ARCH AI

Demo

About

The problem it solves Most existing platforms were designed for developers, requiring significant coding, complex setups, and constant tweaking just to get basic tasks working. This excluded non-technical creators, slowed down innovation, and made it nearly impossible to deploy AI agents that interacted with web3 infrastructure, APIs, and multi-step reasoning logic. **Arch AI** changes that. It’s a low-code platform powered by our visual open source framework, [ChainGraph](https://github.com/archailabs/chaingraph), that lets anyone—founders, ops, growth, or community leads—build, test, and deploy intelligent agents that connect to Web3 infrastructure, reason through multi-step logic, interact with external APIs, and even **collaborate with other agents**. No complex setup. No engineering bottlenecks. Just fast, modular agent creation that actually ships hassle-free.

How it's made

Challenges we ran into In Arch AI we try to make the experience feel magical for the end user. One major challenge was developing a real-time chat engine that could handle loads and multiple agent interactions and render dynamic markdown components on the fly. Not only we achieved this, but we also let users connect their wallets to in-chat widgets without affecting the main app.  Another challenge was creating our graph execution engine with built-in type safety. We didn’t want users to build broken flows, so we engineered the system to visually guide users—using color-coded ports, contextual warnings, and type-locked connections. Behind the scenes, we built a system that rejects invalid graphs at design time, not runtime—catching logic errors before they ever happen.  We faced one more challenge when we first hit peak hours. It was related to external APIs and scaling issues. We used to hit rate limits with LLMs and X API. Eventually, we had to rebuilt our integration layers with smarter batching, request queuing, and fallback logic—ensuring reliability even when upstream services choke.