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Basic Information

Index is an open-source browser agent designed to autonomously execute complex web tasks and to turn any website into an accessible API for integration into projects. The repository provides a Python package and a CLI that let developers run agents locally or via a serverless API. It is built to work with reasoning LLMs that include vision capabilities and supports multiple model providers such as Gemini, Claude, and OpenAI. Index emphasizes reliable structured extraction via Pydantic schemas, persistent browser state across sessions, and optional integration with a tracing platform called Laminar for browser observability. The README includes quickstart examples, installation instructions for dependencies and Playwright, environment setup for model API keys, and sample code demonstrating navigation, extraction and summarization, making it suitable for programmatic web automation, data extraction, and embedding browser-driven agents into applications.

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App URL
https://github.com/lmnr-ai/index
Github Repository
https://github.com/lmnr-ai/index/blob/main/README.md

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App Details

Features
Index offers a set of developer-centric features for building and running browser agents. It supports multiple LLM providers and models with vision-capable reasoning. Distribution is via pip and a CLI command index run for interactive sessions. It provides structured output using Pydantic schemas for reliable data extraction and supports serverless API usage to manage remote browser sessions. Observability and tracing of agent actions are available through Laminar integration. Other features include Playwright-based browser automation, an option to use a personal Chrome instance to preserve logged-in sessions, real-time streaming updates in the CLI, follow-up messages with a human control action, terminal UI using Textual, and browser state persistence between runs.
Use Cases
Index helps developers automate web workflows by providing a ready-made browser agent that can navigate sites, extract structured data, summarize content, and perform multi-step web tasks without building scraping logic from scratch. It simplifies integration by offering both a local library and a serverless API so teams can choose between running agents in-app or delegating session management. Structured Pydantic outputs increase reliability for downstream processing, and Laminar tracing provides synchronized observability of agent actions and browser sessions for debugging and auditing. Support for multiple LLMs and a local Chrome option make it flexible for handling authenticated workflows, cost/speed tradeoffs, and reproducible automation across projects.

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