AgentScope is more than just a library—it's a comprehensive ecosystem designed for transparency, controllability, and production readiness. Say goodbye to "magic" and embrace truly robust, debuggable agent development.
Designed specifically to solve real-world complex problems, providing unparalleled transparency and control.
From core development framework, production-grade runtime to visualization tools, covering the complete application lifecycle.
"No deep encapsulation or implicit magic." All components are visible and controllable to developers, making debugging and customization no longer a challenge.
Achieved SOTA resolution rate on the challenging SWE-Bench benchmark, proving its powerful ability to solve complex tasks.
Provides independent runtime and sandboxed tool execution environment, ensuring safe deployment for your agent applications.
Covering the complete closed loop from development, debugging to secure deployment.
Core Development Framework
The foundation of the ecosystem, providing basic components like messages, models, memory, tools and orchestration tools like pipelines, message hubs. Friendly to beginners, powerful for experts.
Production Deployment Engine
Independent, production-ready runtime providing sandboxed tool execution environment, ensuring security and scalability. The key is: it's framework-agnostic!
Visualization & Debugging Tools
Locally-run visual development and debugging suite, real-time tracking of message flows and agent thinking processes, making the "transparency" principle truly land.
AgentScope has proven its value in multiple complex application scenarios and authoritative benchmark tests.
Through an elegant "reproduce-fix-test" multi-agent specialized pipeline, AgentScope successfully solved software issues from real GitHub projects, achieving an impressive 63.4% resolution rate, proving its excellent ability to handle complex tasks.
Fully demonstrates AgentScope's ability to handle multi-role dialogue systems and dynamic group interactions through its core abstractions like message hubs. Agents need advanced social intelligence including deception, negotiation, alliance, and strategic reasoning.
Demonstrates "agentified RAG" capabilities, where agents can autonomously retrieve, filter, and integrate information from multiple heterogeneous knowledge sources, generating answers with reliable citations, solving core challenges of traditional RAG.
Understand AgentScope's unique position in the agent framework landscape.
| Feature Dimension | AgentScope | LangGraph | AutoGen | CrewAI |
|---|---|---|---|---|
| Core Paradigm | Explicit message passing and workflow orchestration | Graph-based state machine | Conversational agents | Role-based task delegation |
| Key Advantages | Transparency, production readiness (runtime/sandbox), SOTA performance | Fine-grained process control, stateful workflows | Flexible inter-agent dialogue, research-grade flexibility | Rapid prototyping, intuitive role-playing design |
| Usability | Beginner-friendly, expert-powerful | Steep learning curve | Moderate learning curve | Very high, beginner-friendly |
| Production Readiness | High (explicit focus through runtime) | High | Medium | Good |
AgentScope stands out in transparency, production deployment support, and verified complex task-solving capabilities.
pip install agentscope