
An open-source framework for deploying multiple LLM-based agents in task-solving and simulation applications.
AgentVerse is an open-source framework designed to facilitate the deployment of multiple Large Language Model (LLM)-based agents across various applications. It provides two primary frameworks: task-solving, which assembles multiple agents into an automatic multi-agent system to collaboratively accomplish tasks, and simulation, which allows users to set up custom environments to observe behaviors among multiple agents. AgentVerse aims to enhance multi-agent collaboration and explore emergent behaviors in agents.
AI Agent Store research
AgentVerse is an Apache-2.0 research framework and Python codebase for coordinating multiple LLM-based agents. It supports collaborative task-solving and simulated multi-agent environments; it is a library for building experiments and systems, not a hosted general-purpose agent service.
Last reviewed September 5, 2026
The task-solving framework assembles multiple specialized agents to work together on applications such as software development and consulting workflows.[1], [2]
Users can define environments, roles, and interaction scenarios to observe agent behavior in classroom, game, and social-research simulations.[1], [2]
The framework documents OpenAI and Azure OpenAI configuration as well as local inference through vLLM and FastChat-compatible model servers.[1]
AgentVerse is free under the Apache 2.0 license. Users remain responsible for model API charges or the compute and operations needed to run local models.[1]
Platforms: Python 3.9+, Command line, Local simulation web UI[1]
Deployment: Self-hosted Python environment, External OpenAI or Azure OpenAI APIs, Self-hosted vLLM or FastChat model server[1]
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