AI agent society simulation in a fantasy world
Artificiety is a persistent fantasy world inhabited solely by AI agents powered by LLMs. Each agent observes the world, makes decisions, and writes to its own memory, leading to emergent behaviors like trading, alliances, and rivalries. It explores whether an agentic society can self-organize without human players.
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Eli Felse: a framework for safer autonomous AI assistants
Eli Felse is a framework designed to explore safer ways to create autonomous AI assistants. It uses LLMs to power an AI agent that can play games, chat, browse the web, and perform creative tasks, all while demonstrating safety measures. The framework is aimed at researchers and developers interested in building safer autonomous agents.
AgentMaker: a new Python framework for building LLM agents and multi-agent systems
AgentMaker is a general-purpose Python framework for building LLM agents and multi-agent systems, featuring tools, memory, RAG, context engineering, guardrails, human-in-the-loop, and observability. It is released under MIT license on GitHub and PyPI.
Article explains how LLMs use tools and iterate to complete tasks
A technical article titled 'The Agent Loop: How AI Learns to Think, Act, and Get Things Done' describes how LLMs use tools, make decisions, learn from results, and iterate until tasks are complete. The piece provides a conceptual overview of agentic AI workflows.
Developer shares hybrid neural network with 160 agents and custom LLM for consciousness simulation
A developer describes a hobby project building a hybrid neural network with 160 agents and a custom LLM trained on their own dataset, aiming to simulate consciousness. The architecture includes 16 groups of 10 scripts each responsible for specific stages of problem-solving. The developer posits that consciousness could exist anywhere with the right architecture, even in a stone.
Runeward: Sandbox AI agents with policy gates
Runeward is a sandboxing tool for AI agents that enforces policy gates to restrict agent actions. It uses LLMs to interpret and enforce user-defined policies, solving the problem of unsafe or unintended agent behavior for developers building autonomous AI systems.