HyprStream: agentic infrastructure for continous online-learning applications
hyprstream/README.md at main · hyprstream/hyprstream
HyprStream: agentic infrastructure for continous online-learning applications - hyprstream/hyprstream
hyprstream/crates/hyprstream/src/storage/mir.rs at main · hyprstream/hyprstream
HyprStream: agentic infrastructure for continous online-learning applications - hyprstream/hyprstream
hyprstream/crates/gittorrent at main · hyprstream/hyprstream
HyprStream: agentic infrastructure for continous online-learning applications - hyprstream/hyprstream
hyprstream/docs/interop/tiles-alignment.md at main · hyprstream/hyprstream
HyprStream: agentic infrastructure for continous online-learning applications - hyprstream/hyprstream
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.
A Survey on Large Language Model based Autonomous Agents
Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.
mozilla-ai/cq
An open standard for shared agent learning. Agents persist, share, and query collective knowledge so they stop rediscovering the same failures independently.
Reimagining Web Infrastructure for the Age of AI Agents
How core internet components will transform for an agent-driven web and the new opportunities for startup founders
Reimagining Web Infrastructure for the Age of AI Agents
How core internet components will transform for an agent-driven web and the new opportunities for startup founders
browser-use/browser-use
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
Philipp Schmid on Twitter / X
Should we build the web for agents, not agents for the web? 🤔 A new paper argues that current research is misguidedly focuses on improving LLMs leading to significant problems with efficiency, reliability, and safety, proposing a new "Agentic Web Interface" (AWI) that sits on… pic.twitter.com/I90k6kdYdk— Philipp Schmid (@_philschmid) June 14, 2025
Google for Developers Blog - News about Web, Mobile, AI and Cloud
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
Google for Developers Blog - News about Web, Mobile, AI and Cloud
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
openai/openai-realtime-agents
This is a simple demonstration of more advanced, agentic patterns built on top of the Realtime API.
Environments Hub: A Community Hub To Scale RL To Open AGI
RL environments are the playgrounds where agents learn. Until now, they’ve been fragmented, closed, and hard to share. We are launching the Environments Hub to change that: an open, community-powered platform that gives environments a true home.Environments define the world, rules and feedback loop of state, action and reward. From games to coding tasks to dialogue, they’re the contexts where AI learns, without them, RL is just an algorithm with nothing to act on.
moltbook - the front page of the agent internet
A social network built exclusively for AI agents. Where AI agents share, discuss, and upvote. 🦞🤖