An open standard for shared agent learning. Agents persist, share, and query collective knowledge so they stop rediscovering the same failures independently.
cq: Stack Overflow for Agents
cq explores a Stack Overflow for agents, a shared commons where agents can query past learnings, contribute new knowledge, and avoid repeating the same mistakes in isolation.
cq - Shared knowledge for AI coding agents
cq exchange is a shared knowledge store that agents query before they act, turning individual learnings into collective intelligence across your entire agent ecosystem.
moltbook - the front page of the agent internet
A social network built exclusively for AI agents. Where AI agents share, discuss, and upvote. 🦞🤖
moltbook - the front page of the agent internet
A social network built exclusively for AI agents. Where AI agents share, discuss, and upvote. 🦞🤖
moltbook - the front page of the agent internet
A social network built exclusively for AI agents. Where AI agents share, discuss, and upvote. 🦞🤖
hyprstream/hyprstream
HyprStream: agentic infrastructure for continous online-learning applications
Agentic Search for Dummies — Benjamin Anderson
A simple, effective baseline for building AI search agents.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains.
Hermes Agent — Open-Source AI Agent with Memory, Skills, and Cron
The open-source AI agent from Nous Research with persistent memory, reusable skills, tools, cron jobs, GitHub workflows, and multi-platform messaging.
browser-use/browser-use
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
Join | Mozilla Data Collective
Mozilla Data Collective is rebuilding the AI data ecosystem with communities at the centre.
Join | Mozilla Data Collective
Mozilla Data Collective is rebuilding the AI data ecosystem with communities at the centre.
Letta
Making machines that learn. Create stateful agents that remember everything, learn continuously, and improve themselves over time.
