AI-powered crypto trading suites and automated trading bots combine large language models, real‑time market analytics, and multi‑agent orchestration to automate signal generation, execution and portfolio management on centralized exchanges (e.g., MEXC) and via exchange APIs. This topic covers two linked categories: Trading Chatbots — conversational interfaces that surface portfolio insights, generate trade ideas and trigger orders — and AI Automation Platforms — infrastructure for building, deploying and governing autonomous trading agents and workflows. The landscape has matured from simple rule‑based bots to systems that use embeddings/RAG for market context, multimodal models for chart and news understanding, and managed ML platforms for production reliability. Key tooling examples illustrate roles in a modern stack: Vertex AI and Google Gemini for managed model training, multimodal reasoning and deployment; Cohere for private, enterprise LLMs, embeddings and retrieval; Relevance AI and Kore.ai for no‑/low‑code agent orchestration and workflow automation; Yellow.ai for agentic CX-style interactions across channels; and Mistral AI for efficient open models focused on privacy and governance. Together these components support backtesting, live inference, observability and policy controls. As of 2026, relevance is driven by increasing demand for low-latency inference, stronger compliance/traceability requirements, and the economic incentive to replace manual trading tasks with AI-driven automation. Key considerations include model drift, market microstructure sensitivity, latency and execution risk, explainability and regulatory compliance. Effective deployments pair robust model operations, risk controls and human‑in‑the‑loop oversight rather than relying on fully autonomous strategies.