# AgentScope — Distributed Multi-Agent Platform

> AgentScope is a multi-agent framework supporting distributed agent communication, built-in fault tolerance, and an actor-based runtime for building complex multi-agent applications at scale.

## Install

Save in your project root:

# AgentScope — Distributed Multi-Agent Platform

## Quick Use
```bash
pip install agentscope
```
```python
import agentscope
agentscope.init(model_configs={"model_type": "openai_chat", "config_name": "gpt4", "model_name": "gpt-4"})
from agentscope.agents import DialogAgent
agent = DialogAgent(name="assistant", model_config_name="gpt4", sys_prompt="You are a helpful assistant.")
msg = agent("Hello, what can you do?")
```

## Introduction
AgentScope is a multi-agent platform designed for building applications where multiple AI agents collaborate or compete. It provides an actor-based distributed runtime, built-in message passing, and fault tolerance so agents can run across processes or machines without custom networking code.

## What AgentScope Does
- Provides a message-based communication protocol for agent interaction
- Supports distributed deployment with an actor-based execution model
- Includes built-in agents for dialogue, tool use, and ReAct reasoning
- Offers a drag-and-drop studio for visual workflow design
- Handles fault tolerance and automatic retry for agent failures

## Architecture Overview
AgentScope uses an actor model where each agent runs as an independent actor that communicates through asynchronous messages. A central service manages agent registration and message routing. The framework wraps LLM calls, tool invocations, and memory operations behind a unified agent interface. For distributed setups, agents can be launched on separate machines and communicate over gRPC.

## Self-Hosting & Configuration
- Install from PyPI and initialize with a model configuration dictionary
- Define model configs for OpenAI, DashScope, Ollama, or custom API endpoints
- Use AgentScope Studio for browser-based workflow design and monitoring
- Deploy distributed agents by specifying host and port in the agent constructor
- Configure logging and checkpointing for long-running multi-agent workflows

## Key Features
- Actor-based distribution lets agents run across machines transparently
- Built-in retry and fallback mechanisms handle LLM API failures gracefully
- Supports pipeline, sequential, and parallel agent orchestration patterns
- AgentScope Studio provides a visual interface for designing and monitoring workflows
- Extensive service toolkit includes web search, code execution, and file operations

## Comparison with Similar Tools
- **CrewAI** — role-based orchestration; less focus on distributed execution
- **AutoGen** — conversation-based multi-agent; no built-in actor-based distribution
- **LangGraph** — graph-based agent workflows; tighter LangChain coupling
- **CAMEL** — focuses on communicative agents for research; less production tooling

## FAQ
**Q: What LLM providers are supported?**
A: OpenAI, DashScope, Ollama, vLLM, and any OpenAI-compatible API endpoint.

**Q: Can agents run on different machines?**
A: Yes. Use the to_dist() method to convert any agent to a distributed actor with gRPC communication.

**Q: Is there a visual builder?**
A: AgentScope Studio provides a drag-and-drop interface for building and monitoring multi-agent workflows.

**Q: How does fault tolerance work?**
A: The framework retries failed LLM calls automatically and supports checkpointing so workflows can resume from the last successful state.

## Sources
- https://github.com/modelscope/agentscope
- https://modelscope.github.io/agentscope/

---
Source: https://tokrepo.com/en/workflows/b9233350-3fda-11f1-9bc6-00163e2b0d79
Author: AI Open Source