

99,750 views • 1 month ago

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

Jerry Liu

18,615 views • 3 months ago

Should you use a search engine or a chatbot for your question? With Brave Search, you don't have to decide. Our new feature, Ask Brave, combines search and AI chat into one. Ask Brave is free and available now on all platforms and browsers.

Should you use a search engine or a chatbot for your question? With Brave Search, you don't have to decide. Our new feature, Ask Brave, combines search and AI chat into one. Ask Brave is free and available now on all platforms and browsers.

Brave

127,311 views • 1 year ago

Today we're launching Metix AI for recruiters. Type your role in one sentence, then pick how far it goes: 👉 Run Fast Search for an immediate list to explore candidate pools and size up the market. 👉 Run Deep Search for the best fits. It starts by clarifying what your role needs and what matters most, then digs into every candidate. 👉 Run the Agent to hand off sourcing, emails and follow-ups. It puts interested candidates on your calendar for interviews. We send interviews, not resumes. Try Fast Search on a role you're hiring for.

Today we're launching Metix AI for recruiters. Type your role in one sentence, then pick how far it goes: 👉 Run Fast Search for an immediate list to explore candidate pools and size up the market. 👉 Run Deep Search for the best fits. It starts by clarifying what your role needs and what matters most, then digs into every candidate. 👉 Run the Agent to hand off sourcing, emails and follow-ups. It puts interested candidates on your calendar for interviews. We send interviews, not resumes. Try Fast Search on a role you're hiring for.

Zhilin Wang

202,805 views • 2 days ago

INTRODUCTION OF SEARCH-TO-EARN 🧵 1/ Search, Earn, Repeat! Imagine a world where every search rewards you. 🔥 With Adot AI Search ( powered by Crypto + AI, you’re not just getting top Web3 insights—you’re also earning Agems! 💎 These can be redeemed for airdrops, NFTs, and more. 🎥 Watch the video to see how it works! 👇

INTRODUCTION OF SEARCH-TO-EARN 🧵 1/ Search, Earn, Repeat! Imagine a world where every search rewards you. 🔥 With Adot AI Search ( powered by Crypto + AI, you’re not just getting top Web3 insights—you’re also earning Agems! 💎 These can be redeemed for airdrops, NFTs, and more. 🎥 Watch the video to see how it works! 👇

Adot (a.xyz) 🔍

56,698 views • 1 year ago

Software developers will love this new AI tool. This is like ChatGPT for code. It understands codebases with GPT-4 and semantic code search and lets you talk with your code.

Software developers will love this new AI tool. This is like ChatGPT for code. It understands codebases with GPT-4 and semantic code search and lets you talk with your code.

Rowan Cheung

638,482 views • 3 years ago

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

OptimAI Network

36,691 views • 5 months ago