Inspiration Minecraft is a great place to explore what it takes for an AI agent to do more than answer questions. We wanted to build a teammate that could understand a goal in plain language, interact with a shared world, and adapt as that world changes. What it does Minecraft Agents lets a player give an assistant a task through in-game chat. The agent observes its surroundings, selects actions, and checks the results as it works. It can attempt tasks such as gathering resources, farming, crafting, and delivering items.
The agent also keeps persistent world memory and can use Tavily to look up Minecraft mechanics when it needs more information. How we built it We built a Python controller that connects NVIDIA Nemotron 3 Super 120B A12B through Nebius Token Factory to a Minecraft bot. The controller communicates over WebSocket with a Node.js Mineflayer bot, which interacts with a local Purpur Minecraft server.
The controller feeds observations and action results back to the model, checks progress against the player’s goal, and stores world checkpoints and experience in SQLite. Optional Tavily searches provide source-linked information about Minecraft mechanics. Challenges we ran into Minecraft is unpredictable. Blocks may be out of reach, inventories change, routes get blocked, and an action that looks successful may not produce the intended result. We had to make the agent check the world and record what actually happened instead of trusting the model’s plan alone.
Longer tasks also exposed challenges with keeping context useful, recovering from partial progress, and distinguishing a completed task from an intermediate step. Accomplishments that we're proud of We connected natural-language requests to actions in a live Minecraft world and built a feedback loop around observations and checked results.
In one hosted run, the agent harvested 32 mature crops and planted 32 potatoes. That run required a controller repair and an explicit resume, but it showed the system carrying progress through a complex task. What we learned An AI agent needs more than a good model. It needs useful observations, tools with clear boundaries, and evidence of what happened after each action. We also learned that recovering from partial progress is just as important as planning the first step. What's next for Minecraft-Agent We want to make longer tasks more reliable, improve recovery when the agent gets stuck, and test more farming and building scenarios. We also want to continue improving world memory and run more end-to-end hosted demos without intervention.
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