Built by Hyperlane community developers Suryansh and Ruddy, hyperlane-mcp connects large language models (LLMs) to Hyperlane’s general message passing infrastructure -  allowing agents to deploy contracts, transfer assets, run validators, and coordinate workflows across multiple blockchains, all through natural language.

hyperlane-mcp works by giving agents a clean, modular interface to interact with Hyperlane, so they can operate across chains as easily as they would on a single one. Try it out: hyperlane-mcp

In this post, we'll cover:

  • Why interchain agents matter
  • What hyperlane-mcp is and how it works  
  • How to get started building

Why Interchain AI Agents Matter

AI agents are evolving from simple chatbots to autonomous systems that can execute tasks. In crypto, we call these "onchain AI agents" - AI systems that have access to crypto wallets and can make blockchain transactions on their own.

These agents are already doing impressive things: they monitor DeFi protocols and automatically rebalance portfolios, execute trades based on market analysis, manage treasury funds, and even respond to social media discussions. 

But there's a big problem: most agents today are stuck on a single chain.

Think about an AI agent managing a treasury that needs to:

  • Move USDC from Ethereum to Base for cheaper operations
  • Deploy contracts across different networks
  • Rebalance a portfolio spanning Ethereum, Solana, and Polygon

Each of these scenarios currently require either multiple specialized agents, manual intervention, or complex custom solutions. The agent can't treat all the chains as one unified environment - instead, it sees a fragmented landscape.

This fragmentation doesn't just limit functionality; it creates operational overhead, increases costs, and introduces points of failure. As onchain activity spans multiple chains, agents stuck on single chains become less useful over time.

This is where Hyperlane comes in. 

What Hyperlane Brings to AI Agents

Hyperlane is a protocol for cross-chain messaging and by utilizing Hyperplane, agents can work across multiple blockchains. It gives them one simple interface for interacting with multiple chains. 

With Hyperlane, agents can:

  • Send messages between chains - Not just token transfers, but any data or instruction
  • Deploy and manage infrastructure automatically - Set up new chains, configure validators and relayers, and manage general message passing without manual work.
  • Run complex multi-chain workflows - Coordinate actions across chains
  • Access all liquidity and data - Treat assets and data across chains as one system instead of separate parts

The main benefit: agents can now work across chains.

How It Works: hyperlane-mcp in Action

Built by Suryansh and Ruddy hyperlane-mcp - is a tool that gives Hyperlane's features to AI agents through a standard interface. This makes any agent work across multiple chains.

To break it down:

  • Model Context Protocol (MCP) is an open standard kickstarted by Anthropic that lets LLMs connect to external tools and data sources in a structured and consistent way.
  • hyperlane-mcp acts as the bridge between an AI agent (like Claude) and the Hyperlane protocol, exposing Hyperlane’s functionality in a format the agent can understand and interact with.

This means any MCP-compatible agent can use Hyperlane features such as sending cross-chain messages without needing to write custom code or complex customization.

Here's what this looks like in practice:

  • General message passing: An agent can send a message from Base to Optimism with one simple command. All the complex Hyperlane work happens behind the scenes.
  • Infrastructure deployment: Deploy Hyperlane contracts to a new testnet by giving chain details - the agent handles validator setup, relayer config, and contract deployment.
  • Token bridging: Set up token transfers between any chains by deploying warp routes with the right settings.
  • Automated operations: Run validators and relayers in containers

Why Hyperlane?

Hyperlane has several features that make your life easier when creating AI agents:

  • Permissionless deployment: Agents can deploy to new chains without waiting for approvals or manual work
  • Modular security: Choose different security models based on your needs
  • Wide network coverage: Access to 150+ chains that already have Hyperlane deployed
  • Existing infrastructure: Agents can build on existing Hyperlane setups instead of starting from scratch

Built Through the Hyperlane Bounty Program

Suryansh and Ruddy began developing the Hyperlane AI agent as part of the Hyperlane Bounty Program

They originally worked on an Eliza OS framework, but as the AI space evolved and MCP became more relevant, they shifted focus to building hyperlane-mcp which gives the developers and the users of the tool a lot more flexibility. .

Their project is a great example of how fast experimentation in AI + crypto can turn into powerful infrastructure as well as  how the Hyperlane ecosystem supports it through bounties and community-led development.

What's Next

AI agents are everywhere right now - they're capturing attention and mindshare across the crypto and ai space. As these systems get smarter, they need infrastructure that's flexible enough to go interchain..

Hyperlane provides that infrastructure. Whether you're building treasury management systems, cross-chain trading, or new types of automation workflows, Hyperlane gives your agents the interchain capabilities they need.

Ready to build interchain apps with Hyperlane?

  • Start building: Check out our docs to learn how to build with Hyperlane
  • See it in action: Head over to the hyperlane-mcp repo or watch the demo to get your interchain AI agent setup!

Join our Discord if you have questions

More about Hyperlane

Hyperlane is the open interoperability framework. It empowers developers to connect anywhere onchain and build applications that can easily and securely communicate between multiple blockchains. Importantly, Hyperlane is fully open-source and always permissionless to build with.

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