AgentScope: review, pricing, security and alternatives

What AgentScope does

AgentScope belongs to the multi-agent family: systems that split a task between several specialised agents passing work to one another. On our grid it ranks #5 of the 43 projects of this family listed in the directory.

A framework is not used as is: it is a library developers build into their own code to describe the agent's tools, decision loop and memory. It requires programming skills, but leaves control over every detail.

You use it from: developer SDK. Usable models: any OpenAI-compatible model. It can run on your own machine or server.

Who is AgentScope for?

It interests technical teams that want to split a complex task between several specialised agents that check each other. Typical tasks people give it:

• pair a writer agent with a reviewer agent
• split a project into subtasks given to roles
• simulate a team (analysis, plan, execution)

It is a good candidate if your data must stay on your machines.

Access and human control

A framework has no access of its own: the tools you plug in define what the agent can do. Start with read-only tools.

We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.

AgentScope pricing

The code is free, under the Apache-2.0 licence.

Getting started with AgentScope

1. Install it: the project ships as a Python package (install with pip or uv). The exact command is in the repository README.
2. Create an API key with the model provider and put it in an environment variable, never in a shared or versioned file.
3. Start with the minimal example from the docs, then add one tool at a time and check what the agent does with it.
4. Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.

Security: risk level

More agents mean higher bills and chained errors: set a budget and a maximum number of steps.

Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.

Project activity and releases

Created on January 12, 2024, the agentscope-ai/agentscope repository has 33,827 stars, 3,715 forks and 379 open issues, with 790 commits in total. It received changes this month.

Over the last 52 weeks: 716 commits, an average of 13.8 per week.

50 releases in total; 30 in the last twelve months. Latest release: v2.0.10, on October 12, 2026.

AgentScope vs other multi-agent

GitHub stars of the best-rated open-source projects in the multi-agent family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.

AgentScope and other multi-agent at a glance

The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.

How we rated it

Score: 85/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 30/40: 33,827 stars on a log scale (40 points from about 200,000).
• Activity 30/30: last commit less than a month ago, 16 releases in six months, 716 commits in a year.
• Framework 25/30: licence, default human approval, self-hosting, model choice and documentation site.

The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.