Bitget Launches Skills and CLI Modules, Enabling Smarter Automated Trading

• Bitget upgrades Agent Hub with Skills & CLI, enabling AI agents like OpenClaw to trade fast.
• Developers can connect AI agents in 3 steps to access real-time market data and automate trading.
• The update completes Bitget’s AI-native trading stack with MCP, REST, and WebSocket API support.

Crypto exchange Bitget has upgraded its Agent Hub with two new tools: Skills and a command-line interface (CLI). The update helps developers and AI agents connect to crypto markets more easily and launch automated trading tools faster.

The upgrade builds on earlier integrations with the Model Context Protocol (MCP) and Bitget’s REST and WebSocket APIs, creating a full system that links AI models, developer tools, and live trading.

Skills Module Enabling AI-Driven Trading Actions

At the center of the upgrade is the Skills mechanism, which allows AI agents to automatically interpret a user’s trading intent and trigger real-time actions.

Through the bgc interface, the module exposes Bitget’s full API suite with standardized JSON output. This allows developers to integrate trading functions into scripts, analytics pipelines, and AI-driven workflows.

AI agents such as Claude Code and OpenClaw can use these capabilities to move directly from market analysis to order execution with minimal setup.

OpenClaw Integration

Bitget says the upgraded Agent Hub now includes nine core capability modules and 58 tools, expanding the functionality available to developers building automated trading agents.

One of the key improvements is the simplified integration process with OpenClaw. Developers can connect Agent Hub to the platform in three steps, which the company says typically takes around three minutes.

Once connected, AI agents can access:

• Real-time market data
• Spot and futures trading tools
• Account and asset management functions
• Strategy execution capabilities

This setup allows AI agents to autonomously monitor markets, execute strategies, and place trades across both on-chain and off-chain environments.

AI-Native Trading Infrastructure

According to Gracy Chen, AI-driven trading infrastructure is a central focus for the company this year. She said the goal is to translate user intent into trading execution more efficiently, giving retail traders tools comparable to institutional platforms.

Chen noted that while AI-based trading requires greater user education and trust, the industry may be approaching an inflection point as the technology matures.

Full AI Trading Stack

The latest upgrade builds on a mid-February 2026 release in which Bitget added native support for the Model Context Protocol, enabling AI systems to directly invoke trading functions on the exchange.

Combined with its low-latency REST and WebSocket APIs, the company says developers can now build automated trading systems, quantitative strategies, and AI-native applications on top of the exchange’s infrastructure.

With the latest update, Bitget positions Agent Hub as a unified gateway that allows AI agents to translate natural-language instructions into live trades. It is creating an ecosystem where AI models, developer tools, and market execution operate within a single environment.