agentscope-skill

pip install agentscope
# or
uv pip install agentscope

from agentscope.message import Msg, TextBlock, ImageBlock, URLSource

msg = Msg(
    name="user",
    content=[TextBlock("Hello world"), ImageBlock(type="image", source=URLSource(type="url", url="..."))],
    role="user"
)

from agentscope.agent import ReActAgent, UserAgent
from agentscope.model import DashScopeChatModel
from agentscope.formatter import DashScopeChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.tool import Toolkit, execute_python_code, execute_shell_command
import os, asyncio

async def main():
    # Initialize toolkit with tools
    toolkit = Toolkit()
    toolkit.register_tool_function(execute_python_code)
    toolkit.register_tool_function(execute_shell_command)

    # Create ReActAgent with model, memory, formatter, and toolkit
    agent = ReActAgent(
        name="Friday",
        sys_prompt="You're a helpful assistant named Friday.",
        model=DashScopeChatModel(
            model_name="qwen-max",
            api_key=os.getenv("DASHSCOPE_API_KEY"),
            stream=True,
        ),
        memory=InMemoryMemory(),
        formatter=DashScopeChatFormatter(),
        toolkit=toolkit,
    )

    # Create user agent for terminal input
    user = UserAgent(name="user")

    # Conversation loop
    msg = None
    while True:
        msg = await agent(msg)  # Agent processes and replies
        msg = await user(msg)   # User inputs next message
        if msg.get_text_content() == "exit":
            break

asyncio.run(main())

alice, bob, carol, david = ReActAgent(...), ReActAgent(...), ReActAgent(...), ReActAgent(...)

msg_alice = await alice()
msg_bob = await bob(msg_alice)  # Bob receives Alice's message and generate a reply. Alice doesn't receive Bob's message unless explicitly passed back.
msg_carol = await carol(msg_alice)  # Similarly, the agent cannot receive messages from other agents unless explicitly passed.

# Broadcasting with MsgHub, a syntactic sugar for message broadcasting within a group of agents
from agentscope.pipeline import MsgHub

async with MsgHub(
    participants=[alice, bob, carol],
    announcement=Msg("Host", "Let's discuss", "user")
) as hub:
    await alice()  # Bob and Carol receive this
    await bob()    # Alice and Carol receive this

    # Manual broadcast
    await hub.broadcast(Msg("Host", "New topic", "user"))

    # Dynamic participant management
    hub.add(david)
    hub.delete(bob)

from agentscope.tool import ToolResponse, Toolkit

async def create_worker(task: str) -> ToolResponse:
    """Create a worker agent for the given task.

    Args:
        task (`str`): The given task, which should be specific and concise.
    """
    task_msg = Msg(name="master", content=task, role="user") # Use the input task or wrap it into a more complex prompt
    worker = ReActAgent(...)
    res = await worker(task_msg)
    return ToolResponse(content=res.content) # Return the worker's response as the tool response

toolkit = Toolkit()
toolkit.register_tool_function(create_worker)

# Clone into this skill directory so that you can refer to it across different sessions
cd /path/to/this/skill/directory
git clone -b main https://github.com/agentscope-ai/agentscope.git

# Or update if already cloned
cd /path/to/this/skill/directory/agentscope
git pull

agentscope/
├── src/agentscope/          # Main library source code
│   ├── agent/               # Agent implementations (ReActAgent, etc.)
│   ├── model/               # LLM API wrappers (OpenAI, Anthropic, DashScope, etc.)
│   ├── formatter/           # Message formatters for different models
│   ├── memory/              # Memory implementations
│   ├── tool/                # Tool management and built-in tools
│   ├── message/             # Msg class and content blocks
│   ├── pipeline/            # Multi-agent orchestration (MsgHub, etc.)
│   ├── session/             # Session/state management
│   ├── mcp/                 # MCP integration
│   ├── rag/                 # RAG functionality
│   ├── realtime/            # Realtime voice interaction
│   ├── tts/                 # Text-to-speech
│   ├── evaluate/            # Evaluation tools
│   └── ...                  # Other modules
│
├── examples/                # Working examples organized by category
│   ├── agent/               # Different agent types
│   │   └── ...
│   ├── workflows/           # Multi-agent workflows
│   │   └── ...
│   ├── functionality/       # Specific features
│   │   └── ...
│   ├── deployment/          # Deployment patterns
│   ├── integration/         # Third-party integrations
│   ├── evaluation/          # Evaluation examples
│   └── game/                # Game examples (e.g., werewolves)
│
├── docs/                    # Documentation
│   ├── tutorial/            # Tutorial markdown files
│   ├── changelog.md         # Version history
│   └── roadmap.md           # Development roadmap
│
└── tests/                   # Test files

User asks: "Build a FastAPI app with AgentScope"
→ Browse: List files in examples/deployment/
→ Check: Are there any web service examples?
→ Search: Look for "fastapi", "flask", "api", "server" in examples/
→ Read: Found examples and adapt to user's needs

cd /path/to/this/skill/directory/scripts/
bash view_pypi_latest_version.sh

cd /path/to/this/skill/directory/scripts/
# View top-level module
python view_module_signature.py --module agentscope
# View specific submodule
python view_module_signature.py --module agentscope.agent
# View specific class
python view_module_signature.py --module agentscope.agent.ReActAgent