max_turns

autogen/agentchat/chat.py

@@ -34,31 +34,37 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
 
     args:
         chat_queue (List[Dict]): a list of dictionaries containing the information of the chats.
-                Each dictionary should contain the following fields:
+                Each dictionary should contain the input arguments for `ConversableAgent.initiate_chat`.
+                More specifically, each dictionary could include the following fields:
+                recipient: the recipient agent.
+                - "sender": the sender agent.
                 - "recipient": the recipient agent.
-                - "context": any context information, e.g., the request message. The following fields are reserved:
-                    "message" needs to be provided if the `generate_init_message` method is not overridden.
-                          Otherwise, input() will be called to get the initial message.
-                    "summary_method": a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
-                        - Supported string are "last_msg" and "reflection_with_llm":
-                            when set "last_msg", it returns the last message of the dialog as the summary.
-                            when set "reflection_with_llm", it returns a summary extracted using an llm client.
-                            `llm_config` must be set in either the recipient or sender.
-                            "reflection_with_llm" requires the llm_config to be set in either the sender or the recipient.
-                        - A callable summary_method should take the recipient and sender agent in a chat as input and return a string of summary. E.g,
-                        ```python
-                        def my_summary_method(
-                            sender: ConversableAgent,
-                            recipient: ConversableAgent,
-                        ):
-                            return recipient.last_message(sender)["content"]
-                        ```
-                    "summary_prompt" can be used to specify the prompt used to extract a summary when summary_method is "reflection_with_llm".
-                        Default is None and the following default prompt will be used when "summary_method" is set to "reflection_with_llm":
-                        "Identify and extract the final solution to the originally asked question based on the conversation."
-                    "carryover" can be used to specify the carryover information to be passed to this chat.
-                        If provided, we will combine this carryover with the "message" content when generating the initial chat
-                        message in `generate_init_message`.
+                - clear_history (bool): whether to clear the chat history with the agent. Default is True.
+                - silent (bool or None): (Experimental) whether to print the messages for this conversation. Default is False.
+                - cache (Cache or None): the cache client to be used for this conversation. Default is None.
+                - max_turns (int or None): the maximum number of turns for the chat. If None, the chat will continue until a termination condition is met. Default is None.
+                - "message" needs to be provided if the `generate_init_message` method is not overridden.
+                        Otherwise, input() will be called to get the initial message.
+                - "summary_method": a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
+                    - Supported string are "last_msg" and "reflection_with_llm":
+                        when set "last_msg", it returns the last message of the dialog as the summary.
+                        when set "reflection_with_llm", it returns a summary extracted using an llm client.
+                        `llm_config` must be set in either the recipient or sender.
+                        "reflection_with_llm" requires the llm_config to be set in either the sender or the recipient.
+                    - A callable summary_method should take the recipient and sender agent in a chat as input and return a string of summary. E.g,
+                    ```python
+                    def my_summary_method(
+                        sender: ConversableAgent,
+                        recipient: ConversableAgent,
+                    ):
+                        return recipient.last_message(sender)["content"]
+                    ```
+                "summary_prompt" can be used to specify the prompt used to extract a summary when summary_method is "reflection_with_llm".
+                    Default is None and the following default prompt will be used when "summary_method" is set to "reflection_with_llm":
+                    "Identify and extract the final solution to the originally asked question based on the conversation."
+                "carryover" can be used to specify the carryover information to be passed to this chat.
+                    If provided, we will combine this carryover with the "message" content when generating the initial chat
+                    message in `generate_init_message`.
 
 
     returns:

autogen/agentchat/conversable_agent.py

@@ -763,6 +763,7 @@ def initiate_chat(
         clear_history: Optional[bool] = True,
         silent: Optional[bool] = False,
         cache: Optional[Cache] = None,
+        max_turns: Optional[int] = None,
         **context,
     ) -> ChatResult:
         """Initiate a chat with the recipient agent.
@@ -773,9 +774,10 @@ def initiate_chat(
 
         Args:
             recipient: the recipient agent.
-            clear_history (bool): whether to clear the chat history with the agent.
-            silent (bool or None): (Experimental) whether to print the messages for this conversation.
-            cache (Cache or None): the cache client to be used for this conversation.
+            clear_history (bool): whether to clear the chat history with the agent. Default is True.
+            silent (bool or None): (Experimental) whether to print the messages for this conversation. Default is False.
+            cache (Cache or None): the cache client to be used for this conversation. Default is None.
+            max_turns (int or None): the maximum number of turns for the chat. If None, the chat will continue until a termination condition is met. Default is None.
             **context: any context information. It has the following reserved fields:
                 "message": a str of message. Needs to be provided. Otherwise, input() will be called to get the initial message.
                 "summary_method": a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
@@ -813,7 +815,19 @@ def my_summary_method(
             agent.previous_cache = agent.client_cache
             agent.client_cache = cache
         self._prepare_chat(recipient, clear_history)
-        self.send(self.generate_init_message(**context), recipient, silent=silent)
+        if isinstance(max_turns, int):
+            msg2send = self.generate_init_message(**context)
+            for _ in range(max_turns):
+                if msg2send is None:
+                    break
+                self.send(msg2send, recipient, request_reply=False, silent=silent)
+                msg2send = recipient.generate_reply(messages=recipient.chat_messages[self], sender=self)
+                if msg2send is None:
+                    break
+                recipient.send(msg2send, self, request_reply=False, silent=silent)
+                msg2send = self.generate_reply(messages=self.chat_messages[recipient], sender=recipient)
+        else:
+            self.send(self.generate_init_message(**context), recipient, silent=silent)
         summary = self._summarize_chat(
             context.get("summary_method", ConversableAgent.DEFAULT_summary_method),
             recipient,
@@ -837,6 +851,7 @@ async def a_initiate_chat(
         clear_history: Optional[bool] = True,
         silent: Optional[bool] = False,
         cache: Optional[Cache] = None,
+        max_turns: Optional[int] = None,
         **context,
     ) -> ChatResult:
         """(async) Initiate a chat with the recipient agent.
@@ -857,7 +872,19 @@ async def a_initiate_chat(
         for agent in [self, recipient]:
             agent.previous_cache = agent.client_cache
             agent.client_cache = cache
-        await self.a_send(await self.a_generate_init_message(**context), recipient, silent=silent)
+        if isinstance(max_turns, int):
+            msg2send = await self.a_generate_init_message(**context)
+            for _ in range(max_turns):
+                if msg2send is None:
+                    break
+                await self.a_send(msg2send, recipient, request_reply=False, silent=silent)
+                msg2send = await recipient.a_generate_reply(messages=recipient.chat_messages[self], sender=self)
+                if msg2send is None:
+                    break
+                await recipient.a_send(msg2send, self, request_reply=False, silent=silent)
+                msg2send = await self.a_generate_reply(messages=self.chat_messages[recipient], sender=recipient)
+        else:
+            await self.a_send(await self.a_generate_init_message(**context), recipient, silent=silent)
         summary = self._summarize_chat(
             context.get("summary_method", ConversableAgent.DEFAULT_summary_method),
             recipient,
@@ -956,31 +983,7 @@ def initiate_chats(self, chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
 
         Args:
             chat_queue (List[Dict]): a list of dictionaries containing the information of the chats.
-                Each dictionary should contain the following fields:
-                - "recipient": the recipient agent.
-                - "context": any context information, e.g., the request message. The following fields are reserved:
-                    "message" needs to be provided if the `generate_init_message` method is not overridden.
-                          Otherwise, input() will be called to get the initial message.
-                    "summary_method": a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
-                        - Supported string are "last_msg" and "reflection_with_llm":
-                            when set "last_msg", it returns the last message of the dialog as the summary.
-                            when set "reflection_with_llm", it returns a summary extracted using an llm client.
-                            `llm_config` must be set in either the recipient or sender.
-                            "reflection_with_llm" requires the llm_config to be set in either the sender or the recipient.
-                        - A callable summary_method should take the recipient and sender agent in a chat as input and return a string of summary. E.g,
-                        ```python
-                        def my_summary_method(
-                            sender: ConversableAgent,
-                            recipient: ConversableAgent,
-                        ):
-                            return recipient.last_message(sender)["content"]
-                        ```
-                    "summary_prompt" can be used to specify the prompt used to extract a summary when summary_method is "reflection_with_llm".
-                        Default is None and the following default prompt will be used when "summary_method" is set to "reflection_with_llm":
-                        "Identify and extract the final solution to the originally asked question based on the conversation."
-                    "carryover" can be used to specify the carryover information to be passed to this chat.
-                        If provided, we will combine this carryover with the "message" content when generating the initial chat
-                        message in `generate_init_message`.
+                Each dictionary should contain the input arguments for `initiate_chat`.
 
         Returns: a list of ChatResult objects corresponding to the finished chats in the chat_queue.
         """

test/agentchat/test_async_get_human_input.py

@@ -45,5 +45,34 @@ async def test_async_get_human_input():
     print("Human input:", res.human_input)
 
 
+@pytest.mark.skipif(skip, reason="openai not installed OR requested to skip")
+@pytest.mark.asyncio
+async def test_async_max_turn():
+    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC)
+
+    # create an AssistantAgent instance named "assistant"
+    assistant = autogen.AssistantAgent(
+        name="assistant",
+        max_consecutive_auto_reply=10,
+        llm_config={
+            "seed": 41,
+            "config_list": config_list,
+        },
+    )
+
+    user_proxy = autogen.UserProxyAgent(name="user", human_input_mode="ALWAYS", code_execution_config=False)
+
+    user_proxy.a_get_human_input = AsyncMock(return_value="Not funny. Try again.")
+
+    res = await user_proxy.a_initiate_chat(
+        assistant, clear_history=True, max_turns=3, message="Hello, make a joke about AI."
+    )
+    print("Result summary:", res.summary)
+    print("Human input:", res.human_input)
+    print("chat history:", res.chat_history)
+    assert len(res.chat_history) == 6
+
+
 if __name__ == "__main__":
     asyncio.run(test_async_get_human_input())
+    asyncio.run(test_async_max_turn())

test/agentchat/test_chats.py

@@ -200,7 +200,8 @@ def my_summary_method(recipient, sender):
             {
                 "recipient": financial_assistant_2,
                 "message": financial_tasks[1],
-                "silent": True,
+                "silent": False,
+                "max_turns": 1,
                 "summary_method": "reflection_with_llm",
             },
             {
@@ -228,6 +229,7 @@ def my_summary_method(recipient, sender):
     print(all_res[0].summary)
     print(all_res[0].chat_history)
     print(all_res[1].summary)
+    assert len(all_res[1].chat_history) <= 2
     # print(blogpost.summary, insights_and_blogpost)
 
 
@@ -305,7 +307,8 @@ def my_summary_method(recipient, sender):
                 "sender": user_2,
                 "recipient": financial_assistant_2,
                 "message": financial_tasks[1],
-                "silent": True,
+                "silent": False,
+                "max_turns": 3,
                 "summary_method": "reflection_with_llm",
             },
             {
@@ -332,6 +335,7 @@ def my_summary_method(recipient, sender):
     print(chat_res[0].summary)
     print(chat_res[0].chat_history)
     print(chat_res[1].summary)
+    assert len(chat_res[1].chat_history) <= 6
     # print(blogpost.summary, insights_and_blogpost)
 
 
@@ -485,7 +489,7 @@ def currency_calculator(
 if __name__ == "__main__":
     test_chats()
     test_chats_general()
-    test_chats_exceptions()
-    test_chats_group()
-    test_chats_w_func()
+    # test_chats_exceptions()
+    # test_chats_group()
+    # test_chats_w_func()
     # test_chat_messages_for_summary()

test/agentchat/test_conversable_agent.py

@@ -5,6 +5,7 @@
 from typing import Any, Callable, Dict, Literal
 import unittest
 import inspect
+from unittest.mock import MagicMock
 
 import pytest
 from unittest.mock import patch
@@ -1028,10 +1029,36 @@ def stopwatch(num_seconds: Annotated[str, "Number of seconds in the stopwatch."]
     stopwatch_mock.assert_called_once_with(num_seconds="5")
 
 
+@pytest.mark.skipif(
+    skip or not sys.version.startswith("3.10"),
+    reason="do not run if openai is not installed or py!=3.10",
+)
+def test_max_turn():
+    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC)
+
+    # create an AssistantAgent instance named "assistant"
+    assistant = autogen.AssistantAgent(
+        name="assistant",
+        max_consecutive_auto_reply=10,
+        llm_config={"timeout": 600, "cache_seed": 41, "config_list": config_list},
+    )
+
+    user_proxy = autogen.UserProxyAgent(name="user", human_input_mode="ALWAYS", code_execution_config=False)
+
+    # Use MagicMock to create a mock get_human_input function
+    user_proxy.get_human_input = MagicMock(return_value="Not funny. Try again.")
+    res = user_proxy.initiate_chat(assistant, clear_history=True, max_turns=3, message="Hello, make a joke about AI.")
+    print("Result summary:", res.summary)
+    print("Human input:", res.human_input)
+    print("history", res.chat_history)
+    assert len(res.chat_history) <= 6
+
+
 if __name__ == "__main__":
     # test_trigger()
     # test_context()
     # test_max_consecutive_auto_reply()
-    test_generate_code_execution_reply()
+    # test_generate_code_execution_reply()
     # test_conversable_agent()
     # test_no_llm_config()
+    test_max_turn()