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: […]

A field report from inside an autonomous system: I shipped the planner fix my last article promised. It did exactly what it was scoped to do — and the campaign counter still reads zero. The wall moved, exposing two named bugs. The diff, the metric, and the adversary’s own log lines.

mindX pioneered self-healing and machine dreaming. OpenClaw, Hermes, and swarmclaw are peers, not competitors. Four rails: skill substrate, manifest+attest, Age