LumaBrowser

Lightpanda Browser is a headless browser built from scratch in Zig for AI agents and web automation. It is not based on Chromium, Blink, or WebKit; its implementation includes an HTML parser, V8 JavaScript support, a DOM tree and APIs, network loading, cookies, form input, proxy support, network interception, and optional robots.txt compliance. It can dump pages as HTML, Markdown, PNG, or PDF, or expose a Chrome DevTools Protocol and WebDriver BiDi server for clients such as Puppeteer and Playwright. Its native agent mode uses an LLM to navigate pages, click through flows, fill forms, and extract structured data. Sessions can be exported as deterministic, token-free PandaScript JavaScript and replayed without a model at runtime. The browser also provides an MCP server over stdio or HTTP, with isolated or shared browsing sessions for multiple agents. The project distributes nightly binaries for Linux and macOS, Docker images for Linux amd64 and arm64, and packages for Homebrew and the Arch User Repository. It can also be built from source with Zig and its V8, Libcurl, and html5ever dependencies.

WebLLM is an in-browser large language model inference engine developed by the MLC community. It runs model inference directly in web browsers using WebGPU hardware acceleration, without server-side processing, and can be used as an npm, Yarn, or CDN package for building web applications. Its MLCEngine exposes an OpenAI-compatible chat-completions interface with streaming, JSON-mode structured generation, seeding, and preliminary function-calling support. It supports model families including Llama, Phi, Gemma, Mistral, and Qwen, and can load custom MLC-format models consisting of model artifacts and WebAssembly model libraries. WebLLM provides Web Worker and Service Worker integrations, Chrome-extension examples, browser caching through Cache API, IndexedDB, or OPFS, and optional Subresource Integrity verification for downloaded model artifacts.

ego lite is a macOS browser from Citro Labs designed for AI-agent browser automation alongside a user's normal browsing. It gives each agent or task an isolated Space within the same browser, allowing multiple tasks to run in parallel without taking over the user's tabs; users can observe, take over, or stop an agent's Space. Through the ego-browser skill, agents such as Claude Code, Codex, Cursor, or custom agents can call in-page JavaScript tools including snapshot, fill, click, wait, navigate, and capture; an agent can compose several operations into one JavaScript execution rather than repeatedly issuing CLI commands. During setup, optional Chrome-data migration can transfer existing logins, cookies, extensions, and bookmarks, while browsing data remains on the user's device. The browser is distributed as a separate free download, the repository is MIT-licensed, and Windows and Linux support are listed as planned.

Fast Browser Use is a local-first browser automation engine and agent skill for Claude Code, Codex, Cursor, and Python workflows. It runs Qwen3.5 models locally through MLX or PyTorch on Apple Silicon, CUDA GPUs, or CPUs, without cloud inference. The engine scans the rendered page for visible, interactable controls and converts them into bounded action candidates such as click, select, type, and done. It maps those candidates to single vocabulary tokens, scores them with one forward pass, and dispatches the selected browser action instead of generating CSS selectors, XPath expressions, or Playwright code. Text generation is used specifically for typing into fields, while structural actions use discrete logits scoring. The runtime also includes page-settling waits, visibility and occlusion checks, DOM-freshness checks, read-only protection, immutable execution traces, and external URL, title, and text assertions for outcome verification. The project provides a command-line interface, a Python API, and installable agent-skill integration, using Playwright Chromium for browser execution. It supports offline model operation after local weights are downloaded and is released under the MIT License.

LLM Space v4 is a local-first desktop workbench for prototyping and developing AI agents. It lets users version prompts, system messages, tools, and model settings; trace each model call and tool run in an agent loop; replay historical runs to debug failures; and evaluate agent performance across runs. It can generate prompts and tools with AI and turn a thread into a runnable LangGraph agent. Threads, project files, and API keys remain on the user's computer, while the project describes the app as cloud-ready for managed agents. The desktop application uses Electrobun with a React, Tailwind CSS, and shadcn/ui interface, and is built as a Bun monorepo around Pi Agent Core.