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ekzhudavorrunjejackgerritsAaronWardsonichi
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Code executors (microsoft#1405)
* code executor * test * revert to main conversable agent * prepare for pr * kernel * run open ai tests only when it's out of draft status * update workflow file * revert workflow changes * ipython executor * check kernel installed; fix tests * fix tests * fix tests * update system prompt * Update notebook, more tests * notebook * raise instead of return None * allow user provided code executor. * fixing types * wip * refactoring * polishing * fixed failing tests * resolved merge conflict * fixing failing test * wip * local command line executor and embedded ipython executor * revert notebook * fix format * fix merged error * fix lmm test * fix lmm test * move warning * name and description should be part of the agent protocol, reset is not as it is only used for ConversableAgent; removing accidentally commited file * version for dependency * Update autogen/agentchat/conversable_agent.py Co-authored-by: Jack Gerrits <[email protected]> * ordering of protocol * description * fix tests * make ipython executor dependency optional * update document optional dependencies * Remove exclude from Agent protocol * Make ConversableAgent consistent with Agent * fix tests * add doc string * add doc string * fix notebook * fix interface * merge and update agents * disable config usage in reply function * description field setter * customize system message update * update doc --------- Co-authored-by: Davor Runje <[email protected]> Co-authored-by: Jack Gerrits <[email protected]> Co-authored-by: Aaron <[email protected]> Co-authored-by: Chi Wang <[email protected]>
1 parent 32b252d commit eb826ed

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‎.github/workflows/build.yml

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@@ -42,6 +42,14 @@ jobs:
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pip install -e .
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python -c "import autogen"
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pip install pytest mock
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pip install jupyter-client ipykernel
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python -m ipykernel install --user --name python3
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- name: Set AUTOGEN_USE_DOCKER based on OS
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shell: bash
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run: |
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if [[ ${{ matrix.os }} != ubuntu-latest ]]; then
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echo "AUTOGEN_USE_DOCKER=False" >> $GITHUB_ENV
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fi
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- name: Test with pytest skipping openai tests
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if: matrix.python-version != '3.10' && matrix.os == 'ubuntu-latest'
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run: |

‎autogen/agentchat/agent.py

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@@ -1,70 +1,136 @@
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from typing import Dict, List, Optional, Union
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from typing import Any, Dict, List, Optional, Protocol, Union, runtime_checkable
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class Agent:
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"""(In preview) An abstract class for AI agent.
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@runtime_checkable
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class Agent(Protocol):
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"""(In preview) A protocol for Agent.
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An agent can communicate with other agents and perform actions.
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Different agents can differ in what actions they perform in the `receive` method.
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"""
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def __init__(
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@property
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def name(self) -> str:
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"""The name of the agent."""
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...
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@property
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def description(self) -> str:
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"""The description of the agent. Used for the agent's introduction in
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a group chat setting."""
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...
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def send(
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self,
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name: str,
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):
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"""
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message: Union[Dict[str, Any], str],
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recipient: "Agent",
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request_reply: Optional[bool] = None,
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) -> None:
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"""Send a message to another agent.
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Args:
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name (str): name of the agent.
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message (dict or str): the message to send. If a dict, it should be
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a JSON-serializable and follows the OpenAI's ChatCompletion schema.
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recipient (Agent): the recipient of the message.
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request_reply (bool): whether to request a reply from the recipient.
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"""
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# a dictionary of conversations, default value is list
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self._name = name
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...
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@property
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def name(self):
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"""Get the name of the agent."""
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return self._name
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async def a_send(
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self,
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message: Union[Dict[str, Any], str],
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recipient: "Agent",
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request_reply: Optional[bool] = None,
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) -> None:
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"""(Async) Send a message to another agent.
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def send(self, message: Union[Dict, str], recipient: "Agent", request_reply: Optional[bool] = None):
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"""(Abstract method) Send a message to another agent."""
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Args:
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message (dict or str): the message to send. If a dict, it should be
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a JSON-serializable and follows the OpenAI's ChatCompletion schema.
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recipient (Agent): the recipient of the message.
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request_reply (bool): whether to request a reply from the recipient.
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"""
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...
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async def a_send(self, message: Union[Dict, str], recipient: "Agent", request_reply: Optional[bool] = None):
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"""(Abstract async method) Send a message to another agent."""
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def receive(
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self,
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message: Union[Dict[str, Any], str],
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sender: "Agent",
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request_reply: Optional[bool] = None,
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) -> None:
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"""Receive a message from another agent.
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def receive(self, message: Union[Dict, str], sender: "Agent", request_reply: Optional[bool] = None):
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"""(Abstract method) Receive a message from another agent."""
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Args:
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message (dict or str): the message received. If a dict, it should be
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a JSON-serializable and follows the OpenAI's ChatCompletion schema.
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sender (Agent): the sender of the message.
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request_reply (bool): whether the sender requests a reply.
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"""
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async def a_receive(self, message: Union[Dict, str], sender: "Agent", request_reply: Optional[bool] = None):
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"""(Abstract async method) Receive a message from another agent."""
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async def a_receive(
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self,
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message: Union[Dict[str, Any], str],
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sender: "Agent",
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request_reply: Optional[bool] = None,
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) -> None:
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"""(Async) Receive a message from another agent.
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def reset(self):
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"""(Abstract method) Reset the agent."""
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Args:
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message (dict or str): the message received. If a dict, it should be
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a JSON-serializable and follows the OpenAI's ChatCompletion schema.
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sender (Agent): the sender of the message.
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request_reply (bool): whether the sender requests a reply.
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"""
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...
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def generate_reply(
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self,
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messages: Optional[List[Dict]] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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sender: Optional["Agent"] = None,
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**kwargs,
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) -> Union[str, Dict, None]:
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"""(Abstract method) Generate a reply based on the received messages.
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**kwargs: Any,
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) -> Union[str, Dict[str, Any], None]:
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"""Generate a reply based on the received messages.
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Args:
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messages (list[dict]): a list of messages received.
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messages (list[dict]): a list of messages received from other agents.
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The messages are dictionaries that are JSON-serializable and
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follows the OpenAI's ChatCompletion schema.
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sender: sender of an Agent instance.
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Returns:
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str or dict or None: the generated reply. If None, no reply is generated.
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"""
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async def a_generate_reply(
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self,
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messages: Optional[List[Dict]] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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sender: Optional["Agent"] = None,
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**kwargs,
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) -> Union[str, Dict, None]:
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"""(Abstract async method) Generate a reply based on the received messages.
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**kwargs: Any,
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) -> Union[str, Dict[str, Any], None]:
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"""(Async) Generate a reply based on the received messages.
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Args:
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messages (list[dict]): a list of messages received.
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messages (list[dict]): a list of messages received from other agents.
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The messages are dictionaries that are JSON-serializable and
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follows the OpenAI's ChatCompletion schema.
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sender: sender of an Agent instance.
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Returns:
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str or dict or None: the generated reply. If None, no reply is generated.
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"""
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@runtime_checkable
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class LLMAgent(Agent, Protocol):
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"""(In preview) A protocol for an LLM agent."""
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@property
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def system_message(self) -> str:
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"""The system message of this agent."""
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def update_system_message(self, system_message: str) -> None:
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"""Update this agent's system message.
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Args:
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system_message (str): system message for inference.
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"""

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