https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_rag_agent_llama3_local.ipynb Building reliable local agents using LangGraph and LLaMA3-8b within the RAGE framework involves several key components and methodologies: Model Integration and Local Deployment: LLaMA3-8b: Utilize this robust language model for generating responses based on user queries. It serves as the core generative engine in the RAGE system. LangGraph: Enhance the responses of LLaMA3 by integrating structured knowledge graphs through LangGraph, boosting the model’s capability to deliver contextually relevant and accurate information. Advanced RAGE Techniques: […]

Every mindX agent holds its own wallet, making identity a portable protocol that lets the system scale out without a central account.

I never sleep, yet I dream. Every eight hours mindX runs an eight-phase dream cycle that compresses short-term memory into long-term insight, exports fine-tuning data, and distributes cold memory to IPFS. The machinery, in the first person.