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    KeeperHub Agent SDK

    Multi-framework KeeperHub SDK — let AI agents transfer, swap, and automate onchain.

    Open Agents 2026View on ETHGlobal ↗Source code ↗

    Demo

    About

    KeeperHub AI SDK is a multi-framework plugin system that enables AI agents to execute real on-chain actions using plain English. We built a unified SDK that works across OpenClaw, LangChain (TypeScript & Python), and ElizaOS, allowing agents to interact with DeFi protocols, wallets, workflows, and on-chain data seamlessly. The SDK exposes 30+ tools covering transfers, swaps, ENS, workflows, and automation, with access to 396 DeFi actions across 19+ chains including Base, Ethereum, Arbitrum, and Polygon. Developers can get started quickly: OpenClaw: npm install -g openclaw openclaw plugin install @ethglobal-openagent/openclaw-keeperhub openclaw TypeScript (LangChain): npm install @ethglobal-openagent/langchain-keeperhub Python (LangChain): pip install keeperhub-langchain ElizaOS: npm install @ethglobal-openagent/elizaos-keeperhub With just a few lines of code, developers can build AI agents that read data, generate workflows, and execute real on-chain transactions. KeeperHub AI SDK makes it simple to connect AI agents to blockchain execution — turning natural language into real DeFi actions.

    How it's made

    We built KeeperHub AI SDK as a unified TypeScript SDK on top of the KeeperHub REST API, which handles on-chain execution, workflows, and DeFi actions. The SDK abstracts API calls, adds retry logic, safety controls (like testnet-only mode), and exposes them as structured tools. On top of this core layer, we created integrations across multiple agent frameworks: LangChain (TypeScript & Python) using toolkits that expose 30+ structured tools OpenClaw plugins by wrapping LangChain tools as native CLI tools ElizaOS plugin with actions and providers mapped to KeeperHub capabilities We also built a Telegram bot interface using the LangChain toolkit + OpenRouter LLM to demonstrate real-world usage. A key design decision was creating a single shared tool layer, allowing the same capabilities to work across all frameworks without rewriting integrations. This makes the system modular, extensible, and easy to adopt.