A production-ready, easy-to-use agent framework with essential abstractions that work with rising model capability and built-in support for finetuning, helping developers build LLM applications efficiently.
One-Minute Overview#
AgentScope is a production-ready framework for building LLM applications. It provides essential abstractions that work with evolving model capabilities rather than constraining them with rigid prompts. Whether you're a developer or researcher, AgentScope lets you start building agents in just 5 minutes, offering a complete toolchain from development to deployment.
Core Value: Streamlines the agent development process while maintaining production-grade stability and scalability.
Quick Start#
Installation Difficulty: Low - Requires only Python 3.10+, with support for both pip and uv installation
# Install from PyPI
pip install agentscope
# Or using uv
uv pip install agentscope
Is this suitable for me?
- ✅ Agent Application Development: Want to quickly build LLM-based agent applications
- ✅ Multi-Agent Systems: Need to orchestrate coordination and communication between multiple agents
- ✅ Production Deployment: Want to deploy agent applications locally, in the cloud, or on K8s clusters
- ❌ Simple Chatbots: If you only need basic chat functionality, this might be overkill
Core Capabilities#
1. Agent Framework - Diverse Agent Types#
- Supports ReAct agents, Voice agents, Deep Research agents, Browser-use agents, and more
- Provides core modules including memory, planning, and tool usage
Actual Value: Developers can choose the appropriate agent type for their application needs without building complex architectures from scratch
2. MCP Integration - Flexible Tool Usage#
- Use MCP tools as local callable functions
- Can call directly, pass to agents as tools, or wrap into more complex tools
Actual Value: Easily integrate various external services and tools to extend agent capabilities
3. Reinforcement Learning Integration - Agent Capability Enhancement#
- Built-in RL support with multiple sample projects
- Includes training examples for various scenarios like math solving, navigation, email search
Actual Value: Continuously optimize agent performance through reinforcement learning to improve task completion quality and accuracy
4. Multi-Agent Workflows - Efficient Collaboration#
- Provides MsgHub and pipelines to streamline multi-agent conversations
- Supports efficient message routing and seamless information sharing
Actual Value: Build complex collaborative systems like multi-agent debates or multiplayer games
5. Production Ready - Deployment and Monitoring#
- Supports local, cloud serverless, and K8s deployment
- Built-in OTel support for monitoring and observability
Actual Value: Simplifies deployment from development to production, ensuring stable application performance and observability
Technology Stack & Integration#
Development Language: Python Key Dependencies: Python 3.10+ Integration Method: SDK / Library
Maintenance Status#
- Development Activity: Very active with multiple commits per week
- Recent Update: v1.0.13 released in January 2026
- Community Response: Active community support with Discord and DingTalk groups
Commercial & License#
License: Apache-2.0
- ✅ Commercial Use: Allowed
- ✅ Modification: Allowed
- ⚠️ Restrictions: Attribution required
Documentation & Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: doc.agentscope.io
- Example Code: Rich examples and tutorials available