AI Frameworks · agentscope-ai

agentscope

AgentScope is a Python framework for building and deploying multi-agent systems with built-in observability, permission controls, and production-grade serving. It supports event-driven architecture, workspace isolation, and extensible middleware for customizing agent behavior.

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Source: GitHub — github.com/agentscope-ai/agentscope

27.6k

GitHub stars

3.1k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	agentscope-ai/agentscope
Owner	agentscope-ai
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	27.6k
Forks	3.1k
Open issues	255
Latest release	v2.0.4 (2026-07-07)
Last updated	2026-07-07
Source	https://github.com/agentscope-ai/agentscope

What agentscope is

AgentScope 2.0 provides event-system abstractions, fine-grained permission controls, multi-tenancy/multi-session support via FastAPI, sandbox execution (local/Docker/E2B), and middleware hooks for the reasoning-acting loop. Targets Python 3.11+ with async/stream-based APIs.

Quickstart

Get the agentscope source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/agentscope-ai/agentscope.git2cd agentscope3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-agent coordination systems

Build teams of agents with leader-worker patterns, task planning, and background task offloading. Suitable for complex orchestration requiring human oversight and permission gates.

Production agent services

Deploy agents as multi-tenant, multi-session services with built-in Web UI, permission systems, and isolation boundaries. Ideal for SaaS platforms or internal tool automation.

Tool-enabled agentic LLM applications

Leverage modern LLM reasoning and tool-use capabilities with workspace sandboxing, RAG integration, and long-term memory support (Agentic Memory, ReMe, Mem0).

Implementation considerations

Requires API credentials for LLM models (e.g., DashScope, others) and management of sandbox backends (Docker daemon, E2B accounts).
Event-driven architecture and async/await patterns demand familiarity with Python async programming and event stream handling.
Permission and workspace isolation require careful configuration in multi-tenant deployments to prevent information leakage or unauthorized tool execution.
Long-term memory integrations (Mem0, ReMe, Agentic Memory) introduce external dependencies and storage management overhead.
Middleware and extension points are powerful but require understanding of the reasoning-acting loop and internal message/event models.

When to avoid it — and what to weigh

Simple chatbot use cases — If you need a basic conversational bot without multi-agent coordination or complex tool workflows, the framework's abstractions may be over-engineered.
No Python requirement or legacy system constraints — Requires Python 3.11+; not suitable if you need JavaScript/TypeScript-first agent orchestration or compatibility with older Python codebases.
Minimal DevOps or infrastructure — Multi-tenancy, sandbox backends (Docker, E2B), and service deployment add operational complexity. Avoid if you need a lightweight, zero-dependency library.
Proprietary/closed-source distribution — Apache 2.0 requires distribution of modifications; if licensing constraints prevent open-source contributions, review terms before use in commercial products.

License & commercial use

Released under Apache License 2.0, a permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution. Patent and liability clauses apply; derivative works must include license and notice of changes.

Apache 2.0 permits commercial use without restrictions. However, any modifications or derivative works must include the Apache 2.0 license and clear notices. If you distribute proprietary extensions, ensure compliance by including source/license notices. Recommended: consult legal for closed-source agent service wrappers.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Framework provides fine-grained permission controls and workspace isolation mechanisms to restrict tool access and execution. Sandbox support (Docker, E2B) isolates code execution. However, security posture depends on correct configuration: misconfigured permissions, unvetted tool integration, or exposed LLM APIs can create vulnerabilities. Credential handling (API keys) and multi-tenant isolation require careful implementation. Review permission system docs and sandbox backend security before production use.

Alternatives to consider

LangChain / LangGraph

More mature, broader model/tool integrations, and larger community. LangGraph has state-machine agent patterns, but less built-in multi-tenancy/permission abstraction.

AutoGen (Microsoft)

Specialized for multi-agent conversations with groupchat, but less production-grade service deployment and permission controls. Good for research/prototyping.

Anthropic's Tool Use / Claude API

Lightweight, native model tool-use support, minimal framework overhead. Suitable if you don't need orchestration, multi-agent coordination, or proprietary agent logic.

Software development agency

Build on agentscope with DEV.co software developers

AgentScope provides the infrastructure for multi-agent coordination, permission control, and observability. Start with pip install agentscope and explore the quickstart examples. Assess sandbox, memory, and multi-tenancy needs for your deployment.

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agentscope FAQ

Can I use AgentScope for a single-agent task-automation app?

Yes. The core Agent class supports single agents with tools, models, and event streams. However, for simple use cases, you may find lighter alternatives (e.g., direct LLM API + function calling) less complex.

What LLM models are supported?

Framework abstracts model interfaces; examples and quickstart show DashScope (Alibaba), but support depends on available credential/model adapters. OpenAI and other providers likely supported; check examples or adapter availability in codebase.

How do I run agents in sandbox/isolated environments?

AgentScope provides workspace/sandbox abstraction with backends for local filesystem, Docker, and E2B. Configure in agent setup; Docker requires daemon running, E2B requires API key. Docs link in resources section.

Is AgentScope suitable for production?

Yes, design targets production with multi-tenancy, multi-session service, permission system, and monitoring (event system). However, operational readiness depends on your infrastructure maturity, credential/secret management, and compliance needs.

Custom software development services

From first prototype to production, DEV.co delivers software development services around tools like agentscope. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

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