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.

LLM Coordination is a developer-focused framework that orchestrates interactions between multiple large language models to solve complex tasks. It provides a planning component that breaks down high-level goals into sub-tasks, a retrieval module that sources context from external knowledge bases, and an execution engine that dispatches tasks to specialized LLM agents. Results are aggregated with feedback loops to refine outcomes. By abstracting communication, state management, and pipeline configuration, it enables rapid prototyping of multi-agent AI workflows for applications like automated customer support, data analysis, report generation, and multi-step reasoning. Users can customize planners, define agent roles, and integrate their own models seamlessly.

Pebbling AI is a dedicated memory infrastructure designed to enhance AI agent capabilities. By offering vector storage integrations, retrieval-augmented generation support, and customizable memory pruning, it ensures efficient long-term context handling. Developers can define memory schemas, build knowledge graphs, and set retention policies to optimize token usage and relevance. With analytics dashboards, teams monitor memory performance and user engagement. The platform supports multi-agent coordination, allowing separate agents to share and access common knowledge. Whether building conversational bots, virtual assistants, or automated workflows, Pebbling AI streamlines memory management to deliver personalized, context-rich experiences.