Bitget’s AI trading stack tops 1 million users and $1.2B in volume

Bitget’s AI trading ecosystem now spans 58 tools, over 1 million users and $1.2B in volume, as the exchange leans into “agent‑native” trading as a core Universal Exchange pillar.

Bitget has launched a dedicated Bitget AI landing page and disclosed new adoption metrics for its AI trading ecosystem, claiming more than 1 million users and over $1.2 billion in total trading volume powered by AI‑driven tools. In a recent overview, the exchange — which describes itself as the world’s largest “Universal Exchange” — said Bitget AI now aggregates 58 AI‑based trading tools into a single infrastructure layer, covering core scenarios such as market analysis, trading assistance, strategy construction and end‑to‑end automation.

Bitget leans into “agent‑native” AI trading

Under Bitget’s UEX multi‑asset trading framework, the company frames Bitget AI as “a key layout” in its push to become a “native intelligent agent exchange,” where artificial intelligence agents are embedded directly into trading workflows rather than bolted on as external bots. As Bitget puts it, the ecosystem is designed to create “a unified AI‑powered trading environment” that serves both retail traders and developers, blending market data ingestion, strategy logic and execution into a closed loop.

At the center of that loop are three flagship products. GetClaw is presented as a no‑install AI agent for real‑time market insights, capable of scanning markets, summarizing conditions and surfacing trade ideas through a conversational interface. GetAgent functions as an AI assistant for strategy execution and automated trading, taking user‑defined rules or signals and turning them into live orders and position management. Agent Hub, meanwhile, is a developer‑focused platform that exposes open APIs and AI‑model integration hooks so third parties can build and deploy their own agents and tools on top of Bitget’s stack. Together, the company says, these components “build a complete closed‑loop process of ‘insight‑strategy‑execution’” for AI‑assisted trading.

Bitget CEO Gracy Chen has flagged a next phase focused on AI Trading Playbooks, currently in internal testing. According to Bitget’s description, these Playbooks will let traders define strategies in natural language, automatically translate them into executable logic, run backtests and then deploy them into live markets, with integrated distribution features that allow successful playbooks to be shared or monetized by their creators.

For now, the headline numbers — 1 million users, $1.2 billion in AI‑driven trading volume and 58 tools — are less about immediate revenue than about Bitget’s attempt to stake out “agent‑native” trading as a differentiator in an increasingly crowded exchange landscape, where AI is shifting from marketing slogan to actual order‑flow source.