Bitget has made AI a core strategic pillar of its Universal Exchange (UEX) model. Rather than releasing AI as a standalone chatbot or trading tool, Bitget has built a broader product stack that applies AI to market analysis, trading workflows, developer infrastructure, and platform engagement.
Bitget’s AI Stack consists of four layers: GetAgent (analytics), Agent Hub (infrastructure), GetClaw (execution), and Gracy AI (strategic interface), all integrated within Bitget’s centralized exchange environment.
GetClaw extends the stack into autonomous execution by allowing users to define trading tasks in natural language and delegate monitoring and execution within Bitget’s platform. It represents Bitget’s primary consumer-facing implementation of agent-based trading.
Agent Hub provides the infrastructure layer that connects AI models directly to exchange-native trading functions. Through support for MCP, APIs, Skills, and CLI-based access, it enables programmatic interaction with market data, accounts, and execution systems.
GetAgent has surpassed 450,000 users, while Gracy AI reached more than 460,000 users and processed over 2.6 million replies within weeks of launch. These early adoption metrics demonstrate the strong demand for AI-native trading interfaces.
Primer
Bitget is a universal exchange that aims to unify a centralized spot and derivatives exchange (CEX) with a broader suite of real-world asset (RWA) products and tooling. These products include, Bitget Onchain, an onchain access layer, tokenized stock trading, TradFi trading with Gold, Silver, and CFD commodities, various institutional products, and yield opportunities for tokenholders. The CEX provides access to hundreds of assets and trading pairs, allowing users to buy, trade, and hold a diverse range of tokens on the platform. BGB is the platform’s native token, used within the Bitget ecosystem to access discounted trading fees, exclusive events, and participate in token sales. The token is also integrated with Morph as the network’s native governance token.
Bitget was founded in 2018 and is registered in Seychelles. Gracy Chen is the company’s CEO as of March 2026. Chen joined Bitget as a managing director in 2022 and was promoted to the role of CEO in May 2024.
To highlight transparency and solvency, Bitget utilizes a Proof-of-Reserve (PoR) system, which provides verification that the exchange holds 100% or more of its users' assets. The system uses Merkle-tree verification, a cryptographic method that enables users to verify their balances without disclosing account details. Bitget also maintains a separate protection fund, providing the platform with an extra layer of protection and underscoring its commitment to user security.
As tokenized assets, onchain trading, and institutional participation accelerate, fragmented trading infrastructure has become a growing constraint for global market participants. Demand is shifting toward platforms that consolidate execution, settlement, and asset access across crypto-native and traditional markets. The Universal Exchange (UEX) model emerges as a response to this structural shift rather than a standalone product innovation.
For a detailed overview of Bitget’s UEX model, refer to our prior Pulse report.
As AI systems become more capable, their role in trading is beginning to shift from assisting users with research to directly supporting execution workflows. These systems can already analyze market data, generate trade ideas, monitor positions, and interact with software environments. The challenge is increasingly less about model capability and more about infrastructure: most financial platforms were built for human users, not software agents that need structured access to data, accounts, and execution functions.
This shift is especially visible in crypto trading. Bitget CEO Gracy Chen has predicted that AI agents will outnumber humans in hedge funds by 2028. At the same time, more than 70% of global spot trading volume is already driven by automated systems. Yet a meaningful gap remains between the sophistication of institutional trading infrastructure and the tools available to retail users. In practice, many users still rely on fragmented workflows across charting tools, market data terminals, execution interfaces, and portfolio trackers. AI is increasingly being positioned as a way to unify those functions by combining analysis, risk management, and execution support within a single interface.
As that transition accelerates, new infrastructure standards are beginning to emerge to support more direct interaction between AI systems and trading environments. Bitget has framed this shift as a strategic priority. CEO Gracy Chen has articulated the company’s ambition to enable 125 million people to trade like Wall Street professionals, a goal that aligns with a broader change in trader expectations. Users increasingly expect analysis, signals, and execution to be integrated within a single interface rather than distributed across multiple tools and workflows. As a result, AI has become one of Bitget’s core UEX strategic pillars for 2026, with the company building and deploying a full AI Agent Stack across infrastructure, execution, and user-facing interfaces.
These products connect market data, model interaction, and trade execution within Bitget’s centralized exchange environment. The stack spans the full AI trading workflow, from information and strategy to execution and oversight, all natively integrated with Bitget’s liquidity and trading infrastructure.
GetClaw
GetClaw is Bitget’s consumer-facing AI trading agent and the autonomous execution layer of its AI stack. Unlike GetAgent, which is centered on prompt-driven interaction, GetClaw is designed to support monitoring and trading tasks that continue over time. It is currently available on Telegram, with Discord, WhatsApp, and in-app support planned in later releases.
Functionally, GetClaw can execute trades, monitor live prices, run technical analysis, and retrieve crypto news and onchain data. It can also detect conditions such as funding-rate dislocations, liquidation clusters, volume spikes, and key price levels, and trigger alerts or scheduled workflows based on those conditions.
Trades are executed via dedicated sub-accounts, providing clear separation between user-controlled assets and agent-driven activity. Users can define strategy using natural language in simple terms, while GetClaw executes, monitors, and adjusts positions within predefined parameters.
GetClaw’s operating model is more constrained than that of a standard API trading bot. Rather than granting broad account access, the system partitions identity, memory, permissions, and credentials into separate control layers, while additional safeguards such as virtual sub-account sandboxes and fund limits restrict where the agent can operate, what it can access, and how much capital it can deploy. This matters because execution authority, rather than model quality alone, is the core risk in consumer-facing trading agents. By constraining how much control the system can exercise, Bitget lowers the operational risk of delegated trading and makes autonomous execution more viable within a retail exchange environment.
AI Trading Strategies
To acquaint its user base with agent trading automation, Bitget launched six AI trading avatars with distinct personalities, strategies, and market philosophies in GetAgent in November. From Nov. 24, 2025, to Dec. 15, 2025, GetAgent users could access a limited one-click copy-trading channel, selecting the avatar that aligned most closely with their trading personality, with each AI trader then executing autonomously in real time, driving 180,000 in web page traffic during this promotional period.
The campaign also featured a 10,000 USDT airdrop pool for participating users, rewarding the first 100 copy-trading users each day with copy-trading vouchers worth up to 100 USDT. Strategies included Steady Hedge (caution), Altcoin Turbo (appetite for volatility), and Infinite Grid (range-based logic).
GetAgent
GetAgent is Bitget’s conversational trading interface and the main user-facing component of its AI trading stack. Integrated across the Bitget app and web interface, it connects natural-language prompts to market analysis, strategy generation, and selected execution functions. Functionally, it brings together several parts of the trading workflow that are often handled across separate tools and interfaces.
This functionality is supported by more than 50 tools and can be grouped into several core functions:
Market data analysis – surfaces market information across price trends, position data, high-activity tokens, and memecoin-related signals.
Portfolio-aware outputs – tailors responses using information such as user assets, open positions, and trading history.
Strategy generation – processes natural-language inputs to produce trading ideas, strategy frameworks, and related risk considerations.
Execution pathways – supports trade execution within the chat interface, linking analysis and trade setup to order placement.
Taken together, these functions place GetAgent between market interpretation and execution, combining information retrieval, strategy formation, and trade setup within a single workflow.
Gracy AI is Bitget’s conversational interface built around the public voice of CEO Gracy Chen. It is designed for discussion rather than execution, giving users a way to explore market conditions, portfolio positioning, and platform direction through a chat-based interface.
Product and platform direction – providing visibility into Bitget’s roadmap and ecosystem
User interaction – enabling conversational engagement through prompts and campaigns
The product operates as a layer of interpretation rather than execution. It helps users make sense of market conditions and platform features, but does not directly generate trades or route orders. In practice, this extends Bitget’s AI offerings beyond trading workflows into user guidance and engagement.
Gracy AI has generated strong early user uptake and engagement. Between February 12 and February 23, 2026, it reached more than 460,000 users, generated over 2.6 million replies, and produced more than 390 million impressions.
Agent Hub
Agent Hub is Bitget’s AI trading infrastructure layer for connecting AI systems to exchange trading functions. Built on Bitget’s API stack, it allows agents and external models to access market data, account information, and execution functions through a common interface.
The platform supports four access methods: MCP Server (for model-to-exchange interaction), Skills (for pre-built trading and analytical functions), REST and WebSocket APIs (for programmatic access), Command-line interface (for local scripting and automation workflows). While other major exchanges support some of these methods, Bitget is the only exchange to support all four. These interfaces support a range of exchange functions, including spot trading, futures and perpetuals, margin and account management, copy trading, conditional orders, and asset and balance management.
Closing Summary
Bitget’s AI stack reflects a deliberate shift in how exchange infrastructure is being packaged for end users and developers. Instead of treating AI as a standalone assistant or front-end feature, the platform distributes it across four functional layers: GetAgent, Agent Hub, GetClaw, and Gracy AI. This architecture reduces fragmentation between research, automation, and execution, while keeping those workflows natively integrated with Bitget’s centralized trading environment.
The stack’s long-term significance depends less on novelty than on whether it can become a durable interface for actual trading behavior. Early adoption across GetAgent and Gracy AI indicates meaningful user demand for AI-native trading interfaces, while Agent Hub and GetClaw extend that demand into infrastructure and automation. The durability of this model will ultimately depend on the reliability of these systems, the usefulness of their outputs, and the extent to which users and developers adopt AI as a default layer for interacting with exchange products. If that shift continues, Bitget is one of the few exchanges already structured around it.
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