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| # Roadmap | |
| ## Long-term Goals | |
| Offering **agent-oriented programming (AOP)** as a new programming paradigm to organize the design and implementation of next-generation LLM-empowered applications. | |
| ## Current Focus (January 2026 - ) | |
| ### 🎙️ Voice Agent | |
| **Voice agents** are a domain we are highly focused on, and AgentScope will continue to invest in this direction. | |
| AgentScope aims to build **production-ready** voice agents rather than demonstration prototypes. This means our voice agents will: | |
| - Support **production-grade** deployment, including seamless frontend integration | |
| - Support **tool invocation**, not just voice conversations | |
| - Support **multi-agent** voice interactions | |
| #### Development Roadmap | |
| Our development strategy for voice agents consists of **three progressive milestones**: | |
| 1. **TTS Models** → 2. **Multimodal Models** → 3. **Real-time Multimodal Models** | |
| --- | |
| #### Phase 1: TTS (Text-to-Speech) Models | |
| - **Build TTS model base class infrastructure** | |
| - Design and implement a unified TTS model base class | |
| - Establish standardized interfaces for TTS model integration | |
| - **Horizontal API expansion** | |
| - Support mainstream TTS APIs (e.g., OpenAI TTS, Google TTS, Azure TTS, ElevenLabs, etc.) | |
| - Ensure consistent behavior across different TTS providers | |
| --- | |
| #### Phase 2: Multimodal Models (Non-Realtime) | |
| - **Enable ReAct agents with multimodal support** | |
| - Integrate multimodal models (e.g., qwen3-omni, gpt-audio) into existing ReAct agent framework | |
| - Support audio input/output in non-realtime mode | |
| - **Advanced multimodal agent capabilities** | |
| - Enable tool invocation within multimodal conversations | |
| - Support multi-agent workflows with multimodal communication | |
| --- | |
| #### Phase 3: Real-time Multimodal Models | |
| - **Beyond request-response**: Explore streaming, interrupt handling, and concurrent multimodal processing | |
| - **New programming paradigms**: Design agent programming models specifically tailored for real-time interactions | |
| - **Production readiness**: Ensure low-latency performance, stability, and scalability for production deployment | |
| ### 🛠️ Agent Skill | |
| Provide **production-ready** agent skill integration solutions. | |
| ### 🌐 Ecosystem Expansion | |
| - **A2UI (Agent-to-UI)**: Enable seamless agent-to-user interface interactions | |
| - **A2A (Agent-to-Agent)**: Enhance agent-to-agent communication capabilities | |
| ### 🚀 Agentic RL | |
| - Support using [Tinker](https://tinker-docs.thinkingmachines.ai/) backend to tune agent applications on devices without GPU. | |
| - Support tuning agent applications based on their run history. | |
| - Integrate with AgentScope Runtime to provide better environment abstraction. | |
| - Add more tutorials and examples on how to build complex judge functions with the help of evaluation module. | |
| - Add more tutorials and examples on data selection and augmentation. | |
| ### 📈 Code Quality | |
| Continuous refinement and improvement of code quality and maintainability. | |
| # Completed Milestones | |
| ### AgentScope V1.0.0 Roadmap | |
| We are deeply grateful for the continuous support from the open-source community that has witnessed AgentScope's | |
| growth. Throughout our journey, we have maintained **developer-centric transparency** as our core principle, | |
| which will continue to guide our future development. | |
| As the AI agent ecosystem rapidly evolves, we recognize the need to adapt AgentScope to meet emerging trends and | |
| requirements. We are excited to announce the upcoming release of AgentScope v1.0.0, which marks a significant shift | |
| towards deployment-focused and secondary development direction. This new version will provide comprehensive support for agent developers | |
| with enhanced deployment capabilities and practical features. Specifically, the update will include: | |
| - ✨New Features | |
| - 🛠️ Tool/MCP | |
| - Support both sync/async tool functions | |
| - Support streaming tool function | |
| - Support parallel execution of tool functions | |
| - Provide more flexible support for the MCP server | |
| - 💾 Memory | |
| - Enhance the existing short-term memory | |
| - Support long-term memory | |
| - 🤖 Agent | |
| - Provide powerful ReAct-based out-of-the-box agents | |
| - 👨💻 Development | |
| - Provide enhanced AgentScope Studio with visual components for developing, tracing and debugging | |
| - Provide a built-in copilot for developing/drafting AgentScope applications | |
| - 🔍 Evaluation | |
| - Provide built-in benchmarking and evaluation toolkit for agents | |
| - Support result visualization | |
| - 🏗️ Deployment | |
| - Support asynchronous agent execution | |
| - Support session/state management | |
| - Provide sandbox for tool execution | |
| Stay tuned for our detailed release notes and beta version, which will be available soon. Follow our GitHub | |
| repository and official channels for the latest updates. We look forward to your valuable feedback and continued | |
| support in shaping the future of AgentScope. |