Github Theagenticai Theagenticbrowser Open Source Ai Agent For Web

Agentic Browser is an agent-based system designed to automate browser interactions using a natural language interface. Built upon the PydanticAI Python agent framework, Agentic Browser allows users to automate tasks such as form filling, product searches on e-commerce platforms, content retrieval, media interaction, and project management on various platforms. Agentic Browser uses three specialized agents working in harmony: Planner Agent: The strategist that breaks down user requests into clear, executable steps. It creates and adapts plans based on feedback and progress. Browser Agent: The executor that directly interacts with web pages.

It performs actions like clicking, typing, navigating, and extracting information using browser automation tools. Critique Agent: The quality controller that analyzes actions, verifies results, and guides the workflow. It determines if tasks are complete or need refinement. This document provides a high-level introduction to TheAgenticBrowser system, explaining its purpose as an AI-powered browser automation platform and outlining its core architectural components. For detailed setup instructions, see Getting Started. For in-depth technical documentation of individual components, see Core Architecture and subsequent sections.

TheAgenticBrowser is an agent-based browser automation system that enables natural language control of web interactions. The system uses a multi-agent architecture built on the PydanticAI framework to decompose complex web tasks into executable browser actions, with continuous evaluation and adaptation capabilities. TheAgenticBrowser implements a three-agent coordination pattern orchestrated by a central management layer. The system maintains separation between planning, execution, and evaluation concerns while providing multiple deployment and integration options. Sources: README.md26-38 Architecture diagrams from context For detailed documentation of individual skills, see Skills and Tools.

A curated collection of interesting GitHub repositories Browser automation using a planner, executor, and critique agent system Hosted on GitHub Pages — Theme by orderedlist We tested proprietary web agents, remote browsers, and benchmarked 8 MCP servers across web search and browser automation tasks. Below are 30+ open-source web agents that enable AI to navigate, interact with, and extract data from the web, including browsing, authentication, and crawling. WebVoyager benchmark runs 643 task instances across Google, GitHub, Wikipedia, and 12 other real-world sites.

Tasks include form submission, multi-page navigation, and search operations. Tools that navigate websites and complete multi-step tasks with minimal guidance. LLM-based agents that operate websites with little to no oversight. Discover and explore top open-source AI tools and projects—updated daily. AI agent for web automation and scraping The Agentic Browser is an open-source AI agent designed for automating web interactions and data scraping through a natural language interface.

It empowers users to automate tasks like e-commerce product searches, data extraction, and content retrieval, benefiting researchers, developers, and power users by streamlining complex web-based workflows. The system employs a three-agent architecture: a Planner Agent to break down tasks, a Browser Agent to execute actions via web automation tools (like Playwright), and a Critique Agent to evaluate results and guide... This feedback loop ensures accurate task completion by allowing agents to adapt plans and refine actions based on analysis of screenshots and DOM changes. The project is built upon the PydanticAI Python Agent Framework and acknowledges Agent-E. Further community or maintenance details are not specified in the README. ModelingGithubposted by ODSC Team December 18, 2025 ODSC Team

The year 2025 has seen an explosion of open-source projects aimed at building and enhancing AI agents. GitHub’s latest Octoverse report highlights that over 4.3 million AI-related repositories now exist (a 178% YoY jump in LLM-focused projects). In this environment, certain new repos skyrocketed in popularity – garnering tens of thousands of stars – by empowering developers to create autonomous workflows, integrate advanced models locally, and streamline AI development. In the blog below, let’s take a look at the ten most-starred GitHub agentic AI repositories in 2025 and explore why they’ve earned such acclaim. Meet leading experts, upskill with hands-on workshops, and connect with thousands of data science and AI practitioners shaping the next wave of innovation. n8n is an open-source workflow automation platform that combines a visual no-code interface with code flexibility, now supercharged with native AI capabilities.

With 400+ integrations, n8n enables technical teams to build powerful automation pipelines while maintaining full control. Notably, n8n introduced an “AI-native” approach, letting users incorporate large language models (e.g. via LangChain) into their workflows to create custom AI agent automations. This ability to build AI-driven agents and multi-system flows – all self-hosted under a fair-code license – has made n8n immensely popular. It surpassed 150,000 GitHub stars in 2025. In this article, we'll explore how to build an AI agent that can automatically browse the web and perform actions using the Supercog Agentic Framework.

This powerful agent combines browser automation with AI vision models to create an intelligent web assistant that can navigate websites, extract information, and perform tasks just like a human would. Let's install the framework from source to get started: To use the OSS Operator Agent, you'll need to set up an API key for your chosen model: You can set the key as an environment variable: You can customize the agent by modifying the oss_operator.py file: Hey everyone!

We are excited to share TheAgenticBrowser with you – our open source project that lets you control your browser with natural language.🎉 Think of it as your personal web assistant that can handle pretty... Whether you need to: - Dig through research papers and make sense of them - Hunt down the best travel deals across different sites - Keep tabs on sports stats and financial data -... We built it on top of PydanticAI because we wanted it to be rock-solid and easy to extend. Plus, it plays nice with any language model you prefer to use. ✴️ Check out the repo here: https://lnkd.in/geMwkByB #TheAgentic #AI #LLMs #OpenSource ⭐Give your AI eyes: Introducing Chrome DevTools MCP👀 Chrome DevTools MCP: a groundbreaking tool that connects AI coding assistants to Chrome's DevTools via the Model Context Protocol.

This innovative integration allows AI agents to see, interact with, and debug live web applications directly in real browsers. With Chrome DevTools MCP, AI can perform a variety of tasks, including: - Running performance traces - Inspecting DOM elements - Monitoring network requests - Simulating user interactions - Automatically fixing issues based on... 🔗 For a deeper dive, read my detailed article here: 🔵 Scraping has changed. Until recently, if you wanted structured data from a website, you needed to: – parse the HTML, – write selectors, – handle structure changes, – and hope the website wouldn’t break your script. Now tools like Firecrawl offer a different approach.

It uses LLMs to understand the content, group it semantically, and serve clean, structured results via API. We tested it in one of our projects. Output? Usable right away, even for complex, unstructured websites. For teams working on AI, automation or search – this kind of tool removes a lot of friction. Full test results, pros & cons, and example use cases, we break it down in this article ⬇️ https://lnkd.in/dTBN89Uh

While searching for Claude API access options, I stumbled upon an interesting JavaScript library from Puter that claims to offer free, unlimited access to Claude 3.5 Sonnet — and it’s powered by a fascinating... 👉 https://lnkd.in/gV4_Pz33 Here’s the core idea: Instead of the app developer paying for servers, APIs, or AI usage — each user brings and pays for their own resources. You build the features; they cover their consumption. This model makes your app practically free to run, regardless of scale — 1 user or 1 million. Key advantages: • 💰 Zero infra cost: No servers, API bills, or AI usage fees for developers. • 🔑 No API keys: Users authenticate directly via Puter, removing key management and secret leakage risks.

• 🧱 Simpler codebase: Many apps can be built purely frontend-only. • 🔒 Built-in security: Auth, permissions, and isolation handled by Puter. • 🚫 No anti-abuse logic needed: Users pay for their own consumption, so bad actors have no free-ride incentive. • 🧾 Unified user billing: Users manage all their usage under their Puter account — simple and transparent. It’s a genuinely novel take on “serverless” — almost developerless infrastructure cost. While it’s great for experimentation, production adoption will still need to weigh: • Vendor dependency and ToS implications • Data privacy and gateway trust • Stability and long-term support Still, the User Pays Model...

What do you think — is this a glimpse into the future of how AI apps will be monetized? #AI #APIs #Serverless #CloudComputing #DeveloperExperience #Claude #PricingModel #Innovation 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐲: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐒𝐨𝐜𝐢𝐚𝐥 𝐌𝐞𝐝𝐢𝐚 𝐏𝐨𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐧𝟖𝐧 + 𝐀𝐈 Manually extracting blog articles, formatting them, and posting across platforms was slowing the team down. So we built an 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐮𝐬𝐢𝐧𝐠 𝐧𝟖𝐧 powered by 𝐀𝐈 that does it all: 1- Fetches new blog articles from the website in real time 2- Extracts title, image, and link automatically 3-... #n8n #AIAutomatic #AI #Socialmediapost #Javascript #Machinelearning #AIagent About a month ago, I came across a highly discussed post on Hacker News — “Stop Building AI Agents”

In the post, the author shared a personal experience: he built a “research crew” with CrewAI: three agents, five tools, perfect coordination on paper. But in practice, the researcher ignored the web scraper, the summarizer forgot to use the citation tool and the coordinator gave up entirely when processing longer documents. It was a beautiful plan falling apart in spectacular ways. The flowchart below was created by the author after countless rounds of debugging and failed attempts, summarizing his decision guide for Should I use an Agent. Image source: https://decodingml.substack.com/p/stop-building-ai-agents The article distilled an important principle: agents work best in unstable processes where humans remain in the loop for oversight — in these scenarios, an agent’s exploratory and creative capabilities often outperform a rigid...

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