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Learn how Nimble's self-learning web search, crawling, and research agents discover deep web data for OpenAI models 👇

Nimble

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144,821 次观看 • 7 天前 •via X (Twitter)

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urik6 天前

@OpenAI Go beyond generic web index

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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

99,735 次观看 • 19 天前

Introducing Web Search Agents: expert level web search that self-learns your domain to deliver perfect web context, while burning less tokens. Generic web search tools start from scratch every time they run. Web Search Agents compound their expertise to adapt web search and crawling to your use case — finding the exact data you need, more efficiently. Learn more below in our blog:

Introducing Web Search Agents: expert level web search that self-learns your domain to deliver perfect web context, while burning less tokens. Generic web search tools start from scratch every time they run. Web Search Agents compound their expertise to adapt web search and crawling to your use case — finding the exact data you need, more efficiently. Learn more below in our blog:

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Agents 🤝 Web Search ASI:One now lets you toggle both Agents and Web Search together. Get answers with memory, logic, and real-time data in one go. → Watch how it works 👇

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Fetch.ai

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Meet Embodied Web Agents that bridge physical-digital realms. Imagine embodied agents that can search for online recipes, shop for ingredients and cook for you. Embodied web agents search internet information for implementing real-world embodied tasks. All data, codes and web environments are available at Paper link:

Meet Embodied Web Agents that bridge physical-digital realms. Imagine embodied agents that can search for online recipes, shop for ingredients and cook for you. Embodied web agents search internet information for implementing real-world embodied tasks. All data, codes and web environments are available at Paper link:

Yining Hong

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I built a Deep Research AI Agent team using OpenAI Agents SDK and Firecrawl. It combines multiple AI agents that can autonomously search the web, extract content, and generate detailed reports with enhanced analysis. 100% Opensource Code with step-by-step tutorials.

I built a Deep Research AI Agent team using OpenAI Agents SDK and Firecrawl. It combines multiple AI agents that can autonomously search the web, extract content, and generate detailed reports with enhanced analysis. 100% Opensource Code with step-by-step tutorials.

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Introducing web-agent, an open framework for building web agents 🔥 Build AI agents that search, scrape, and interact with the web - powered by the same architecture behind our /agent endpoint. 100% open source. Bring any model. Anthropic, OpenAI, or your own.

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figuring out how to upgrade go version using gpt-oss:120b, ollama's web search MCP server, and OpenAI codex open source models for coding are here

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You built an agent and you're still doing research by hand? Give it web search made for agents — CLI, REST, MCP.

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We're having a blast at Databricks Data + AI Summit! We're talking with builders about three easy ways to bring Nimble's live web search into Databricks: 1. Ask Genie to search the web and bring any data into any table 2. Call Nimble via SQL to enrich your tables 3. Build an end-to-end app on Databricks to monitor and visualize anything on the web

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You can now officially use Exa web search inside Grok Build! Exa is the fastest and most accurate web search, built for agents.

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Bytedance, opensourced DeerFlow (Deep Exploration and Efficient Research Flow) LLM + Langchain + tools (web search, crawler, code exec). Its a community-driven Deep Research framework.

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104,614 次观看 • 1 年前

Introducing the new Firecrawl CLI 🔥 The toolkit for agents to scrape, search, and browse the web. - Scrape clean data from any page - Search the web and get full results back - Spin up cloud browsers for interactive flows npx -y firecrawl-cli@latest init --all --browser

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Firecrawl

357,170 次观看 • 7 个月前

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

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Parallel is now integrated with Zapier Use this integration to give any automation access to powerful structured web search, for example: - Slack bots that enrich leads - Inbound emails to trigger deep research reports - Market research agents Here's Kumar Saunack for a demo:

Parallel is now integrated with Zapier Use this integration to give any automation access to powerful structured web search, for example: - Slack bots that enrich leads - Inbound emails to trigger deep research reports - Market research agents Here's Kumar Saunack for a demo:

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This is a wild use case! I used Gamma + n8n to automatically generate a complete presentation on AI Agents research. In just minutes! It combines web search (for research), GPT-5 (narrative), and Gamma (for slide content generation). Full workflow breakdown below 👇

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elvis

50,758 次观看 • 11 个月前