Ext 扩展机制总览
集成第三方模型、工具和服务
📖 概述
autogen-ext 提供了丰富的扩展，包括：
- 模型客户端（Model Clients）
- 工具（Tools）
- 代码执行器（Code Executors）
- MCP 集成
- 记忆系统（Memory）
1. 模型客户端
OpenAI
python
from autogen_ext.models.openai import OpenAIChatCompletionClient
client = OpenAIChatCompletionClient(
model="gpt-4.1",
api_key="sk-..."
)Azure OpenAI
python
from autogen_ext.models.azure import AzureOpenAIChatCompletionClient
client = AzureOpenAIChatCompletionClient(
azure_deployment="gpt-4",
azure_endpoint="https://...",
api_version="2024-01-01"
)Ollama
python
from autogen_ext.models.ollama import OllamaChatCompletionClient
client = OllamaChatCompletionClient(
model="llama3.1",
base_url="http://localhost:11434"
)Anthropic
python
from autogen_ext.models.anthropic import AnthropicChatCompletionClient
client = AnthropicChatCompletionClient(
model="claude-sonnet-4-20250514",
api_key="..."
)2. 工具 (Tools)
FunctionTool
python
from autogen_core.tools import FunctionTool
def add(a: int, b: int) -> int:
"""计算两数之和"""
return a + b
tool = FunctionTool(add)MCP 工具
python
from autogen_ext.tools.mcp import McpWorkbench, StdioServerParams
server_params = StdioServerParams(
command="npx",
args=["@playwright/mcp@latest"]
)
async with McpWorkbench(server_params) as workbench:
tools = await workbench.list_tools()LangChain 工具
python
from autogen_ext.tools.langchain import LangChainAdapter
from langchain_community.tools import DuckDuckGoSearchRun
tool = LangChainAdapter(DuckDuckGoSearchRun())3. 代码执行器
Docker 执行器
python
from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
executor = DockerCommandLineCodeExecutor(
work_dir="coding",
container_name="autogen-code-exec"
)Jupyter 执行器
python
from autogen_ext.code_executors.jupyter import JupyterCodeExecutor
executor = JupyterCodeExecutor()Azure 执行器
python
from autogen_ext.code_executors.azure import AzureContainerCodeExecutor
executor = AzureContainerCodeExecutor(
pool_management_endpoint="...",
credential=credential
)4. 记忆系统
ChromaDB 记忆
python
from autogen_ext.memory.chromadb import ChromaDBMemory
memory = ChromaDBMemory(
collection_name="my_memory"
)Redis 记忆
python
from autogen_ext.memory.redis import RedisMemory
memory = RedisMemory(
redis_url="redis://localhost:6379"
)5. 缓存
DiskCache
python
from autogen_ext.cache_store.diskcache import DiskCacheStore
cache = DiskCacheStore(cache_dir="./cache")Redis 缓存
python
from autogen_ext.cache_store.redis import RedisStore
cache = RedisStore(redis_url="redis://localhost:6379")6. 安装
完整安装
bash
pip install "autogen-ext[all]"按需安装
bash
# OpenAI
pip install "autogen-ext[openai]"
# MCP
pip install "autogen-ext[mcp]"
# 代码执行
pip install "autogen-ext[docker]"
# 记忆
pip install "autogen-ext[chromadb]"7. 创建自定义扩展
自定义模型客户端
python
from autogen_core.models import ChatCompletionClient, CreateResult
class MyModelClient(ChatCompletionClient):
async def create(self, messages, tools=None, **kwargs) -> CreateResult:
# 实现创建逻辑
return result自定义工具
python
from autogen_core.tools import BaseTool
class MyTool(BaseTool[InputModel, OutputModel]):
def __init__(self):
super().__init__(
InputModel,
OutputModel,
name="my_tool",
description="我的工具"
)
async def run(self, args: InputModel, cancellation_token) -> OutputModel:
# 实现工具逻辑
return result