Sec-ConvAgent is a specialized AI agent module focused on secure, privacy-preserving conversational interactions. Built on the Agentic-OS framework, it encrypts messages and context logs using robust cryptographic protocols, ensuring that sensitive user inputs remain protected during transmission and storage. It integrates with popular LLMs, automatically managing encryption and decryption without developer intervention. By leveraging secure key management, role-based access controls, and end-to-end encryption pipelines, Sec-ConvAgent enables organizations to deploy conversational agents for healthcare, finance, legal, and other regulated domains. Developers can configure secure channels, define encryption policies, and seamlessly integrate secure dialogue flows within their existing Agentic-OS agent orchestration. Sec-ConvAgent bridges the gap between powerful AI-driven conversational interfaces and stringent security requirements.

The AI Agent Terraform Module provides a reusable Terraform configuration that automates the end-to-end provisioning of an AI agent backend. It creates an AWS VPC, IAM roles with least-privilege policies, Lambda functions wired to OpenAI or custom model APIs, API Gateway REST interfaces, and optional Step Functions for workflow orchestration. Users can customize environment variables, scale settings, logging, and monitoring. The module abstracts complex cloud setup into simple inputs, enabling rapid, consistent, and secure deployment of conversational AI agents, task automations, or data processing bots in minutes.

AnYi AI Agent Framework helps developers integrate autonomous AI agents into their applications. Agents can plan and execute multi-step tasks, leverage external tools and APIs, and maintain conversation context through configurable memory modules. The framework abstracts interactions with various LLM providers and supports custom tool and memory backends. With built-in logging, monitoring, and asynchronous execution, AnYi accelerates deployment of intelligent assistants for research, customer support, data analysis, or any workflow requiring automated reasoning and action.

AskAtlasAI-Agent empowers developers to deploy AI agents that answer natural language queries against any document set stored in MongoDB Atlas. It orchestrates LLM calls for embedding, search, and response generation, handles conversational context, and offers configurable prompt chains. Built on JavaScript/TypeScript, it requires minimal setup: connect your Atlas cluster, supply OpenAI credentials, ingest or reference your documents, and start querying via a simple API. It also supports extension with custom ranking functions, memory backends, and multi-model orchestration.

GuruBase is a SaaS platform that enables non-technical users to create powerful AI chatbots by uploading documents, connecting websites, or linking knowledge bases. Users can select from pre-built conversational templates or customize prompts and flows to fit specific use cases, then deploy agents across web widgets, Slack, and Microsoft Teams. GuruBase provides analytics dashboards to track usage, performance, and user satisfaction, enabling continuous optimization. Security features and role-based access ensure that sensitive data remains protected.

Mastra provides a visual interface for designing, testing, and deploying AI agents without writing code. Users can drag and drop nodes to create multi-step workflows that integrate language models, computer vision, and custom scripts. Data connectors allow seamless retrieval from databases, CRMs, or web services, while built-in analytics track agent performance. Teams can collaborate in real time, version control workflows, and roll back changes. Once tested, agents can be deployed with a single click to web chat widgets, messaging apps, or REST APIs. Monitoring dashboards surface usage metrics, error alerts, and optimization suggestions to maintain and improve agent effectiveness over time.