Add notebook executor as service function and add codeact agent.

README.md

@@ -167,6 +167,8 @@ the following libraries.
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Conversation with Customized Tools](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_customized_services/)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Mixture of Agents Algorithm](https://github.com/modelscope/agentscope/blob/main/examples/conversation_mixture_of_agents/)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Conversation in Stream Mode](https://github.com/modelscope/agentscope/blob/main/examples/conversation_in_stream_mode/)
+  - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Conversation with CodeAct Agent](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_codeact_agent/)
+
 
 - Game
   - [Gomoku](https://github.com/modelscope/agentscope/blob/main/examples/game_gomoku)

README_ZH.md

@@ -153,10 +153,14 @@ AgentScope支持使用以下库快速部署本地模型服务。
   - [通过对话查询SQL信息](./examples/conversation_nl2sql/)
   - [与RAG智能体对话](./examples/conversation_with_RAG_agents)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[与gpt-4o模型对话](./examples/conversation_with_gpt-4o)
+  - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[与自定义服务对话](./examples/conversation_with_customized_services/)
+
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[与SoftWare Engineering智能体对话](./examples/conversation_with_swe-agent/)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[自定义工具函数](./examples/conversation_with_customized_services/)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Mixture of Agents算法](https://github.com/modelscope/agentscope/blob/main/examples/conversation_mixture_of_agents/)
   - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[流式对话](https://github.com/modelscope/agentscope/blob/main/examples/conversation_in_stream_mode/)
+  - <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[与CodeAct智能体对话](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_codeact_agent/)
+
 
 - 游戏
   - [五子棋](./examples/game_gomoku)

docs/sphinx_doc/en/source/tutorial/204-service.md

@@ -15,6 +15,7 @@ The following table outlines the various Service functions by type. These functi
 | Service Scene               | Service Function Name      | Description                                                                                                    |
 |-----------------------------|----------------------------|----------------------------------------------------------------------------------------------------------------|
 | Code                        | `execute_python_code`      | Execute a piece of Python code, optionally inside a Docker container.                                          |
+|                             | `NoteBookExecutor.run_code_on_notebook`                  | Compute Execute a segment of Python code in the IPython environment of the NoteBookExecutor, adhering to the IPython interactive computing style.                                       |
 | Retrieval                   | `retrieve_from_list`       | Retrieve a specific item from a list based on given criteria.                                                  |
 |                             | `cos_sim`                  | Compute the cosine similarity between two different embeddings.                                                |
 | SQL Query                   | `query_mysql`              | Execute SQL queries on a MySQL database and return results.                                                    |

docs/sphinx_doc/zh_CN/source/tutorial/204-service.md

@@ -12,6 +12,7 @@
 | Service场景  | Service函数名称           | 描述                                      |
 |------------|-----------------------|-----------------------------------------|
 | 代码         | `execute_python_code` | 执行一段 Python 代码，可选择在 Docker <br/>容器内部执行。 |
+|            | `NoteBookExecutor.run_code_on_notebook`             | 在 NoteBookExecutor 的 IPython 环境中执行一段 Python 代码，遵循 IPython 交互式计算风格。                |
 | 检索         | `retrieve_from_list`  | 根据给定的标准从列表中检索特定项目。                      |
 |            | `cos_sim`             | 计算2个embedding的余弦相似度。                    |
 | SQL查询      | `query_mysql`         | 在 MySQL 数据库上执行 SQL 查询并返回结果。             |

examples/conversation_with_codeact_agent/codeact_agent.py

@@ -0,0 +1,166 @@
+# -*- coding: utf-8 -*-
+# pylint: disable=C0301
+"""An agent class that implements the CodeAct agent.
+This agent can execute code interactively as actions.
+More details can be found at the paper of codeact agent
+https://arxiv.org/abs/2402.01030
+and the original repo of codeact https://github.com/xingyaoww/code-act
+"""
+from agentscope.agents import AgentBase
+from agentscope.message import Msg
+from agentscope.service import (
+    ServiceResponse,
+    ServiceExecStatus,
+    NoteBookExecutor,
+)
+from agentscope.parsers import RegexTaggedContentParser
+
+SYSTEM_MESSAGE = """
+You are a helpful assistant that gives helpful, detailed, and polite answers to the user's questions.
+You should interact with the interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code written by assistant should be enclosed using <execute> tag, for example: <execute> print('Hello World!') </execute>.
+You should attempt fewer things at a time instead of putting too much code in one <execute> block. You can install packages through PIP by <execute> !pip install [package needed] </execute> and should always import packages and define variables before starting to use them.
+You should stop <execute> and provide an answer when they have already obtained the answer from the execution result. Whenever possible, execute the code for the user using <execute> instead of providing it.
+Your response should be concise, but do express their thoughts. Always write the code in <execute> block to execute them.
+You should not ask for the user's input unless necessary. Solve the task on your own and leave no unanswered questions behind.
+You should do every thing by your self.
+"""  # noqa
+
+EXAMPLE_MESSAGE = """
+Additionally, you are provided with the following code available:
+{example_code}
+The above code is already available in your interactive Python (Jupyter Notebook) environment, allowing you to directly use these variables and functions without needing to redeclare them.
+"""  # noqa
+
+
+class CodeActAgent(AgentBase):
+    """
+    The implementation of CodeAct-agent.
+    The agent can execute code interactively as actions.
+    More details can be found at the paper of codeact agent
+    https://arxiv.org/abs/2402.01030
+    and the original repo of codeact https://github.com/xingyaoww/code-act
+    """
+
+    def __init__(
+        self,
+        name: str,
+        model_config_name: str,
+        example_code: str = "",
+    ) -> None:
+        """
+        Initialize the CodeActAgent.
+        Args:
+            name(`str`):
+                The name of the agent.
+            model_config_name(`str`):
+                The name of the model configuration.
+            example_code(Optional`str`):
+                The example code to be executed bewfore the interaction.
+                You can import reference libs, define variables
+                and functions to be called. For example:
+
+                    ```python
+                    from agentscope.service import bing_search
+                    import os
+
+                    api_key = "{YOUR_BING_API_KEY}"
+
+                    def search(question: str):
+                        return bing_search(question, api_key=api_key, num_results=3).content
+                    ```
+
+        """  # noqa
+        super().__init__(
+            name=name,
+            model_config_name=model_config_name,
+        )
+        self.n_max_executions = 5
+        self.example_code = example_code
+        self.code_executor = NoteBookExecutor()
+
+        sys_msg = Msg(name="system", role="system", content=SYSTEM_MESSAGE)
+        example_msg = Msg(
+            name="user",
+            role="user",
+            content=EXAMPLE_MESSAGE.format(example_code=self.example_code),
+        )
+
+        self.memory.add(sys_msg)
+
+        if self.example_code != "":
+            code_execution_result = self.code_executor.run_code_on_notebook(
+                self.example_code,
+            )
+            code_exec_msg = self.handle_code_result(
+                code_execution_result,
+                "Example Code excuted: ",
+            )
+            self.memory.add(example_msg)
+            self.memory.add(code_exec_msg)
+            self.speak(code_exec_msg)
+
+        self.parser = RegexTaggedContentParser(try_parse_json=False)
+
+    def handle_code_result(
+        self,
+        code_execution_result: ServiceResponse,
+        content_pre_sring: str = "",
+    ) -> Msg:
+        """return the message from code result"""
+        code_exec_content = content_pre_sring
+        if code_execution_result.status == ServiceExecStatus.SUCCESS:
+            code_exec_content += "Excution Successful:\n"
+        else:
+            code_exec_content += "Excution Failed:\n"
+        code_exec_content += "Execution Output:\n" + str(
+            code_execution_result.content,
+        )
+        return Msg(name="user", role="user", content=code_exec_content)
+
+    def reply(self, x: Msg = None) -> Msg:
+        """The reply function that implements the codeact agent."""
+
+        self.memory.add(x)
+
+        excution_count = 0
+        while (
+            self.memory.get_memory(1)[-1].role == "user"
+            and excution_count < self.n_max_executions
+        ):
+            prompt = self.model.format(self.memory.get_memory())
+            model_res = self.model(prompt)
+            msg_res = Msg(
+                name=self.name,
+                content=model_res.text,
+                role="assistant",
+            )
+            self.memory.add(msg_res)
+            self.speak(msg_res)
+            res = self.parser.parse(model_res)
+            code = res.parsed.get("execute")
+            if code is not None:
+                code = code.strip()
+                code_execution_result = (
+                    self.code_executor.run_code_on_notebook(code)
+                )
+                excution_count += 1
+                code_exec_msg = self.handle_code_result(code_execution_result)
+                self.memory.add(code_exec_msg)
+                self.speak(code_exec_msg)
+
+        if excution_count == self.n_max_executions:
+            assert self.memory.get_memory(1)[-1].role == "user"
+            code_max_exec_msg = Msg(
+                name="assitant",
+                role="assistant",
+                content=(
+                    "I have reached the maximum number "
+                    f"of executions ({self.n_max_executions=}). "
+                    "Can you assist me or ask me another question?"
+                ),
+            )
+            self.memory.add(code_max_exec_msg)
+            self.speak(code_max_exec_msg)
+            return code_max_exec_msg
+
+        return msg_res