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.

Kaiban Agents Aggregator provides a unified dashboard to browse and manage AI agents built using the KaibanJS framework. Users can filter agents by category, view detailed documentation, test agent behavior, and deploy to staging or production with one click. The platform tracks runtime metrics and usage logs, enabling performance monitoring and quick iteration. Built-in collaboration tools allow multiple stakeholders to annotate, comment, and share configurations, while API integrations streamline embedding agents into existing applications or workflows.

Arcade Vercel AI Template is an open-source boilerplate designed to kickstart AI-powered web projects using Vercel’s AI SDK. It provides pre-built components for chat interfaces, serverless API routes, and agent configuration files. Through a simple file structure, developers define their AI agents, prompts, and model parameters. The template handles authentication, routing, and deployment settings out of the box, enabling rapid iteration. By leveraging ArcadeAI’s APIs, users can integrate generative text, database lookups, and custom business logic. The result is a scalable, maintainable AI website that can be deployed in minutes to Vercel’s edge network.

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.