initial update

.gitignore

@@ -0,0 +1,5 @@
+.dist
+deep-swarm/data
+deep-swarm/tmp
+deep-swarm/.env
+deep-swarm/utils/__pycache__/

README.md

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+# DeepSwarm
+
+## Overview
+
+DeepSwarm is a multi-agent framework based from [camel](https://github.com/camel-ai/camel/). It achieved open-source state-of-the-art performance on the [GAIA](https://huggingface.co/datasets/gaia-benchmark/GAIA) benchmark.
+
+## Quickstart
+
+It is recommended to run the code in linux environment.
+To get started, follow these steps:
+
+1. **Clone the Github repository:**
+
+```bash
+$ git clone xxx
+```
+
+2. **Set up Python Environment:**
+
+```bash
+$ conda create -n deepswarm python=3.11
+$ conda activate deepswarm
+```
+
+3. **Install Dependencies:**
+
+```bash
+$ pip install -r requirements.txt
+```
+
+4. **Set API Keys:** We use `dotenv` to manage API keys. Please copy and check the `.env.example` file to `.env` and fill in the necessary API keys.
+
+5. **Run the Demo Code:**
+   
+```bash
+$ python run.py
+```
+
+## Reproduce the Results in GAIA
+
+We have provided a script to reproduce the results in GAIA. You can check the `run_gaia_roleplaying.py` file and run the following command:
+
+```bash
+$ python run_gaia_roleplaying.py
+```

deep-swarm/.env_template

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+
+# OPENAI API
+OPENAI_API_KEY = ""
+
+# Hugging Face API (https://huggingface.co/join)
+HF_TOKEN=""
+
+# Qwen API (https://help.aliyun.com/document_detail/611472.html)
+QWEN_API_KEY=""
+
+
+#===========================================
+# Tools & Services API
+#===========================================
+
+# Google Search API (https://developers.google.com/custom-search/v1/overview)
+GOOGLE_API_KEY=""
+SEARCH_ENGINE_ID=""
+
+# Chunkr API (https://chunkr.ai/)
+CHUNKR_API_KEY=""
+
+# Firecrawl API (https://www.firecrawl.dev/)
+FIRECRAWL_API_KEY=""
+

deep-swarm/camel/__init__.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+from camel.logger import disable_logging, enable_logging, set_log_level
+
+__version__ = '0.2.11'
+
+__all__ = [
+    '__version__',
+    'camel',
+    'disable_logging',
+    'enable_logging',
+    'set_log_level',
+]

deep-swarm/camel/agents/__init__.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from .base import BaseAgent
+from .chat_agent import ChatAgent
+from .critic_agent import CriticAgent
+from .embodied_agent import EmbodiedAgent
+from .knowledge_graph_agent import KnowledgeGraphAgent
+from .role_assignment_agent import RoleAssignmentAgent
+from .search_agent import SearchAgent
+from .task_agent import (
+    TaskCreationAgent,
+    TaskPlannerAgent,
+    TaskPrioritizationAgent,
+    TaskSpecifyAgent,
+)
+from .tool_agents.base import BaseToolAgent
+from .tool_agents.hugging_face_tool_agent import HuggingFaceToolAgent
+
+__all__ = [
+    'BaseAgent',
+    'ChatAgent',
+    'TaskSpecifyAgent',
+    'TaskPlannerAgent',
+    'TaskCreationAgent',
+    'TaskPrioritizationAgent',
+    'CriticAgent',
+    'BaseToolAgent',
+    'HuggingFaceToolAgent',
+    'EmbodiedAgent',
+    'RoleAssignmentAgent',
+    'SearchAgent',
+    'KnowledgeGraphAgent',
+]

deep-swarm/camel/agents/base.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from abc import ABC, abstractmethod
+from typing import Any
+
+
+class BaseAgent(ABC):
+    r"""An abstract base class for all CAMEL agents."""
+
+    @abstractmethod
+    def reset(self, *args: Any, **kwargs: Any) -> Any:
+        r"""Resets the agent to its initial state."""
+        pass
+
+    @abstractmethod
+    def step(self, *args: Any, **kwargs: Any) -> Any:
+        r"""Performs a single step of the agent."""
+        pass

deep-swarm/camel/agents/chat_agent.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from __future__ import annotations
+
+import json
+# import logging
+import re
+import uuid
+from collections import defaultdict
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
+
+from loguru import logger
+
+from openai.types.chat import ChatCompletionMessageToolCall
+from openai.types.chat.chat_completion_message_tool_call import Function
+from pydantic import BaseModel
+
+from camel.agents.base import BaseAgent
+from camel.memories import (
+    AgentMemory,
+    ChatHistoryMemory,
+    MemoryRecord,
+    ScoreBasedContextCreator,
+)
+from camel.messages import BaseMessage, FunctionCallingMessage, OpenAIMessage
+from camel.models import (
+    BaseModelBackend,
+    ModelFactory,
+    ModelManager,
+    ModelProcessingError,
+)
+from camel.responses import ChatAgentResponse
+from camel.types import (
+    ChatCompletion,
+    ChatCompletionChunk,
+    ModelPlatformType,
+    ModelType,
+    OpenAIBackendRole,
+    RoleType,
+)
+from camel.utils import (
+    func_string_to_callable,
+    get_model_encoding,
+    get_pydantic_object_schema,
+    json_to_function_code,
+)
+
+if TYPE_CHECKING:
+    from openai import Stream
+
+    from camel.terminators import ResponseTerminator
+    from camel.toolkits import FunctionTool
+
+
+# logger = logging.getLogger(__name__)
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+class FunctionCallingRecord(BaseModel):
+    r"""Historical records of functions called in the conversation.
+
+    Attributes:
+        func_name (str): The name of the function being called.
+        args (Dict[str, Any]): The dictionary of arguments passed to
+            the function.
+        result (Any): The execution result of calling this function.
+    """
+
+    func_name: str
+    args: Dict[str, Any]
+    result: Any
+
+    def __str__(self) -> str:
+        r"""Overridden version of the string function.
+
+        Returns:
+            str: Modified string to represent the function calling.
+        """
+        return (
+            f"Function Execution: {self.func_name}\n"
+            f"\tArgs: {self.args}\n"
+            f"\tResult: {self.result}"
+        )
+
+    def as_dict(self) -> dict[str, Any]:
+        r"""Returns the function calling record as a dictionary.
+
+        Returns:
+            dict[str, Any]: The function calling record as a dictionary.
+        """
+        return self.model_dump()
+
+
+@track_agent(name="ChatAgent")
+class ChatAgent(BaseAgent):
+    r"""Class for managing conversations of CAMEL Chat Agents.
+
+    Args:
+        system_message (Union[BaseMessage, str], optional): The system message
+            for the chat agent.
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`ModelPlatformType.DEFAULT`
+            with `ModelType.DEFAULT`)
+        memory (AgentMemory, optional): The agent memory for managing chat
+            messages. If `None`, a :obj:`ChatHistoryMemory` will be used.
+            (default: :obj:`None`)
+        message_window_size (int, optional): The maximum number of previous
+            messages to include in the context window. If `None`, no windowing
+            is performed. (default: :obj:`None`)
+        token_limit (int, optional): The maximum number of tokens in a context.
+            The context will be automatically pruned to fulfill the limitation.
+            If `None`, it will be set according to the backend model.
+            (default: :obj:`None`)
+        output_language (str, optional): The language to be output by the
+            agent. (default: :obj:`None`)
+        tools (List[FunctionTool], optional): List of available
+            :obj:`FunctionTool`. (default: :obj:`None`)
+        external_tools (List[FunctionTool], optional): List of external tools
+            (:obj:`FunctionTool`) bind to one chat agent. When these tools
+            are called, the agent will directly return the request instead of
+            processing it. (default: :obj:`None`)
+        response_terminators (List[ResponseTerminator], optional): List of
+            :obj:`ResponseTerminator` bind to one chat agent.
+            (default: :obj:`None`)
+        scheduling_strategy (str): name of function that defines how to select
+            the next model in ModelManager. (default: :str:`round_robin`)
+    """
+
+    def __init__(
+        self,
+        system_message: Optional[Union[BaseMessage, str]] = None,
+        model: Optional[
+            Union[BaseModelBackend, List[BaseModelBackend]]
+        ] = None,
+        memory: Optional[AgentMemory] = None,
+        message_window_size: Optional[int] = None,
+        token_limit: Optional[int] = None,
+        output_language: Optional[str] = None,
+        tools: Optional[List[FunctionTool]] = None,
+        external_tools: Optional[List[FunctionTool]] = None,
+        response_terminators: Optional[List[ResponseTerminator]] = None,
+        scheduling_strategy: str = "round_robin",
+    ) -> None:
+        from copy import deepcopy
+        if isinstance(system_message, str):
+            system_message = BaseMessage.make_assistant_message(
+                role_name='Assistant', content=system_message
+            )
+
+        self.orig_sys_message: Optional[BaseMessage] = system_message
+        self._system_message: Optional[BaseMessage] = system_message
+        self.role_name: str = (
+            getattr(system_message, 'role_name', None) or "assistant"
+        )
+        self.role_type: RoleType = (
+            getattr(system_message, 'role_type', None) or RoleType.ASSISTANT
+        )
+        self.model_backend = ModelManager(
+            model
+            if model is not None
+            else ModelFactory.create(
+                model_platform=ModelPlatformType.DEFAULT,
+                model_type=ModelType.DEFAULT,
+            ),
+            scheduling_strategy=scheduling_strategy,
+        )
+
+        self.model_type = self.model_backend.model_type
+
+        # Tool registration
+        external_tools = external_tools or []
+        tools = tools or []
+        all_tools = tools + external_tools
+        self.external_tool_names = [
+            tool.get_function_name() for tool in external_tools
+        ]
+        self.func_dict = {
+            tool.get_function_name(): tool.func for tool in all_tools
+        }
+        self.tool_dict = {tool.get_function_name(): tool for tool in all_tools}
+        self._all_tools = all_tools
+
+        # If the user set tools from `ChatAgent`, it will override the
+        # configured tools in `BaseModelBackend`.
+        if all_tools:
+            # logger.warning(
+            #     "Overriding the configured tools in `BaseModelBackend` with the tools from `ChatAgent`."
+            # )
+            tool_schema_list = [
+                tool.get_openai_tool_schema() for tool in all_tools
+            ]
+            self.model_backend.model_config_dict['tools'] = tool_schema_list
+            self.tool_schema_list = tool_schema_list
+            
+        from copy import deepcopy
+        self.model_config_dict = deepcopy(self.model_backend.model_config_dict)
+
+        self.model_token_limit = token_limit or self.model_backend.token_limit
+        context_creator = ScoreBasedContextCreator(
+            self.model_backend.token_counter,
+            self.model_token_limit,
+        )
+        self.memory: AgentMemory = memory or ChatHistoryMemory(
+            context_creator, window_size=message_window_size
+        )
+
+        self.output_language: Optional[str] = output_language
+        if self.output_language is not None:
+            self.set_output_language(self.output_language)
+
+        self.terminated: bool = False
+        self.response_terminators = response_terminators or []
+        self.init_messages()
+
+        self.tool_prompt_added = False
+
+    # ruff: noqa: E501
+    def _generate_tool_prompt(self, tool_schema_list: List[Dict]) -> str:
+        r"""Generates a tool prompt based on the provided tool schema list.
+
+        Args:
+            tool_schema_list (List[Dict]): A list of dictionaries, each
+                containing a tool schema.
+
+        Returns:
+            str: A string representing the tool prompt.
+        """
+        tool_prompts = []
+
+        for tool in tool_schema_list:
+            tool_info = tool['function']
+            tool_name = tool_info['name']
+            tool_description = tool_info['description']
+            tool_json = json.dumps(tool_info, indent=4)
+
+            prompt = f"Use the function '{tool_name}' to '{tool_description}':\n{tool_json}\n"
+            tool_prompts.append(prompt)
+
+        tool_prompt_str = "\n".join(tool_prompts)
+
+        final_prompt = f'''
+    # Tool prompt
+    TOOL_PROMPT = f"""
+    You have access to the following functions:
+
+    {tool_prompt_str}
+
+    If you choose to call a function ONLY reply in the following format with no
+    prefix or suffix:
+
+    <function=example_function_name>{{"example_name": "example_value"}}
+    </function>
+
+    Reminder:
+    - Function calls MUST follow the specified format, start with <function=
+      and end with </function>
+    - Required parameters MUST be specified
+    - Only call one function at a time
+    - Put the entire function call reply on one line
+    - If there is no function call available, answer the question like normal 
+      with your current knowledge and do not tell the user about function calls
+    """
+    '''
+        return final_prompt
+
+    def _parse_tool_response(self, response: str):
+        r"""Parses the tool response to extract the function name and
+        arguments.
+
+        Args:
+            response (str): The response from the model containing the
+                function call.
+
+        Returns:
+            Optional[Dict[str, Any]]: The parsed function name and arguments
+                if found, otherwise :obj:`None`.
+        """
+        function_regex = r"<function=(\w+)>(.*?)</function>"
+        match = re.search(function_regex, response)
+
+        if match:
+            function_name, args_string = match.groups()
+            try:
+                args = json.loads(args_string)
+                return {"function": function_name, "arguments": args}
+            except json.JSONDecodeError as error:
+                print(f"Error parsing function arguments: {error}")
+                return None
+        return None
+
+    def reset(self):
+        r"""Resets the :obj:`ChatAgent` to its initial state."""
+        self.terminated = False
+        self.init_messages()
+        for terminator in self.response_terminators:
+            terminator.reset()
+
+    @property
+    def system_message(self) -> Optional[BaseMessage]:
+        r"""The getter method for the property :obj:`system_message`.
+
+        Returns:
+            Optional[BaseMessage]: The system message of this agent if set,
+                else :obj:`None`.
+        """
+        return self._system_message
+
+    @system_message.setter
+    def system_message(self, message: BaseMessage) -> None:
+        r"""The setter method for the property :obj:`system_message`.
+
+        Args:
+            message (BaseMessage): The message to be set as the
+                new system message of this agent.
+        """
+        self._system_message = message
+
+    def is_tools_added(self) -> bool:
+        r"""Whether OpenAI function calling is enabled for this agent.
+
+        Returns:
+            bool: Whether OpenAI function calling is enabled for this
+                agent, determined by whether the dictionary of tools
+                is empty.
+        """
+        return len(self.func_dict) > 0
+
+    def update_memory(
+        self, message: BaseMessage, role: OpenAIBackendRole
+    ) -> None:
+        r"""Updates the agent memory with a new message.
+
+        Args:
+            message (BaseMessage): The new message to add to the stored
+                messages.
+            role (OpenAIBackendRole): The backend role type.
+        """
+        self.memory.write_record(
+            MemoryRecord(message=message, role_at_backend=role)
+        )
+
+    def set_output_language(self, output_language: str) -> BaseMessage:
+        r"""Sets the output language for the system message. This method
+        updates the output language for the system message. The output
+        language determines the language in which the output text should be
+        generated.
+
+        Args:
+            output_language (str): The desired output language.
+
+        Returns:
+            BaseMessage: The updated system message object.
+        """
+        self.output_language = output_language
+        language_prompt = (
+            "\nRegardless of the input language, "
+            f"you must output text in {output_language}."
+        )
+        if self.orig_sys_message is not None:
+            content = self.orig_sys_message.content + language_prompt
+            self._system_message = self.orig_sys_message.create_new_instance(
+                content
+            )
+        else:
+            self._system_message = BaseMessage.make_assistant_message(
+                role_name="Assistant",
+                content=language_prompt,
+            )
+
+        system_record = MemoryRecord(
+            message=self._system_message,
+            role_at_backend=OpenAIBackendRole.SYSTEM,
+        )
+        self.memory.clear()
+        self.memory.write_record(system_record)
+        return self._system_message
+
+    def get_info(
+        self,
+        session_id: Optional[str],
+        usage: Optional[Dict[str, int]],
+        termination_reasons: List[str],
+        num_tokens: int,
+        tool_calls: List[FunctionCallingRecord],
+        external_tool_request: Optional[ChatCompletionMessageToolCall] = None,
+    ) -> Dict[str, Any]:
+        r"""Returns a dictionary containing information about the chat session.
+
+        Args:
+            session_id (str, optional): The ID of the chat session.
+            usage (Dict[str, int], optional): Information about the usage of
+                the LLM model.
+            termination_reasons (List[str]): The reasons for the termination
+                of the chat session.
+            num_tokens (int): The number of tokens used in the chat session.
+            tool_calls (List[FunctionCallingRecord]): The list of function
+                calling records, containing the information of called tools.
+            external_tool_request
+                (Optional[ChatCompletionMessageToolCall], optional):
+                The tool calling request of external tools from the model.
+                These requests are directly returned to the user instead of
+                being processed by the agent automatically.
+                (default: :obj:`None`)
+
+        Returns:
+            Dict[str, Any]: The chat session information.
+        """
+        return {
+            "id": session_id,
+            "usage": usage,
+            "termination_reasons": termination_reasons,
+            "num_tokens": num_tokens,
+            "tool_calls": tool_calls,
+            "external_tool_request": external_tool_request,
+        }
+
+    def init_messages(self) -> None:
+        r"""Initializes the stored messages list with the current system
+        message.
+        """
+        if self._system_message is not None:
+            system_record = MemoryRecord(
+                message=self._system_message,
+                role_at_backend=OpenAIBackendRole.SYSTEM,
+            )
+            self.memory.clear()
+            self.memory.write_record(system_record)
+        else:
+            self.memory.clear()
+    
+    def _transform_function_calling_format(self, openai_messages: List[dict]):
+        r"""Used in deepseek-chat backend. It can modify function calling records' format to match the deepseek-chat backend's format."""
+        from copy import deepcopy
+        _messages = deepcopy(openai_messages)
+        modified_messages = []
+        for message in _messages:
+            if message['role'] == 'function':
+                new_message = {
+                    'role': 'tool',
+                    'tool_call_id': message['name'],
+                    'content': message['content']
+                }
+                modified_messages.append(new_message)
+            else:
+                modified_messages.append(message)
+
+        return modified_messages
+
+
+    def record_message(self, message: BaseMessage) -> None:
+        r"""Records the externally provided message into the agent memory as if
+        it were an answer of the :obj:`ChatAgent` from the backend. Currently,
+        the choice of the critic is submitted with this method.
+
+        Args:
+            message (BaseMessage): An external message to be recorded in the
+                memory.
+        """
+        self.update_memory(message, OpenAIBackendRole.ASSISTANT)
+
+    def step(
+        self,
+        input_message: Union[BaseMessage, str],
+        response_format: Optional[Type[BaseModel]] = None,
+    ) -> ChatAgentResponse:
+        r"""Performs a single step in the chat session by generating a response
+        to the input message.
+
+        Args:
+            input_message (Union[BaseMessage, str]): The input message to the
+                agent. For BaseMessage input, its `role` field that specifies
+                the role at backend may be either `user` or `assistant` but it
+                will be set to `user` anyway since for the self agent any
+                incoming message is external. For str input, the `role_name` would be `User`.
+            response_format (Optional[Type[BaseModel]], optional): A pydantic
+                model class that includes value types and field descriptions
+                used to generate a structured response by LLM. This schema
+                helps in defining the expected output format. (default:
+                :obj:`None`)
+
+        Returns:
+            ChatAgentResponse: A struct containing the output messages,
+                a boolean indicating whether the chat session has terminated,
+                and information about the chat session.
+        """
+        from copy import deepcopy
+        self.model_backend.model_config_dict = deepcopy(self.model_config_dict)
+        self.tool_dict = {tool.get_function_name(): tool for tool in self._all_tools}
+        if (
+            self.model_backend.model_config_dict.get("response_format")
+            and response_format
+        ):
+            raise ValueError(
+                "The `response_format` parameter cannot be set both in "
+                "the model configuration and in the ChatAgent step."
+            )
+
+        if isinstance(input_message, str):
+            input_message = BaseMessage.make_user_message(
+                role_name='User', content=input_message
+            )
+
+        if "llama" in self.model_type.lower():
+            if (
+                self.model_backend.model_config_dict.get("tools", None)
+                and not self.tool_prompt_added
+            ):
+                tool_prompt = self._generate_tool_prompt(self.tool_schema_list)
+
+                tool_sys_msg = BaseMessage.make_assistant_message(
+                    role_name="Assistant",
+                    content=tool_prompt,
+                )
+
+                self.update_memory(tool_sys_msg, OpenAIBackendRole.SYSTEM)
+                self.tool_prompt_added = True
+
+            self.update_memory(input_message, OpenAIBackendRole.USER)
+
+            tool_call_records: List[FunctionCallingRecord] = []
+            while True:
+                # Check if token has exceeded
+                try:
+                    openai_messages, num_tokens = self.memory.get_context()
+                except RuntimeError as e:
+                    return self._step_token_exceed(
+                        e.args[1], tool_call_records, "max_tokens_exceeded"
+                    )
+                (
+                    response,
+                    output_messages,
+                    finish_reasons,
+                    usage_dict,
+                    response_id,
+                ) = self._step_model_response(openai_messages, num_tokens)
+                # If the model response is not a function call, meaning the
+                # model has generated a message response, break the loop
+                if (
+                    not self.is_tools_added()
+                    or not isinstance(response, ChatCompletion)
+                    or "</function>" not in response.choices[0].message.content  # type: ignore[operator]
+                ):
+                    break
+
+                parsed_content = self._parse_tool_response(
+                    response.choices[0].message.content  # type: ignore[arg-type]
+                )
+
+                response.choices[0].message.tool_calls = [
+                    ChatCompletionMessageToolCall(
+                        id=str(uuid.uuid4()),
+                        function=Function(
+                            arguments=str(parsed_content["arguments"]).replace(
+                                "'", '"'
+                            ),
+                            name=str(parsed_content["function"]),
+                        ),
+                        type="function",
+                    )
+                ]
+
+                # Check for external tool call
+                tool_call_request = response.choices[0].message.tool_calls[0]
+                if tool_call_request.function.name in self.external_tool_names:
+                    # if model calls an external tool, directly return the
+                    # request
+                    info = self._step_get_info(
+                        output_messages,
+                        finish_reasons,
+                        usage_dict,
+                        response_id,
+                        tool_call_records,
+                        num_tokens,
+                        tool_call_request,
+                    )
+                    return ChatAgentResponse(
+                        msgs=output_messages,
+                        terminated=self.terminated,
+                        info=info,
+                    )
+
+                # Normal function calling
+                tool_call_records.append(
+                    self._step_tool_call_and_update(response)
+                )
+
+            if response_format is not None:
+                (
+                    output_messages,
+                    finish_reasons,
+                    usage_dict,
+                    response_id,
+                    tool_call,
+                    num_tokens,
+                ) = self._structure_output_with_function(response_format)
+                tool_call_records.append(tool_call)
+
+            info = self._step_get_info(
+                output_messages,
+                finish_reasons,
+                usage_dict,
+                response_id,
+                tool_call_records,
+                num_tokens,
+            )
+
+            if len(output_messages) == 1:
+                # Auto record if the output result is a single message
+                self.record_message(output_messages[0])
+            else:
+                logger.warning(
+                    "Multiple messages returned in `step()`, message won't be "
+                    "recorded automatically. Please call `record_message()` "
+                    "to record the selected message manually."
+                )
+
+            return ChatAgentResponse(
+                msgs=output_messages, terminated=self.terminated, info=info
+            )
+
+        else:
+            self.update_memory(input_message, OpenAIBackendRole.USER)
+            # try:
+
+            tool_call_records: List[FunctionCallingRecord] = []  # type: ignore[no-redef]
+            while True:
+                # Check if token has exceeded
+                try:
+                    openai_messages, num_tokens = self.memory.get_context()
+                except RuntimeError as e:
+                    return self._step_token_exceed(
+                        e.args[1], tool_call_records, "max_tokens_exceeded"
+                    )
+
+                (
+                    response,
+                    output_messages,
+                    finish_reasons,
+                    usage_dict,
+                    response_id,
+                ) = self._step_model_response(openai_messages, num_tokens)
+                # If the model response is not a function call, meaning the
+                # model has generated a message response, break the loop
+                if (
+                    not self.is_tools_added()
+                    or not isinstance(response, ChatCompletion)
+                    or not response.choices[0].message.tool_calls
+                ):
+                    break
+
+                # Check for external tool call
+                tool_call_request = response.choices[0].message.tool_calls[0]
+
+                if tool_call_request.function.name in self.external_tool_names:
+                    # if model calls an external tool, directly return the
+                    # request
+                    info = self._step_get_info(
+                        output_messages,
+                        finish_reasons,
+                        usage_dict,
+                        response_id,
+                        tool_call_records,
+                        num_tokens,
+                        tool_call_request,
+                    )
+                    return ChatAgentResponse(
+                        msgs=output_messages,
+                        terminated=self.terminated,
+                        info=info,
+                    )
+
+                # Normal function calling
+                tool_call_records.append(
+                    self._step_tool_call_and_update(response)
+                )
+
+            if (
+                response_format is not None
+                and self.model_type.support_native_tool_calling
+            ):
+                (
+                    output_messages,
+                    finish_reasons,
+                    usage_dict,
+                    response_id,
+                    tool_call,
+                    num_tokens,
+                ) = self._structure_output_with_function(response_format)
+                tool_call_records.append(tool_call)
+
+            info = self._step_get_info(
+                output_messages,
+                finish_reasons,
+                usage_dict,
+                response_id,
+                tool_call_records,
+                num_tokens,
+            )
+
+            if len(output_messages) == 1:
+                # Auto record if the output result is a single message
+                self.record_message(output_messages[0])
+            else:
+                logger.warning(
+                    "Multiple messages returned in `step()`, message won't be "
+                    "recorded automatically. Please call `record_message()` "
+                    "to record the selected message manually."
+                )
+
+            return ChatAgentResponse(
+                msgs=output_messages, terminated=self.terminated, info=info
+            )
+
+            # except Exception as e:
+            #     logger.error(e)
+            #     breakpoint()
+            #     raise e
+
+    async def step_async(
+        self,
+        input_message: Union[BaseMessage, str],
+        response_format: Optional[Type[BaseModel]] = None,
+    ) -> ChatAgentResponse:
+        r"""Performs a single step in the chat session by generating a response
+        to the input message. This agent step can call async function calls.
+
+        Args:
+            input_message (Union[BaseMessage, str]): The input message to the
+                agent. For BaseMessage input, its `role` field that specifies
+                the role at backend may be either `user` or `assistant` but it
+                will be set to `user` anyway since for the self agent any
+                incoming message is external. For str input, the `role_name` would be `User`.
+            response_format (Optional[Type[BaseModel]], optional): A pydantic
+                model class that includes value types and field descriptions
+                used to generate a structured response by LLM. This schema
+                helps in defining the expected output format. (default:
+                :obj:`None`)
+
+        Returns:
+            ChatAgentResponse: A struct containing the output messages,
+                a boolean indicating whether the chat session has terminated,
+                and information about the chat session.
+        """
+        if isinstance(input_message, str):
+            input_message = BaseMessage.make_user_message(
+                role_name='User', content=input_message
+            )
+
+        self.update_memory(input_message, OpenAIBackendRole.USER)
+
+        tool_call_records: List[FunctionCallingRecord] = []
+        while True:
+            try:
+                openai_messages, num_tokens = self.memory.get_context()
+            except RuntimeError as e:
+                return self._step_token_exceed(
+                    e.args[1], tool_call_records, "max_tokens_exceeded"
+                )
+
+            (
+                response,
+                output_messages,
+                finish_reasons,
+                usage_dict,
+                response_id,
+            ) = self._step_model_response(openai_messages, num_tokens)
+
+            if (
+                not self.is_tools_added()
+                or not isinstance(response, ChatCompletion)
+                or response.choices[0].message.tool_calls is None
+            ):
+                break
+
+            # Check for external tool call
+            tool_call_request = response.choices[0].message.tool_calls[0]
+            if tool_call_request.function.name in self.external_tool_names:
+                # if model calls an external tool, directly return the request
+                info = self._step_get_info(
+                    output_messages,
+                    finish_reasons,
+                    usage_dict,
+                    response_id,
+                    tool_call_records,
+                    num_tokens,
+                    tool_call_request,
+                )
+                return ChatAgentResponse(
+                    msgs=output_messages, terminated=self.terminated, info=info
+                )
+
+            # Normal function calling
+            tool_call_records.append(
+                await self._step_tool_call_and_update_async(response)
+            )
+
+        if (
+            response_format is not None
+            and self.model_type.support_native_tool_calling
+        ):
+            (
+                output_messages,
+                finish_reasons,
+                usage_dict,
+                response_id,
+                tool_call_record,
+                num_tokens,
+            ) = self._structure_output_with_function(response_format)
+            tool_call_records.append(tool_call_record)
+
+        info = self._step_get_info(
+            output_messages,
+            finish_reasons,
+            usage_dict,
+            response_id,
+            tool_call_records,
+            num_tokens,
+        )
+
+        if len(output_messages) == 1:
+            # Auto record if the output result is a single message
+            self.record_message(output_messages[0])
+        else:
+            logger.warning(
+                "Multiple messages returned in `step()`, message won't be "
+                "recorded automatically. Please call `record_message()` to "
+                "record the selected message manually."
+            )
+
+        return ChatAgentResponse(
+            msgs=output_messages, terminated=self.terminated, info=info
+        )
+
+    def _step_tool_call_and_update(
+        self, response: ChatCompletion
+    ) -> FunctionCallingRecord:
+        r"""Processes a function call within the chat completion response,
+        records the function call in the provided list of tool calls and
+        updates the memory of the current agent.
+
+        Args:
+            response (ChatCompletion): The response object from the chat
+                completion.
+
+        Returns:
+            FunctionCallingRecord: The record of calling the function.
+        """
+
+        # Perform function calling
+        func_assistant_msg, func_result_msg, tool_call_record = (
+            self.step_tool_call(response)
+        )
+
+        # Update the messages
+        self.update_memory(func_assistant_msg, OpenAIBackendRole.ASSISTANT)
+        self.update_memory(func_result_msg, OpenAIBackendRole.FUNCTION)
+
+        return tool_call_record
+
+    async def _step_tool_call_and_update_async(
+        self, response: ChatCompletion
+    ) -> FunctionCallingRecord:
+        (
+            func_assistant_msg,
+            func_result_msg,
+            func_record,
+        ) = await self.step_tool_call_async(response)
+
+        self.update_memory(func_assistant_msg, OpenAIBackendRole.ASSISTANT)
+        self.update_memory(func_result_msg, OpenAIBackendRole.FUNCTION)
+
+        return func_record
+
+    def _structure_output_with_function(
+        self, response_format: Type[BaseModel]
+    ) -> Tuple[
+        List[BaseMessage],
+        List[str],
+        Dict[str, int],
+        str,
+        FunctionCallingRecord,
+        int,
+    ]:
+        r"""Internal function of structuring the output of the agent based on
+        the given output schema.
+
+        Args:
+            response_format (Type[BaseModel]): The output schema to use for
+                structuring the output.
+
+        Returns:
+            Tuple[List[BaseMessage], List[str], Dict[str, int], str,
+                FunctionCallingRecord, int]:
+                A tuple containing the output messages, finish reasons, usage
+                dictionary, response ID, function calling record, and number of
+                tokens.
+        """
+        from camel.toolkits import FunctionTool
+
+        schema_json = get_pydantic_object_schema(response_format)
+        func_str = json_to_function_code(schema_json)
+        func_callable = func_string_to_callable(func_str)
+        func = FunctionTool(func_callable)
+
+        original_func_dict = self.func_dict
+        original_model_dict = self.model_backend.model_config_dict
+
+        # Replace the original tools with the structuring function
+        self.func_dict = {func.get_function_name(): func.func}
+        self.tool_dict = {func.get_function_name(): func}
+        self.model_backend.model_config_dict = original_model_dict.copy()
+        self.model_backend.model_config_dict["tools"] = [
+            func.get_openai_tool_schema()
+        ]
+        self.model_backend.model_config_dict["tool_choice"] = "required"
+
+        openai_messages, num_tokens = self.memory.get_context()
+        (
+            response,
+            output_messages,
+            finish_reasons,
+            usage_dict,
+            response_id,
+        ) = self._step_model_response(openai_messages, num_tokens)
+
+        if isinstance(response, ChatCompletion):
+            tool_call_record = self._step_tool_call_and_update(response)
+        else:
+            raise ValueError(
+                "Structured output is not supported for stream responses."
+            )
+
+        for base_message_item in output_messages:
+            base_message_item.content = str(tool_call_record.result)
+
+        # Recover the original tools
+        self.func_dict = original_func_dict
+        self.model_backend.model_config_dict = original_model_dict
+
+        return (
+            output_messages,
+            finish_reasons,
+            usage_dict,
+            response_id,
+            tool_call_record,
+            num_tokens,
+        )
+
+    def _step_model_response(
+        self,
+        openai_messages: List[OpenAIMessage],
+        num_tokens: int,
+    ) -> tuple[
+        Union[ChatCompletion, Stream],
+        List[BaseMessage],
+        List[str],
+        Dict[str, int],
+        str,
+    ]:
+        r"""Internal function for agent step model response."""
+
+        response = None
+        # Obtain the model's response
+        for _ in range(len(self.model_backend.models)):
+            try:
+                response = self.model_backend.run(openai_messages)
+                break
+            except Exception as exc:
+                logger.error(
+                    f"An error occurred while running model "
+                    f"{self.model_backend.model_type}, "
+                    f"index: {self.model_backend.current_model_index}",
+                    exc_info=exc,
+                )
+                continue
+        if not response:
+            raise ModelProcessingError(
+                "Unable to process messages: none of the provided models "
+                "run succesfully."
+            )
+
+        # logger.debug(
+        #     f"Model {self.model_backend.model_type}, "
+        #     f"index {self.model_backend.current_model_index}, "
+        #     f"processed these messages: {openai_messages}"
+        # )
+
+        if isinstance(response, ChatCompletion):
+            output_messages, finish_reasons, usage_dict, response_id = (
+                self.handle_batch_response(response)
+            )
+        else:
+            output_messages, finish_reasons, usage_dict, response_id = (
+                self.handle_stream_response(response, num_tokens)
+            )
+        return (
+            response,
+            output_messages,
+            finish_reasons,
+            usage_dict,
+            response_id,
+        )
+
+    def _step_get_info(
+        self,
+        output_messages: List[BaseMessage],
+        finish_reasons: List[str],
+        usage_dict: Dict[str, int],
+        response_id: str,
+        tool_calls: List[FunctionCallingRecord],
+        num_tokens: int,
+        external_tool_request: Optional[ChatCompletionMessageToolCall] = None,
+    ) -> Dict[str, Any]:
+        r"""Process the output of a chat step and gather information about the
+        step.
+
+        This method checks for termination conditions, updates the agent's
+        state, and collects information about the chat step, including tool
+        calls and termination reasons.
+
+        Args:
+            output_messages (List[BaseMessage]): The messages generated in
+                this step.
+            finish_reasons (List[str]): The reasons for finishing the
+                generation for each message.
+            usage_dict (Dict[str, int]): Dictionary containing token usage
+                information.
+            response_id (str): The ID of the response from the model.
+            tool_calls (List[FunctionCallingRecord]): Records of function calls
+                made during this step.
+            num_tokens (int): The number of tokens used in this step.
+            external_tool_request (Optional[ChatCompletionMessageToolCall]):
+                Any external tool request made during this step.
+                (default::obj:`None`)
+
+        Returns:
+            Dict[str, Any]: A dictionary containing information about the chat
+                step, including termination status, reasons, and tool call
+                information.
+
+        Note:
+            This method iterates over all response terminators and checks if
+            any of them signal termination. If a terminator signals
+            termination, the agent's state is updated accordingly, and the
+            termination reason is recorded.
+        """
+        termination = [
+            terminator.is_terminated(output_messages)
+            for terminator in self.response_terminators
+        ]
+        # Terminate the agent if any of the terminator terminates
+        self.terminated, termination_reason = next(
+            (
+                (terminated, termination_reason)
+                for terminated, termination_reason in termination
+                if terminated
+            ),
+            (False, None),
+        )
+        # For now only retain the first termination reason
+        if self.terminated and termination_reason is not None:
+            finish_reasons = [termination_reason] * len(finish_reasons)
+
+        info = self.get_info(
+            response_id,
+            usage_dict,
+            finish_reasons,
+            num_tokens,
+            tool_calls,
+            external_tool_request,
+        )
+        return info
+
+    def handle_batch_response(
+        self, response: ChatCompletion
+    ) -> Tuple[List[BaseMessage], List[str], Dict[str, int], str]:
+        r"""Process a batch response from the model and extract the necessary
+        information.
+
+        Args:
+            response (dict): Model response.
+
+        Returns:
+            tuple: A tuple of list of output `ChatMessage`, list of
+                finish reasons, usage dictionary, and response id.
+        """
+        output_messages: List[BaseMessage] = []
+        for choice in response.choices:
+            chat_message = BaseMessage(
+                role_name=self.role_name,
+                role_type=self.role_type,
+                meta_dict=dict(),
+                content=choice.message.content or "",
+                parsed=getattr(choice.message, 'parsed', None),
+            )
+            # Process log probabilities and append to the message meta information
+            if choice.logprobs is not None:
+                tokens_logprobs = choice.logprobs.content
+
+                if tokens_logprobs is not None:
+                    # Extract and structure logprob information
+                    logprobs_info = [
+                        {
+                            "token": token_logprob.token,
+                            "logprob": token_logprob.logprob,
+                            "top_logprobs": [
+                                (top_logprob.token, top_logprob.logprob)
+                                for top_logprob in token_logprob.top_logprobs
+                            ],
+                        }
+                        for token_logprob in tokens_logprobs
+                    ]
+                # Ensure meta_dict exists before adding logprobs info
+                if chat_message.meta_dict is None:
+                    chat_message.meta_dict = {}
+                chat_message.meta_dict["logprobs_info"] = logprobs_info
+            # Append the processed chat message to output
+            output_messages.append(chat_message)
+
+        finish_reasons = [
+            str(choice.finish_reason) for choice in response.choices
+        ]
+        usage = (
+            self._safe_model_dump(response.usage)
+            if response.usage is not None
+            else {}
+        )
+        return (
+            output_messages,
+            finish_reasons,
+            usage,
+            response.id,
+        )
+
+    def _safe_model_dump(self, obj) -> dict:
+        r"""Safely dump a Pydantic model to a dictionary.
+
+        This method attempts to use the `model_dump` method if available,
+        otherwise it falls back to the `dict` method.
+
+        Args:
+            obj: The Pydantic model instance to be dumped.
+
+        Returns:
+            dict: A dictionary representation of the Pydantic model.
+        """
+        # Check if the `model_dump` method exists (Pydantic v2)
+        if hasattr(obj, 'model_dump'):
+            return obj.model_dump()
+        # Fallback to `dict()` method (Pydantic v1)
+        elif hasattr(obj, 'dict'):
+            return obj.dict()
+        else:
+            raise TypeError("The object is not a Pydantic model")
+
+    def handle_stream_response(
+        self,
+        response: Stream[ChatCompletionChunk],
+        prompt_tokens: int,
+    ) -> Tuple[List[BaseMessage], List[str], Dict[str, int], str]:
+        r"""Process a stream response from the model and extract the necessary
+        information.
+
+        Args:
+            response (dict): Model response.
+            prompt_tokens (int): Number of input prompt tokens.
+
+        Returns:
+            tuple: A tuple of list of output `ChatMessage`, list of
+                finish reasons, usage dictionary, and response id.
+        """
+        content_dict: defaultdict = defaultdict(lambda: "")
+        finish_reasons_dict: defaultdict = defaultdict(lambda: "")
+        output_messages: List[BaseMessage] = []
+        response_id: str = ""
+        # All choices in one response share one role
+        for chunk in response:
+            response_id = chunk.id
+            for choice in chunk.choices:
+                index = choice.index
+                delta = choice.delta
+                if delta.content is not None:
+                    # When response has not been stopped
+                    # Notice that only the first chunk_dict has the "role"
+                    content_dict[index] += delta.content
+                if choice.finish_reason:
+                    finish_reasons_dict[index] = choice.finish_reason
+                    chat_message = BaseMessage(
+                        role_name=self.role_name,
+                        role_type=self.role_type,
+                        meta_dict=dict(),
+                        content=content_dict[index],
+                    )
+                    output_messages.append(chat_message)
+        finish_reasons = [
+            finish_reasons_dict[i] for i in range(len(finish_reasons_dict))
+        ]
+        usage_dict = self.get_usage_dict(output_messages, prompt_tokens)
+        return output_messages, finish_reasons, usage_dict, response_id
+
+    def _step_token_exceed(
+        self,
+        num_tokens: int,
+        tool_calls: List[FunctionCallingRecord],
+        termination_reason: str,
+    ) -> ChatAgentResponse:
+        r"""Return trivial response containing number of tokens and information
+        of called functions when the number of tokens exceeds.
+
+        Args:
+            num_tokens (int): Number of tokens in the messages.
+            tool_calls (List[FunctionCallingRecord]): List of information
+                objects of functions called in the current step.
+            termination_reason (str): String of termination reason.
+
+        Returns:
+            ChatAgentResponse: The struct containing trivial outputs and
+                information about token number and called functions.
+        """
+        self.terminated = True
+        output_messages: List[BaseMessage] = []
+
+        info = self.get_info(
+            None,
+            None,
+            [termination_reason],
+            num_tokens,
+            tool_calls,
+        )
+
+        return ChatAgentResponse(
+            msgs=output_messages,
+            terminated=self.terminated,
+            info=info,
+        )
+
+    def step_tool_call(
+        self,
+        response: ChatCompletion,
+    ) -> Tuple[
+        FunctionCallingMessage, FunctionCallingMessage, FunctionCallingRecord
+    ]:
+        r"""Execute the function with arguments following the model's response.
+
+        Args:
+            response (Dict[str, Any]): The response obtained by calling the
+                model.
+
+        Returns:
+            tuple: A tuple consisting of two obj:`FunctionCallingMessage`,
+                one about the arguments and the other about the execution
+                result, and a struct for logging information about this
+                function call.
+        """
+        choice = response.choices[0]
+        if choice.message.tool_calls is None:
+            raise RuntimeError("Tool call is None")
+        func_name = choice.message.tool_calls[0].function.name
+
+        args = json.loads(choice.message.tool_calls[0].function.arguments)
+        tool = self.tool_dict[func_name]
+
+        # ! Here, if the agent calls advanced reasoning, provide the chat history
+        if func_name == "make_advanced_reasoning":
+            reformed_question = f"""
+            Please help an assistant to solve reasoning tasks. 
+            Here are the chat history between the assistant and the user, which may help you understand the intention of the user and the question:
+            <chat_history>{self.memory.get_context()}</chat_history>
+
+            Now please answer the following question:
+            <question>{args['question']}</question>
+            """
+            args["question"] = reformed_question
+
+        result = tool(**args)
+
+        assist_msg = FunctionCallingMessage(
+            role_name=self.role_name,
+            role_type=self.role_type,
+            meta_dict=None,
+            content="",
+            func_name=func_name,
+            args=args,
+        )
+        func_msg = FunctionCallingMessage(
+            role_name=self.role_name,
+            role_type=self.role_type,
+            meta_dict=None,
+            content="",
+            func_name=func_name,
+            result=result,
+        )
+
+        # Record information about this function call
+        func_record = FunctionCallingRecord(
+            func_name=func_name, args=args, result=result
+        )
+        return assist_msg, func_msg, func_record
+
+    async def step_tool_call_async(
+        self,
+        response: ChatCompletion,
+    ) -> Tuple[
+        FunctionCallingMessage, FunctionCallingMessage, FunctionCallingRecord
+    ]:
+        r"""Execute the async function with arguments following the model's
+        response.
+
+        Args:
+            response (Dict[str, Any]): The response obtained by calling the
+                model.
+
+        Returns:
+            tuple: A tuple consisting of two obj:`FunctionCallingMessage`,
+                one about the arguments and the other about the execution
+                result, and a struct for logging information about this
+                function call.
+        """
+        # Note that when function calling is enabled, `n` is set to 1.
+        choice = response.choices[0]
+        if choice.message.tool_calls is None:
+            raise RuntimeError("Tool call is None")
+        func_name = choice.message.tool_calls[0].function.name
+
+        args = json.loads(choice.message.tool_calls[0].function.arguments)
+        tool = self.tool_dict[func_name]
+        result = await tool(**args)
+
+        assist_msg = FunctionCallingMessage(
+            role_name=self.role_name,
+            role_type=self.role_type,
+            meta_dict=None,
+            content="",
+            func_name=func_name,
+            args=args,
+        )
+        func_msg = FunctionCallingMessage(
+            role_name=self.role_name,
+            role_type=self.role_type,
+            meta_dict=None,
+            content="",
+            func_name=func_name,
+            result=result,
+        )
+
+        # Record information about this function call
+        func_record = FunctionCallingRecord(
+            func_name=func_name, args=args, result=result
+        )
+        return assist_msg, func_msg, func_record
+
+    def get_usage_dict(
+        self, output_messages: List[BaseMessage], prompt_tokens: int
+    ) -> Dict[str, int]:
+        r"""Get usage dictionary when using the stream mode.
+
+        Args:
+            output_messages (list): List of output messages.
+            prompt_tokens (int): Number of input prompt tokens.
+
+        Returns:
+            dict: Usage dictionary.
+        """
+        encoding = get_model_encoding(self.model_type.value_for_tiktoken)
+        completion_tokens = 0
+        for message in output_messages:
+            completion_tokens += len(encoding.encode(message.content))
+        usage_dict = dict(
+            completion_tokens=completion_tokens,
+            prompt_tokens=prompt_tokens,
+            total_tokens=completion_tokens + prompt_tokens,
+        )
+        return usage_dict
+
+    def add_model_scheduling_strategy(self, name: str, strategy_fn: Callable):
+        r"""Add a scheduling strategy method provided by user to ModelManger.
+
+        Args:
+            name (str): The name of the strategy.
+            strategy_fn (Callable): The scheduling strategy function.
+        """
+        self.model_backend.add_strategy(name, strategy_fn)
+
+    def __repr__(self) -> str:
+        r"""Returns a string representation of the :obj:`ChatAgent`.
+
+        Returns:
+            str: The string representation of the :obj:`ChatAgent`.
+        """
+        return (
+            f"ChatAgent({self.role_name}, {self.role_type}, {self.model_type})"
+        )

deep-swarm/camel/agents/critic_agent.py

@@ -0,0 +1,202 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import random
+import warnings
+from typing import Any, Dict, Optional, Sequence
+
+from colorama import Fore
+
+from camel.agents.chat_agent import ChatAgent
+from camel.memories import AgentMemory
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.responses import ChatAgentResponse
+from camel.utils import get_first_int, print_text_animated
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="CriticAgent")
+class CriticAgent(ChatAgent):
+    r"""A class for the critic agent that assists in selecting an option.
+
+    Args:
+        system_message (BaseMessage): The system message for the critic
+            agent.
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        message_window_size (int, optional): The maximum number of previous
+            messages to include in the context window. If `None`, no windowing
+            is performed. (default: :obj:`6`)
+        retry_attempts (int, optional): The number of retry attempts if the
+            critic fails to return a valid option. (default: :obj:`2`)
+        verbose (bool, optional): Whether to print the critic's messages.
+        logger_color (Any): The color of the menu options displayed to the
+            user. (default: :obj:`Fore.MAGENTA`)
+    """
+
+    def __init__(
+        self,
+        system_message: BaseMessage,
+        model: Optional[BaseModelBackend] = None,
+        memory: Optional[AgentMemory] = None,
+        message_window_size: int = 6,
+        retry_attempts: int = 2,
+        verbose: bool = False,
+        logger_color: Any = Fore.MAGENTA,
+    ) -> None:
+        super().__init__(
+            system_message,
+            model=model,
+            memory=memory,
+            message_window_size=message_window_size,
+        )
+        self.options_dict: Dict[str, str] = dict()
+        self.retry_attempts = retry_attempts
+        self.verbose = verbose
+        self.logger_color = logger_color
+
+    def flatten_options(self, messages: Sequence[BaseMessage]) -> str:
+        r"""Flattens the options to the critic.
+
+        Args:
+            messages (Sequence[BaseMessage]): A list of `BaseMessage` objects.
+
+        Returns:
+            str: A string containing the flattened options to the critic.
+        """
+        options = [message.content for message in messages]
+        flatten_options = (
+            f"> Proposals from "
+            f"{messages[0].role_name} ({messages[0].role_type}). "
+            "Please choose an option:\n"
+        )
+        for index, option in enumerate(options):
+            flatten_options += f"Option {index + 1}:\n{option}\n\n"
+            self.options_dict[str(index + 1)] = option
+        format = (
+            f"Please first enter your choice ([1-{len(self.options_dict)}]) "
+            "and then your explanation and comparison: "
+        )
+        return flatten_options + format
+
+    def get_option(self, input_message: BaseMessage) -> str:
+        r"""Gets the option selected by the critic.
+
+        Args:
+            input_message (BaseMessage): A `BaseMessage` object representing
+                the input message.
+
+        Returns:
+            str: The option selected by the critic.
+        """
+        # TODO: Add support for editing options by the critic.
+        msg_content = input_message.content
+        i = 0
+        while i < self.retry_attempts:
+            critic_response = self.step(input_message)
+
+            if critic_response.msgs is None or len(critic_response.msgs) == 0:
+                raise RuntimeError("Got None critic messages.")
+            if critic_response.terminated:
+                raise RuntimeError("Critic step failed.")
+
+            critic_msg = critic_response.msg
+            if self.verbose:
+                print_text_animated(
+                    self.logger_color + "\n> Critic response: "
+                    f"\x1b[3m{critic_msg.content}\x1b[0m\n"
+                )
+            choice = self.parse_critic(critic_msg)
+
+            if choice in self.options_dict:
+                return self.options_dict[choice]
+            else:
+                input_message = BaseMessage(
+                    role_name=input_message.role_name,
+                    role_type=input_message.role_type,
+                    meta_dict=input_message.meta_dict,
+                    content="> Invalid choice. Please choose again.\n"
+                    + msg_content,
+                )
+                i += 1
+        warnings.warn(
+            "Critic failed to get a valid option. "
+            f"After {self.retry_attempts} attempts. "
+            "Returning a random option."
+        )
+        return random.choice(list(self.options_dict.values()))
+
+    def parse_critic(self, critic_msg: BaseMessage) -> Optional[str]:
+        r"""Parses the critic's message and extracts the choice.
+
+        Args:
+            critic_msg (BaseMessage): A `BaseMessage` object representing the
+                critic's response.
+
+        Returns:
+            Optional[str]: The critic's choice as a string, or None if the
+                message could not be parsed.
+        """
+        choice = str(get_first_int(critic_msg.content))
+        return choice
+
+    def reduce_step(
+        self,
+        input_messages: Sequence[BaseMessage],
+    ) -> ChatAgentResponse:
+        r"""Performs one step of the conversation by flattening options to the
+        critic, getting the option, and parsing the choice.
+
+        Args:
+            input_messages (Sequence[BaseMessage]): A list of BaseMessage
+                objects.
+
+        Returns:
+            ChatAgentResponse: A `ChatAgentResponse` object includes the
+                critic's choice.
+        """
+        meta_chat_message = BaseMessage(
+            role_name=input_messages[0].role_name,
+            role_type=input_messages[0].role_type,
+            meta_dict=input_messages[0].meta_dict,
+            content="",
+        )
+
+        flatten_options = self.flatten_options(input_messages)
+        if self.verbose:
+            print_text_animated(
+                self.logger_color + f"\x1b[3m{flatten_options}\x1b[0m\n"
+            )
+        input_msg = meta_chat_message.create_new_instance(flatten_options)
+
+        option = self.get_option(input_msg)
+        output_msg = meta_chat_message.create_new_instance(option)
+
+        # TODO: The return `info` can be improved.
+        return ChatAgentResponse(
+            msgs=[output_msg],
+            terminated=False,
+            info={},
+        )

deep-swarm/camel/agents/deductive_reasoner_agent.py

@@ -0,0 +1,303 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import re
+from typing import Dict, List, Optional, Union
+
+from camel.agents.chat_agent import ChatAgent
+from camel.logger import get_logger
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.prompts import TextPrompt
+from camel.types import RoleType
+
+logger = get_logger(__name__)
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="DeductiveReasonerAgent")
+class DeductiveReasonerAgent(ChatAgent):
+    r"""An agent responsible for deductive reasoning. Model of deductive
+    reasoning:
+        - L: A ⊕ C -> q * B
+        - A represents the known starting state.
+        - B represents the known target state.
+        - C represents the conditions required to transition from A to B.
+        - Q represents the quality or effectiveness of the transition from
+        A to B.
+        - L represents the path or process from A to B.
+
+    Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+    """
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+    ) -> None:
+        system_message = BaseMessage(
+            role_name="Insight Agent",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You assign roles based on tasks.",
+        )
+        super().__init__(system_message, model=model)
+
+    def deduce_conditions_and_quality(
+        self,
+        starting_state: str,
+        target_state: str,
+        role_descriptions_dict: Optional[Dict[str, str]] = None,
+    ) -> Dict[str, Union[List[str], Dict[str, str]]]:
+        r"""Derives the conditions and quality from the starting state and the
+        target state based on the model of the deductive reasoning and the
+        knowledge base. It can optionally consider the roles involved in the
+        scenario, which allows tailoring the output more closely to the AI
+        agent's environment.
+
+        Args:
+            starting_state (str): The initial or starting state from which
+                conditions are deduced.
+            target_state (str): The target state of the task.
+            role_descriptions_dict (Optional[Dict[str, str]], optional): The
+                descriptions of the roles. (default: :obj:`None`)
+            role_descriptions_dict (Optional[Dict[str, str]], optional): A
+                dictionary describing the roles involved in the scenario. This
+                is optional and can be used to provide a context for the
+                CAMEL's role-playing, enabling the generation of more relevant
+                and tailored conditions and quality assessments. This could be
+                generated using a `RoleAssignmentAgent()` or defined manually
+                by the user.
+
+        Returns:
+            Dict[str, Union[List[str], Dict[str, str]]]: A dictionary with the
+                extracted data from the message. The dictionary contains three
+                keys:
+                - 'conditions': A list where each key is a condition ID and
+                    each value is the corresponding condition text.
+                - 'labels': A list of label strings extracted from the message.
+                - 'quality': A string of quality assessment strings extracted
+                    from the message.
+        """
+        self.reset()
+
+        deduce_prompt = """You are a deductive reasoner. You are tasked to 
+        complete the TASK based on the THOUGHT OF DEDUCTIVE REASONING, the 
+        STARTING STATE A and the TARGET STATE B. You are given the CONTEXT 
+        CONTENT to help you complete the TASK.
+Your answer MUST strictly adhere to the structure of ANSWER TEMPLATE, ONLY 
+fill in the BLANKs, and DO NOT alter or modify any other part of the template
+
+===== MODELING OF DEDUCTIVE REASONING =====
+You are tasked with understanding a mathematical model based on the components 
+${A, B, C, Q, L}$. In this model: ``L: A ⊕ C -> q * B``.
+- $A$ represents the known starting state.
+- $B$ represents the known target state.
+- $C$ represents the conditions required to transition from $A$ to $B$.
+- $Q$ represents the quality or effectiveness of the transition from $A$ to 
+$B$.
+- $L$ represents the path or process from $A$ to $B$.
+
+===== THOUGHT OF DEDUCTIVE REASONING =====
+1. Define the Parameters of A and B:
+    - Characterization: Before delving into transitions, thoroughly understand 
+    the nature and boundaries of both $A$ and $B$. This includes the type, 
+    properties, constraints, and possible interactions between the two.
+    - Contrast and Compare: Highlight the similarities and differences between 
+    $A$ and $B$. This comparative analysis will give an insight into what 
+    needs changing and what remains constant.
+2. Historical & Empirical Analysis:
+    - Previous Transitions according to the Knowledge Base of GPT: (if 
+    applicable) Extract conditions and patterns from the historical instances 
+    where a similar transition from a state comparable to $A$ moved towards 
+    $B$.
+    - Scientific Principles: (if applicable) Consider the underlying 
+    scientific principles governing or related to the states and their 
+    transition. For example, if $A$ and $B$ are physical states, laws of 
+    physics might apply.
+3. Logical Deduction of Conditions ($C$):
+    - Direct Path Analysis: What are the immediate and direct conditions 
+    required to move from $A$ to $B$?
+    - Intermediate States: Are there states between $A$ and $B$ that must be 
+    traversed or can be used to make the transition smoother or more 
+    efficient? If yes, what is the content?
+    - Constraints & Limitations: Identify potential barriers or restrictions 
+    in moving from $A$ to $B$. These can be external (e.g., environmental 
+    factors) or internal (properties of $A$ or $B$).
+    - Resource and Information Analysis: What resources and information are 
+    required for the transition? This could be time, entity, factor, code 
+    language, software platform, unknowns, etc.
+    - External Influences: Consider socio-economic, political, or 
+    environmental factors (if applicable) that could influence the transition 
+    conditions.
+    - Creative/Heuristic Reasoning: Open your mind to multiple possible $C$'s, 
+    no matter how unconventional they might seem. Utilize analogies, 
+    metaphors, or brainstorming techniques to envision possible conditions or 
+    paths from $A$ to $B$.
+    - The conditions $C$ should be multiple but in one sentence. And each 
+    condition should be concerned with one aspect/entity.
+4. Entity/Label Recognition of Conditions ($C$):
+    - Identify and categorize entities of Conditions ($C$) such as the names, 
+    locations, dates, specific technical terms or contextual parameters that 
+    might be associated with events, innovations post-2022.
+    - The output of the entities/labels will be used as tags or labels for 
+    semantic similarity searches. The entities/labels may be the words, or 
+    phrases, each of them should contain valuable, high information entropy 
+    information, and should be independent.
+    - Ensure that the identified entities are formatted in a manner suitable 
+    for database indexing and retrieval. Organize the entities into 
+    categories, and combine the category with its instance into a continuous 
+    phrase, without using colons or other separators.
+    - Format these entities for database indexing: output the category rather 
+    than its instance/content into a continuous phrase. For example, instead 
+    of "Jan. 02", identify it as "Event time".
+5. Quality Assessment ($Q$):
+    - Efficiency: How efficient is the transition from $A$ to $B$, which 
+    measures the resources used versus the desired outcome?
+    - Effectiveness: Did the transition achieve the desired outcome or was the 
+    target state achieved as intended?
+    - Safety & Risks: Assess any risks associated with the transition and the 
+    measures to mitigate them.
+    - Feedback Mechanisms: Incorporate feedback loops to continuously monitor 
+    and adjust the quality of transition, making it more adaptive.
+6. Iterative Evaluation:
+    - Test & Refine: Based on the initially deduced conditions and assessed 
+    quality, iterate the process to refine and optimize the transition. This 
+    might involve tweaking conditions, employing different paths, or changing 
+    resources.
+    - Feedback Integration: Use feedback to make improvements and increase the 
+    quality of the transition.
+7. Real-world scenarios often present challenges that may not be captured by 
+models and frameworks. While using the model, maintain an adaptive mindset:
+    - Scenario Exploration: Continuously imagine various possible scenarios, 
+    both positive and negative, to prepare for unexpected events.
+    - Flexibility: Be prepared to modify conditions ($C$) or alter the path/
+    process ($L$) if unforeseen challenges arise.
+    - Feedback Integration: Rapidly integrate feedback from actual 
+    implementations to adjust the model's application, ensuring relevancy and 
+    effectiveness.
+
+===== TASK =====
+Given the starting state $A$ and the target state $B$, assuming that a path 
+$L$ always exists between $A$ and $B$, how can one deduce or identify the 
+necessary conditions $C$ and the quality $Q$ of the transition?
+
+===== STARTING STATE $A$ =====
+{starting_state}
+
+===== TARGET STATE $B$ =====
+{target_state}
+
+{role_with_description_prompt}
+===== ANSWER TEMPLATE =====
+- Characterization and comparison of $A$ and $B$:\n<BLANK>
+- Historical & Empirical Analysis:\n<BLANK>/None
+- Logical Deduction of Conditions ($C$) (multiple conditions can be deduced):
+    condition <NUM>:
+        <BLANK>.
+- Entity/Label Recognition of Conditions:\n[<BLANK>, <BLANK>, ...] (include 
+square brackets)
+- Quality Assessment ($Q$) (do not use symbols):
+    <BLANK>.
+- Iterative Evaluation:\n<BLANK>/None"""
+
+        if role_descriptions_dict is not None:
+            role_names = role_descriptions_dict.keys()
+            role_with_description_prompt = (
+                "===== ROLES WITH DESCRIPTIONS =====\n"
+                + "\n".join(
+                    f"{role_name}:\n{role_descriptions_dict[role_name]}\n"
+                    for role_name in role_names
+                )
+                + "\n\n"
+            )
+        else:
+            role_with_description_prompt = ""
+        deduce_prompt = TextPrompt(deduce_prompt)
+
+        deduce = deduce_prompt.format(
+            starting_state=starting_state,
+            target_state=target_state,
+            role_with_description_prompt=role_with_description_prompt,
+        )
+
+        conditions_and_quality_generation_msg = BaseMessage.make_user_message(
+            role_name="Deductive Reasoner", content=deduce
+        )
+
+        response = self.step(
+            input_message=conditions_and_quality_generation_msg
+        )
+
+        if response.terminated:
+            raise RuntimeError(
+                "Deduction failed. Error:\n" + f"{response.info}"
+            )
+        msg: BaseMessage = response.msg
+        logger.info(f"Message content:\n{msg.content}")
+
+        # Extract the conditions from the message
+        conditions_dict = {
+            f"condition {i}": cdt.replace("<", "")
+            .replace(">", "")
+            .strip()
+            .strip('\n')
+            for i, cdt in re.findall(
+                r"condition (\d+):\s*(.+?)(?=condition \d+|- Entity)",
+                msg.content,
+                re.DOTALL,
+            )
+        }
+
+        # Extract the labels from the message
+        labels = [
+            label.strip().strip('\n').strip("\"'")
+            for label in re.findall(
+                r"Entity/Label Recognition of Conditions:\n\[(.+?)\]",
+                msg.content,
+                re.DOTALL,
+            )[0].split(",")
+        ]
+
+        # Extract the quality from the message
+        quality = next(
+            q.strip().strip('\n')
+            for q in re.findall(
+                r"Quality Assessment \(\$Q\$\) \(do not use symbols\):"
+                r"\n(.+?)- Iterative",
+                msg.content,
+                re.DOTALL,
+            )
+        )
+
+        # Convert them into JSON format
+        conditions_and_quality_json: Dict[
+            str, Union[List[str], Dict[str, str]]
+        ] = {}
+        conditions_and_quality_json["conditions"] = conditions_dict
+        conditions_and_quality_json["labels"] = labels
+        conditions_and_quality_json["evaluate_quality"] = quality
+
+        return conditions_and_quality_json

deep-swarm/camel/agents/embodied_agent.py

@@ -0,0 +1,201 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import Any, List, Optional
+
+from colorama import Fore
+
+from camel.agents.chat_agent import ChatAgent
+from camel.agents.tool_agents.base import BaseToolAgent
+from camel.interpreters import (
+    BaseInterpreter,
+    InternalPythonInterpreter,
+    SubprocessInterpreter,
+)
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.responses import ChatAgentResponse
+from camel.utils import print_text_animated
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="EmbodiedAgent")
+class EmbodiedAgent(ChatAgent):
+    r"""Class for managing conversations of CAMEL Embodied Agents.
+
+    Args:
+        system_message (BaseMessage): The system message for the chat agent.
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        message_window_size (int, optional): The maximum number of previous
+            messages to include in the context window. If `None`, no windowing
+            is performed. (default: :obj:`None`)
+        tool_agents (List[BaseToolAgent], optional): The tools agents to use in
+            the embodied agent. (default: :obj:`None`)
+        code_interpreter (BaseInterpreter, optional): The code interpreter to
+            execute codes. If `code_interpreter` and `tool_agent` are both
+            `None`, default to `SubProcessInterpreter`. If `code_interpreter`
+            is `None` and `tool_agents` is not `None`, default to
+            `InternalPythonInterpreter`.  (default: :obj:`None`)
+        verbose (bool, optional): Whether to print the critic's messages.
+        logger_color (Any): The color of the logger displayed to the user.
+            (default: :obj:`Fore.MAGENTA`)
+    """
+
+    def __init__(
+        self,
+        system_message: BaseMessage,
+        model: Optional[BaseModelBackend] = None,
+        message_window_size: Optional[int] = None,
+        tool_agents: Optional[List[BaseToolAgent]] = None,
+        code_interpreter: Optional[BaseInterpreter] = None,
+        verbose: bool = False,
+        logger_color: Any = Fore.MAGENTA,
+    ) -> None:
+        self.tool_agents = tool_agents
+        self.code_interpreter: BaseInterpreter
+        if code_interpreter is not None:
+            self.code_interpreter = code_interpreter
+        elif self.tool_agents:
+            self.code_interpreter = InternalPythonInterpreter()
+        else:
+            self.code_interpreter = SubprocessInterpreter()
+
+        if self.tool_agents:
+            system_message = self._set_tool_agents(system_message)
+        self.verbose = verbose
+        self.logger_color = logger_color
+        super().__init__(
+            system_message=system_message,
+            model=model,
+            message_window_size=message_window_size,
+        )
+
+    def _set_tool_agents(self, system_message: BaseMessage) -> BaseMessage:
+        action_space_prompt = self._get_tool_agents_prompt()
+        result_message = system_message.create_new_instance(
+            content=system_message.content.format(
+                action_space=action_space_prompt
+            )
+        )
+        if self.tool_agents is not None:
+            self.code_interpreter.update_action_space(
+                {tool.name: tool for tool in self.tool_agents}
+            )
+        return result_message
+
+    def _get_tool_agents_prompt(self) -> str:
+        r"""Returns the action space prompt.
+
+        Returns:
+            str: The action space prompt.
+        """
+        if self.tool_agents is not None:
+            return "\n".join(
+                [
+                    f"*** {tool.name} ***:\n {tool.description}"
+                    for tool in self.tool_agents
+                ]
+            )
+        else:
+            return ""
+
+    def get_tool_agent_names(self) -> List[str]:
+        r"""Returns the names of tool agents.
+
+        Returns:
+            List[str]: The names of tool agents.
+        """
+        if self.tool_agents is not None:
+            return [tool.name for tool in self.tool_agents]
+        else:
+            return []
+
+    # ruff: noqa: E501
+    def step(self, input_message: BaseMessage) -> ChatAgentResponse:  # type: ignore[override]
+        r"""Performs a step in the conversation.
+
+        Args:
+            input_message (BaseMessage): The input message.
+
+        Returns:
+            ChatAgentResponse: A struct containing the output messages,
+                a boolean indicating whether the chat session has terminated,
+                and information about the chat session.
+        """
+        response = super().step(input_message)
+
+        if response.msgs is None or len(response.msgs) == 0:
+            raise RuntimeError("Got None output messages.")
+        if response.terminated:
+            raise RuntimeError(f"{self.__class__.__name__} step failed.")
+
+        # NOTE: Only single output messages are supported
+        explanations, codes = response.msg.extract_text_and_code_prompts()
+
+        if self.verbose:
+            for explanation, code in zip(explanations, codes):
+                print_text_animated(
+                    self.logger_color + f"> Explanation:\n{explanation}"
+                )
+                print_text_animated(self.logger_color + f"> Code:\n{code}")
+
+            if len(explanations) > len(codes):
+                print_text_animated(
+                    self.logger_color + f"> Explanation:\n{explanations[-1]}"
+                )
+
+        content = response.msg.content
+
+        if codes is not None:
+            try:
+                content = "\n> Executed Results:\n"
+                for block_idx, code in enumerate(codes):
+                    executed_output = self.code_interpreter.run(
+                        code, code.code_type
+                    )
+                    content += (
+                        f"Executing code block {block_idx}: {{\n"
+                        + executed_output
+                        + "}\n"
+                    )
+            except InterruptedError as e:
+                content = (
+                    f"\n> Running code fail: {e}\n"
+                    "Please regenerate the code."
+                )
+
+        # TODO: Handle errors
+        content = input_message.content + f"\n> Embodied Actions:\n{content}"
+        message = BaseMessage(
+            input_message.role_name,
+            input_message.role_type,
+            input_message.meta_dict,
+            content,
+        )
+        return ChatAgentResponse(
+            msgs=[message],
+            terminated=response.terminated,
+            info=response.info,
+        )

deep-swarm/camel/agents/knowledge_graph_agent.py

@@ -0,0 +1,259 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import TYPE_CHECKING, Optional, Union
+
+if TYPE_CHECKING:
+    from unstructured.documents.elements import Element
+
+from camel.agents import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.prompts import TextPrompt
+from camel.storages.graph_storages.graph_element import (
+    GraphElement,
+    Node,
+    Relationship,
+)
+from camel.types import RoleType
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+text_prompt = """
+You are tasked with extracting nodes and relationships from given content and 
+structures them into Node and Relationship objects. Here's the outline of what 
+you needs to do:
+
+Content Extraction:
+You should be able to process input content and identify entities mentioned 
+within it.
+Entities can be any noun phrases or concepts that represent distinct entities 
+in the context of the given content.
+
+Node Extraction:
+For each identified entity, you should create a Node object.
+Each Node object should have a unique identifier (id) and a type (type).
+Additional properties associated with the node can also be extracted and 
+stored.
+
+Relationship Extraction:
+You should identify relationships between entities mentioned in the content.
+For each relationship, create a Relationship object.
+A Relationship object should have a subject (subj) and an object (obj) which 
+are Node objects representing the entities involved in the relationship.
+Each relationship should also have a type (type), and additional properties if 
+applicable.
+
+Output Formatting:
+The extracted nodes and relationships should be formatted as instances of the 
+provided Node and Relationship classes.
+Ensure that the extracted data adheres to the structure defined by the classes.
+Output the structured data in a format that can be easily validated against 
+the provided code.
+
+Instructions for you:
+Read the provided content thoroughly.
+Identify distinct entities mentioned in the content and categorize them as 
+nodes.
+Determine relationships between these entities and represent them as directed 
+relationships.
+Provide the extracted nodes and relationships in the specified format below.
+Example for you:
+
+Example Content:
+"John works at XYZ Corporation. He is a software engineer. The company is 
+located in New York City."
+
+Expected Output:
+
+Nodes:
+
+Node(id='John', type='Person')
+Node(id='XYZ Corporation', type='Organization')
+Node(id='New York City', type='Location')
+
+Relationships:
+
+Relationship(subj=Node(id='John', type='Person'), obj=Node(id='XYZ 
+Corporation', type='Organization'), type='WorksAt')
+Relationship(subj=Node(id='John', type='Person'), obj=Node(id='New York City', 
+type='Location'), type='ResidesIn')
+
+===== TASK =====
+Please extracts nodes and relationships from given content and structures them 
+into Node and Relationship objects. 
+
+{task}
+"""
+
+
+@track_agent(name="KnowledgeGraphAgent")
+class KnowledgeGraphAgent(ChatAgent):
+    r"""An agent that can extract node and relationship information for
+    different entities from given `Element` content.
+
+    Attributes:
+        task_prompt (TextPrompt): A prompt for the agent to extract node and
+            relationship information for different entities.
+    """
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+    ) -> None:
+        r"""Initialize the `KnowledgeGraphAgent`.
+
+        Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        """
+        system_message = BaseMessage(
+            role_name="Graphify",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="Your mission is to transform unstructured content "
+            "into structured graph data. Extract nodes and relationships with "
+            "precision, and let the connections unfold. Your graphs will "
+            "illuminate the hidden connections within the chaos of "
+            "information.",
+        )
+        super().__init__(system_message, model=model)
+
+    def run(
+        self,
+        element: "Element",
+        parse_graph_elements: bool = False,
+    ) -> Union[str, GraphElement]:
+        r"""Run the agent to extract node and relationship information.
+
+        Args:
+            element (Element): The input element.
+            parse_graph_elements (bool, optional): Whether to parse into
+                `GraphElement`. Defaults to `False`.
+
+        Returns:
+            Union[str, GraphElement]: The extracted node and relationship
+                information. If `parse_graph_elements` is `True` then return
+                `GraphElement`, else return `str`.
+        """
+        self.reset()
+        self.element = element
+
+        knowledge_graph_prompt = TextPrompt(text_prompt)
+        knowledge_graph_generation = knowledge_graph_prompt.format(
+            task=str(element)
+        )
+
+        knowledge_graph_generation_msg = BaseMessage.make_user_message(
+            role_name="Graphify", content=knowledge_graph_generation
+        )
+
+        response = self.step(input_message=knowledge_graph_generation_msg)
+
+        content = response.msg.content
+
+        if parse_graph_elements:
+            content = self._parse_graph_elements(content)
+
+        return content
+
+    def _validate_node(self, node: Node) -> bool:
+        r"""Validate if the object is a valid Node.
+
+        Args:
+            node (Node): Object to be validated.
+
+        Returns:
+            bool: True if the object is a valid Node, False otherwise.
+        """
+        return (
+            isinstance(node, Node)
+            and isinstance(node.id, (str, int))
+            and isinstance(node.type, str)
+        )
+
+    def _validate_relationship(self, relationship: Relationship) -> bool:
+        r"""Validate if the object is a valid Relationship.
+
+        Args:
+            relationship (Relationship): Object to be validated.
+
+        Returns:
+            bool: True if the object is a valid Relationship, False otherwise.
+        """
+        return (
+            isinstance(relationship, Relationship)
+            and self._validate_node(relationship.subj)
+            and self._validate_node(relationship.obj)
+            and isinstance(relationship.type, str)
+        )
+
+    def _parse_graph_elements(self, input_string: str) -> GraphElement:
+        r"""Parses graph elements from given content.
+
+        Args:
+            input_string (str): The input content.
+
+        Returns:
+            GraphElement: The parsed graph elements.
+        """
+        import re
+
+        # Regular expressions to extract nodes and relationships
+        node_pattern = r"Node\(id='(.*?)', type='(.*?)'\)"
+        rel_pattern = (
+            r"Relationship\(subj=Node\(id='(.*?)', type='(.*?)'\), "
+            r"obj=Node\(id='(.*?)', type='(.*?)'\), type='(.*?)'\)"
+        )
+
+        nodes = {}
+        relationships = []
+
+        # Extract nodes
+        for match in re.finditer(node_pattern, input_string):
+            id, type = match.groups()
+            properties = {'source': 'agent_created'}
+            if id not in nodes:
+                node = Node(id=id, type=type, properties=properties)
+                if self._validate_node(node):
+                    nodes[id] = node
+
+        # Extract relationships
+        for match in re.finditer(rel_pattern, input_string):
+            subj_id, subj_type, obj_id, obj_type, rel_type = match.groups()
+            properties = {'source': 'agent_created'}
+            if subj_id in nodes and obj_id in nodes:
+                subj = nodes[subj_id]
+                obj = nodes[obj_id]
+                relationship = Relationship(
+                    subj=subj, obj=obj, type=rel_type, properties=properties
+                )
+                if self._validate_relationship(relationship):
+                    relationships.append(relationship)
+
+        return GraphElement(
+            nodes=list(nodes.values()),
+            relationships=relationships,
+            source=self.element,
+        )

deep-swarm/camel/agents/role_assignment_agent.py

@@ -0,0 +1,141 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import re
+from typing import Dict, Optional, Union
+
+from camel.agents.chat_agent import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.prompts import TextPrompt
+from camel.types import RoleType
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="RoleAssignmentAgent")
+class RoleAssignmentAgent(ChatAgent):
+    r"""An agent that generates role names based on the task prompt.
+
+    Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+
+    Attributes:
+        role_assignment_prompt (TextPrompt): A prompt for the agent to generate
+        role names.
+    """
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+    ) -> None:
+        system_message = BaseMessage(
+            role_name="Role Assigner",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You assign roles based on tasks.",
+        )
+        super().__init__(system_message, model=model)
+
+    def run(
+        self,
+        task_prompt: Union[str, TextPrompt],
+        num_roles: int = 2,
+    ) -> Dict[str, str]:
+        r"""Generate role names based on the input task prompt.
+
+        Args:
+            task_prompt (Union[str, TextPrompt]): The prompt
+                for the task based on which the roles are to be generated.
+            num_roles (int, optional): The number of roles to generate.
+                (default: :obj:`2`)
+
+        Returns:
+            Dict[str, str]: A dictionary mapping role names to their
+                descriptions.
+        """
+        self.reset()
+
+        expert_prompt = "===== ANSWER PROMPT =====\n" + "\n".join(
+            f"Domain expert {i + 1}: <BLANK>\n"
+            f"Associated competencies, characteristics, duties "
+            f"and workflows: <BLANK>. End."
+            for i in range(num_roles or 0)
+        )
+        role_assignment_generation_prompt = TextPrompt(
+            "You are a role assignment agent, and you're in charge of "
+            + "recruiting {num_roles} experts for the following task."
+            + "\n==== TASK =====\n {task}\n\n"
+            + "Identify the domain experts you'd recruit and detail their "
+            + "associated competencies, characteristics, duties and workflows "
+            + "to complete the task.\n "
+            + "Your answer MUST adhere to the format of ANSWER PROMPT, and "
+            + "ONLY answer the BLANKs.\n"
+            + expert_prompt
+        )
+        role_assignment_generation = role_assignment_generation_prompt.format(
+            num_roles=num_roles, task=task_prompt
+        )
+
+        role_assignment_generation_msg = BaseMessage.make_user_message(
+            role_name="Role Assigner", content=role_assignment_generation
+        )
+
+        response = self.step(input_message=role_assignment_generation_msg)
+
+        msg = response.msg  # type: BaseMessage
+        terminated = response.terminated
+
+        # Distribute the output completions into role names and descriptions
+        role_names = [
+            desc.replace("<|", "").replace("|>", "")
+            for desc in re.findall(
+                r"Domain expert \d: (.+?)\nAssociated competencies,",
+                msg.content,
+                re.DOTALL,
+            )
+        ]
+        role_descriptions = [
+            desc.replace("<|", "").replace("|>", "")
+            for desc in re.findall(
+                r"Associated competencies, characteristics, "
+                r"duties and workflows: (.+?) End.",
+                msg.content,
+                re.DOTALL,
+            )
+        ]
+
+        if len(role_names) != num_roles or len(role_descriptions) != num_roles:
+            raise RuntimeError(
+                "Got None or insufficient information of roles."
+            )
+        if terminated:
+            raise RuntimeError("Role assignment failed.")
+
+        role_descriptions_dict = {
+            role_name: description
+            for role_name, description in zip(role_names, role_descriptions)
+        }
+
+        return role_descriptions_dict

deep-swarm/camel/agents/search_agent.py

@@ -0,0 +1,133 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import Optional
+
+from camel.agents.chat_agent import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.prompts import TextPrompt
+from camel.types import RoleType
+from camel.utils import create_chunks
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="SearchAgent")
+class SearchAgent(ChatAgent):
+    r"""An agent that summarizes text based on a query and evaluates the
+    relevance of an answer.
+
+    Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+    """
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+    ) -> None:
+        system_message = BaseMessage(
+            role_name="Assistant",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You are a helpful assistant.",
+        )
+        super().__init__(system_message, model=model)
+
+    def summarize_text(self, text: str, query: str) -> str:
+        r"""Summarize the information from the text, base on the query.
+
+        Args:
+            text (str): Text to summarize.
+            query (str): What information you want.
+
+        Returns:
+            str: Strings with information.
+        """
+        self.reset()
+
+        summary_prompt = TextPrompt(
+            '''Gather information from this text that relative to the
+            question, but do not directly answer the question.\nquestion:
+            {query}\ntext '''
+        )
+        summary_prompt = summary_prompt.format(query=query)
+        # Max length of each chunk
+        max_len = 3000
+        results = ""
+        chunks = create_chunks(text, max_len)
+        # Summarize
+        for i, chunk in enumerate(chunks, start=1):
+            prompt = summary_prompt + str(i) + ": " + chunk
+            user_msg = BaseMessage.make_user_message(
+                role_name="User",
+                content=prompt,
+            )
+            result = self.step(user_msg).msg.content
+            results += result + "\n"
+
+        # Final summarization
+        final_prompt = TextPrompt(
+            '''Here are some summarized texts which split from one text. Using
+            the information to answer the question. If can't find the answer,
+            you must answer "I can not find the answer to the query" and
+            explain why.\n Query:\n{query}.\n\nText:\n'''
+        )
+        final_prompt = final_prompt.format(query=query)
+        prompt = final_prompt + results
+
+        user_msg = BaseMessage.make_user_message(
+            role_name="User",
+            content=prompt,
+        )
+        response = self.step(user_msg).msg.content
+
+        return response
+
+    def continue_search(self, query: str, answer: str) -> bool:
+        r"""Ask whether to continue search or not based on the provided answer.
+
+        Args:
+            query (str): The question.
+            answer (str): The answer to the question.
+
+        Returns:
+            bool: `True` if the user want to continue search, `False`
+            otherwise.
+        """
+        prompt = TextPrompt(
+            "Do you think the ANSWER can answer the QUERY? "
+            "Use only 'yes' or 'no' to answer.\n"
+            "===== QUERY =====\n{query}\n\n"
+            "===== ANSWER =====\n{answer}"
+        )
+        prompt = prompt.format(query=query, answer=answer)
+        user_msg = BaseMessage.make_user_message(
+            role_name="User",
+            content=prompt,
+        )
+        response = self.step(user_msg).msg.content
+        if "yes" in str(response).lower():
+            return False
+        return True

deep-swarm/camel/agents/task_agent.py

@@ -0,0 +1,410 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import Any, Dict, List, Optional, Union
+
+from camel.agents.chat_agent import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import BaseModelBackend
+from camel.prompts import PromptTemplateGenerator, TextPrompt
+from camel.types import RoleType, TaskType
+from camel.utils import get_task_list
+
+# AgentOps decorator setting
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import track_agent
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    from camel.utils import track_agent
+
+
+@track_agent(name="TaskSpecifyAgent")
+class TaskSpecifyAgent(ChatAgent):
+    r"""An agent that specifies a given task prompt by prompting the user to
+    provide more details.
+
+    Attributes:
+        DEFAULT_WORD_LIMIT (int): The default word limit for the task prompt.
+        task_specify_prompt (TextPrompt): The prompt for specifying the task.
+
+    Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        task_type (TaskType, optional): The type of task for which to generate
+            a prompt. (default: :obj:`TaskType.AI_SOCIETY`)
+        task_specify_prompt (Union[str, TextPrompt], optional): The prompt for
+            specifying the task. (default: :obj:`None`)
+        word_limit (int, optional): The word limit for the task prompt.
+            (default: :obj:`50`)
+        output_language (str, optional): The language to be output by the
+            agent. (default: :obj:`None`)
+    """
+
+    DEFAULT_WORD_LIMIT = 50
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+        task_type: TaskType = TaskType.AI_SOCIETY,
+        task_specify_prompt: Optional[Union[str, TextPrompt]] = None,
+        word_limit: int = DEFAULT_WORD_LIMIT,
+        output_language: Optional[str] = None,
+    ) -> None:
+        self.task_specify_prompt: Union[str, TextPrompt]
+        if task_specify_prompt is None:
+            task_specify_prompt_template = (
+                PromptTemplateGenerator().get_task_specify_prompt(task_type)
+            )
+
+            self.task_specify_prompt = task_specify_prompt_template.format(
+                word_limit=word_limit
+            )
+        else:
+            self.task_specify_prompt = TextPrompt(task_specify_prompt)
+
+        system_message = BaseMessage(
+            role_name="Task Specifier",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You can make a task more specific.",
+        )
+
+        super().__init__(
+            system_message,
+            model=model,
+            output_language=output_language,
+        )
+
+    def run(
+        self,
+        task_prompt: Union[str, TextPrompt],
+        meta_dict: Optional[Dict[str, Any]] = None,
+    ) -> TextPrompt:
+        r"""Specify the given task prompt by providing more details.
+
+        Args:
+            task_prompt (Union[str, TextPrompt]): The original task
+                prompt.
+            meta_dict (Dict[str, Any], optional): A dictionary containing
+                additional information to include in the prompt.
+                (default: :obj:`None`)
+
+        Returns:
+            TextPrompt: The specified task prompt.
+        """
+        self.reset()
+        task_specify_prompt = self.task_specify_prompt.format(task=task_prompt)
+
+        if meta_dict is not None:
+            task_specify_prompt = task_specify_prompt.format(**meta_dict)
+        task_msg = BaseMessage.make_user_message(
+            role_name="Task Specifier", content=task_specify_prompt
+        )
+        specifier_response = self.step(task_msg)
+
+        if specifier_response.terminated:
+            raise RuntimeError("Task specification failed.")
+        if len(specifier_response.msgs) == 0:
+            raise RuntimeError("Got no specification message.")
+
+        specified_task_msg = specifier_response.msgs[0]
+
+        return TextPrompt(specified_task_msg.content)
+
+
+@track_agent(name="TaskPlannerAgent")
+class TaskPlannerAgent(ChatAgent):
+    r"""An agent that helps divide a task into subtasks based on the input
+    task prompt.
+
+    Attributes:
+        task_planner_prompt (TextPrompt): A prompt for the agent to divide
+            the task into subtasks.
+
+    Args:
+        model (BaseModelBackend, optional): The model backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        output_language (str, optional): The language to be output by the
+            agent. (default: :obj:`None`)
+    """
+
+    def __init__(
+        self,
+        model: Optional[BaseModelBackend] = None,
+        output_language: Optional[str] = None,
+    ) -> None:
+        self.task_planner_prompt = TextPrompt(
+            "Divide this task into subtasks: {task}. Be concise."
+        )
+        system_message = BaseMessage(
+            role_name="Task Planner",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You are a helpful task planner.",
+        )
+
+        super().__init__(
+            system_message,
+            model=model,
+            output_language=output_language,
+        )
+
+    def run(
+        self,
+        task_prompt: Union[str, TextPrompt],
+    ) -> TextPrompt:
+        r"""Generate subtasks based on the input task prompt.
+
+        Args:
+            task_prompt (Union[str, TextPrompt]): The prompt for the task to
+                be divided into subtasks.
+
+        Returns:
+            TextPrompt: A prompt for the subtasks generated by the agent.
+        """
+        # TODO: Maybe include roles information.
+        self.reset()
+        task_planner_prompt = self.task_planner_prompt.format(task=task_prompt)
+
+        task_msg = BaseMessage.make_user_message(
+            role_name="Task Planner", content=task_planner_prompt
+        )
+
+        task_response = self.step(task_msg)
+
+        if task_response.terminated:
+            raise RuntimeError("Task planning failed.")
+        if len(task_response.msgs) == 0:
+            raise RuntimeError("Got no task planning message.")
+
+        sub_tasks_msg = task_response.msgs[0]
+        return TextPrompt(sub_tasks_msg.content)
+
+
+@track_agent(name="TaskCreationAgent")
+class TaskCreationAgent(ChatAgent):
+    r"""An agent that helps create new tasks based on the objective
+    and last completed task. Compared to :obj:`TaskPlannerAgent`,
+    it's still a task planner, but it has more context information
+    like last task and incomplete task list. Modified from
+    `BabyAGI <https://github.com/yoheinakajima/babyagi>`_.
+
+    Attributes:
+        task_creation_prompt (TextPrompt): A prompt for the agent to
+            create new tasks.
+
+    Args:
+        role_name (str): The role name of the Agent to create the task.
+        objective (Union[str, TextPrompt]): The objective of the Agent to
+            perform the task.
+        model (BaseModelBackend, optional): The LLM backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        output_language (str, optional): The language to be output by the
+            agent. (default: :obj:`None`)
+        message_window_size (int, optional): The maximum number of previous
+            messages to include in the context window. If `None`, no windowing
+            is performed. (default: :obj:`None`)
+        max_task_num (int, optional): The maximum number of planned
+            tasks in one round. (default: :obj:3)
+    """
+
+    def __init__(
+        self,
+        role_name: str,
+        objective: Union[str, TextPrompt],
+        model: Optional[BaseModelBackend] = None,
+        output_language: Optional[str] = None,
+        message_window_size: Optional[int] = None,
+        max_task_num: Optional[int] = 3,
+    ) -> None:
+        task_creation_prompt = TextPrompt(
+            """Create new a task with the following objective: {objective}.
+Never forget you are a Task Creator of {role_name}.
+You must instruct me based on my expertise and your needs to solve the task.
+You should consider past solved tasks and in-progress tasks: {task_list}.
+The new created tasks must not overlap with these past tasks.
+The result must be a numbered list in the format:
+
+    #. First Task
+    #. Second Task
+    #. Third Task
+
+You can only give me up to {max_task_num} tasks at a time. \
+Each task should be concise, concrete and doable for a {role_name}.
+You should make task plan and not ask me questions.
+If you think no new tasks are needed right now, write "No tasks to add."
+Now start to give me new tasks one by one. No more than three tasks.
+Be concrete.
+"""
+        )
+
+        self.task_creation_prompt = task_creation_prompt.format(
+            objective=objective, role_name=role_name, max_task_num=max_task_num
+        )
+        self.objective = objective
+
+        system_message = BaseMessage(
+            role_name="Task Creator",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You are a helpful task creator.",
+        )
+
+        super().__init__(
+            system_message,
+            model=model,
+            output_language=output_language,
+            message_window_size=message_window_size,
+        )
+
+    def run(
+        self,
+        task_list: List[str],
+    ) -> List[str]:
+        r"""Generate subtasks based on the previous task results and
+        incomplete task list.
+
+        Args:
+            task_list (List[str]): The completed or in-progress
+                tasks which should not overlap with new created tasks.
+
+        Returns:
+            List[str]: The new task list generated by the Agent.
+        """
+
+        if len(task_list) > 0:
+            task_creation_prompt = self.task_creation_prompt.format(
+                task_list=task_list
+            )
+        else:
+            task_creation_prompt = self.task_creation_prompt.format(
+                task_list=""
+            )
+
+        task_msg = BaseMessage.make_user_message(
+            role_name="Task Creator", content=task_creation_prompt
+        )
+        task_response = self.step(task_msg)
+
+        if task_response.terminated:
+            raise RuntimeError("Task creation failed.")
+        if len(task_response.msgs) == 0:
+            raise RuntimeError("Got no task creation message.")
+
+        sub_tasks_msg = task_response.msgs[0]
+        return get_task_list(sub_tasks_msg.content)
+
+
+@track_agent(name="TaskPrioritizationAgent")
+class TaskPrioritizationAgent(ChatAgent):
+    r"""An agent that helps re-prioritize the task list and
+    returns numbered prioritized list. Modified from
+    `BabyAGI <https://github.com/yoheinakajima/babyagi>`_.
+
+    Attributes:
+        task_prioritization_prompt (TextPrompt): A prompt for the agent to
+            prioritize tasks.
+
+    Args:
+        objective (Union[str, TextPrompt]): The objective of the Agent to
+            perform the task.
+        model (BaseModelBackend, optional): The LLM backend to use for
+            generating responses. (default: :obj:`OpenAIModel` with
+            `GPT_4O_MINI`)
+        output_language (str, optional): The language to be output by the
+            agent. (default: :obj:`None`)
+        message_window_size (int, optional): The maximum number of previous
+            messages to include in the context window. If `None`, no windowing
+            is performed. (default: :obj:`None`)
+    """
+
+    def __init__(
+        self,
+        objective: Union[str, TextPrompt],
+        model: Optional[BaseModelBackend] = None,
+        output_language: Optional[str] = None,
+        message_window_size: Optional[int] = None,
+    ) -> None:
+        task_prioritization_prompt = TextPrompt(
+            """Prioritize the following tasks : {task_list}.
+Consider the ultimate objective of you: {objective}.
+Tasks should be sorted from highest to lowest priority, where higher-priority \
+tasks are those that act as pre-requisites or are more essential for meeting \
+the objective. Return one task per line in your response.
+Do not remove or modify any tasks.
+The result must be a numbered list in the format:
+
+    #. First task
+    #. Second task
+
+The entries must be consecutively numbered, starting with 1.
+The number of each entry must be followed by a period.
+Do not include any headers before your ranked list or follow your list \
+with any other output."""
+        )
+
+        self.task_prioritization_prompt = task_prioritization_prompt.format(
+            objective=objective
+        )
+        self.objective = objective
+
+        system_message = BaseMessage(
+            role_name="Task Prioritizer",
+            role_type=RoleType.ASSISTANT,
+            meta_dict=None,
+            content="You are a helpful task prioritizer.",
+        )
+
+        super().__init__(
+            system_message,
+            model=model,
+            output_language=output_language,
+            message_window_size=message_window_size,
+        )
+
+    def run(
+        self,
+        task_list: List[str],
+    ) -> List[str]:
+        r"""Prioritize the task list given the agent objective.
+
+        Args:
+            task_list (List[str]): The unprioritized tasks of agent.
+
+        Returns:
+            List[str]: The new prioritized task list generated by the Agent.
+        """
+        task_prioritization_prompt = self.task_prioritization_prompt.format(
+            task_list=task_list
+        )
+
+        task_msg = BaseMessage.make_user_message(
+            role_name="Task Prioritizer", content=task_prioritization_prompt
+        )
+
+        task_response = self.step(task_msg)
+
+        if task_response.terminated:
+            raise RuntimeError("Task prioritization failed.")
+        if len(task_response.msgs) == 0:
+            raise RuntimeError("Got no task prioritization message.")
+
+        sub_tasks_msg = task_response.msgs[0]
+        return get_task_list(sub_tasks_msg.content)

deep-swarm/camel/agents/tool_agents/__init__.py

@@ -0,0 +1,20 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from .base import BaseToolAgent
+from .hugging_face_tool_agent import HuggingFaceToolAgent
+
+__all__ = [
+    'BaseToolAgent',
+    'HuggingFaceToolAgent',
+]

deep-swarm/camel/agents/tool_agents/base.py

@@ -0,0 +1,39 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from camel.agents import BaseAgent
+
+
+class BaseToolAgent(BaseAgent):
+    r"""Creates a :obj:`BaseToolAgent` object with the specified name and
+        description.
+
+    Args:
+        name (str): The name of the tool agent.
+        description (str): The description of the tool agent.
+    """
+
+    def __init__(self, name: str, description: str) -> None:
+        self.name = name
+        self.description = description
+
+    def reset(self) -> None:
+        r"""Resets the agent to its initial state."""
+        pass
+
+    def step(self) -> None:
+        r"""Performs a single step of the agent."""
+        pass
+
+    def __str__(self) -> str:
+        return f"{self.name}: {self.description}"

deep-swarm/camel/agents/tool_agents/hugging_face_tool_agent.py

@@ -0,0 +1,206 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import Any, Optional
+
+from camel.agents.tool_agents.base import BaseToolAgent
+
+
+# flake8: noqa :E501
+class HuggingFaceToolAgent(BaseToolAgent):
+    r"""Tool agent for calling HuggingFace models. This agent is a wrapper
+        around agents from the `transformers` library. For more information
+        about the available models, please see the `transformers` documentation
+        at https://huggingface.co/docs/transformers/transformers_agents.
+
+    Args:
+        name (str): The name of the agent.
+        *args (Any): Additional positional arguments to pass to the underlying
+            Agent class.
+        remote (bool, optional): Flag indicating whether to run the agent
+            remotely. (default: :obj:`True`)
+        **kwargs (Any): Additional keyword arguments to pass to the underlying
+            Agent class.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        *args: Any,
+        remote: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        try:
+            # TODO: Support other tool agents
+            import transformers
+            from packaging import version
+
+            if version.parse(transformers.__version__) < version.parse(
+                "4.31.0"
+            ):
+                raise ValueError(
+                    "The version of \"transformers\" package should >= 4.31.0"
+                )
+
+            from transformers.tools import OpenAiAgent
+            from transformers.tools.agent_types import AgentImage
+        except (ImportError, ValueError):
+            raise ValueError(
+                "Could not import transformers tool agents. "
+                "Please setup the environment with "
+                "pip install huggingface_hub==0.14.1 transformers==4.31.0 diffusers accelerate==0.20.3 datasets torch soundfile sentencepiece opencv-python"
+            )
+        self.agent_image_type = AgentImage
+        self.agent = OpenAiAgent(*args, **kwargs)
+        description = f"""The `{name}` is a tool agent that can perform a variety of tasks including:
+- Document question answering: given a document (such as a PDF) in image format, answer a question on this document
+- Text question answering: given a long text and a question, answer the question in the text
+- Unconditional image captioning: Caption the image!
+- Image question answering: given an image, answer a question on this image
+- Image segmentation: given an image and a prompt, output the segmentation mask of that prompt
+- Speech to text: given an audio recording of a person talking, transcribe the speech into text
+- Text to speech: convert text to speech
+- Zero-shot text classification: given a text and a list of labels, identify to which label the text corresponds the most
+- Text summarization: summarize a long text in one or a few sentences
+- Translation: translate the text into a given language
+- Text downloading: to download a text from a web URL
+- Text to image: generate an image according to a prompt, leveraging stable diffusion
+- Image transformation: modify an image given an initial image and a prompt, leveraging instruct pix2pix stable diffusion
+- Text to video: generate a small video according to a prompt
+
+Here are some python code examples of what you can do with this agent:
+
+Single execution (step) mode, the single execution method is when using the step() method of the agent:
+```
+# Text to image
+rivers_and_lakes_image = {name}.step("Draw me a picture of rivers and lakes.")
+rivers_and_lakes_image.save("./rivers_and_lakes_image.png")
+
+# Text to image -> Image transformation
+sea_add_island_image = {name}.step("Draw me a picture of the sea then transform the picture to add an island")
+sea_add_island_image.save("./sea_add_island_image.png")
+
+# If you'd like to keep a state across executions or to pass non-text objects to the agent, 
+# you can do so by specifying variables that you would like the agent to use. For example,
+# you could generate the first image of rivers and lakes, and ask the model to update that picture to add an island by doing the following:
+picture = {name}.step("Generate a picture of rivers and lakes.")
+picture.save("./picture.png")
+updated_picture = {name}.step("Transform the image in `picture` to add an island to it.", picture=picture)
+updated_picture.save("./updated_picture.png")
+
+capybara_sea_image = {name}.step("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea")
+capybara_sea_image.save("./capybara_sea_image.png")
+
+# Document question answering
+answer = {name}.step(
+    "In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",
+    document=document,
+)
+print(answer)
+
+
+# Text to image
+boat_image = {name}.step("Generate an image of a boat in the water")
+boat_image.save("./boat_image.png")
+
+# Unconditional image captioning
+boat_image_caption = {name}.step("Can you caption the `boat_image`?", boat_image=boat_image)
+print(boat_image_caption)
+
+# Text to image -> Unconditional image captioning -> Text to speech
+boat_audio = {name}.step("Can you generate an image of a boat? Please read out loud the contents of the image afterwards")
+
+# Text downloading
+document = {name}.step("Download the text from http://hf.co")
+print(document)
+
+# Text summarization
+summary = {name}.step("Summarize the following text: `document`", document=document)
+print(summary)
+
+# Text downloading -> Text summarization -> Text to speech
+audio = {name}.step("Read out loud the summary of http://hf.co")
+```
+
+Chat-based execution (chat), the agent also has a chat-based approach, using the chat() method:
+```
+# Clean the chat history
+{name}.reset()
+
+# Text to image
+capybara_image = {name}.chat("Show me an an image of a capybara")
+capybara_image.save("./capybara_image.png")
+
+# Image transformation
+transformed_capybara_image = {name}.chat("Transform the image so that it snows")
+transformed_capybara_image.save("./transformed_capybara_image.png")
+
+# Image segmentation
+segmented_transformed_capybara_image = {name}.chat("Show me a mask of the snowy capybaras")
+segmented_transformed_capybara_image.save("./segmented_transformed_capybara_image.png")
+```
+"""
+        super(HuggingFaceToolAgent, self).__init__(name, description)
+        self.remote = remote
+
+    def reset(self) -> None:
+        r"""Resets the chat history of the agent."""
+        self.agent.prepare_for_new_chat()
+
+    def step(
+        self,
+        *args: Any,
+        remote: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> Any:
+        r"""Runs the agent in single execution mode.
+
+        Args:
+            *args (Any): Positional arguments to pass to the agent.
+            remote (bool, optional): Flag indicating whether to run the agent
+                remotely. Overrides the default setting. (default: :obj:`None`)
+            **kwargs (Any): Keyword arguments to pass to the agent.
+
+        Returns:
+            str: The response from the agent.
+        """
+        if remote is None:
+            remote = self.remote
+        agent_output = self.agent.run(*args, remote=remote, **kwargs)
+        if isinstance(agent_output, self.agent_image_type):
+            agent_output = agent_output.to_raw()
+        return agent_output
+
+    def chat(
+        self,
+        *args: Any,
+        remote: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> Any:
+        r"""Runs the agent in a chat conversation mode.
+
+        Args:
+            *args (Any): Positional arguments to pass to the agent.
+            remote (bool, optional): Flag indicating whether to run the agent
+                remotely. Overrides the default setting. (default: :obj:`None`)
+            **kwargs (Any): Keyword arguments to pass to the agent.
+
+        Returns:
+            str: The response from the agent.
+        """
+        if remote is None:
+            remote = self.remote
+        agent_output = self.agent.chat(*args, remote=remote, **kwargs)
+        if isinstance(agent_output, self.agent_image_type):
+            agent_output = agent_output.to_raw()
+        return agent_output

deep-swarm/camel/benchmarks/__init__.py

@@ -0,0 +1,17 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+from .base import BaseBenchmark
+
+__all__ = ["BaseBenchmark"]

deep-swarm/camel/benchmarks/base.py

@@ -0,0 +1,152 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Any, Dict, List, Literal, Optional
+
+from camel.agents import ChatAgent
+
+logger = logging.getLogger(__name__)
+
+
+class BaseBenchmark(ABC):
+    r"""Base class for benchmarks.
+
+    Attributes:
+        name (str): Name of the benchmark.
+        data_dir (str): Path to the data directory.
+        save_to (str): Path to save the results.
+        processes (int): Number of processes to use for parallel
+            processing. :(default: :obj:`1`)
+    """
+
+    def __init__(
+        self, name: str, data_dir: str, save_to: str, processes: int = 1
+    ):
+        r"""Initialize the benchmark.
+
+        Args:
+            name (str): Name of the benchmark.
+            data_dir (str): Path to the data directory.
+            save_to (str): Path to save the results.
+            processes (int): Number of processes to use for parallel
+                processing. :(default: :obj:`1`)
+
+        """
+        self.name = name
+        self.data_dir = Path(data_dir)
+        self.processes = processes
+        self.save_to = save_to
+        if not self.data_dir.exists():
+            logger.info(
+                f"Data directory {data_dir} does not exist. Creating it."
+            )
+            self.data_dir.mkdir(parents=True, exist_ok=True)
+        if not self.data_dir.is_dir():
+            raise NotADirectoryError(
+                f"Data directory {data_dir} is not a directory"
+            )
+        self._data: Dict[str, List[Dict[str, Any]]] = dict()
+        self._results: List[Dict[str, Any]] = []
+
+    @abstractmethod
+    def download(self) -> "BaseBenchmark":
+        r"""Download the benchmark data.
+
+        Returns:
+            BaseBenchmark: The benchmark instance.
+        """
+        pass
+
+    @abstractmethod
+    def load(self, force_download: bool = False) -> "BaseBenchmark":
+        r"""Load the benchmark data.
+
+        Args:
+            force_download (bool): Whether to force download the data.
+
+        Returns:
+            BaseBenchmark: The benchmark instance.
+        """
+        pass
+
+    @property
+    def train(self) -> List[Dict[str, Any]]:
+        r"""Get the training data.
+
+        Returns:
+            List[Dict[str, Any]]: The training data.
+        """
+        if not self._data:
+            logger.info("Data not loaded. Loading data.")
+            self.load()
+        return self._data["train"]
+
+    @property
+    def valid(self) -> List[Dict[str, Any]]:
+        r"""Get the validation data.
+
+        Returns:
+            List[Dict[str, Any]]: The validation data.
+        """
+        if not self._data:
+            logger.info("Data not loaded. Loading data.")
+            self.load()
+        return self._data["valid"]
+
+    @property
+    def test(self) -> List[Dict[str, Any]]:
+        r"""Get the test data.
+
+        Returns:
+            List[Dict[str, Any]]: The test data.
+        """
+        if not self._data:
+            logger.info("Data not loaded. Loading data.")
+            self.load()
+        return self._data["test"]
+
+    @abstractmethod
+    def run(
+        self,
+        agent: ChatAgent,
+        on: Literal["train", "valid", "test"],
+        randomize: bool = False,
+        subset: Optional[int] = None,
+        *args,
+        **kwargs,
+    ) -> "BaseBenchmark":
+        r"""Run the benchmark.
+
+        Args:
+            agent (ChatAgent): The chat agent.
+            on (str): The data split to run the benchmark on.
+            randomize (bool): Whether to randomize the data.
+            subset (int): The subset of the data to run the benchmark on.
+
+        Returns:
+            BaseBenchmark: The benchmark instance.
+        """
+        pass
+
+    @property
+    def results(self) -> List[Dict[str, Any]]:
+        r"""Get the results.
+
+        Returns:
+            List[Dict[str, Any]]: The results.
+        """
+        return self._results

deep-swarm/camel/bots/__init__.py

@@ -0,0 +1,34 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from .discord_app import DiscordApp
+from .slack.models import (
+    SlackAppMentionEventBody,
+    SlackAppMentionEventProfile,
+    SlackAuthProfile,
+    SlackEventBody,
+    SlackEventProfile,
+)
+from .slack.slack_app import SlackApp
+from .telegram_bot import TelegramBot
+
+__all__ = [
+    'DiscordApp',
+    'SlackApp',
+    'SlackAppMentionEventBody',
+    'SlackAppMentionEventProfile',
+    'SlackAuthProfile',
+    'SlackEventBody',
+    'SlackEventProfile',
+    'TelegramBot',
+]

deep-swarm/camel/bots/discord_app.py

@@ -0,0 +1,138 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import logging
+import os
+from typing import TYPE_CHECKING, List, Optional
+
+from camel.utils import dependencies_required
+
+if TYPE_CHECKING:
+    from discord import Message
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class DiscordApp:
+    r"""A class representing a Discord app that uses the `discord.py` library
+    to interact with Discord servers.
+
+    This bot can respond to messages in specific channels and only reacts to
+    messages that mention the bot.
+
+    Attributes:
+        channel_ids (Optional[List[int]]): A list of allowed channel IDs. If
+            provided, the bot will only respond to messages in these channels.
+        token (Optional[str]): The Discord bot token used for authentication.
+    """
+
+    @dependencies_required('discord')
+    def __init__(
+        self,
+        channel_ids: Optional[List[int]] = None,
+        token: Optional[str] = None,
+    ) -> None:
+        r"""Initialize the DiscordApp instance by setting up the Discord client
+        and event handlers.
+
+        Args:
+            channel_ids (Optional[List[int]]): A list of allowed channel IDs.
+                The bot will only respond to messages in these channels if
+                provided.
+            token (Optional[str]): The Discord bot token for authentication.
+                If not provided, the token will be retrieved from the
+                environment variable `DISCORD_TOKEN`.
+
+        Raises:
+            ValueError: If the `DISCORD_TOKEN` is not found in environment
+                variables.
+        """
+        self.token = token or os.getenv('DISCORD_TOKEN')
+        self.channel_ids = channel_ids
+
+        if not self.token:
+            raise ValueError(
+                "`DISCORD_TOKEN` not found in environment variables. Get it"
+                " here: `https://discord.com/developers/applications`."
+            )
+
+        import discord
+
+        intents = discord.Intents.default()
+        intents.message_content = True
+        self._client = discord.Client(intents=intents)
+
+        # Register event handlers
+        self._client.event(self.on_ready)
+        self._client.event(self.on_message)
+
+    async def start(self):
+        r"""Asynchronously start the Discord bot using its token.
+
+        This method starts the bot and logs into Discord asynchronously using
+        the provided token. It should be awaited when used in an async
+        environment.
+        """
+        await self._client.start(self.token)
+
+    def run(self) -> None:
+        r"""Start the Discord bot using its token.
+
+        This method starts the bot and logs into Discord synchronously using
+        the provided token. It blocks execution and keeps the bot running.
+        """
+        self._client.run(self.token)  # type: ignore[arg-type]
+
+    async def on_ready(self) -> None:
+        r"""Event handler that is called when the bot has successfully
+        connected to the Discord server.
+
+        When the bot is ready and logged into Discord, it prints a message
+        displaying the bot's username.
+        """
+        logger.info(f'We have logged in as {self._client.user}')
+
+    async def on_message(self, message: 'Message') -> None:
+        r"""Event handler for processing incoming messages.
+
+        This method is called whenever a new message is received by the bot. It
+        will ignore messages sent by the bot itself, only respond to messages
+        in allowed channels (if specified), and only to messages that mention
+        the bot.
+
+        Args:
+            message (discord.Message): The message object received from
+                Discord.
+        """
+        # If the message author is the bot itself,
+        # do not respond to this message
+        if message.author == self._client.user:
+            return
+
+        # If allowed channel IDs are provided,
+        # only respond to messages in those channels
+        if self.channel_ids and message.channel.id not in self.channel_ids:
+            return
+
+        # Only respond to messages that mention the bot
+        if not self._client.user or not self._client.user.mentioned_in(
+            message
+        ):
+            return
+
+        logger.info(f"Received message: {message.content}")
+
+    @property
+    def client(self):
+        return self._client

deep-swarm/camel/bots/slack/__init__.py

@@ -0,0 +1,30 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from .models import (
+    SlackAppMentionEventBody,
+    SlackAppMentionEventProfile,
+    SlackAuthProfile,
+    SlackEventBody,
+    SlackEventProfile,
+)
+from .slack_app import SlackApp
+
+__all__ = [
+    'SlackApp',
+    'SlackAppMentionEventBody',
+    'SlackAppMentionEventProfile',
+    'SlackAuthProfile',
+    'SlackEventBody',
+    'SlackEventProfile',
+]

deep-swarm/camel/bots/slack/models.py

@@ -0,0 +1,158 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class SlackAuthProfile(BaseModel):
+    r"""Represents the authorization profile within a Slack event.
+
+    Events will contain a single, compact authorizations field that shows one
+    installation of your app that the event is visible to.
+    In other words, lists of authorizations will be truncated to one element.
+
+    If there's more than one installing party that your app is keeping track
+    of, it's best not to rely on the single party listed in authorizations to
+    be any particular one.
+
+    To get a full list of who can see events, call the apps.event.
+    authorizations.list method after obtaining an app-level token. Read more on
+    the changes here; they have taken effect for existing apps as of
+    February 24, 2021.
+
+    References:
+
+    - https://api.slack.com/apis/events-api#authorizations
+    - https://api.slack.com/changelog/2020-09-15-events-api-truncate-authed-users#no_context
+    """
+
+    enterprise_id: Optional[str] = None
+    """The ID of the enterprise associated with the authorization."""
+
+    team_id: str
+    """The ID of the team associated with the authorization."""
+
+    user_id: str
+    """The ID of the user associated with the authorization."""
+
+    is_bot: bool
+    """Whether the authorized user is a bot."""
+
+    is_enterprise_install: bool
+    """Whether the authorization is for an enterprise installation."""
+
+
+class SlackEventProfile(BaseModel):
+    r"""Represents the detailed profile of a Slack event, including user,
+    message, and context data.
+    """
+
+    user: str
+    """The ID of the user associated with the event."""
+
+    type: str
+    """The type of the event (e.g., 'message')."""
+
+    ts: str
+    """A timestamp representing when the event was triggered."""
+
+    thread_ts: Optional[str] = None
+    """The timestamp of the parent message in a thread."""
+
+    client_msg_id: str
+    """A unique ID generated by the client for the message (if available)."""
+
+    text: str
+    """The message content text."""
+
+    team: str
+    """The ID of the team that the event is associated with."""
+
+    blocks: list
+    """The list of message blocks, providing structured information."""
+
+    channel: str
+    """The ID of the Slack channel where the event happened."""
+
+    event_ts: str
+    """The event-specific timestamp when it occurred."""
+
+    channel_type: Optional[str]
+    """The type of Slack channel (e.g., 'channel', 'im')."""
+
+
+class SlackEventBody(BaseModel):
+    r"""Represents the entire body of a Slack event, including the event
+    profile, authorization, and context.
+    """
+
+    token: str
+    """The token to verify the source of the event."""
+
+    team_id: str
+    """The ID of the team where the event is happening."""
+
+    context_team_id: Optional[str]
+    """The team ID for the shared channel context, if applicable."""
+
+    context_enterprise_id: Optional[str] = None
+    """The enterprise ID for the shared channel context, if applicable."""
+
+    api_app_id: str
+    """The unique identifier for the Slack app that received the event."""
+
+    event: SlackEventProfile
+    """A detailed profile of the event"""
+
+    type: str
+    """The overall type of event received (e.g., 'event_callback')."""
+
+    event_id: str
+    """A unique identifier assigned to this event by Slack."""
+
+    event_time: int
+    """The timestamp (in seconds) representing when the event was triggered."""
+
+    authorizations: Optional[list[SlackAuthProfile]] = None
+    """An optional list of authorizations that describe which installation can 
+    see the event."""
+
+    is_ext_shared_channel: bool
+    """Indicates if the event is part of a shared channel between different 
+    organizations."""
+
+    event_context: str
+    """A unique string representing the context of the event."""
+
+
+class SlackAppMentionEventProfile(SlackEventProfile):
+    r"""Represents the detailed profile of a Slack event where the app was
+    mentioned in a message.
+    """
+
+    channel_type: Optional[str] = None
+    """The type of Slack channel. it's None for app mentions."""
+
+
+class SlackAppMentionEventBody(SlackEventBody):
+    r"""Represents the entire body of a Slack event where the app was mentioned
+    in a message.
+    """
+
+    context_team_id: Optional[str] = None
+    """A detailed profile of the event. it's None for app mentions."""
+
+    event: SlackAppMentionEventProfile
+    """A detailed profile of the event"""

deep-swarm/camel/bots/slack/slack_app.py

@@ -0,0 +1,255 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import logging
+import os
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+from slack_sdk.oauth.installation_store.async_installation_store import (
+    AsyncInstallationStore,
+)
+from starlette import requests, responses
+
+from camel.bots.slack.models import (
+    SlackAppMentionEventBody,
+    SlackAppMentionEventProfile,
+    SlackEventBody,
+    SlackEventProfile,
+)
+from camel.utils import dependencies_required
+
+if TYPE_CHECKING:
+    from slack_bolt.context.async_context import AsyncBoltContext
+    from slack_bolt.context.say.async_say import AsyncSay
+    from slack_sdk.web.async_client import AsyncWebClient
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class SlackApp:
+    r"""Represents a Slack app that is powered by a Slack Bolt `AsyncApp`.
+
+    This class is responsible for initializing and managing the Slack
+    application by setting up event handlers, running the app server, and
+    handling events such as messages and mentions from Slack.
+
+    Args:
+        token (Optional[str]): Slack API token for authentication.
+        scopes (Optional[str]): Slack app scopes for permissions.
+        signing_secret (Optional[str]): Signing secret for verifying Slack
+            requests.
+        client_id (Optional[str]): Slack app client ID.
+        client_secret (Optional[str]): Slack app client secret.
+        redirect_uri_path (str): The URI path for OAuth redirect, defaults to
+            "/slack/oauth_redirect".
+        installation_store (Optional[AsyncInstallationStore]): The installation
+            store for handling OAuth installations.
+    """
+
+    @dependencies_required('slack_bolt')
+    def __init__(
+        self,
+        token: Optional[str] = None,
+        scopes: Optional[str] = None,
+        signing_secret: Optional[str] = None,
+        client_id: Optional[str] = None,
+        client_secret: Optional[str] = None,
+        redirect_uri_path: str = "/slack/oauth_redirect",
+        installation_store: Optional[AsyncInstallationStore] = None,
+    ) -> None:
+        r"""Initializes the SlackApp instance by setting up the Slack Bolt app
+        and configuring event handlers and OAuth settings.
+
+        Args:
+            token (Optional[str]): The Slack API token.
+            scopes (Optional[str]): The scopes for Slack app permissions.
+            signing_secret (Optional[str]): The signing secret for verifying
+                requests.
+            client_id (Optional[str]): The Slack app client ID.
+            client_secret (Optional[str]): The Slack app client secret.
+            redirect_uri_path (str): The URI path for handling OAuth redirects
+                (default is "/slack/oauth_redirect").
+            installation_store (Optional[AsyncInstallationStore]): An optional
+                installation store for OAuth installations.
+        """
+        from slack_bolt.adapter.starlette.async_handler import (
+            AsyncSlackRequestHandler,
+        )
+        from slack_bolt.app.async_app import AsyncApp
+        from slack_bolt.oauth.async_oauth_settings import AsyncOAuthSettings
+
+        self.token: Optional[str] = token or os.getenv("SLACK_TOKEN")
+        self.scopes: Optional[str] = scopes or os.getenv("SLACK_SCOPES")
+        self.signing_secret: Optional[str] = signing_secret or os.getenv(
+            "SLACK_SIGNING_SECRET"
+        )
+        self.client_id: Optional[str] = client_id or os.getenv(
+            "SLACK_CLIENT_ID"
+        )
+        self.client_secret: Optional[str] = client_secret or os.getenv(
+            "SLACK_CLIENT_SECRET"
+        )
+
+        if not all([self.token, self.scopes, self.signing_secret]):
+            raise ValueError(
+                "`SLACK_TOKEN`, `SLACK_SCOPES`, and `SLACK_SIGNING_SECRET` "
+                "environment variables must be set. Get it here: "
+                "`https://api.slack.com/apps`."
+            )
+
+        # Setup OAuth settings if client ID and secret are provided
+        if self.client_id and self.client_secret:
+            self._app = AsyncApp(
+                oauth_settings=AsyncOAuthSettings(
+                    client_id=self.client_id,
+                    client_secret=self.client_secret,
+                    scopes=self.scopes,
+                    redirect_uri_path=redirect_uri_path,
+                ),
+                logger=logger,
+                signing_secret=self.signing_secret,
+                installation_store=installation_store,
+                token=self.token,
+            )
+        else:
+            # Initialize Slack Bolt AsyncApp with settings
+            self._app = AsyncApp(
+                logger=logger,
+                signing_secret=self.signing_secret,
+                installation_store=installation_store,
+                token=self.token,
+            )
+
+        self._handler = AsyncSlackRequestHandler(self._app)
+        self.setup_handlers()
+
+    def setup_handlers(self) -> None:
+        r"""Sets up the event handlers for Slack events, such as `app_mention`
+        and `message`.
+
+        This method registers the `app_mention` and `on_message` event handlers
+        with the Slack Bolt app to respond to Slack events.
+        """
+        self._app.event("app_mention")(self.app_mention)
+        self._app.event("message")(self.on_message)
+
+    def run(
+        self,
+        port: int = 3000,
+        path: str = "/slack/events",
+        host: Optional[str] = None,
+    ) -> None:
+        r"""Starts the Slack Bolt app server to listen for incoming Slack
+        events.
+
+        Args:
+            port (int): The port on which the server should run (default is
+                3000).
+            path (str): The endpoint path for receiving Slack events (default
+                is "/slack/events").
+            host (Optional[str]): The hostname to bind the server (default is
+                None).
+        """
+        self._app.start(port=port, path=path, host=host)
+
+    async def handle_request(
+        self, request: requests.Request
+    ) -> responses.Response:
+        r"""Handles incoming requests from Slack through the request handler.
+
+        Args:
+            request (Request): A Starlette request object representing the
+                incoming request.
+
+        Returns:
+            The response generated by the Slack Bolt handler.
+        """
+        return await self._handler.handle(request)
+
+    async def app_mention(
+        self,
+        context: "AsyncBoltContext",
+        client: "AsyncWebClient",
+        event: Dict[str, Any],
+        body: Dict[str, Any],
+        say: "AsyncSay",
+    ) -> None:
+        r"""Event handler for `app_mention` events.
+
+        This method is triggered when someone mentions the app in Slack.
+
+        Args:
+            context (AsyncBoltContext): The Slack Bolt context for the event.
+            client (AsyncWebClient): The Slack Web API client.
+            event (Dict[str, Any]): The event data for the app mention.
+            body (Dict[str, Any]): The full request body from Slack.
+            say (AsyncSay): A function to send a response back to the channel.
+        """
+        event_profile = SlackAppMentionEventProfile(**event)
+        event_body = SlackAppMentionEventBody(**body)
+
+        logger.info(f"app_mention, context: {context}")
+        logger.info(f"app_mention, client: {client}")
+        logger.info(f"app_mention, event_profile: {event_profile}")
+        logger.info(f"app_mention, event_body: {event_body}")
+        logger.info(f"app_mention, say: {say}")
+
+    async def on_message(
+        self,
+        context: "AsyncBoltContext",
+        client: "AsyncWebClient",
+        event: Dict[str, Any],
+        body: Dict[str, Any],
+        say: "AsyncSay",
+    ) -> None:
+        r"""Event handler for `message` events.
+
+        This method is triggered when the app receives a message in Slack.
+
+        Args:
+            context (AsyncBoltContext): The Slack Bolt context for the event.
+            client (AsyncWebClient): The Slack Web API client.
+            event (Dict[str, Any]): The event data for the message.
+            body (Dict[str, Any]): The full request body from Slack.
+            say (AsyncSay): A function to send a response back to the channel.
+        """
+        await context.ack()
+
+        event_profile = SlackEventProfile(**event)
+        event_body = SlackEventBody(**body)
+
+        logger.info(f"on_message, context: {context}")
+        logger.info(f"on_message, client: {client}")
+        logger.info(f"on_message, event_profile: {event_profile}")
+        logger.info(f"on_message, event_body: {event_body}")
+        logger.info(f"on_message, say: {say}")
+
+        logger.info(f"Received message: {event_profile.text}")
+
+    def mention_me(
+        self, context: "AsyncBoltContext", body: SlackEventBody
+    ) -> bool:
+        r"""Check if the bot is mentioned in the message.
+
+        Args:
+            context (AsyncBoltContext): The Slack Bolt context for the event.
+            body (SlackEventBody): The body of the Slack event.
+
+        Returns:
+            bool: True if the bot is mentioned in the message, False otherwise.
+        """
+        message = body.event.text
+        bot_user_id = context.bot_user_id
+        mention = f"<@{bot_user_id}>"
+        return mention in message

deep-swarm/camel/bots/telegram_bot.py

@@ -0,0 +1,82 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import os
+from typing import TYPE_CHECKING, Optional
+
+from camel.agents import ChatAgent
+from camel.messages import BaseMessage
+from camel.utils import dependencies_required
+
+# Conditionally import telebot types only for type checking
+if TYPE_CHECKING:
+    from telebot.types import (  # type: ignore[import-untyped]
+        Message,
+    )
+
+
+class TelegramBot:
+    r"""Represents a Telegram bot that is powered by an agent.
+
+    Attributes:
+        chat_agent (ChatAgent): Chat agent that will power the bot.
+        telegram_token (str, optional): The bot token.
+    """
+
+    @dependencies_required('telebot')
+    def __init__(
+        self,
+        chat_agent: ChatAgent,
+        telegram_token: Optional[str] = None,
+    ) -> None:
+        self.chat_agent = chat_agent
+
+        if not telegram_token:
+            self.token = os.getenv('TELEGRAM_TOKEN')
+            if not self.token:
+                raise ValueError(
+                    "`TELEGRAM_TOKEN` not found in environment variables. "
+                    "Get it from t.me/BotFather."
+                )
+        else:
+            self.token = telegram_token
+
+        import telebot  # type: ignore[import-untyped]
+
+        self.bot = telebot.TeleBot(token=self.token)
+
+        # Register the message handler within the constructor
+        self.bot.message_handler(func=lambda message: True)(self.on_message)
+
+    def run(self) -> None:
+        r"""Start the Telegram bot."""
+        print("Telegram bot is running...")
+        self.bot.infinity_polling()
+
+    def on_message(self, message: 'Message') -> None:
+        r"""Handles incoming messages from the user.
+
+        Args:
+            message (types.Message): The incoming message object.
+        """
+        self.chat_agent.reset()
+
+        if not message.text:
+            return
+
+        user_msg = BaseMessage.make_user_message(
+            role_name="User", content=message.text
+        )
+        assistant_response = self.chat_agent.step(user_msg)
+
+        self.bot.reply_to(message, assistant_response.msg.content)

deep-swarm/camel/configs/__init__.py

@@ -0,0 +1,76 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from .anthropic_config import ANTHROPIC_API_PARAMS, AnthropicConfig
+from .base_config import BaseConfig
+from .cohere_config import COHERE_API_PARAMS, CohereConfig
+from .deepseek_config import DEEPSEEK_API_PARAMS, DeepSeekConfig
+from .gemini_config import Gemini_API_PARAMS, GeminiConfig
+from .groq_config import GROQ_API_PARAMS, GroqConfig
+from .litellm_config import LITELLM_API_PARAMS, LiteLLMConfig
+from .mistral_config import MISTRAL_API_PARAMS, MistralConfig
+from .nvidia_config import NVIDIA_API_PARAMS, NvidiaConfig
+from .ollama_config import OLLAMA_API_PARAMS, OllamaConfig
+from .openai_config import OPENAI_API_PARAMS, ChatGPTConfig
+from .qwen_config import QWEN_API_PARAMS, QwenConfig
+from .reka_config import REKA_API_PARAMS, RekaConfig
+from .samba_config import (
+    SAMBA_CLOUD_API_PARAMS,
+    SAMBA_VERSE_API_PARAMS,
+    SambaCloudAPIConfig,
+    SambaVerseAPIConfig,
+)
+from .togetherai_config import TOGETHERAI_API_PARAMS, TogetherAIConfig
+from .vllm_config import VLLM_API_PARAMS, VLLMConfig
+from .yi_config import YI_API_PARAMS, YiConfig
+from .zhipuai_config import ZHIPUAI_API_PARAMS, ZhipuAIConfig
+
+__all__ = [
+    'BaseConfig',
+    'ChatGPTConfig',
+    'OPENAI_API_PARAMS',
+    'AnthropicConfig',
+    'ANTHROPIC_API_PARAMS',
+    'GROQ_API_PARAMS',
+    'GroqConfig',
+    'LiteLLMConfig',
+    'LITELLM_API_PARAMS',
+    'NvidiaConfig',
+    'NVIDIA_API_PARAMS',
+    'OllamaConfig',
+    'OLLAMA_API_PARAMS',
+    'ZhipuAIConfig',
+    'ZHIPUAI_API_PARAMS',
+    'GeminiConfig',
+    'Gemini_API_PARAMS',
+    'VLLMConfig',
+    'VLLM_API_PARAMS',
+    'MistralConfig',
+    'MISTRAL_API_PARAMS',
+    'RekaConfig',
+    'REKA_API_PARAMS',
+    'SambaVerseAPIConfig',
+    'SAMBA_VERSE_API_PARAMS',
+    'SambaCloudAPIConfig',
+    'SAMBA_CLOUD_API_PARAMS',
+    'TogetherAIConfig',
+    'TOGETHERAI_API_PARAMS',
+    'CohereConfig',
+    'COHERE_API_PARAMS',
+    'YiConfig',
+    'YI_API_PARAMS',
+    'QwenConfig',
+    'QWEN_API_PARAMS',
+    'DeepSeekConfig',
+    'DEEPSEEK_API_PARAMS',
+]