AI‑powered crypto trading platforms and ML trading tools combine large language models, agentic workflows, and traditional quantitative pipelines to automate strategy discovery, backtesting, and live execution. This topic spans three categories — AI Tool Marketplaces (for models and components), AI Automation Platforms (for agent orchestration and execution), and AI Data Platforms (for ingestion, labeling, and feature stores) — and is directly informed by recent shifts in the tooling ecosystem. Key building blocks include engineering frameworks such as LangChain for building stateful agentic trading apps; no‑code/low‑code platforms like MindStudio for rapid prototype-to-production agent design; prompt galleries such as Vellum for reusable agent recipes; and developer utilities (GitHub Copilot, Windsurf, Tabnine) and code models (Code Llama, Salesforce CodeT5) that accelerate strategy implementation and testing. Infrastructure consolidation — exemplified by the Deci→NVIDIA transition observed in audits — highlights ongoing centralization of ML optimization and deployment tooling, with implications for latency, hardware access, and model performance tuning. Why it matters now: by late 2025, multi‑model agent architectures, improved developer toolchains, and easier access to production data have lowered the barrier to building automated crypto strategies, while tighter governance and private/self‑hosted options (a focus of tools like Tabnine) respond to security and compliance needs. Practitioners should weigh benefits against risks: model overfitting, data leakage, execution latency, and regulatory scrutiny remain central concerns. Successful adoption requires combining robust data platforms, rigorous backtesting, explainability and risk controls, and reproducible, governed deployment pipelines driven by the tooling landscape described above.