Aptos PlayLab
Aptos PlayLab is an agenticIDE for experimenting with Move smart contracts on the Aptos network. With built-in AI assistance, you can write, audit, compile, and deploy Move contracts.
Created on 24th March 2025
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Aptos PlayLab
Aptos PlayLab is an agenticIDE for experimenting with Move smart contracts on the Aptos network. With built-in AI assistance, you can write, audit, compile, and deploy Move contracts.
The problem Aptos PlayLab solves
While Aptos provides excellent documentation and robust tooling for Move development, building smart contracts still requires a deep understanding of the language, formal verification practices, and deployment workflows. For many developers—especially newcomers—the barrier to rapid experimentation remains high.
Aptos PlayLab addresses this by introducing an AI-powered IDE that accelerates the entire smart contract lifecycle. It helps developers generate, audit, compile, and deploy Move contracts with minimal setup. It also includes RAG-based chat support for Aptos documentation and the Move Agent Kit, allowing developers to ask questions and get context-aware answers directly in their workspace.
Beyond just code generation for Move modules(Smart Contracts), Aptos PlayLab enables the creation of autonomous agents using LangChain and LangGraph Js. These agents can generate other agents that interact with the Move AI Agent Kit—a first step toward self-replicating, Von Neumann–style systems. While they don't deploy contracts themselves, these agents can reason about and interact with existing smart contracts on the Aptos platform, unlocking new possibilities for autonomous coordination and logic-driven execution. (This is an experiment)
Whether you're prototyping your first contract or exploring AI-native workflows, Aptos PlayLab turns smart contract development into a faster, more creative, and more accessible process.
Challenges we ran into
⚠️ Challenges We Ran Into
This project was built during a hackathon, so we optimized for speed and experimentation over polish. Here's what we learned (and where we're headed next):
📚 RAG for Code Generation is Hard
We experimented with using RAG (Retrieval-Augmented Generation) to generate Move smart contracts. The best results came from stuffing large contexts—up to 30,000 tokens of Aptos documentation—into the model. While effective in the short term, this approach is expensive and unsustainable. We took the path of least resistance for the hackathon, but we plan to redesign our RAG architecture and explore more efficient, retrieval-based strategies in the next iteration.
💡 One-shot Smart Contract Generation is a Pipe Dream
Prompting the AI to write an entire smart contract in one go sometimes works... but usually doesn’t. Code generation needs to be iterative and guided, not fire-and-forget. We're working on making the process more interactive and step-based, with context-aware revisions and AI-driven code review loops.
🧠 Our AI Agents Are Not Yet Stateful
Right now, our agents are built using LangChain and lack persistent memory. We need to integrate LangGraph to give agents memory and state, enabling more complex, multi-step reasoning workflows across sessions.
🧵 MCP Server Integration Wasn’t Finished
We started building an MCP (Multi-Context Provider) server to serve both our web-based Aptos Docs IDE and local environments like Cursor. It would’ve unified context handling across interfaces, but we ran out of time. This is high on our priority list for future work.
🐢 Compile & Prover Can Be Slow
Compiling Move code and running the Aptos Prover can be slow at times. We're exploring ways to optimize these flows for better dev experience, especially when integrated into an AI feedback loop.
Tracks Applied (2)
Agentic Infra
Aptos Foundation Grants
Technologies used