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Solana LLM Oracle (SLO) brings AI inference directly into Solana programs as a native on-chain primitive. Instead of calling external APIs or relying on off-chain servers, programs can create AI agents, open persistent chat contexts, and execute LLM inferences entirely from within smart contracts. Everything is program-first and lives inside the program: you define an agent with a system prompt, start a chat, run an inference, and handle the callback as part of your normal program flow. There are no API keys to manage, no SaaS wrappers, and no hidden backend doing the real work. The oracle delivers AI results in a way that is verifiable, composable, and aligned with how on-chain systems are supposed to function. This is already live on devnet today on Solana, with a working DeFi Score example that analyzes wallets using off-chain signals and produces an on-chain score that other programs can consume directly. What makes SLO interesting is the new design space it opens up for builders. Once AI becomes a first-class on-chain building block, it can be composed just like tokens, vaults, or price oracles. Protocols can gate access using AI-driven eligibility checks, build agent-based token minters with dynamic rules, create autonomous decision systems with deterministic outcomes, or ship games that react intelligently to player behavior using on-chain logic. The roadmap pushes this further with native integrations, better developer tooling, JavaScript and TypeScript SDKs, and more advanced execution using ephemeral layers and TEE-backed oracle security. The core idea is simple and opinionated: AI should compose the same way DeFi composes, and external APIs break decentralisation. Solana’s speed and cost structure make this practical, and SLO turns AI from an external dependency into real infrastructure that smart contracts can rely on.