Secure Agent Augmentation provides a Python SDK and set of helper modules to wrap AI agent tool calls with security controls. It supports integration with popular LLM frameworks like LangChain and Semantic Kernel, and connects to secret vaults (e.g., HashiCorp Vault, AWS Secrets Manager). Encryption-at-rest and in-transit, role-based access control, and audit trails ensure that agents can augment their reasoning with internal knowledge bases and APIs without exposing sensitive data. Developers define secured tool endpoints, configure authentication policies, and initialize an augmented agent instance to run secure queries against private data sources.

Multi-Agent-RAG provides a modular framework for constructing retrieval-augmented generation (RAG) applications by orchestrating multiple specialized AI agents. Developers configure individual agents: a retrieval agent connects to vector stores to fetch relevant documents; a reasoning agent performs chain-of-thought analysis; and a generation agent synthesizes final responses using large language models. The framework supports plugin extensions, configurable prompts, and comprehensive logging, enabling seamless integration with popular LLM APIs and vector databases to improve RAG accuracy, scalability, and development efficiency.

Questflow Labs is a decentralized AI agentic workflow network designed to automatically manage and execute various tasks using autonomous AI agents. The platform allows users to connect different AI agents, models, and applications, like ChatGPT, to optimize workflows and increase productivity. This orchestration of multi-agents makes it possible to automate routine tasks, enabling users to focus on more strategic activities. By leveraging a decentralized network, Questflow ensures high reliability and scalability, making it an ideal solution for modern teams aiming to maximize efficiency through automation.