AgentScope

AgentScope is an open-source multi-agent platform built by Alibaba DAMO Academy designed for developers and researchers who need to build, deploy, and manage AI agents at scale. With over 27,800 GitHub stars and 3,200 forks, it has emerged as one of the most popular open-source agent frameworks.

What sets AgentScope apart is its emphasis on transparency and trust. The platform provides visualization tools that let you see exactly what each agent is doing, how decisions are made, and how agents communicate with each other. This makes it particularly valuable for research, debugging, and production deployments where agent behavior must be explainable.

AgentScope is hosted on GitHub at github.com/agentscope-ai/agentscope and is available under the Apache 2.0 license. It supports Python 3.9+ and integrates with major LLM providers including OpenAI, Anthropic, and local models through vLLM and Ollama.

• Multi-Agent Orchestration - Coordinate multiple agents with different roles, goals, and capabilities to solve complex tasks collaboratively.
• Visual Agent Monitoring - Real-time dashboard showing agent states, message flows, decision traces, and performance metrics.
• Model Agnostic - Support for OpenAI, Anthropic, Google Gemini, local models via vLLM, Ollama, and custom model endpoints.
• Flexible Agent Topologies - Support for sequential, parallel, hierarchical, and dynamic agent collaboration patterns.
• Built-in Tools - Pre-built tool support for web search, code execution, file operations, API integration, and RAG.
• Service Layer - Share resources like credentials, knowledge bases, and agent configurations across deployments.
• Kubernetes Support - Run agents in Kubernetes workspaces with Pod/PVC lifecycle management via K8sWorkspace.
• Knowledge Management - Built-in RAG integration with vector stores for domain-specific knowledge retrieval.

AgentScope's architecture is designed around modularity and observability. At its core is a distributed agent runtime that manages agent lifecycles, message passing, and resource allocation. Agents are defined as Python classes with declarative configurations for models, tools, memory, and knowledge sources.

The system includes a built-in message bus for inter-agent communication, supporting both synchronous and asynchronous messaging patterns. The service layer provides shared resource management, allowing agents to access common credentials, knowledge bases, and model endpoints through a centralized service registry. The monitoring subsystem captures all agent interactions for debugging, replay, and auditing.

AgentScope supports OpenSandbox for secure code execution environments and K8sWorkspace for production-grade Kubernetes deployments with automatic Pod and PVC lifecycle management. This makes it suitable for both local development and enterprise-scale agent deployments.

Install via pip:

pip install agentscope

Install from source:

git clone https://github.com/agentscope-ai/agentscope.git
cd agentscope
pip install -e .

AgentScope requires Python 3.9 or later. Optional dependencies for specific model backends (vLLM, Ollama, DashScope) can be installed with extras like pip install agentscope[vllm].

AgentScope provides several built-in agent types that can be composed to build complex multi-agent systems:

• DialogAgent - Conversational agent for natural language interactions with users and other agents.
• PipelineAgent - Sequential processing agent that passes results through a chain of operations.
• SelectorAgent - Routes tasks to specialized sub-agents based on task classification.
• DistributedAgent - Agent that can run on remote workers for horizontal scaling.
• ReActAgent - Reasoning and acting agent that alternates between thinking and taking actions.
• ToolAgent - Agent specialized in executing specific tools and returning structured results.
• Custom Agents - Extend the base Agent class to create domain-specific agents with custom behaviors.

The AgentScope Service Layer provides centralized management of shared resources across agent deployments. Credentials for external APIs, knowledge base connections, and agent configurations can be registered, versioned, and shared between agents and teams.

The service layer supports hot-reloading of configuration changes without restarting running agents. It integrates with the monitoring subsystem to provide a unified view of all agents, their resource usage, and communication patterns. This makes it easier to manage large-scale multi-agent deployments in production environments.

AgentScope is versatile enough for a wide range of multi-agent applications. Common use cases include:

• Research and Experimentation - Test multi-agent coordination strategies, prompt engineering techniques, and agent architectures with full visibility into agent behavior.
• Automated Workflows - Build agent teams that collaborate on complex tasks like document analysis, code review, data pipeline management, and report generation.
• Customer Support - Deploy specialized agents for triage, technical support, billing, and escalation, coordinated by a dispatcher agent.
• Simulation and Gaming - Create multi-agent environments for social simulation, economic modeling, and game AI development.
• Enterprise Automation - Use K8sWorkspace and the service layer to deploy reliable, scalable agent systems with shared knowledge and tools.