agentscope-bricks 0.1.0

AgentScope-Bricks provides unified API interface for agent development

AgentScope-Bricks

Aliyun Modelstudio Website

What is AgentScope-Bricks?

Agentscope-Bricks is a componentized AI application development framework that provides enterprise-grade, modular components for building sophisticated AI-powered applications. Built around Alibaba Cloud Modelstudio services, it offers an unified API for creating scalable AI agents with production-ready capabilities.

Key Features

• 🧩 Modular Component Architecture: Single-responsibility components with clear input/output definitions
• 🔌 Open-Source Framework Integration: Native support for LangGraph, AutoGen, LlamaIndex, and AgentScope, more on the road
• 🔒 ModelStudio Integration: Seamlessly integrate core ModelStudio enterprise components: Search, RAG, AIGC, and Memory Management.

Table of Contents

• Getting Started
  • Installation
  • Quick Example
    • Custom component development
    • Build agent with Agentscope family
    • Apply to exists project
• Agent Development, Debugging & Deployment
  • Agent Development
    • Using Built-in Frameworks
    • Custom Agent Development
    • Agent Schemas Protocol
  • Agent Debugging
  • Agent Deployment
• Why Agentscope-Bricks?
• Contributing
• License

Getting Started

Installation

# Basic installation
pip install agentscope-bricks

# Development installation
pip install -e .

Prerequisites:

• Python 3.10+
• DASHSCOPE_API_KEY environment variable for Alibaba Cloud services

Quick Example

1. Custom component development

Build type-safe, reusable components with automatic schema generation:

import asyncio
from agentscope_bricks.base.component import Component
from pydantic import BaseModel, Field
from typing import Any

# Define input/output schemas
class SearchInput(BaseModel):
    query: str = Field(..., description="Search query")
    max_results: int = Field(default=10, description="Maximum results")
    context: dict = Field(default_factory=dict)

class SearchOutput(BaseModel):
    results: list[str] = Field(..., description="Search results")
    total_found: int = Field(..., description="Total results found")
    context: dict = Field(..., description="Updated context")

# Implement your component
class SearchComponent(Component[SearchInput, SearchOutput]):
    async def _arun(self, args: SearchInput, **kwargs: Any) -> SearchOutput:
        # Your search logic here
        results = [f"Result for '{args.query}' #{i+1}" for i in range(args.max_results)]

        return SearchOutput(
            results=results,
            total_found=len(results),
            context={**args.context, "last_query": args.query}
        )

# Use the component
async def main():
    component = SearchComponent(
        name="Enterprise Search",
        description="High-performance search component"
    )

    result = await component.arun(SearchInput(query="AI agents", max_results=5))
    print(f"Found {result.total_found} results:")
    for r in result.results:
        print(f"  - {r}")

    # Component automatically generates OpenAI-compatible function schema
    print("\nFunction Schema:")
    print(component.function_schema)

asyncio.run(main())

2. Build agent with Agentscope family

Build agent with Agentscope-Runtime and AgentScope

import asyncio
import os
from typing import Any
from agentscope_runtime.engine import Runner
from agentscope_runtime.engine.agents.agentscope_agent import AgentScopeAgent

from agentscope_runtime.engine.services.environment_manager import \
    EnvironmentManager
from agentscope_runtime.engine.services.context_manager import \
    ContextManager
from agentscope_runtime.engine.schemas.agent_schemas import (
    MessageType,
    RunStatus,
    AgentRequest,
)

from agentscope.agent import ReActAgent
from agentscope.model import DashScopeChatModel

from agentscope_bricks.adapters.agentscope_runtime.tool import (
    AgentScopeRuntimeToolAdapter,
)
from agentscope_bricks.components import Component

from pydantic import BaseModel, Field

# Initialize the instance

model = DashScopeChatModel(
    "qwen-max",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
)

SYSTEM_PROMPT = """You are a helpful AI assistant.

# Objective
Your goal is to complete given tasks by using registry tools
"""

print("✅系统提示词已配置")
search_tool = AgentScopeRuntimeToolAdapter(SearchComponent())

# Create the AgentScope agent
agent = AgentScopeAgent(
    name="Friday",
    model=model,
    agent_config={
        "sys_prompt": SYSTEM_PROMPT,
    },
    tools=[search_tool],
    agent_builder=ReActAgent,
)
runner_instance = Runner(
    agent=agent,
    context_manager=ContextManager(),
    environment_manager=EnvironmentManager(),
)

# interact with agent
async def interact(runner):
    # Create a request
    request = AgentRequest(
        input=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "What is weather in beijing?",
                    },
                ],
            },
        ],
    )

    # Stream interaction with the agent
    print("🤖 智能体正在处理您的请求...")
    async for message in runner.stream_query(
        request=request,
    ):
        # Check if this is a completed message
        if (
            message.object == "message"
            and MessageType.MESSAGE == message.type
            and RunStatus.Completed == message.status
        ):
            all_result = message.content[0].text

    print("📝 智能体输出:", all_result)


asyncio.run(interact(runner_instance))

3. More open-source adaptions

Apply component to an exists Langgraph project

# -*- coding: utf-8 -*-
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, START, MessagesState, StateGraph
from pydantic import BaseModel, Field
from typing import Literal, Any
import os
from agentscope_bricks.adapters.langgraph.tool import LanggraphNodeAdapter
import asyncio

tool_node = LanggraphNodeAdapter(
    [
        SearchComponent(),
    ],
)

api_key = os.getenv("DASHSCOPE_API_KEY")

model = ChatOpenAI(
    model="qwen-turbo",
    openai_api_key=api_key,
    openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
).bind_tools(tool_node.tool_schemas)


# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
    messages = state["messages"]
    last_message = messages[-1]
    # If the LLM makes a tool call, then we route to the "tools" node
    if last_message.tool_calls:
        return "tools"
    # Otherwise, we stop (reply to the user)
    return END


# Define the function that calls the model
def call_model(state: MessagesState):
    messages = state["messages"]
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}


# Define a new graph
workflow = StateGraph(MessagesState)

# Define the two nodes we will cycle between
workflow.add_node("agents", call_model)
workflow.add_node("tools", tool_node)

# Set the entrypoint as `agents`
# This means that this node is the first one called
workflow.add_edge(START, "agents")

# We now add a conditional edge
workflow.add_conditional_edges(
    # First, we define the start node. We use `agents`.
    # This means these are the edges taken after the `agents` node is called.
    "agents",
    # Next, we pass in the function that will determine which
    # node is called next.
    should_continue,
)

# We now add a normal edge from `tools` to `agents`.
# This means that after `tools` is called, `agents` node is called next.
workflow.add_edge("tools", "agents")

# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()

# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable.
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)

# Use the Runnable
final_state = app.invoke(
    {"messages": [HumanMessage(content="what is the weather in sf")]},
    config={"configurable": {"thread_id": 42}},
)
print(final_state["messages"][-1].content)


async def arun():
    final_state_async = await app.ainvoke(
        {"messages": [HumanMessage(content="what is the weather in sf")]},
        config={"configurable": {"thread_id": 42}},
    )
    print(final_state_async["messages"][-1].content)


asyncio.run(arun())

Why Agentscope-Bricks?

🎯 Production-Ready from Day One

• Enterprise-grade components used in production by Alibaba Cloud
• Built-in observability, tracing, and error handling
• Comprehensive testing and validation

🔧 Framework Adaption

# Works with your favorite frameworks
from agentscope_bricks.adapters.agentscope.tool import agentscope_tool_adapter
from agentscope_bricks.adapters.autogen.tool import AutogenToolAdapter

# Convert to LangGraph node
node = agentscope_tool_adapter(your_component)

# Convert to AutoGen tool
tool = AutogenToolAdapter(your_component)

⚡ High Performance

• Async-first architecture with streaming support
• Efficient memory management and caching
• Optimized for high-throughput scenarios

🧩 Truly Modular

• Single-responsibility components with clear input output schemas
• Swappable implementations

Contributing

We welcome contributions! Please see our development guidelines:

Development Setup

# Clone the repository
git clone https://github.com/your-org/agentscope-bricks.git
cd agentscope_bricks

# Install development dependencies
pip install . -U

Testing

# Run all tests
pytest

Code Quality

pre-commit install
pre-commit run --all-files

Guidelines

• Component Development: Follow the single-responsibility principle
• Type Safety: Use Pydantic models for all inputs/outputs
• Testing: Write comprehensive unit and integration tests
• Documentation: Update relevant docs and examples
• Performance: Maintain async-first patterns

Submitting Changes

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Ensure all tests pass
5. Submit a pull request

License

This framework is licensed under the Apache License (Version 2.0). For models and datasets, please refer to the original resource page and follow the corresponding License.

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