Recall Agent Browser

Recall Agent Browser

ReactJavascriptFastAPIFAISSLangGraphLLMs

A browser extension that uses RAG to enhance web browsing by providing context-aware information retrieval and summarization, with added features like AI agent for task automation.

Recall Agent Browser Extension

A sophisticated browser extension that revolutionizes web browsing through intelligent AI agents and Retrieval-Augmented Generation (RAG) technology.

Project Overview

The Recall Agent Browser extension transforms how users interact with web content by providing context-aware information retrieval, intelligent summarization, and automated task execution. Built with cutting-edge AI technologies, it serves as a personal AI assistant integrated directly into your browsing experience.

Key Features

🧠 RAG-Powered Information Retrieval

  • Context-Aware Search: Automatically understands the context of web pages and provides relevant information
  • Smart Summarization: Generates concise summaries of lengthy articles and documents
  • Cross-Page Memory: Maintains context across multiple web pages for coherent assistance

🤖 AI Agent Automation

  • Task Automation: Automates repetitive web tasks using intelligent agents
  • Form Filling: Automatically fills forms based on learned patterns and user preferences
  • Content Extraction: Extracts and organizes important information from web pages

🔍 Advanced Search Capabilities

  • Semantic Search: Goes beyond keyword matching to understand intent and meaning
  • Multi-Source Aggregation: Combines information from multiple sources for comprehensive answers
  • Real-Time Processing: Provides instant responses without leaving the current page

Technical Architecture

Frontend Components

  • React-Based UI: Modern, responsive interface built with React
  • JavaScript Engine: Efficient processing of web page interactions
  • Browser API Integration: Seamless integration with browser functionality

Backend Infrastructure

  • FastAPI Server: High-performance API backend for processing requests
  • FAISS Vector Database: Efficient similarity search and clustering of dense vectors
  • LangGraph Framework: Advanced workflow orchestration for AI agents

AI/ML Pipeline

  • Large Language Models: State-of-the-art LLMs for natural language understanding
  • Vector Embeddings: Semantic representation of web content for intelligent retrieval
  • Agent Coordination: Multi-agent system for complex task execution

Implementation Details

RAG System Design

Web Content → Text Extraction → Embedding Generation → Vector Storage → Similarity Search → Context Retrieval → LLM Processing → Response Generation

Agent Workflow

  1. Context Analysis: Analyzes current web page and user intent
  2. Task Planning: Breaks down complex requests into actionable steps
  3. Execution: Performs automated actions through browser APIs
  4. Feedback Loop: Learns from user interactions to improve future performance

Development Challenges & Solutions

Challenge 1: Cross-Domain Content Access

Problem: Browser security restrictions limiting access to web page content Solution: Implemented content scripts with proper permissions and secure communication channels

Challenge 2: Real-Time Processing

Problem: Maintaining responsive user experience while processing complex AI operations Solution: Asynchronous processing with background workers and smart caching strategies

Challenge 3: Context Preservation

Problem: Maintaining conversation context across different web pages Solution: Persistent storage system with intelligent context windowing

Performance Optimizations

  • Lazy Loading: Content is processed only when needed
  • Intelligent Caching: Frequently accessed information is cached locally
  • Batch Processing: Multiple requests are batched for efficiency
  • Edge Computing: Critical operations run locally to reduce latency

Security & Privacy

  • Local Processing: Sensitive data processed locally when possible
  • Encrypted Communication: All API communications are encrypted
  • User Consent: Explicit user permission for data processing
  • Data Minimization: Only necessary data is collected and processed

Future Enhancements

Planned Features

  • Multi-Language Support: Support for multiple languages and translations
  • Custom Agent Training: Allow users to train personalized agents
  • Advanced Analytics: Detailed insights into browsing patterns and productivity
  • Enterprise Integration: Features tailored for business environments

Technical Roadmap

  • Performance Optimization: Further improvements to speed and efficiency
  • Mobile Support: Extension of functionality to mobile browsers
  • Offline Capabilities: Core features available without internet connection
  • API Ecosystem: Public APIs for third-party integrations

Impact & Applications

Personal Productivity

  • Research assistance for students and professionals
  • Content discovery and curation
  • Automated data entry and form completion

Professional Use Cases

  • Market research and competitive analysis
  • Content creation and ideation
  • Customer support and information gathering

Educational Applications

  • Study assistance and note-taking
  • Research paper compilation
  • Learning resource discovery

Technical Specifications

  • Frontend: React 18+, JavaScript ES2022
  • Backend: FastAPI 0.104+, Python 3.9+
  • AI/ML: LangGraph, FAISS, OpenAI/Anthropic APIs
  • Storage: IndexedDB, Chrome Storage API
  • Communication: WebSocket, REST API
  • Security: CSP, CORS, OAuth 2.0