AgentScope is a production-ready multi-agent framework designed to simplify the development of LLM-powered applications. It provides high-level abstractions for agent communication, memory management, and tool usage. Rather than using restrictive prompts, AgentScope leverages the inherent reasoning and tool-use capabilities of LLMs. Key features include Real-time Voice, Human-in-the-loop steering, Agentic RL for model tuning, and support for MCP and A2A protocols. It is engineered for scalability, supporting local, serverless, and K8s deployments with integrated OTel monitoring for enterprise-grade observability.
The official repository for the book 'Build a Large Language Model (From Scratch)' by Sebastian Raschka. It provides a comprehensive, step-by-step guide to developing, pretraining, and finetuning a GPT-like LLM from the ground up. By using pure PyTorch instead of high-level LLM libraries, it explains the inner workings of components like attention mechanisms and transformer blocks. The project includes end-to-end pipelines for pretraining on unlabeled data and finetuning for instruction-following, designed to be accessible on standard consumer hardware.