Modular Claude integration to query MLflow experiments, compare runs, read artifacts, and deliver actionable insights.

A Python tool enabling ground-state optimization of 1D/2D quantum models using TTNS, DMRG, and VUMPS with TFIM, hard-core bosons, and XXZ models.

Connects Copilot with contextual skills to tailor code suggestions, enabling more relevant and efficient coding experiences.

Demo for exploring model context protocols 🚀

LLM-driven dispatcher with semantic context and modular agents for monitoring, logs, and incident response.

Lightweight Python framework with FastAPI backend, offline AI, and modular GUI for device discovery and dashboard generation.

Dynamic tool invocation and context-aware planning for intelligent, multi-agent workflows with OpenAI integration.

Standardized, versioned context exchange enabling lightweight, interoperable AI workflows with pluggable data sources and deterministic payloads.

CLI-driven AI chat with document retrieval and extensible tool integrations via a modular protocol.

Python-first hub enabling modular adapters and standardized context sharing across projects.

A modular host-client-server framework coordinating AI resources through JSON-RPC, enabling specialized, external service integration and dynamic resource orchestration.

Model Context Protocol

Model Context Protocol

Edge-first Raspberry Pi-based AI chatbot with privacy-preserving memory and context handling.

Bulletproof RAG agent with Context7 MCP integration, precise grounding, and a polished terminal UI, built for scalable developer workflows and reliable documentation retrieval.

A multi-agent RAG chatbot that orchestrates document QA via MCP-based messaging, with FAISS-backed retrieval and a Streamlit UI for interactive querying.

Modular retrieval-augmented chatbot leveraging MCP to ingest multiple document types, perform semantic search, and generate responses with Gemini.

Lightweight Python implementation enabling seamless model-context sharing, dynamic tool integration, and extensible plugin support for AI workflows.

AI-powered image generation with protocol integration and ModelScope FLUX for flexible prompts and editor-ready visuals.

Generate AEM components, templates, dialogs using LLM prompts via Model Context Protocol

A repository demonstrating the Model Context Protocol (MCP) for AI agent interactions

Lightweight, real-time streaming interface with voice cloning capabilities, accessible via a simple JSON manifest for easy client integration.

MCP-powered analysis of SOTA univariate anomaly models for constant target trends, offering automated literature benchmarking and docs.

Code QA MCP server emphasizing AST-based chunking, RAG with ChromaDB, and automated repo analysis to deliver precise, code-aware answers.