add tool adapters

cookbook/en/tools/tools.md

@@ -74,7 +74,7 @@ Use this pattern for every custom tool: define Pydantic models, extend `Tool`, i
 
 ## AgentScope Integration Example
 
-We use `tool_adapter` to add tools to AgentScope's `Toolkit`:
+We use `agentscope_tool_adapter` to add tools to AgentScope's `Toolkit`:
 
 ```python
 import asyncio
@@ -87,42 +87,18 @@ from agentscope.tool import Toolkit
 from agentscope.message import Msg
 
 from agentscope_runtime.tools.searches import (
-    ModelstudioSearch,
+    ModelstudioSearchLite,
     SearchInput,
     SearchOptions,
 )
-from agentscope_runtime.adapters.agentscope.tool import tool_adapter
-
-search_tool = ModelstudioSearch()
-
-
-@tool_adapter(description=search_tool.description)
-def modelstudio_search_tool(
-    messages: list[dict],
-    search_options: dict | None = None,
-    search_timeout: int | None = None,
-    _type: str | None = None,
-):
-    payload_kwargs = {
-        "messages": messages,
-        "search_options": SearchOptions(**(search_options or {})),
-    }
-    if search_timeout is not None:
-        payload_kwargs["search_timeout"] = search_timeout
-    if _type is not None:
-        payload_kwargs["type"] = _type
-
-    payload = SearchInput(**payload_kwargs)
-    result = search_tool.run(
-        payload,
-        user_id=os.environ["MODELSTUDIO_USER_ID"],
-    )
-    return ModelstudioSearch.return_value_as_string(result)
+from agentscope_runtime.adapters.agentscope.tool import agentscope_tool_adapter
 
+search_tool = ModelstudioSearchLite()
+search_tool = agentscope_tool_adapter(search_tool)
 
-toolkit = Toolkit()
 
-toolkit.register_tool_function(modelstudio_search_tool)
+toolkit = Toolkit()
+toolkit.tools[search_tool.name] = search_tool
 
 agent = ReActAgent(
     name="Friday",
@@ -150,7 +126,10 @@ if __name__ == "__main__":
 
 ## LangGraph Integration Example
 
-To reproduce the “Apply to existing LangGraph project” flow, wrap the tool as a LangChain `StructuredTool`, bind it to a model, and wire it into a LangGraph workflow. The tool schema comes directly from the tool’s input model, so tool calls remain type-safe.
+To reproduce the “Apply to existing LangGraph project” flow, wrap the tool as a Langgraph Node
+by `LanggraphNodeAdapter`, and bind it to a model, and wire it into a LangGraph workflow.
+
+The tool schema comes directly from the tool’s input model, so tool calls remain type-safe.
 
 ```python
 import os
@@ -161,47 +140,28 @@ from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, END, MessagesState, StateGraph
 from langgraph.prebuilt import ToolNode
 
-from agentscope_runtime.common.tools.searches import (
-    ModelstudioSearch,
+from agentscope_runtime.tools.searches import (
+    ModelstudioSearchLite,
     SearchInput,
     SearchOptions,
 )
 
-search_tool = ModelstudioSearch()
-
-
-def search_tool_func(
-    messages: list[dict],
-    search_options: dict | None = None,
-    search_timeout: int | None = None,
-    type: str | None = None,
-):
-    kwargs = {
-        "messages": messages,
-        "search_options": SearchOptions(**(search_options or {})),
-    }
-    if search_timeout is not None:
-        kwargs["search_timeout"] = search_timeout
-    if type is not None:
-        kwargs["type"] = type
-    result = search_tool.run(
-        SearchInput(**kwargs),
-        user_id=os.environ["MODELSTUDIO_USER_ID"],
-    )
-    return ModelstudioSearch.return_value_as_string(result)
+from agentscope_runtime.adapters.langgraph.tool import LanggraphNodeAdapter
+search_tool = ModelstudioSearchLite()
 
-
-search_tool = StructuredTool.from_function(
-    func=search_tool_func,
-    name=search_tool.name,
-    description=search_tool.description,
+tool_node = LanggraphNodeAdapter(
+    [
+        ModelstudioSearchLite(),
+    ],
 )
 
+api_key = os.getenv("DASHSCOPE_API_KEY")
+
 llm = ChatOpenAI(
     model="qwen-turbo",
     openai_api_key=os.environ["DASHSCOPE_API_KEY"],
     openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
-).bind_tools([search_tool])
+).bind_tools(tool_node.tool_schemas)
 
 
 def should_continue(state: MessagesState):
@@ -229,6 +189,45 @@ final_state = app.invoke(
 print(final_state["messages"][-1].content)
 ```
 
+## AutoGen Integration Example
+
+We use `AutogenToolAdapter` to convert our tool to AutogenTool:
+
+```python
+import asyncio
+from agentscope_runtime.tools.searches import ModelstudioSearchLite
+from agentscope_runtime.adapters.autogen.tool import AutogenToolAdapter
+from autogen_ext.models.openai import OpenAIChatCompletionClient
+from autogen_agentchat.agents import AssistantAgent
+from autogen_agentchat.messages import TextMessage
+from autogen_core import CancellationToken
+
+async def main():
+    # Create the search tool
+    search_tool = ModelstudioSearchLite()
+
+    # Create the autogen tool adapter
+    search_tool = AutogenToolAdapter(search_tool)
+
+    # Create an agents with the search tool
+    model = OpenAIChatCompletionClient(model="gpt-4")
+    agents = AssistantAgent(
+        "assistant",
+        tools=[search_tool],
+        model_client=model,
+    )
+
+    # Use the agents
+    response = await agents.on_messages(
+        [TextMessage(content="What's the weather in Beijing?",
+        source="user")],
+        CancellationToken(),
+    )
+    print(response.chat_message)
+
+asyncio.run(main())
+```
+
 ## Using Tools inside Agents
 1. **Configure credentials**: declare environment variables (DashScope keys, Alipay secrets, etc.) before running the agent process so tools can authenticate.
 2. **Instantiate once**: create tool objects during agent initialization; reuse them instead of re-instantiating per call to keep connections warm.

cookbook/zh/tools/tools.md

@@ -74,7 +74,7 @@ asyncio.run(main())
 
 ## AgentScope集成示例
 
-我们使用`tool_adapter`将工具添加到AgentScope的`Toolkit`中：
+我们使用`agentscope_tool_adapter`将工具添加到AgentScope的`Toolkit`中：
 
 ```python
 import asyncio
@@ -87,42 +87,18 @@ from agentscope.tool import Toolkit
 from agentscope.message import Msg
 
 from agentscope_runtime.tools.searches import (
-    ModelstudioSearch,
+    ModelstudioSearchLite,
     SearchInput,
     SearchOptions,
 )
-from agentscope_runtime.adapters.agentscope.tool import tool_adapter
-
-search_tool = ModelstudioSearch()
-
-
-@tool_adapter(description=search_tool.description)
-def modelstudio_search_tool(
-    messages: list[dict],
-    search_options: dict | None = None,
-    search_timeout: int | None = None,
-    _type: str | None = None,
-):
-    payload_kwargs = {
-        "messages": messages,
-        "search_options": SearchOptions(**(search_options or {})),
-    }
-    if search_timeout is not None:
-        payload_kwargs["search_timeout"] = search_timeout
-    if _type is not None:
-        payload_kwargs["type"] = _type
-
-    payload = SearchInput(**payload_kwargs)
-    result = search_tool.run(
-        payload,
-        user_id=os.environ["MODELSTUDIO_USER_ID"],
-    )
-    return ModelstudioSearch.return_value_as_string(result)
+from agentscope_runtime.adapters.agentscope.tool import agentscope_tool_adapter
 
+search_tool = ModelstudioSearchLite()
+search_tool = agentscope_tool_adapter(search_tool)
 
-toolkit = Toolkit()
 
-toolkit.register_tool_function(modelstudio_search_tool)
+toolkit = Toolkit()
+toolkit.tools[search_tool.name] = search_tool
 
 agent = ReActAgent(
     name="Friday",
@@ -150,7 +126,8 @@ if __name__ == "__main__":
 
 ## LangGraph 集成示例
 
-如果要在 LangGraph 项目中沿用上一示例，只需把 Tool 包成 LangChain `StructuredTool`，绑定模型并接入 LangGraph 工作流。工具 Schema 直接来自 Tool 的输入模型，保证调用依旧类型安全。
+如果要在 LangGraph 项目中沿用上一示例，只需用`LanggraphNodeAdapter`把 Tool 包成 Langgraph Node，
+绑定模型并接入 LangGraph 工作流。工具 Schema 直接来自 Tool 的输入模型，保证调用依旧类型安全。
 
 ```python
 import os
@@ -161,47 +138,28 @@ from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, END, MessagesState, StateGraph
 from langgraph.prebuilt import ToolNode
 
-from agentscope_runtime.common.tools.searches import (
-    ModelstudioSearch,
+from agentscope_runtime.tools.searches import (
+    ModelstudioSearchLite,
     SearchInput,
     SearchOptions,
 )
 
-search_tool = ModelstudioSearch()
-
-
-def search_tool_func(
-    messages: list[dict],
-    search_options: dict | None = None,
-    search_timeout: int | None = None,
-    type: str | None = None,
-):
-    kwargs = {
-        "messages": messages,
-        "search_options": SearchOptions(**(search_options or {})),
-    }
-    if search_timeout is not None:
-        kwargs["search_timeout"] = search_timeout
-    if type is not None:
-        kwargs["type"] = type
-    result = search_tool.run(
-        SearchInput(**kwargs),
-        user_id=os.environ["MODELSTUDIO_USER_ID"],
-    )
-    return ModelstudioSearch.return_value_as_string(result)
-
+from agentscope_runtime.adapters.langgraph.tool import LanggraphNodeAdapter
+search_tool = ModelstudioSearchLite()
 
-search_tool = StructuredTool.from_function(
-    func=search_tool_func,
-    name=search_tool.name,
-    description=search_tool.description,
+tool_node = LanggraphNodeAdapter(
+    [
+        ModelstudioSearchLite(),
+    ],
 )
 
+api_key = os.getenv("DASHSCOPE_API_KEY")
+
 llm = ChatOpenAI(
     model="qwen-turbo",
     openai_api_key=os.environ["DASHSCOPE_API_KEY"],
     openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
-).bind_tools([search_tool])
+).bind_tools(tool_node.tool_schemas)
 
 
 def should_continue(state: MessagesState):
@@ -229,6 +187,46 @@ final_state = app.invoke(
 print(final_state["messages"][-1].content)
 ```
 
+## AutoGen 集成示例
+
+利用 `AutogenToolAdapter` 把 Tool 转换成AutogenTool
+
+```python
+import asyncio
+from agentscope_runtime.tools.searches import ModelstudioSearchLite
+from agentscope_runtime.adapters.autogen.tool import AutogenToolAdapter
+from autogen_ext.models.openai import OpenAIChatCompletionClient
+from autogen_agentchat.agents import AssistantAgent
+from autogen_agentchat.messages import TextMessage
+from autogen_core import CancellationToken
+
+async def main():
+    # Create the search tool
+    search_tool = ModelstudioSearchLite()
+
+    # Create the autogen tool adapter
+    search_tool = AutogenToolAdapter(search_tool)
+
+    # Create an agents with the search tool
+    model = OpenAIChatCompletionClient(model="gpt-4")
+    agents = AssistantAgent(
+        "assistant",
+        tools=[search_tool],
+        model_client=model,
+    )
+
+    # Use the agents
+    response = await agents.on_messages(
+        [TextMessage(content="What's the weather in Beijing?",
+        source="user")],
+        CancellationToken(),
+    )
+    print(response.chat_message)
+
+asyncio.run(main())
+```
+
+
 ## 在 Agent 中使用 Tool 的操作建议
 1. **配置凭证**：在启动 Agent 前准备好 DashScope Key、支付宝密钥等环境变量，以便 Tool 读取并完成鉴权。
 2. **实例化一次**：在 Agent 初始化阶段创建 Tool 对象并复用，避免每次调用重新实例化导致连接冷启动。

pyproject.toml

@@ -21,6 +21,8 @@ dependencies = [
     "shortuuid>=1.0.13",
     "celery[redis]>=5.3.1",
     "a2a-sdk>=0.3.0",
+    "jsonref",
+    "asgiref",
 ]
 
 [tool.setuptools]

src/agentscope_runtime/adapters/agentscope/tool/__init__.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
-from .tool import tool_adapter
+from .tool import agentscope_tool_adapter, agentscope_toolkit_adapter
 from .sandbox_tool import sandbox_tool_adapter
 
 __all__ = [
-    "tool_adapter",
+    "agentscope_tool_adapter",
+    "agentscope_toolkit_adapter",
     "sandbox_tool_adapter",
 ]