Multi-Agents provides a structured environment where different AI agents—such as planners, executors, and critics—coordinate to solve multi-step tasks. The planner agent breaks down high-level goals into sub-tasks, the executor agent interacts with external APIs or tools to carry out each step, and the critic agent reviews outcomes for accuracy and consistency. Memory modules allow agents to store context across interactions, while a messaging system ensures seamless communication. The framework is extensible, letting users add custom roles, integrate proprietary tools, or swap LLM backends for specialized use cases.

Ruler is a no-code AI agent that streamlines rule-based decision workflows. It allows users to define conditional rules, chain multiple steps, and integrate external data sources to automate complex processes. With a drag-and-drop interface, Ruler makes it simple to create branching logic, trigger actions across applications, and send automated notifications. Real-time dashboards and logs provide insights into rule performance, while built-in version control ensures safe updates. Ruler’s API-first architecture supports seamless integration with CRMs, ERPs, and messaging platforms. Teams can rapidly model business policies, compliance checks, and approval processes, reducing manual intervention and accelerating decision cycles. Whether automating loan approvals, customer support routing, or supply chain alerts, Ruler delivers consistent, reliable operations without writing code.

SCOUT-2 provides a modular architecture for building autonomous agents powered by large language models. It includes goal decomposition, task planning, an execution engine, and a feedback-driven reflection module. Developers define a top-level objective, and SCOUT-2 automatically generates a task tree, dispatches worker agents for execution, monitors progress, and refines tasks based on outcomes. It integrates with OpenAI APIs and can be extended with custom prompts and templates to support a wide range of workflows.

Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.

Bot-With-Plan provides a modular Python template for building AI agents that first generate a detailed plan before execution. It uses OpenAI GPT to parse user instructions, decompose tasks into sequential steps, validate the plan, and then execute each step through external tools like web search or calculators. The framework includes prompt management, plan parsing, execution orchestration, and error handling. By separating planning and execution phases, it offers better oversight, easier debugging, and a clear structure for extending with new tools or capabilities.

Desktop Commander is an AI desktop automation agent designed to reduce repetitive work and boost productivity. Users type or speak simple commands—such as “organize my downloads by date” or “open my email and draft a summary”—and the agent executes them across applications. It supports file operations, application control, script execution, and system settings adjustments. With customizable workflows and API integrations, Desktop Commander adapts to both personal and enterprise use cases, enabling complex multi-step processes with a single instruction.

Llama-Agent is a developer-focused toolkit for creating intelligent AI agents powered by large language models. It offers tool integration to call external APIs or functions, memory management to store and retrieve context, and chain-of-thought planning to break down complex tasks. Agents can execute actions, interact with custom environments, and adapt through a plugin system. As an open-source project, it supports easy extension of core components, enabling rapid experimentation and deployment of automated workflows across various domains.

Scint is a powerful no-code AI Agent platform enabling users to compose, deploy, and manage autonomous multi-step workflows. With Scint’s drag-and-drop interface, users define agent behaviors, connect APIs and data sources, and set triggers. The platform offers built-in debugging, version control, and real-time monitoring dashboards. Designed for both technical and non-technical teams, Scint accelerates automation development, ensuring reliable execution of complex tasks from data processing to customer support handling.

Agentic App Template is a fully configured Next.js project that serves as a foundation for developing AI-driven agentic applications. It incorporates a modular folder structure, environment variable management, and example agent workflows leveraging OpenAI’s GPT models and vector databases like Pinecone. The template demonstrates key patterns such as sequential multi-step chains, conversational Q&A agents, and text generation endpoints. Developers can easily customize chain logic, integrate additional services, and deploy to platforms like Vercel or Netlify. With TypeScript support and built-in error handling, the scaffold reduces initial setup time and provides clear documentation for further extension.