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.

NPI.ai offers a comprehensive platform where users can graphically design AI agents through drag-and-drop modules. Each agent comprises components such as language model prompts, function calls, decision logic, and memory vectors. The platform supports integration with APIs, databases, and third-party services. Agents can maintain context through built-in memory layers, allowing them to engage in multi-turn conversations, retrieve past interactions, and perform dynamic reasoning. NPI.ai includes versioning, testing environments, and deployment pipelines, making it easy to iterate and launch agents into production. With real-time logging and monitoring, teams gain insights into agent performance and user interactions, facilitating continuous improvement and ensuring reliability at scale.

ROSA (Rover Sequencing & Autonomy) is a comprehensive autonomy framework developed by NASA’s Jet Propulsion Laboratory for space robotics. It features a modular AI planner, constraint-aware scheduler, and built-in simulators that produce validated command sequences for rover operations. Users can define mission objectives, resource constraints, and safety rules; ROSA will generate optimal execution plans, detect conflicts, and support rapid replanning in response to unexpected events. Its plugin architecture allows integration with custom sensors, actuators, and telemetry analysis tools, facilitating end-to-end mission autonomy for planetary exploration.

QueryCraft is a Python-based prompt engineering toolkit designed to streamline the development of AI agents. It enables users to define structured prompts through a modular pipeline, connect seamlessly to multiple LLM APIs, and conduct automated evaluations against custom metrics. With built-in logging of token usage and costs, developers can measure performance, compare prompt variations, and identify inefficiencies. QueryCraft also includes debugging tools to inspect model outputs, visualize workflow steps, and benchmark across different models. Its CLI and SDK interfaces allow integration into CI/CD pipelines, supporting rapid iteration and collaboration. By providing a comprehensive environment for prompt design, testing, and optimization, QueryCraft helps teams deliver more accurate, efficient, and cost-effective AI agent solutions.