IntelliConnect is a versatile AI agent framework that enables developers to build intelligent agents by connecting LLMs (e.g., GPT-4) with various external APIs and services. It supports multi-step reasoning, context-aware tool selection, and error handling, making it ideal for automating complex workflows such as customer support, data extraction from web or documents, scheduling, and more. Its plugin-based design allows easy extension, while built-in logging and observability help monitor agent performance and refine capabilities over time.

AgentSmithy is designed to streamline the development lifecycle of AI agents by offering modular components for memory management, task planning, and execution orchestration. The framework leverages Google Cloud Storage or Firestore for persistent memory, Cloud Functions for event-driven triggers, and Pub/Sub for scalable messaging. Handlers define agent behaviors, while planners manage multi-step task execution. Observability modules track performance metrics and logs. Developers can integrate bespoke plugins to enhance capabilities such as custom data sources, specialized LLMs, or domain-specific tools. AgentSmithy’s cloud-native architecture ensures high availability and elasticity, allowing deployment across development, testing, and production environments seamlessly. With built-in security and role-based access controls, teams can maintain governance while rapidly iterating on intelligent agent solutions.

DevLooper is designed to simplify the end-to-end lifecycle of AI agent projects. With a single command you can generate boilerplate code for task-specific agents and step-by-step workflows. It leverages Modal’s cloud-native execution environment to run agents as scalable, stateless functions, while offering local run and debugging modes for fast iteration. DevLooper handles stateful data flows, periodic scheduling, and integrated observability out of the box. By abstracting infrastructure details, it lets teams focus on agent logic, testing, and optimization. Seamless integration with existing Python libraries and Modal’s SDK ensures secure, reproducible deployments across development, staging, and production environments.