agentscope-ai / agentscope

**中文主页** | **Tutorial** | **Roadmap (Jan 2026 -)** | **FAQ** What is AgentScope? AgentScope is a production-ready, easy-to-use agent framework with essential abstractions that work with rising model capability and built-in support for finetuning. We design for increasingly agentic LLMs. Our approach leverages the models' reasoning and tool use abilities rather than constraining them with strict prompts and opinionated orchestrations. Why use AgentScope? • **Simple**: start building your agents in 5 minutes with built-in ReAct agent, tools, skills, human-in-the-loop steering, memory, planning, realtime voice, evaluation and model finetuning • **Extensible**: large number of ecosystem integrations for tools, memory and observability; built-in support for MCP and A2A; message hub for flexible multi-agent orchestration and workflows • **Production-ready**: deploy and serve your agents locally, as serverless in the cloud, or on your K8s cluster with built-in OTel support The AgentScope Ecosystem News • **[2026-02] :** Realtime Voice Agent support. Example | Multi-Agent Realtime Example | Tutorial • **[2026-01] :** Biweekly Meetings launched to share ecosystem updates and development plans - join us! Details & Schedule • **[2026-01] :** Database support & memory compression in memory module. Example | Tutorial • **[2025-12] :** A2A (Agent-to-Agent) protocol support. Example | Tutorial • **[2025-12] :** TTS (Text-to-Speech) support. Example | Tutorial • **[2025-11] :** Anthropic Agent Skill support. Example | Tutorial • **[2025-11] :** Alias-Agent for diverse real-world tasks and Data-Juicer Agent for data processing open-sourced. Alias-Agent | Data-Juicer Agent • **[2025-11] :** Agentic RL via Trinity-RFT library. Example | Trinity-RFT • **[2025-11] :** ReMe for enhanced long-term memory. Example • **[2025-11] :** agentscope-samples repository launched and agentscope-runtime upgraded with Docker/K8s deployment and VNC-powered GUI sandboxes. Samples | Runtime More news → Community Welcome to join our community on | Discord | DingTalk | |----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------| | | | 📑 Table of Contents • Quickstart • Installation • From PyPI • From source • Example • Hello AgentScope! • Voice Agent • Realtime Voice Agent • Human-in-the-loop • Flexible MCP Usage • Agentic RL • Multi-Agent Workflows • Documentation • More Examples & Samples • Functionality • Agent • Game • Workflow • Evaluation • Tuner • Contributing • License • Publications • Contributors Quickstart Installation > AgentScope requires **Python 3.10** or higher. From PyPI Or with uv: From source Example Hello AgentScope! Start with a conversation between user and a ReAct agent 🤖 named "Friday"! Voice Agent Create a voice-enabled ReAct agent that can understand and respond with speech, even playing a multi-agent werewolf game with voice interactions. https://github.com/user-attachments/assets/c5f05254-aff6-4375-90df-85e8da95d5da Realtime Voice Agent Build a realtime voice agent with web interface that can interact with users via voice input and output. Realtime chatbot | Realtime Multi-Agent Example https://github.com/user-attachments/assets/1b7b114b-e995-4586-9b3f-d3bb9fcd2558 Human-in-the-loop Support realtime interruption in ReActAgent: conversation can be interrupted via cancellation in realtime and resumed seamlessly via robust memory preservation. Flexible MCP Usage Use individual MCP tools as **local callable functions** to compose toolkits or wrap into a more complex tool. Agentic RL Train your agentic application seamlessly with Reinforcement Learning integration. We also prepare multiple sample projects covering various scenarios: | Example | Description | Model | Training Result | |--------------------------------------------------------------------------------------------------|-------------------------------------------------------------|------------------------|-----------------------------| | Math Agent | Tune a math-solving agent with multi-step reasoning. | Qwen3-0.6B | Accuracy: 75% → 85% | | Frozen Lake | Train an agent to navigate the Frozen Lake environment. | Qwen2.5-3B-Instruct | Success rate: 15% → 86% | | Learn to Ask | Tune agents using LLM-as-a-judge for automated feedback. | Qwen2.5-7B-Instruct | Accuracy: 47% → 92% | | Email Search | Improve tool-use capabilities without labeled ground truth. | Qwen3-4B-Instruct-2507 | Accuracy: 60% | | Werewolf Game | Train agents for strategic multi-agent game interactions. | Qwen2.5-7B-Instruct | Werewolf win rate: 50% → 80% | | Data Augment | Generate synthetic training data to enhance tuning results. | Qwen3-0.6B | AIME-24 accuracy: 20% → 60% | Multi-Agent Workflows AgentScope provides MsgHub and pipelines to streamline multi-agent conversations, offering efficient message routing and seamless information sharing Documentation • Tutorial • FAQ • API Docs More Examples & Samples Functionality • MCP • Anthropic Agent Skill • Plan • Structured Output • RAG • Long-Term Memory • Session with SQLite • Stream Printing Messages • TTS • Code-first Deployment • Memory Compression Agent • ReAct Agent • Voice Agent • Deep Research Agent • Browser-use Agent • Meta Planner Agent • A2A Agent • Realtime Voice Agent Game • Nine-player Werewolves Workflow • Multi-agent Debate • Multi-agent Conversation • Multi-agent Concurrent • Multi-agent Realtime Conversation Evaluation • ACEBench Tuner • Tune ReAct Agent Contributing We welcome contributions from the community! Please refer to our CONTRIBUTING.md for guidelines on how to contribute. License AgentScope is released under Apache License 2.0. Publications If you find our work helpful for your research or application, please cite our papers. • AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applicati…