base on The SOTA Open-Source Browser Agent for autonomously performing complex tasks on the web <a href="https://www.ycombinator.com/companies/laminar-ai"></a> <a href="https://x.com/lmnrai"></a> <a href="https://discord.gg/nNFUUDAKub">  </a> # Index Index is the SOTA open-source browser agent for autonomously executing complex tasks on the web. - [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] Index is also available as a [serverless API.](https://docs.lmnr.ai/laminar-index/introduction) - [x] You can also try out Index via [Chat UI](https://lmnr.ai/chat). - [x] Supports advanced [browser agent observability](https://docs.lmnr.ai/laminar-index/observability) 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) ## Index API The easiest way to use Index in production is via the [serverless API](https://docs.lmnr.ai/laminar-index/introduction). Index API manages remote browser sessions, agent infrastructure and [browser observability](https://docs.lmnr.ai/laminar-index/tracing). To get started, [sign up](https://lmnr.ai/sign-in) and create project API key. Read the [docs](https://docs.lmnr.ai/laminar-index/introduction) to learn more. ### 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-03-25", prompt="Navigate to news.ycombinator.com, find a post about AI, and summarize it" ): print(chunk) ``` ## Local Quick Start ### Install dependencies ```bash pip install lmnr-index # Install playwright playwright install chromium ``` ### Setup model API keys Setup your model API keys in `.env` file in your project root: ``` ANTHROPIC_API_KEY= GEMINI_API_KEY= OPENAI_API_KEY= ``` ### Run the agent with CLI You can run Index via interactive CLI. It 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 the agent with the following command. Remember to set API key for the selected model in the `.env` file. ```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 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 existing logged in sessions will be available. ```bash # Basic usage with default Chrome path index run --local-chrome # With custom Chrome path and debugging port index run --local-chrome --chrome-path="/path/to/chrome" --port=9223 ``` This will launch Chrome with remote debugging enabled and connect Index to it. #### OS-specific Chrome paths Default Chrome executable paths on different operating systems: **macOS**: ```bash index run --local-chrome --chrome-path="/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" ``` **Windows**: ```bash index run --local-chrome --chrome-path="C:\Program Files\Google\Chrome\Application\chrome.exe" ``` #### Connecting to an already running Chrome instance If you already have Chrome running with remote debugging enabled, you can connect to it: 1. Launch Chrome with debugging enabled: ```bash # macOS /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --remote-debugging-port=9222 # Windows "C:\Program Files\Google\Chrome\Application\chrome.exe" --remote-debugging-port=9222 ``` 2. Then run Index with the same port: ```bash index run --local-chrome --port=9222 ``` ### Run the agent with code ```python import asyncio from index import Agent, AnthropicProvider async def main(): llm = AnthropicProvider( model="claude-3-7-sonnet-20250219", enable_thinking=True, thinking_token_budget=2048) # llm = OpenAIProvider(model="o4-mini") you can also use OpenAI models agent = Agent(llm=llm) output = await agent.run( prompt="Navigate to news.ycombinator.com, find a post about AI, and summarize it" ) print(output.result) if __name__ == "__main__": asyncio.run(main()) ``` ### Stream the agent's output ```python async for chunk in agent.run_stream( prompt="Navigate to news.ycombinator.com, find a post about AI, and summarize it" ): print(chunk) ``` ### Enable browser agent 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. <picture> <img src="./static/traces.png" alt="Index observability" width="800"/> </picture> ### Run with remote CDP url ```python import asyncio from index import Agent, AnthropicProvider, BrowserConfig async def main(): # Configure browser to connect to an existing Chrome DevTools Protocol endpoint browser_config = BrowserConfig( cdp_url="<cdp_url>" ) llm = AnthropicProvider(model="claude-3-7-sonnet-20250219", enable_thinking=True, thinking_token_budget=2048) agent = Agent(llm=llm, browser_config=browser_config) output = await agent.run( prompt="Navigate to news.ycombinator.com and find the top story" ) print(output.result) if __name__ == "__main__": asyncio.run(main()) ``` ### Run with local Chrome instance (programmatically) ```python import asyncio from index import Agent, AnthropicProvider, BrowserConfig async def main(): # Configure browser to connect to a local Chrome instance browser_config = BrowserConfig( cdp_url="http://localhost:9222" ) llm = AnthropicProvider(model="claude-3-7-sonnet-20250219", enable_thinking=True, thinking_token_budget=2048) agent = Agent(llm=llm, browser_config=browser_config) output = await agent.run( prompt="Navigate to news.ycombinator.com and find the top story" ) print(output.result) if __name__ == "__main__": asyncio.run(main()) ``` ### Customize browser window size ```python import asyncio from index import Agent, AnthropicProvider, BrowserConfig async def main(): # Configure browser with custom viewport size browser_config = BrowserConfig( viewport_size={"width": 1200, "height": 900} ) llm = AnthropicProvider(model="claude-3-7-sonnet-20250219") agent = Agent(llm=llm, browser_config=browser_config) output = await agent.run( "Navigate to a responsive website and capture how it looks in full HD resolution" ) print(output.result) if __name__ == "__main__": asyncio.run(main()) ``` --- Made with ❤️ by the [Laminar team](https://lmnr.ai) ", Assign "at most 3 tags" to the expected json: {"id":"13507","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"