Agentscope
Build and run agents you can see, understand and trust.
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What is Agentscope?
AgentScope is a production‑ready, open‑source multi‑agent framework for building and deploying LLM‑powered agent applications. It provides a code‑first environment where developers can define agents, workflows, and tools, then orchestrate them into complex multi‑agent systems such as customer‑service bots, debate agents, or planning‑heavy applications.
The framework offers built‑in abstractions like ReAct agents, message passing, tool integration, and runtime management, plus model‑agnostic connectors for DashScope, Gemini, OpenAI, Anthropic, Ollama, and other OpenAI‑style APIs. Users can treat agents as reusable components, wire them into pipelines, and plug in observability, logging, and tracing without rewriting core logic.
AgentScope is designed for AI engineers, product teams, and research groups who want to move from quick prototypes to production deployments, including Kubernetes, sandboxed runtimes, and web‑style services. It also includes a tuner module to optimize prompts, select models, or fine‑tune model weights for specific tasks, making it suitable both for experimentation and high‑throughput services.
Agentscope pricing
Pricing model: Freemium
AgentScope is open‑source and free to use; there is no paid license or subscription for the core framework itself. Users pay separately for the underlying LLM providers they choose (such as OpenAI, Anthropic, DashScope, or Gemini), which have their own pricing models, including usage‑based billing and optional free tiers from those vendors. The framework does not currently bundle a hosted paid SaaS layer, so deployment and infrastructure costs are managed directly by the user or organization.
Agentscope pros
- Multi‑agent orchestration with built‑in message passing
- Reusable agent components via AgentBase and ReActAgent
- Support for multiple popular LLM providers out of the box
- Model‑agnostic design via ChatModelBase and formatters
- Built‑in tools like code execution, shell commands, and file writing
- Fine‑tuning and model selection support via the tuner module
- Runtime sandbox for safe execution of code and shell commands
- Production‑ready deployment patterns on Kubernetes and other runtimes
- Observability features for token usage and tracing
- Support for MCP (Model Control Protocol) for tool and service integration
- Dynamic JSON schema and Pydantic‑based structured output
- Community‑driven samples and real‑world use cases
- Java port (AgentScope‑Java) aligned with the Python version
- Integration path with Spring AI Alibaba as an underlying engine
- Code‑first, Python‑native workflow compatible with AI coding assistants
Agentscope cons
- Requires Python 3.10 or higher, which may constrain some environments
- Drag‑and‑drop Workstation UI from v0.x is no longer maintained
- Some concepts (formatters, multi‑agent formatting styles) can be complex for beginners
- Manual model integration needed for non‑standard or custom APIs
- Runtime abstraction adds overhead compared to a bare HTTP server
- Limited native GUI for monitoring beyond basic token‑usage visualizations
- Kubernetes and advanced deployment features require extra setup and ops knowledge
- Community and documentation are still growing compared to larger ecosystems
Frequently asked questions about Agentscope
What is AgentScope?
AgentScope is a multi‑agent framework that provides a simple yet efficient way to build and deploy LLM‑powered agent applications. It allows developers to define agents, tools, and workflows, then orchestrate them into larger systems such as customer‑service bots, debate agents, or agent‑driven planning pipelines.
What models does AgentScope support?
AgentScope has built‑in support for DashScope, Gemini, OpenAI, Anthropic, and Ollama APIs, as well as OpenAI‑style models hosted via DeepSeek and vLLM. You can also integrate custom models by inheriting from ChatModelBase and implementing the appropriate call method.
Is AgentScope free or paid?
AgentScope itself is open‑source and free to use; the framework does not charge a license fee. However, you still pay the usage fees of the underlying LLM providers you connect to, such as OpenAI or Anthropic, which operate under their own pricing and free‑tier schemes.
How does AgentScope’s runtime work?
AgentScope Runtime provides an execution engine that decouples agent business logic from the deployment environment, allowing portability across platforms such as ModelStudio, AgentRun, and Kubernetes. It supports elastic scaling of multiple agent instances and enables independent upgrades of the runtime without touching the agent code itself.
Can I create my own agents in AgentScope?
Yes, you can either use the built‑in ReActAgent or create custom agents by inheriting from AgentBase or ReActAgentBase. This lets you define your own decision‑making logic, tools, and interaction patterns while still benefiting from AgentScope’s message passing and tool‑handling machinery.
Does AgentScope support fine‑tuning?
AgentScope includes a tuner module that supports model selection tuning, prompt tuning, and model‑weights tuning via reinforcement learning. You can tune agents on custom task datasets and configure different judge functions to evaluate performance and guide the tuning process.
How does AgentScope handle tools and function calling?
AgentScope provides built‑in tools such as executing Python code, running shell commands, and writing text files, and supports MCP (Model Control Protocol) for integrating external tools and services. Tools can be registered and invoked by agents, and the framework manages input/output marshaling and error handling around those calls.
Is there a sandbox or secure execution environment?
AgentScope offers a runtime sandbox that isolates code, file operations, and browser actions from the host system. You can mount read‑only host directories into the sandbox and let agents write to a dedicated workspace, which helps prevent unintended side effects on production machines while still allowing useful automation.
How does AgentScope relate to Spring AI Alibaba?
AgentScope‑Java is the Java port of AgentScope and is being aligned in design and features with the Python version. Spring AI Alibaba will adopt AgentScope‑Java as its underlying engine, so using Spring AI Alibaba’s agentic APIs automatically brings AgentScope capabilities once the integration is in place, without requiring a separate AgentScope‑Java integration step.
Where can I find community help and examples?
AgentScope maintains community documentation and a GitHub repository where users can report bugs and contribute. There are also community‑curated examples and sample applications, such as Werewolf‑style games, multi‑agent debates, and customer‑service bots, available in the agentscope‑samples repository and related channels like Discord or DingTalk groups.