This topic covers the platforms, developer frameworks and security tooling that enable machine learning to run, interact and persist on blockchain networks. As projects move from proofs‑of‑concept to production, architects combine decentralized AI infrastructure (verifiable compute, onchain data marketplaces and cryptographic proofs), AI data platforms for retrieval‑augmented generation (RAG) and post‑quantum cryptography for long‑lived keys and signatures. Key components include agent orchestration and model‑interface frameworks such as LangChain (agent building, observability and deployment patterns) and LlamaIndex (turning unstructured onchain/offchain content into RAG-ready indices), developer IDEs and agentic coding platforms like Windsurf, Cursor and Replit that embed AI throughout the coding lifecycle, and productivity assistants (GitHub Copilot) and code models (StarCoder, Code Llama, CodeGeeX) used to generate and audit smart contracts and onchain integrations. Specialized offerings such as AI‑integrated wallets (Aptos‑focused examples) combine user UX with automated signing, transaction suggestion and local model inference. Practically, modern stacks blend offchain LLMs with lightweight onchain verification: compute and heavy inference remain offchain or in attested enclaves while proofs, hashes and state transitions live on the ledger. That hybrid pattern also underscores why post‑quantum blockchain tools matter: long‑term key security and signature schemes must evolve as AI‑powered attack surfaces expand. By 2026 this mix—frameworks for orchestration and RAG, AI‑native IDEs, code LLMs for smart contract work, wallet automation and PQ readiness—defines the pragmatic path for deploying trustworthy, auditable onchain ML systems.