@CycleDecoded: Guys doing automation and scraping can throw away their previous solutions. An open-source project called Index (built by the AI developer platform Laminar team) suddenly went viral on GitHub, turning the web browser directly into a callable API. In essence, it's an ultra-smooth “AI browser Agen…

Brothers working on automation and web scraping can throw away their old solutions. A new open-source project called Index (built by the AI developer platform team Laminar) has suddenly gone viral on GitHub — it turns your web browser into a callable API. At its core, this is an extremely smooth “AI browser agent”: type a plain-English command in your terminal, and it can click around, scrape data, fill forms, and even chain actions across multiple sites to complete an entire complex workflow — just like a real human. It hits a stunning 92% accuracy on the WebVoyager benchmark. One command to run: pip install, then type index run and call it directly from your terminal. Works with your local Chrome out of the box: pass the --local-chrome flag to automatically reuse your logged-in account state. Top-tier visual reasoning engine: natively supports leading multimodal LLMs like Claude 3.7 Sonnet and Gemini 2.5 Pro. Full session replay and debugging: ships with out-of-the-box visual session recording and step-by-step Trace monitoring. Automatically scrape and generate spreadsheets: one sentence handles “go scrape data from a website and write it to a new Google Sheet.” License: Apache-2.0 GitHub link: https://github.com/lmnr-ai/index — # lmnr-ai/index Source: https://github.com/lmnr-ai/index GitHub stars Static Badge X (formerly Twitter) Follow Static Badge # Index Index is a state-of-the-art open-source browser agent that autonomously executes complex web tasks. It turns any website into an accessible API and can be seamlessly integrated with just a few lines of code. - [x] Powered by reasoning LLMs with vision capabilities. - [x] Gemini 2.5 Pro (really fast and accurate) - [x] Claude 3.7 Sonnet with extended thinking (reliable and accurate) - [x] OpenAI o4-mini (depending on the reasoning effort, provides good balance between speed, cost and accuracy) - [x] Gemini 2.5 Flash (really fast, cheap, and good for less complex tasks) - [x] pip install lmnr-index and use it in your project - [x] index run to run the agent in the interactive CLI - [x] Supports structured output via Pydantic schemas for reliable data extraction. - [x] Index is also available as a serverless API. (https://docs.lmnr.ai/index-agent/api/getting-started) - [x] You can also try out Index via Chat UI (https://lmnr.ai/chat). - [x] Supports advanced browser agent observability (https://docs.lmnr.ai/index-agent/tracing) powered by open-source platform Laminar (https://github.com/lmnr-ai/lmnr). prompt: go to ycombinator.com. summarize first 3 companies in the W25 batch and make new spreadsheet in google sheets. https://github.com/user-attachments/assets/2b46ee20-81b6-4188-92fb-4d97fe0b3d6a ## Documentation Check out full documentation here (https://docs.lmnr.ai/index-agent/getting-started) ## Quickstart ### Install dependencies bash pip install lmnr-index 'lmnr[all]' # Install playwright playwright install chromium ### Setup model API keys Setup your model API keys in .env file in your project root: GEMINI_API_KEY= ANTHROPIC_API_KEY= OPENAI_API_KEY= # Optional, to trace the agent's actions and record browser session LMNR_PROJECT_API_KEY= ### Run Index with code python import asyncio from index import Agent, GeminiProvider from pydantic import BaseModel from lmnr import Laminar import os # to trace the agent's actions and record browser session Laminar.initialize() # Define Pydantic schema for structured output class NewsSummary(BaseModel): title: str summary: str async def main(): llm = GeminiProvider(model="gemini-2.5-pro-preview-05-06") agent = Agent(llm=llm) # Example of getting structured output output = await agent.run( prompt="Navigate to news.ycombinator.com, find a post about AI, extract its title and provide a concise summary.", output_model=NewsSummary ) summary = NewsSummary.model_validate(output.result.content) print(f"Title: {summary.title}") print(f"Summary: {summary.summary}") if __name__ == "__main__": asyncio.run(main()) ### Run Index with CLI Index CLI features: - Browser state persistence between sessions - Follow-up messages with support for “give human control” action - Real-time streaming updates - Beautiful terminal UI using Textual You can run Index CLI with the following command. bash index run Output will look like this: Loaded existing browser state ╭───────────────────── Interactive Mode ─────────────────────╮ │ Index Browser Agent Interactive Mode │ │ Type your message and press Enter. The agent will respond. │ │ Press Ctrl+C to exit. │ ╰────────────────────────────────────────────────────────────╯ Choose an LLM model: 1. Gemini 2.5 Flash 2. Claude 3.7 Sonnet 3. OpenAI o4-mini Select model [1/2] (1): 3 Using OpenAI model: o4-mini Loaded existing browser state Your message: go to lmnr.ai, summarize pricing page Agent is working... Step 1: Opening lmnr.ai Step 2: Opening Pricing page Step 3: Scrolling for more pricing details Step 4: Scrolling back up to view pricing tiers Step 5: Provided concise summary of the three pricing tiers ### Running CLI with a personal Chrome instance You can use Index with personal Chrome browser instance instead of launching a new browser. Main advantage is that all your existing logged-in sessions will be available. bash # Basic usage with default Chrome path index run --local-chrome ## Use Index via API The easiest way to use Index in production is with serverless API (https://docs.lmnr.ai/index-agent/api/getting-started). Index API manages remote browser sessions, agent infrastructure and browser observability (https://docs.lmnr.ai/index-agent/api/tracing). To get started, create a project API key in Laminar (https://lmnr.ai). ### Install Laminar bash pip install lmnr ### Use Index via API python from lmnr import Laminar, LaminarClient # you can also set LMNR_PROJECT_API_KEY environment variable # Initialize tracing Laminar.initialize(project_api_key="your_api_key") # Initialize the client client = LaminarClient(project_api_key="your_api_key") for chunk in client.agent.run( stream=True, model_provider="gemini", model="gemini-2.5-pro-preview-05-06", prompt="Navigate to news.ycombinator.com, find a post about AI, and summarize it" ): print(chunk) ## Browser agent observability Both code run and API run provide advanced browser observability. To trace Index agent’s actions and record browser session you simply need to initialize Laminar tracing before running the agent. python from lmnr import Laminar Laminar.initialize(project_api_key="your_api_key") Then you will get full observability on the agent’s actions synced with the browser session in the Laminar platform. Learn more about browser agent observability in the documentation (https://docs.lmnr.ai/index-agent/tracing). — Made with ❤️ by the Laminar team (https://lmnr.ai)