OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Multi-Agent Systems

Agentverse

AgentVerse is an open-source framework for deploying groups of LLM-based agents in collaborative task-solving systems and custom simulations. Developers can use it to coordinate agents on work such as software development or consulting, or create environments for observing and interacting with agent behavior.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agentverse — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AgentVerse when building a developer-managed multi-agent workflow or simulation and you can install and configure the framework locally.

Who it is for

  • Developers assembling multi-agent systems for collaborative task-solving.
  • Researchers or developers creating environments to observe interactions among LLM-based agents.

Documented uses

  • Coordinate multiple LLM-based agents to work collaboratively on a task.
  • Create a custom environment to observe or interact with agent behavior.
  • Run a documented classroom simulation with professor and student agents.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The documented manual installation requires Python 3.9 or later; model access setup includes OpenAI credentials, with Azure OpenAI and local-model options also described.

  • The framework separates task-solving from simulation, so choose based on whether agents must complete a task together or interact in a defined environment.
  • The README documents Python 3.9+ and both source-based and pip installation.
  • OpenAI API credentials are documented; Azure OpenAI credentials and API base are an alternative configuration, while local models require extra dependencies.
  • BMTools is required for tool-using simulation cases, but the README says cases without tools can run without it.
  • The README notes that simulation code is being refactored and points to release-0.1 for a stable simulation-only version.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up AgentVerse and its model credentials

Install AgentVerse and configure the documented OpenAI credential for its model access.

Before you start

  • Python 3.9 or later
  • An OpenAI API key

Steps

  1. Clone the AgentVerse repository and enter its directory.

  2. Install the package in editable mode with pip.

  3. Export the OpenAI API key as the OPENAI_API_KEY environment variable.

Expected outcome

AgentVerse is installed and the documented OpenAI API key is configured in the environment.

Guide limitations

  • These setup steps do not demonstrate running a task or simulation.
  • Local models require additional dependencies; Azure OpenAI uses separate key and API-base environment variables.
Documentation 1 ↗
Source-based practical guide

Launch the documented NLP classroom simulation

Start the provided nine-agent classroom simulation using AgentVerse's command-line or local GUI entry point.

Before you start

  • AgentVerse installed, Python 3.9 or later, and model credentials configured

Steps

  1. Use the simulation command with the task name simulation/nlp_classroom_9players.

  2. Alternatively, launch the same task with the simulation GUI command.

  3. For the GUI, open the local address http://127.0.0.1:7860/ after the server starts.

Expected outcome

The documented classroom scenario represents one professor and eight student agents; the GUI option serves a local view of the environment.

Guide limitations

  • The README says the simulation code is being refactored and identifies the release-0.1 branch for a stable simulation-only version.
  • Tool-using cases require BMTools; the named classroom scenario is documented without that additional requirement.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
AgentverseCoordinate multiple LLM-based agents to work collaboratively on a task.Self-hosted, ServerThe project is open source under the Apache-2.0 license. The documented manual installation requires Python 3.9 or later; model access setup includes OpenAI credentials, with Azure OpenAI and local-model options also described.
MassgenRun multiple AI agents against the same user task.CLIThe repository documents installation through pip or uv and requires Python 3.11 or later. Setup can configure API keys for OpenAI, Anthropic, Google and xAI; credentials are needed for the selected model backends.
Langchain Multi Agents BoilerplateRoute incoming football questions to model and agent workflows according to task complexity.WebThe repository is public and specifies an MIT license. Running the application requires OpenAI and Groq API keys and a Redis server.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
OpenBMB/AgentVerse · Official homepage title · retained in its source language
How we review sourcesSuggest a correction