[RFC] Implementing global run

autogen/__init__.py

@@ -19,6 +19,7 @@
     ConversableAgent,
     GroupChat,
     GroupChatManager,
+    LLMMessageType,
     OnCondition,
     OnContextCondition,
     SwarmAgent,
@@ -53,6 +54,7 @@
     filter_config,
     get_config_list,
 )
+from .run import a_run, run
 from .version import __version__
 
 # Set the root logger.
@@ -79,6 +81,7 @@
     "GroupChatManager",
     "InvalidCarryOverTypeError",
     "LLMConfig",
+    "LLMMessageType",
     "ModelClient",
     "NoEligibleSpeakerError",
     "OnCondition",
@@ -92,6 +95,7 @@
     "UserProxyAgent",
     "__version__",
     "a_initiate_swarm_chat",
+    "a_run",
     "config_list_from_dotenv",
     "config_list_from_json",
     "config_list_from_models",
@@ -104,4 +108,5 @@
     "initiate_swarm_chat",
     "register_function",
     "register_hand_off",
+    "run",
 ]

autogen/agentchat/__init__.py

@@ -4,7 +4,7 @@
 #
 # Portions derived from  https://github.com/microsoft/autogen are under the MIT License.
 # SPDX-License-Identifier: MIT
-from .agent import Agent, LLMAgent
+from .agent import Agent, LLMAgent, LLMMessageType
 from .assistant_agent import AssistantAgent
 from .chat import ChatResult, a_initiate_chats, initiate_chats
 
@@ -43,6 +43,7 @@
     "GroupChat",
     "GroupChatManager",
     "LLMAgent",
+    "LLMMessageType",
     "OnCondition",
     "OnContextCondition",
     "SwarmAgent",

autogen/agentchat/agent.py

@@ -4,11 +4,23 @@
 #
 # Portions derived from  https://github.com/microsoft/autogen are under the MIT License.
 # SPDX-License-Identifier: MIT
-from typing import TYPE_CHECKING, Any, Optional, Protocol, Union, runtime_checkable
+from typing import TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional, Protocol, Union, runtime_checkable
 
+from ..cache.abstract_cache_base import AbstractCache
 from ..doc_utils import export_module
 
-__all__ = ["Agent", "LLMAgent"]
+if TYPE_CHECKING:
+    # mypy will fail if Conversible agent does not implement Agent protocol
+    from ..tools.tool import Tool
+    from .chat import ChatResult
+    from .conversable_agent import ConversableAgent
+
+
+__all__ = ["DEFAULT_SUMMARY_METHOD", "Agent", "LLMAgent", "LLMMessageType"]
+
+DEFAULT_SUMMARY_METHOD = "last_msg"
+
+LLMMessageType = dict[str, Any]
 
 
 @runtime_checkable
@@ -32,9 +44,14 @@ def description(self) -> str:
         """
         ...
 
+    @property
+    def llm_config(self) -> Union[dict[str, Any], Literal[False]]:
+        """The LLM configuration of the agent."""
+        ...
+
     def send(
         self,
-        message: Union[dict[str, Any], str],
+        message: Union["LLMMessageType", str],
         recipient: "Agent",
         request_reply: Optional[bool] = None,
     ) -> None:
@@ -50,7 +67,7 @@ def send(
 
     async def a_send(
         self,
-        message: Union[dict[str, Any], str],
+        message: Union["LLMMessageType", str],
         recipient: "Agent",
         request_reply: Optional[bool] = None,
     ) -> None:
@@ -66,7 +83,7 @@ async def a_send(
 
     def receive(
         self,
-        message: Union[dict[str, Any], str],
+        message: Union["LLMMessageType", str],
         sender: "Agent",
         request_reply: Optional[bool] = None,
     ) -> None:
@@ -81,7 +98,7 @@ def receive(
 
     async def a_receive(
         self,
-        message: Union[dict[str, Any], str],
+        message: Union["LLMMessageType", str],
         sender: "Agent",
         request_reply: Optional[bool] = None,
     ) -> None:
@@ -97,7 +114,7 @@ async def a_receive(
 
     def generate_reply(
         self,
-        messages: Optional[list[dict[str, Any]]] = None,
+        messages: Optional[list["LLMMessageType"]] = None,
         sender: Optional["Agent"] = None,
         **kwargs: Any,
     ) -> Union[str, dict[str, Any], None]:
@@ -116,7 +133,7 @@ def generate_reply(
 
     async def a_generate_reply(
         self,
-        messages: Optional[list[dict[str, Any]]] = None,
+        messages: Optional[list["LLMMessageType"]] = None,
         sender: Optional["Agent"] = None,
         **kwargs: Any,
     ) -> Union[str, dict[str, Any], None]:
@@ -134,6 +151,58 @@ async def a_generate_reply(
         """
         ...
 
+    def initiate_chat(
+        self,
+        recipient: "ConversableAgent",
+        clear_history: bool = True,
+        silent: Optional[bool] = False,
+        cache: Optional[AbstractCache] = None,
+        max_turns: Optional[int] = None,
+        summary_method: Optional[Union[str, Callable[..., Any]]] = DEFAULT_SUMMARY_METHOD,
+        summary_args: Optional[dict[str, Any]] = {},
+        message: Optional[Union["LLMMessageType", str, Callable[..., Any]]] = None,
+        **kwargs: Any,
+    ) -> "ChatResult": ...
+
+    async def a_initiate_chat(
+        self,
+        recipient: "ConversableAgent",
+        clear_history: bool = True,
+        silent: Optional[bool] = False,
+        cache: Optional[AbstractCache] = None,
+        max_turns: Optional[int] = None,
+        summary_method: Optional[Union[str, Callable[..., Any]]] = DEFAULT_SUMMARY_METHOD,
+        summary_args: Optional[dict[str, Any]] = {},
+        message: Optional[Union["LLMMessageType", str, Callable[..., Any]]] = None,
+        **kwargs: Any,
+    ) -> "ChatResult": ...
+
+    def run(
+        self,
+        message: str,
+        *,
+        tools: Optional[Union["Tool", Iterable["Tool"]]] = None,
+        executor_kwargs: Optional[dict[str, Any]] = None,
+        max_turns: Optional[int] = None,
+        msg_to: Literal["agent", "user"] = "agent",
+        clear_history: bool = False,
+        user_input: bool = True,
+        summary_method: Optional[Union[str, Callable[..., Any]]] = DEFAULT_SUMMARY_METHOD,
+    ) -> "ChatResult": ...
+
+    async def a_run(
+        self,
+        message: str,
+        *,
+        tools: Optional[Union["Tool", Iterable["Tool"]]] = None,
+        executor_kwargs: Optional[dict[str, Any]] = None,
+        max_turns: Optional[int] = None,
+        msg_to: Literal["agent", "user"] = "agent",
+        clear_history: bool = False,
+        user_input: bool = True,
+        summary_method: Optional[Union[str, Callable[..., Any]]] = DEFAULT_SUMMARY_METHOD,
+    ) -> "ChatResult": ...
+
 
 @runtime_checkable
 @export_module("autogen")
@@ -154,7 +223,5 @@ def update_system_message(self, system_message: str) -> None:
 
 if TYPE_CHECKING:
     # mypy will fail if Conversible agent does not implement Agent protocol
-    from .conversable_agent import ConversableAgent
-
-    def _check_protocol_implementation(agent: ConversableAgent) -> Agent:
+    def _check_protocol_implementation(agent: ConversableAgent) -> LLMAgent:
         return agent

autogen/agentchat/chat.py

@@ -14,8 +14,8 @@
 from typing import Any
 
 from ..doc_utils import export_module
+from ..events.agent_events import PostCarryoverProcessingEvent
 from ..io.base import IOStream
-from ..messages.agent_messages import PostCarryoverProcessingMessage
 from .utils import consolidate_chat_info
 
 logger = logging.getLogger(__name__)
@@ -133,7 +133,7 @@ def __post_carryover_processing(chat_info: dict[str, Any]) -> None:
             UserWarning,
         )
 
-    iostream.send(PostCarryoverProcessingMessage(chat_info=chat_info))
+    iostream.send(PostCarryoverProcessingEvent(chat_info=chat_info))
 
 
 @export_module("autogen")

autogen/agentchat/contrib/capabilities/generate_images.py

@@ -7,7 +7,7 @@
 import re
 from typing import Any, Literal, Optional, Protocol, Union
 
-from .... import Agent, ConversableAgent, code_utils
+from .... import Agent, ConversableAgent, LLMMessageType, code_utils
 from ....cache import AbstractCache
 from ....import_utils import optional_import_block, require_optional_import
 from ....llm_config import LLMConfig
@@ -216,7 +216,7 @@ def add_to_agent(self, agent: ConversableAgent):
     def _image_gen_reply(
         self,
         recipient: ConversableAgent,
-        messages: Optional[list[dict[str, Any]]],
+        messages: Optional[list["LLMMessageType"]],
         sender: Optional[Agent] = None,
         config: Optional[Any] = None,
     ) -> tuple[bool, Optional[Union[str, dict[str, Any]]]]:

autogen/agentchat/contrib/capabilities/teachability.py

@@ -11,6 +11,7 @@
 from ....formatting_utils import colored
 from ....import_utils import optional_import_block, require_optional_import
 from ....llm_config import LLMConfig
+from ...agent import LLMMessageType
 from ...assistant_agent import ConversableAgent
 from ..text_analyzer_agent import TextAnalyzerAgent
 from .agent_capability import AgentCapability
@@ -92,7 +93,7 @@ def prepopulate_db(self):
         """Adds a few arbitrary memos to the DB."""
         self.memo_store.prepopulate()
 
-    def process_last_received_message(self, text: Union[dict[str, Any], str]):
+    def process_last_received_message(self, text: Union["LLMMessageType", str]):
         """Appends any relevant memos to the message text, and stores any apparent teachings in new memos.
         Uses TextAnalyzerAgent to make decisions about memo storage and retrieval.
         """
@@ -107,7 +108,7 @@ def process_last_received_message(self, text: Union[dict[str, Any], str]):
         # Return the (possibly) expanded message text.
         return expanded_text
 
-    def _consider_memo_storage(self, comment: Union[dict[str, Any], str]):
+    def _consider_memo_storage(self, comment: Union["LLMMessageType", str]):
         """Decides whether to store something from one user comment in the DB."""
         memo_added = False
 
@@ -165,7 +166,7 @@ def _consider_memo_storage(self, comment: Union[dict[str, Any], str]):
             # Yes. Save them to disk.
             self.memo_store._save_memos()
 
-    def _consider_memo_retrieval(self, comment: Union[dict[str, Any], str]):
+    def _consider_memo_retrieval(self, comment: Union["LLMMessageType", str]):
         """Decides whether to retrieve memos from the DB, and add them to the chat context."""
         # First, use the comment directly as the lookup key.
         if self.verbosity >= 1:
@@ -228,7 +229,9 @@ def _concatenate_memo_texts(self, memo_list: list) -> str:
             memo_texts = memo_texts + "\n" + info
         return memo_texts
 
-    def _analyze(self, text_to_analyze: Union[dict[str, Any], str], analysis_instructions: Union[dict[str, Any], str]):
+    def _analyze(
+        self, text_to_analyze: Union["LLMMessageType", str], analysis_instructions: Union["LLMMessageType", str]
+    ):
         """Asks TextAnalyzerAgent to analyze the given text according to specific instructions."""
         self.analyzer.reset()  # Clear the analyzer's list of messages.
         self.teachable_agent.send(

autogen/agentchat/contrib/capabilities/transform_messages.py

@@ -5,12 +5,15 @@
 # Portions derived from  https://github.com/microsoft/autogen are under the MIT License.
 # SPDX-License-Identifier: MIT
 import copy
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from ....formatting_utils import colored
 from ...conversable_agent import ConversableAgent
 from .transforms import MessageTransform
 
+if TYPE_CHECKING:
+    from ... import LLMMessageType
+
 
 class TransformMessages:
     """Agent capability for transforming messages before reply generation.
@@ -65,7 +68,7 @@ def add_to_agent(self, agent: ConversableAgent):
         """
         agent.register_hook(hookable_method="process_all_messages_before_reply", hook=self._transform_messages)
 
-    def _transform_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    def _transform_messages(self, messages: list["LLMMessageType"]) -> list[dict[str, Any]]:
         post_transform_messages = copy.deepcopy(messages)
         system_message = None