Historically, the complexity of identifying vulnerabilities in smart contracts required human-intensive audits to supplement imprecise automated code scans. The growing smart contract market highlights the urgency for effective automated security auditing. Furthermore, new AI-powered coding assistants can introduce vulnerabilities that evade engineers, and autonomous AI can now write 100% code changes without human oversight. This research introduces LLMSmartSec, a new approach that accurately identifies and fixes smart contract vulnerabilities, eliminating human audit requirements. To develop LLMSmartSec, we first fine-tuned Open AI GPT-4 to understand Solidity, the programming language of Ethereum blockchain, and to micro-analyze smart contracts holistically from the viewpoints of the developer (LLMdev), auditor (LLMAudit), and ethical hacker (LLMehack) and to identify vulnerabilities and generate code fixes for them. We used GPT-4 again to store the smart contract code in a control flow graph (CFG) annotated with the code vulnerabilities and their fixes. Finally, we trained an LLMGraphAgent with open-source LLMs on the annotated CFG so that it can run locally, identifying vulnerabilities and fixes without costly calls to GPT-4. This research advances blockchain-based projects in Web3 by supplementing a costly human bottleneck with a low-cost automated security tool based upon the innovative creation of an annotated CFG with GPT-4.