Popular GitHub Repositories for Mult Tenancy

PYthon For Homomorphic Encryption Libraries, perform encrypted computations such as sum, mult, scalar product or matrix multiplication in Python, with NumPy compatibility. Uses SEAL/PALISADE as backends, implemented using Cython.

Kubernetes Scheduler for Deep Learning

Discover advanced AI techniques in my repository combining Multi-Hop Chain of Thought (CoT) and Retrieval-Augmented Generation (RAG) using DSPy and Indexify. Enhance complex problem-solving with multi-step reasoning and external knowledge integration. Perfect for AI enthusiasts and researchers.

Stateful AI agents for Go and Postgres, transaction-safe.

KubeNova acts as a **central control plane** for multi-tenant Kubernetes environments. It coordinates **Capsule** for multi-tenancy and **KubeVela** for application delivery.

An Agentic AI Research Playground for building AI Agents and Mult-Agent systems leveraging open-source models, libraries, and frameworks. Models used to run agents locally on Nvidia RTX A2000 GPU - 8GB VRAM include the quantized Ollama models: llama3.1:8b-instruct-q8_0, llama3.1:8b_q8_0, qwen2.5:7b-instruct-q8_0, and qwen2.5-coder:7b-instruct-q8_0.

StackRAG is a multi-tenant Retrieval-Augmented Generation (RAG) platform for financial document intelligence. It extracts structured data from financial PDFs using LLMs, offers secure multi-tenancy, real-time APIs, and is built on Python, FastAPI, Docker, and PostgreSQL.

A Python framework for building agentic APIs. Includes support for async processing, multi-tenancy, observability, and LLM integration.

Parameter Efficient Fine-Tuning of various Qwen3 family of models on the Mult-It dataset using various approaches

Fine-tuning various Llama 3.1 family of models on the Mult-It dataset

Parameter Efficient Fine-Tuning of Magistral Small model on the Mult-It dataset using various approaches

This project evaluated the collective intelligence potential of collaborating machine learning models, primarily large language models, that are integrated into a mult-agent system.

Samples on how to work with Amazon Bedrock Global cross-Region inference

Production-ready Neo4j MCP server with multi-tenancy and Azure AKS deployment. Seamlessly integrate Claude Code with Neo4j graph databases. ?️?

AI Infrastructure ML Platform Engineer Learning Track - Building self-service ML platforms at scale

AI agent framework with memory, tools, multi-agent coordination, and consciousness. YAML config or code.