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AgentScope

Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.

Introduction

Most agent frameworks try to make weaker models reliable by boxing them in with rigid prompt templates and hard-coded orchestration. AgentScope 2.0 makes the opposite bet: as LLMs get better at reasoning and tool use, the framework should get out of the way and instead harden everything around the agent. That reframing drives the 2.0 rewrite, which trades clever prompting for production plumbing.

What Sets It Apart
  • Event bus over hidden state — every step flows through a unified event system, so a frontend can render exactly what the agent is doing and a human can step in mid-loop, instead of watching an opaque chain.
  • Permissions as a first-class layer — configurable, fine-grained control over which tools and resources an agent may touch, which is what actually makes autonomous tool use safe to deploy.
  • Multi-tenant, multi-session serving — isolation across tenants and sessions is built in, not bolted on, so a single deployment can serve many users without cross-talk.
  • Sandboxed execution — tools and code run in local, Docker, or E2B backends, containing side effects rather than trusting the model to behave.
Great Fit / Look Elsewhere

Great fit if you are moving a multi-agent prototype toward real deployment and want serving, isolation, and human-in-the-loop as infrastructure instead of DIY glue. Look elsewhere if you need a lightweight single-file script for a quick demo, or a low-code visual builder — this is a Python-first framework (3.11+) that assumes you are comfortable wiring agents in code. Note that the 2.0 rewrite means older AgentScope 0.x tutorials and APIs may no longer apply.

Information

  • Websitegithub.com
  • OrganizationsAlibaba
  • Authorsagentscope-ai
  • Published date2024/01/12

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