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qKnow is an open-source knowledge platform built around Knowledge Graphs, offering capabilities for knowledge extraction, knowledge fusion, graph construction, and visualization. It helps enterprises and organizations build structured, queryable, and inferable knowledge systems.
Hybrid AI is the future of explainable intelligence. This article explores how combining vector search, knowledge graphs, and retrieval-augmented generation (RAG) creates AI systems that can reason, cite, and explain their answers with insights learned from building a real Graph-Powered RAG Engine.
PR Guardian AI automatically reviews pull requests using advanced AI analysis. It identifies code issues, security risks, performance problems, and provides actionable suggestions directly inside GitHub pull requests.
The official implementation of "RouteExplainer: An Explanation Framework for Vehicle Routing Problem" (PAKDD 2024, oral)
Turn plain English into Robot Framework files with AI. No dependencies, no hassle — just validated, ready-to-run tests
High-quality scientific PDF translation using Claude Code Max - Transform scanned academic documents from English to French with precision
An interactive dashboard for exploring mathematical research trends on arXiv
Augmented Intelligence Framework: Create orchestrated multi-agent AI teams for software development, user research & content creation. Features 45+ specialized agents with structured workflows delivering the full benefits of human-AI collaboration
🤖 Production-ready samples for building multi-modal AI agents that understand images, documents, videos, and text using Amazon Bedrock and Strands Agents. Features Claude integration, MCP tools, streaming responses, and enterprise-grade architecture.
AI-powered stock analysis and investment recommendation app using CrewAI agents, Gemini LLM, DuckDuckGo Search and real-time market data. Generates comprehensive reports, news summaries, and buy/hold/sell advice.
An open-source, reproducible framework for labelling maintenance logs from wind farms based on free-entry descriptors and benchmarking Large Language Models (LLMs) in this task. The repository is from a study evaluating proprietary and open-source models on performance, cost, and calibration to support data-driven O&M in the wind energy sector.
ScholarLens analyzes research papers using RAG with AI models from OpenAI, Anthropic, and Google. It identifies research gaps, assesses novelty, extracts key concepts, visualizes citations, and enables natural language queries of academic content. Features include PDF processing, arXiv/Semantic Scholar integration, batch processing, and intelligent
An enterprise-grade, full-stack AI travel planner which provides data-driven itineraries for Lucknow, India and showcases production-ready architecture, combining a FastAPI backend with a Streamlit frontend. It leverages an advanced agentic RAG system, context-aware responses by integrating a local knowledge base with live, external APIs.
RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.
⚙️ Automated LLM-based Prompt Engineering for Structured Data Processing
An experimental framework for building collaborative coding agents that design and implement software through structured debate and consensus.
Production-style LangChain project demonstrating a financial research assistant that analyzes US stock market data using: LangChain, yfinance market and OpenAI
Production-ready Phidata AI agent implementations covering 16+ use cases: data analysis, financial advisory, healthcare diagnostics, content generation, web research, and multi-agent systems. Perfect starter kit for developers building intelligent, tool-equipped agents with memory and reasoning.
Multi-agent fitness coaching without the framework bloat. Phase-based reasoning pipeline (Analyze → Think → Summarize → Decide) that actually improves training plans — not just chat.
Successfully developed an interview preparation guide using Langchain which can effectively guide users in their interview preparation process and job search journeys by providing valuable insights and feedback regarding their performance. It generates a comprehensive list of questions pertaining to a user query as well.
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