AgentScope Runtime Skill

AgentScope is a production-ready multi-agent framework with ReAct agents, memory, human-in-the-loop steering, MCP/A2A integrations, and voice support, while AgentScope Runtime adds hardened sandboxes, Agent-as-a-Service APIs, and FastAPI-native deployment adapters.¹ ² Use this skill when you want BioKernel missions to tap into AgentScope’s ecosystem or when you must expose an agent over HTTP with observability and sandbox isolation baked in.

When to Use

• You need asynchronous sandboxes (GUI, browser, filesystem, mobile) with isolation guarantees before executing untrusted tool calls.²
• You want to host an AgentScope ReAct or planning workflow behind a FastAPI endpoint and call it from other agents.
• You must integrate with MCP/A2A compatible tools or run K8s/Function Compute deployments without rewriting orchestration.

Setup

1. Install both framework + runtime (Python 3.10+):

   uv pip install "agentscope>=0.10" "agentscope-runtime>=1.1"
   # or pip install agentscope agentscope-runtime
   

2. Export provider keys (DashScope, OpenAI, Gemini, etc.) plus sandbox registry settings if you want non-default Docker images:

   export DASHSCOPE_API_KEY=sk-...
   export RUNTIME_SANDBOX_REGISTRY="agentscope-registry.ap-southeast-1.cr.aliyuncs.com"
   

Workflow (Agent-as-a-Service)

1. Create

   agent_app.py

   based on the runtime quickstart:

   import os
   from contextlib import asynccontextmanager
   from agentscope.agent import ReActAgent
   from agentscope.model import DashScopeChatModel
   from agentscope.tool import Toolkit, execute_python_code
   from agentscope_runtime.engine import AgentApp
   from agentscope_runtime.sandbox import BaseSandboxAsync
   
   @asynccontextmanager
   async def lifespan(app):
       async with BaseSandboxAsync() as box:
           app.state.sandbox = box
           yield
   
   agent_app = AgentApp(app_name="Friday", lifespan=lifespan)
   
   @agent_app.query(framework="agentscope")
   async def query(messages, **kwargs):
       toolkit = Toolkit()
       toolkit.register_tool_function(execute_python_code)
       agent = ReActAgent(
           name="Friday",
           sys_prompt="Reason carefully about biomedical code changes.",
           model=DashScopeChatModel("qwen-max", api_key=os.environ["DASHSCOPE_API_KEY"], stream=True),
           toolkit=toolkit,
       )
       async for msg, last in agent.stream_chat(messages):
           yield msg, last
   
   if __name__ == "__main__":
       agent_app.run(port=8090)
   

2. Launch the service:

   python agent_app.py
   # or use the helper runner (handles env injection + cwd)
   python Skills/Agentic_AI/AgentScope_Runtime/agentscope_runner.py \
     agent_app.py --workdir Skills/Agentic_AI/AgentScope_Runtime/examples \
     --env DASHSCOPE_API_KEY=sk-...
   

   Runtime exposes

   POST /process

   with SSE streaming just like the README example.
3. From BioKernel, call the endpoint via

   platform/adapters/runtime_adapter.py

   and treat it like any other remote agent. Attach mission metadata so Reviewer/SafetyOfficer agents can audit the AgentScope trace.

Sandbox-First Tooling

• Switch between synchronous/asynchronous sandboxes depending on mission latency requirements.
• Use

  BrowserSandboxAsync

  for GUI Operator-like actions,

  FilesystemSandboxAsync

  for editing patient files, and

  MobileSandboxAsync

  for validating digital therapeutics.
• Configure Docker/tag fields with

  RUNTIME_SANDBOX_IMAGE_NAMESPACE

  /

  RUNTIME_SANDBOX_IMAGE_TAG

  to pull gVisor, BoxLite, or custom hardened images before handing control to the runtime.

Integration Notes

• Keep mission templates under

  Skills/Agentic_AI/AgentScope_Runtime/examples/

  so other contributors can spin up the same AgentApp quickly. Ship ready-made

  agent_app.py

  samples plus

  .env.example

  for provider keys.
• Use AgentScope’s

  MsgHub

  if you want to route sub-agents locally inside the runtime and only send summarized responses back to the swarm.
• Stream the SSE trace plus sandbox logs into

  platform/compliance/agent_logs/

  for after-action audits.

References

1. GitHub – agentscope-ai/agentscope (

   README

   details ReAct agents, MCP/A2A, memory, realtime voice, roadmap). https://github.com/agentscope-ai/agentscope
2. GitHub – agentscope-ai/agentscope-runtime (

   README

   covers AgentApp, asynchronous sandboxes, deployment, and async tool execution). https://github.com/agentscope-ai/agentscope-runtime