What it is

AgentScope 2.0 is a production-ready agent framework written in Python and released under the Apache-2.0 licence. It provides a set of essential abstractions for building agents, and it is designed explicitly for increasingly agentic large language models. Rather than wrapping models in strict prompts and opinionated orchestrations, the framework leans on the reasoning and tool use abilities the models already have, so the agent logic follows rising model capability instead of fighting it. The project lives in the Python AI and machine learning ecosystem, with a documented homepage at docs.agentscope.io and a repository that has accumulated 31,686 stars and 3,508 forks over roughly three years.

The concrete problem it solves is the gap between a raw model API and a working agent that can be inspected, debugged and deployed. AgentScope supplies the SDK layer for composing agents out of building blocks: a ReAct reasoning-acting loop with structured output, realtime interruption and resume, and batched sequential or concurrent tool acting; a Toolkit that manages Python tools, MCP servers and skills, including built-in coding tools for shell access, file editing and search plus task and planning tools; and a Model layer spanning LLM, embedding and text-to-speech providers such as OpenAI, Anthropic and Gemini. On top of that layer it addresses the deployment side through channels, pipelines, a terminal console and protocol bridges, so an agent can move from a local script to a service reachable from group chat platforms.

Key capabilities

• ReAct agent loop with structured output, realtime interruption and resume, and batched tool acting run either sequentially or concurrently.
• Toolkit for agentic tool management over Python tools, MCP servers and skills, shipping built-in coding tools (shell, file edit, search) and task/plan tools.
• Model abstraction covering LLM, embedding and TTS across major providers including OpenAI, Anthropic and Gemini.
• RealtimeAgent with support for DashScope, OpenAI, Gemini and xAI realtime APIs, and an experimental realtime voice agent.
• A2A protocol support through A2AAgent, which allows chatting with any remote A2A agent.
• Pipeline building block that runs multiple agents under a fixed logic behind one event stream.
• Channels that connect agents to IM platforms, with DingTalk, Feishu (Lark) and Discord integrations, plus GitHub MCP Registry and ClawHub as built-in hubs.

Who uses it and how

• Developers prototyping agents locally use the console building block to test and debug agents directly in the terminal.
• Teams building multi-agent systems compose several agents into a pipeline so a fixed orchestration logic emits a single event stream.
• Teams wiring agents into workplace chat deploy the agent service and attach channels such as DingTalk, Feishu or Discord.
• Builders of voice interfaces adopt RealtimeAgent against a supported realtime API for speech-to-speech interaction.
• Integ

uv pip install agentscope

# Pull the source code from GitHub
git clone -b main https://github.com/agentscope-ai/agentscope.git

# Install the package in editable mode
cd agentscope

uv pip install -e .