KI-Agenten
eventcatalog
Living architecture documentation that both your team and AI agents can read.
★ 2,9 Tsd. SterneCustom
Anleitung ansehen →generative_agents ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von joonspk-research (joonspk-research/generative_agents auf GitHub). Es hat 22.200 Sterne auf GitHub und 3.112 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 5. August 2024 auf GitHub aktualisiert.
Stanford's Generative Agents simulation - 25 AI characters living in a virtual town
Read the paper summary and decide whether you want to replay a demo or run a live simulation.
Get the repository, which contains the environment front-end and the simulation server.
Create the utils file with your OpenAI API key, then install the Python requirements.
Start the environment server and the simulation server, then watch or replay the agents' day.
Implements the paper's memory stream, retrieval, reflection and planning loop for each agent.
Runs 25 agents in the Smallville environment ('the Ville') with daily routines and emergent social behavior.
Simulations can be saved, replayed and demoed, so experiments are reproducible.
Author and load agent history files to change what agents remember.
Create your own base simulations with different agents and starting states.
Watch agents move and interact through the web-based environment server.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/joonspk-research/generative_agents.git
cd generative_agentsAfter creating the utils file with your OpenAI API key (README Step 1), install the Python dependencies.
pip install -r requirements.txt # see README for the exact pathThe README walks through starting the environment server, then the simulation server, then running or replaying a simulation.
# Start the environment (frontend) server, then the simulation server
# per README 'Running a Simulation' steps 1-2It is maintained by the paper's authors (Joon Sung Park and collaborators, Stanford) as research code on GitHub.
Yes - the code is Apache-2.0 licensed and free to use under that license.
Yes, for live simulations you need your own OpenAI API key, and a town full of agents making LLM calls can consume meaningful API credit; replaying saved simulations avoids most of that cost.
Compared with agent frameworks like LangChain, this is not a toolkit for building your own agent app - it is a complete, paper-faithful social simulation you run, replay and modify.
Clone the repo, add your OpenAI key in the utils file, install requirements.txt, and follow the README's two-server run instructions.
Living architecture documentation that both your team and AI agents can read.
180 free gradients for CSS, Figma, and Sketch, plus a dataset for AI agents.
Babysitter enforces the process: deterministic, quality-gated orchestration for fleets of coding agents.
Management console for installing and monitoring AI agents and pipelines.
Context layer so AI agents query your analytics databases accurately.
Compression gateway that cuts AI coding agents' token bills up to 85%.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls generative_agents nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die generative_agents bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Yohei Nakajima's autonomous task agent - now a self-building agent framework in Python
Swarm — OpenAI's experimental framework for lightweight multi-agent orchestration with agents and handoffs, MIT
Open-source LLM observability: trace, evaluate and monitor apps and agents
Google's code-first toolkit for building real AI agents.
NVIDIA's reference stack for running AI agents more securely inside OpenShell sandboxes
A visual all-in-one platform for building, debugging, and deploying AI agents.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.