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.

Swarms SDK simplifies creation, configuration, and execution of collaborative multi-agent systems using large language models. Developers define agents with distinct roles—researcher, synthesizer, critic—and group them into swarms that exchange messages via a shared bus. The SDK handles scheduling, context persistence, and memory storage, enabling iterative problem solving. With native support for OpenAI, Anthropic, and other LLM providers, it offers flexible integrations. Utilities for logging, result aggregation, and performance evaluation help teams prototype and deploy AI-driven workflows for brainstorming, content generation, summarization, and decision support.

ModelScope Agent provides a modular, Python‐based framework to orchestrate autonomous AI agents. It features plugin integration for external tools (APIs, databases, search), conversation memory for context preservation, and customizable agent chains to handle complex tasks such as knowledge retrieval, document processing, and decision support. Developers can configure agent roles, behaviors, and prompts, as well as leverage multiple LLM backends to optimize performance and reliability in production.