Introducing Snowball Fight ☃️, our first ML-Agents environment

AI Agents Are Here. What Now?

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Agent Lightning v1.0 is a lightweight 3,500‑line framework designed to connect existing AI agents to reinforcement learning training. It simplifies the process of improving agents by handling tools, context, and decision‑making within a single, easy‑to‑integrate system. The framework allows developers to train agents with real harnesses without the need to rebuild complex underlying structures.

Neural MMO: A massively multiagent game environment

We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task.

Introducing ⚔️ AI vs. AI ⚔️ a deep reinforcement learning multi-agents competition system

Build AI agents with the Mistral Agents API

Aligning to What? Rethinking Agent Generalization in MiniMax M2

Tiny Agents in Python: a MCP-powered agent in ~70 lines of code

A team of Gemini 4 Argon agents beats Minecraft

A team of four Gemini 4 Argon agents autonomously defeated the Ender Dragon in a sandboxed Minecraft survival world. Using only the open‑source Mineflayer library, the agents coordinated via in‑game chat and shared memory, writing 2,783 programs over 27 hours and 14 minutes while dying 605 times. This marks the first end‑to‑end autonomous completion of Minecraft by AI agents without pre‑scouted seeds or human‑written game‑specific tools.

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

arXiv:2608. 14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists".

Computer-Using Agent

The next evolution of the Agents SDK

OpenAI updates the Agents SDK with native sandbox execution and a model-native harness, helping developers build secure, long-running agents across files and tools.

Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents