API Reference

Agent

Source code in agency_swarm/agents/agent.py

class Agent():
    _shared_state: SharedState = None

    @property
    def assistant(self):
        if not hasattr(self, '_assistant') or self._assistant is None:
            raise Exception("Assistant is not initialized. Please run init_oai() first.")
        return self._assistant

    @assistant.setter
    def assistant(self, value):
        self._assistant = value

    @property
    def functions(self):
        return [tool for tool in self.tools if issubclass(tool, BaseTool)]

    @property
    def shared_state(self):
        return self._shared_state

    @shared_state.setter
    def shared_state(self, value):
        self._shared_state = value
        for tool in self.tools:
            if issubclass(tool, BaseTool):
                tool._shared_state = value

    def response_validator(self, message: str | list) -> str:
        """
        Validates the response from the agent. If the response is invalid, it must raise an exception with instructions
        for the caller agent on how to proceed.

        Parameters:
            message (str): The response from the agent.

        Returns:
            str: The validated response.
        """
        return message

    def __init__(
            self,
            id: str = None,
            name: str = None,
            description: str = None,
            instructions: str = "",
            tools: List[Union[Type[BaseTool], Type[FileSearch], Type[CodeInterpreter], type[Retrieval]]] = None,
            tool_resources: ToolResources = None,
            temperature: float = None,
            top_p: float = None,
            response_format: Union[str, dict, type] = "auto",
            tools_folder: str = None,
            files_folder: Union[List[str], str] = None,
            schemas_folder: Union[List[str], str] = None,
            api_headers: Dict[str, Dict[str, str]] = None,
            api_params: Dict[str, Dict[str, str]] = None,
            file_ids: List[str] = None,
            metadata: Dict[str, str] = None,
            model: str = "gpt-4o-2024-08-06",
            validation_attempts: int = 1,
            max_prompt_tokens: int = None,
            max_completion_tokens: int = None,
            truncation_strategy: dict = None,
            examples: List[ExampleMessage] = None,
            file_search: FileSearchConfig = None,
            parallel_tool_calls: bool = True,
            refresh_from_id: bool = True,
    ):
        """
        Initializes an Agent with specified attributes, tools, and OpenAI client.

        Parameters:
            id (str, optional): Loads the assistant from OpenAI assistant ID. Assistant will be created or loaded from settings if ID is not provided. Defaults to None.
            name (str, optional): Name of the agent. Defaults to the class name if not provided.
            description (str, optional): A brief description of the agent's purpose. Defaults to None.
            instructions (str, optional): Path to a file containing specific instructions for the agent. Defaults to an empty string.
            tools (List[Union[Type[BaseTool], Type[Retrieval], Type[CodeInterpreter]]], optional): A list of tools (as classes) that the agent can use. Defaults to an empty list.
            tool_resources (ToolResources, optional): A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs. Defaults to None.
            temperature (float, optional): The temperature parameter for the OpenAI API. Defaults to None.
            top_p (float, optional): The top_p parameter for the OpenAI API. Defaults to None.
            response_format (Union[str, Dict, type], optional): The response format for the OpenAI API. If BaseModel is provided, it will be converted to a response format. Defaults to None.
            tools_folder (str, optional): Path to a directory containing tools associated with the agent. Each tool must be defined in a separate file. File must be named as the class name of the tool. Defaults to None.
            files_folder (Union[List[str], str], optional): Path or list of paths to directories containing files associated with the agent. Defaults to None.
            schemas_folder (Union[List[str], str], optional): Path or list of paths to directories containing OpenAPI schemas associated with the agent. Defaults to None.
            api_headers (Dict[str,Dict[str, str]], optional): Headers to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.
            api_params (Dict[str, Dict[str, str]], optional): Extra params to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.
            metadata (Dict[str, str], optional): Metadata associated with the agent. Defaults to an empty dictionary.
            model (str, optional): The model identifier for the OpenAI API. Defaults to "gpt-4o".
            validation_attempts (int, optional): Number of attempts to validate the response with response_validator function. Defaults to 1.
            max_prompt_tokens (int, optional): Maximum number of tokens allowed in the prompt. Defaults to None.
            max_completion_tokens (int, optional): Maximum number of tokens allowed in the completion. Defaults to None.
            truncation_strategy (TruncationStrategy, optional): Truncation strategy for the OpenAI API. Defaults to None.
            examples (List[Dict], optional): A list of example messages for the agent. Defaults to None.
            file_search (FileSearchConfig, optional): A dictionary containing the file search tool configuration. Defaults to None.
            parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.
            refresh_from_id (bool, optional): Whether to load and update the agent from the OpenAI assistant ID when provided. Defaults to True.

        This constructor sets up the agent with its unique properties, initializes the OpenAI client, reads instructions if provided, and uploads any associated files.
        """
        # public attributes
        self.id = id
        self.name = name if name else self.__class__.__name__
        self.description = description
        self.instructions = instructions
        self.tools = tools[:] if tools is not None else []
        self.tools = [tool for tool in self.tools if tool.__name__ != "ExampleTool"]
        self.tool_resources = tool_resources
        self.temperature = temperature
        self.top_p = top_p
        self.response_format = response_format
        # use structured outputs if response_format is a BaseModel
        if isinstance(self.response_format, type):
            self.response_format = type_to_response_format_param(self.response_format)
        self.tools_folder = tools_folder
        self.files_folder = files_folder if files_folder else []
        self.schemas_folder = schemas_folder if schemas_folder else []
        self.api_headers = api_headers if api_headers else {}
        self.api_params = api_params if api_params else {}
        self.metadata = metadata if metadata else {}
        self.model = model
        self.validation_attempts = validation_attempts
        self.max_prompt_tokens = max_prompt_tokens
        self.max_completion_tokens = max_completion_tokens
        self.truncation_strategy = truncation_strategy
        self.examples = examples
        self.file_search = file_search
        self.parallel_tool_calls = parallel_tool_calls
        self.refresh_from_id = refresh_from_id

        self.settings_path = './settings.json'

        # private attributes
        self._assistant: Any = None
        self._shared_instructions = None

        # init methods
        self.client = get_openai_client()
        self._read_instructions()

        # upload files
        self._upload_files()
        if file_ids:
            print("Warning: 'file_ids' parameter is deprecated. Please use 'tool_resources' parameter instead.")
            self.add_file_ids(file_ids, "file_search")

        self._parse_schemas()
        self._parse_tools_folder()

    # --- OpenAI Assistant Methods ---

    def init_oai(self):
        """
        Initializes the OpenAI assistant for the agent.

        This method handles the initialization and potential updates of the agent's OpenAI assistant. It loads the assistant based on a saved ID, updates the assistant if necessary, or creates a new assistant if it doesn't exist. After initialization or update, it saves the assistant's settings.

        Output:
            self: Returns the agent instance for chaining methods or further processing.
        """

        # check if settings.json exists
        path = self.get_settings_path()

        # load assistant from id
        if self.id:
            if not self.refresh_from_id:
                return self

            self.assistant = self.client.beta.assistants.retrieve(self.id)
            # Assign attributes to self if they are None
            self.instructions = self.instructions or self.assistant.instructions
            self.name = self.name if self.name != self.__class__.__name__ else self.assistant.name
            self.description = self.description or self.assistant.description
            self.temperature = self.assistant.temperature if self.temperature is None else self.temperature
            self.top_p = self.top_p or self.assistant.top_p
            self.response_format = self.response_format or self.assistant.response_format
            if not isinstance(self.response_format, str):
                self.response_format = self.response_format or self.response_format.model_dump()
            else:
                self.response_format = self.response_format or self.assistant.response_format
            self.tool_resources = self.tool_resources or self.assistant.tool_resources.model_dump()
            self.metadata = self.metadata or self.assistant.metadata
            self.model = self.model or self.assistant.model
            self.tool_resources = self.tool_resources or self.assistant.tool_resources.model_dump()

            for tool in self.assistant.tools:
                # update assistants created with v1
                if tool.type == "retrieval":
                    self.client.beta.assistants.update(self.id, tools=self.get_oai_tools())

            # update assistant if parameters are different
            if not self._check_parameters(self.assistant.model_dump()):
                self._update_assistant()

            return self

        # load assistant from settings
        if os.path.exists(path):
            with open(path, 'r') as f:
                settings = json.load(f)
                # iterate settings and find the assistant with the same name
                for assistant_settings in settings:
                    if assistant_settings['name'] == self.name:
                        try:
                            self.assistant = self.client.beta.assistants.retrieve(assistant_settings['id'])
                            self.id = assistant_settings['id']

                            # update assistant if parameters are different
                            if not self._check_parameters(self.assistant.model_dump()):
                                print("Updating agent... " + self.name)
                                self._update_assistant()

                            if self.assistant.tool_resources:
                                self.tool_resources = self.assistant.tool_resources.model_dump()

                            self._update_settings()
                            return self
                        except NotFoundError:
                            continue

        # create assistant if settings.json does not exist or assistant with the same name does not exist
        self.assistant = self.client.beta.assistants.create(
            model=self.model,
            name=self.name,
            description=self.description,
            instructions=self.instructions,
            tools=self.get_oai_tools(),
            tool_resources=self.tool_resources,
            metadata=self.metadata,
            temperature=self.temperature,
            top_p=self.top_p,
            response_format=self.response_format,
        )

        if self.assistant.tool_resources:
            self.tool_resources = self.assistant.tool_resources.model_dump()

        self.id = self.assistant.id

        self._save_settings()

        return self

    def _update_assistant(self):
        """
        Updates the existing assistant's parameters on the OpenAI server.

        This method updates the assistant's details such as name, description, instructions, tools, file IDs, metadata, and the model. It only updates parameters that have non-empty values. After updating the assistant, it also updates the local settings file to reflect these changes.

        No input parameters are directly passed to this method as it uses the agent's instance attributes.

        No output parameters are returned, but the method updates the assistant's details on the OpenAI server and locally updates the settings file.
        """
        tool_resources = copy.deepcopy(self.tool_resources)
        if tool_resources and tool_resources.get('file_search'):
            tool_resources['file_search'].pop('vector_stores', None)

        params = {
            "name": self.name,
            "description": self.description,
            "instructions": self.instructions,
            "tools": self.get_oai_tools(),
            "tool_resources": tool_resources,
            "temperature": self.temperature,
            "top_p": self.top_p,
            "response_format": self.response_format,
            "metadata": self.metadata,
            "model": self.model
        }
        params = {k: v for k, v in params.items() if v}
        self.assistant = self.client.beta.assistants.update(
            self.id,
            **params,
        )
        self._update_settings()

    def _upload_files(self):
        def add_id_to_file(f_path, id):
            """Add file id to file name"""
            if os.path.isfile(f_path):
                file_name, file_ext = os.path.splitext(f_path)
                f_path_new = file_name + "_" + id + file_ext
                os.rename(f_path, f_path_new)
                return f_path_new

        def get_id_from_file(f_path):
            """Get file id from file name"""
            if os.path.isfile(f_path):
                file_name, file_ext = os.path.splitext(f_path)
                file_name = os.path.basename(file_name)
                file_name = file_name.split("_")
                if len(file_name) > 1:
                    return file_name[-1] if "file-" in file_name[-1] else None
                else:
                    return None

        files_folders = self.files_folder if isinstance(self.files_folder, list) else [self.files_folder]

        file_search_ids = []
        code_interpreter_ids = []

        for files_folder in files_folders:
            if isinstance(files_folder, str):
                f_path = files_folder

                if not os.path.isdir(f_path):
                    f_path = os.path.join(self.get_class_folder_path(), files_folder)
                    f_path = os.path.normpath(f_path)

                if os.path.isdir(f_path):
                    f_paths = os.listdir(f_path)

                    f_paths = [f for f in f_paths if not f.startswith(".")]

                    f_paths = [os.path.join(f_path, f) for f in f_paths]

                    code_interpreter_file_extensions = [
                        ".json",  # JSON
                        ".csv",  # CSV
                        ".xml",  # XML
                        ".jpeg",  # JPEG
                        ".jpg",  # JPEG
                        ".gif",  # GIF
                        ".png",  # PNG
                        ".zip"  # ZIP
                    ]

                    for f_path in f_paths:
                        file_ext = os.path.splitext(f_path)[1]

                        f_path = f_path.strip()
                        file_id = get_id_from_file(f_path)
                        if file_id:
                            print("File already uploaded. Skipping... " + os.path.basename(f_path))
                        else:
                            print("Uploading new file... " + os.path.basename(f_path))
                            with open(f_path, 'rb') as f:
                                file_id = self.client.with_options(
                                    timeout=80 * 1000,
                                ).files.create(file=f, purpose="assistants").id
                            add_id_to_file(f_path, file_id)

                        if file_ext in code_interpreter_file_extensions:
                            code_interpreter_ids.append(file_id)
                        else:
                            file_search_ids.append(file_id)
                else:
                    print(f"Files folder '{f_path}' is not a directory. Skipping...", )
            else:
                print("Files folder path must be a string or list of strings. Skipping... ", files_folder)

        if FileSearch not in self.tools and file_search_ids:
            print("Detected files without FileSearch. Adding FileSearch tool...")
            self.add_tool(FileSearch)
        if CodeInterpreter not in self.tools and code_interpreter_ids:
            print("Detected files without CodeInterpreter. Adding CodeInterpreter tool...")
            self.add_tool(CodeInterpreter)

        self.add_file_ids(file_search_ids, "file_search")
        self.add_file_ids(code_interpreter_ids, "code_interpreter")

    # --- Tool Methods ---

    # TODO: fix 2 methods below
    def add_tool(self, tool):
        if not isinstance(tool, type):
            raise Exception("Tool must not be initialized.")

        subclasses = [FileSearch, CodeInterpreter, Retrieval]
        for subclass in subclasses:
            if issubclass(tool, subclass):
                if not any(issubclass(t, subclass) for t in self.tools):
                    self.tools.append(tool)
                return

        if issubclass(tool, BaseTool):
            if tool.__name__ == "ExampleTool":
                print("Skipping importing ExampleTool...")
                return
            self.tools = [t for t in self.tools if t.__name__ != tool.__name__]
            self.tools.append(tool)
        else:
            raise Exception("Invalid tool type.")

    def get_oai_tools(self):
        tools = []
        for tool in self.tools:
            if not isinstance(tool, type):
                print(tool)
                raise Exception("Tool must not be initialized.")

            if issubclass(tool, FileSearch):
                tools.append(tool(file_search=self.file_search).model_dump(exclude_none=True))
            elif issubclass(tool, CodeInterpreter):
                tools.append(tool().model_dump())
            elif issubclass(tool, Retrieval):
                tools.append(tool().model_dump())
            elif issubclass(tool, BaseTool):
                tools.append({
                    "type": "function",
                    "function": tool.openai_schema
                })
            else:
                raise Exception("Invalid tool type.")
        return tools

    def _parse_schemas(self):
        schemas_folders = self.schemas_folder if isinstance(self.schemas_folder, list) else [self.schemas_folder]

        for schemas_folder in schemas_folders:
            if isinstance(schemas_folder, str):
                f_path = schemas_folder

                if not os.path.isdir(f_path):
                    f_path = os.path.join(self.get_class_folder_path(), schemas_folder)
                    f_path = os.path.normpath(f_path)

                if os.path.isdir(f_path):
                    f_paths = os.listdir(f_path)

                    f_paths = [f for f in f_paths if not f.startswith(".")]

                    f_paths = [os.path.join(f_path, f) for f in f_paths]

                    for f_path in f_paths:
                        with open(f_path, 'r') as f:
                            openapi_spec = f.read()
                        try:
                            validate_openapi_spec(openapi_spec)
                        except Exception as e:
                            print("Invalid OpenAPI schema: " + os.path.basename(f_path))
                            raise e
                        try:
                            headers = None
                            params = None
                            if os.path.basename(f_path) in self.api_headers:
                                headers = self.api_headers[os.path.basename(f_path)]
                            if os.path.basename(f_path) in self.api_params:
                                params = self.api_params[os.path.basename(f_path)]
                            tools = ToolFactory.from_openapi_schema(openapi_spec, headers=headers, params=params)
                        except Exception as e:
                            print("Error parsing OpenAPI schema: " + os.path.basename(f_path))
                            raise e
                        for tool in tools:
                            self.add_tool(tool)
                else:
                    print("Schemas folder path is not a directory. Skipping... ", f_path)
            else:
                print("Schemas folder path must be a string or list of strings. Skipping... ", schemas_folder)

    def _parse_tools_folder(self):
        if not self.tools_folder:
            return

        if not os.path.isdir(self.tools_folder):
            self.tools_folder = os.path.join(self.get_class_folder_path(), self.tools_folder)
            self.tools_folder = os.path.normpath(self.tools_folder)

        if os.path.isdir(self.tools_folder):
            f_paths = os.listdir(self.tools_folder)
            f_paths = [f for f in f_paths if not f.startswith(".") and not f.startswith("__")]
            f_paths = [os.path.join(self.tools_folder, f) for f in f_paths]
            for f_path in f_paths:
                if not f_path.endswith(".py"):
                    continue
                if os.path.isfile(f_path):
                    try:
                        tool = ToolFactory.from_file(f_path)
                        self.add_tool(tool)
                    except Exception as e:
                        print(f"Error parsing tool file {os.path.basename(f_path)}: {e}. Skipping...")
                else:
                    print("Items in tools folder must be files. Skipping... ", f_path)
        else:
            print("Tools folder path is not a directory. Skipping... ", self.tools_folder)

    def get_openapi_schema(self, url):
        """Get openapi schema that contains all tools from the agent as different api paths. Make sure to call this after agency has been initialized."""
        if self.assistant is None:
            raise Exception(
                "Assistant is not initialized. Please initialize the agency first, before using this method")

        return ToolFactory.get_openapi_schema(self.tools, url)

    # --- Settings Methods ---

    def _check_parameters(self, assistant_settings, debug=False):
        """
        Checks if the agent's parameters match with the given assistant settings.

        Parameters:
            assistant_settings (dict): A dictionary containing the settings of an assistant.
            debug (bool): If True, prints debug statements. Default is False.

        Returns:
            bool: True if all the agent's parameters match the assistant settings, False otherwise.

        This method compares the current agent's parameters such as name, description, instructions, tools, file IDs, metadata, and model with the given assistant settings. It uses DeepDiff to compare complex structures like tools and metadata. If any parameter does not match, it returns False; otherwise, it returns True.
        """
        if self.name != assistant_settings['name']:
            if debug:
                print(f"Name mismatch: {self.name} != {assistant_settings['name']}")
            return False

        if self.description != assistant_settings['description']:
            if debug:
                print(f"Description mismatch: {self.description} != {assistant_settings['description']}")
            return False

        if self.instructions != assistant_settings['instructions']:
            if debug:
                print(f"Instructions mismatch: {self.instructions} != {assistant_settings['instructions']}")
            return False

        def clean_tool(tool):
            if isinstance(tool, dict):
                if 'function' in tool and 'strict' in tool['function'] and not tool['function']['strict']:
                    tool['function'].pop('strict', None)
            return tool

        local_tools = [clean_tool(tool) for tool in self.get_oai_tools()]
        assistant_tools = [clean_tool(tool) for tool in assistant_settings['tools']]

        # find file_search and code_interpreter tools in local_tools and assistant_tools
        # Find file_search tools in local and assistant tools
        local_file_search = next((tool for tool in local_tools if tool['type'] == 'file_search'), None)
        assistant_file_search = next((tool for tool in assistant_tools if tool['type'] == 'file_search'), None)

        if local_file_search:
            # If local file_search doesn't have a 'file_search' key, use assistant's if available
            if 'file_search' not in local_file_search and assistant_file_search and 'file_search' in assistant_file_search:
                local_file_search['file_search'] = assistant_file_search['file_search']
            elif 'file_search' in local_file_search:
                # Update max_num_results if not set locally but available in assistant
                if 'max_num_results' not in local_file_search['file_search'] and assistant_file_search and \
                   assistant_file_search['file_search'].get('max_num_results') is not None:
                    local_file_search['file_search']['max_num_results'] = assistant_file_search['file_search']['max_num_results']

                # Update ranking_options if not set locally but available in assistant
                if 'ranking_options' not in local_file_search['file_search'] and assistant_file_search and \
                   assistant_file_search['file_search'].get('ranking_options') is not None:
                    local_file_search['file_search']['ranking_options'] = assistant_file_search['file_search']['ranking_options']

        local_tools.sort(key=lambda x: json.dumps(x, sort_keys=True))
        assistant_tools.sort(key=lambda x: json.dumps(x, sort_keys=True))

        tools_diff = DeepDiff(local_tools, assistant_tools, ignore_order=True)
        if tools_diff:
            if debug:
                print(f"Tools mismatch: {tools_diff}")
                print("Local tools:", local_tools)
                print("Assistant tools:", assistant_tools)
            return False

        if self.temperature != assistant_settings['temperature']:
            if debug:
                print(f"Temperature mismatch: {self.temperature} != {assistant_settings['temperature']}")
            return False

        if self.top_p != assistant_settings['top_p']:
            if debug:
                print(f"Top_p mismatch: {self.top_p} != {assistant_settings['top_p']}")
            return False

        # adjust differences between local and assistant tool resources
        tool_resources_settings = copy.deepcopy(self.tool_resources)
        if tool_resources_settings is None:
            tool_resources_settings = {}
        if tool_resources_settings.get('file_search'):
            tool_resources_settings['file_search'].pop('vector_stores', None)
        if tool_resources_settings.get('file_search') is None:
            tool_resources_settings['file_search'] = {'vector_store_ids': []}
        if tool_resources_settings.get('code_interpreter') is None:
            tool_resources_settings['code_interpreter'] = {"file_ids": []}

        assistant_tool_resources = assistant_settings['tool_resources']
        if assistant_tool_resources is None:
            assistant_tool_resources = {}
        if assistant_tool_resources.get('code_interpreter') is None:
            assistant_tool_resources['code_interpreter'] = {"file_ids": []}
        if assistant_tool_resources.get('file_search') is None:
            assistant_tool_resources['file_search'] = {'vector_store_ids': []}

        tool_resources_diff = DeepDiff(tool_resources_settings, assistant_tool_resources, ignore_order=True)
        if tool_resources_diff != {}:
            if debug:
                print(f"Tool resources mismatch: {tool_resources_diff}")
                print("Local tool resources:", tool_resources_settings)
                print("Assistant tool resources:", assistant_settings['tool_resources'])
            return False

        metadata_diff = DeepDiff(self.metadata, assistant_settings['metadata'], ignore_order=True)
        if metadata_diff != {}:
            if debug:
                print(f"Metadata mismatch: {metadata_diff}")
            return False

        if self.model != assistant_settings['model']:
            if debug:
                print(f"Model mismatch: {self.model} != {assistant_settings['model']}")
            return False

        response_format_diff = DeepDiff(self.response_format, assistant_settings['response_format'], ignore_order=True)
        if response_format_diff != {}:
            if debug:
                print(f"Response format mismatch: {response_format_diff}")
            return False

        return True

    def _save_settings(self):
        path = self.get_settings_path()
        # check if settings.json exists
        if not os.path.isfile(path):
            with open(path, 'w') as f:
                json.dump([self.assistant.model_dump()], f, indent=4)
        else:
            settings = []
            with open(path, 'r') as f:
                settings = json.load(f)
                settings.append(self.assistant.model_dump())
            with open(path, 'w') as f:
                json.dump(settings, f, indent=4)

    def _update_settings(self):
        path = self.get_settings_path()
        # check if settings.json exists
        if os.path.isfile(path):
            settings = []
            with open(path, 'r') as f:
                settings = json.load(f)
                for i, assistant_settings in enumerate(settings):
                    if assistant_settings['id'] == self.id:
                        settings[i] = self.assistant.model_dump()
                        break
            with open(path, 'w') as f:
                json.dump(settings, f, indent=4)

    # --- Helper Methods ---

    def add_file_ids(self, file_ids: List[str], tool_resource: Literal["code_interpreter", "file_search"]):
        if not file_ids:
            return

        if self.tool_resources is None:
            self.tool_resources = {}

        if tool_resource == "code_interpreter":
            if CodeInterpreter not in self.tools:
                raise Exception("CodeInterpreter tool not found in tools.")

            if tool_resource not in self.tool_resources or self.tool_resources[
                tool_resource] is None:
                self.tool_resources[tool_resource] = {
                    "file_ids": file_ids
                }

            self.tool_resources[tool_resource]['file_ids'] = file_ids
        elif tool_resource == "file_search":
            if FileSearch not in self.tools:
                raise Exception("FileSearch tool not found in tools.")

            if tool_resource not in self.tool_resources or self.tool_resources[
                tool_resource] is None:
                self.tool_resources[tool_resource] = {
                    "vector_stores": [{
                        "file_ids": file_ids
                    }]
                }
            elif not self.tool_resources[tool_resource].get('vector_store_ids'):
                self.tool_resources[tool_resource]['vector_stores'] = [{
                    "file_ids": file_ids
                }]
            else:
                vector_store_id = self.tool_resources[tool_resource]['vector_store_ids'][0]
                self.client.beta.vector_stores.file_batches.create(
                    vector_store_id=vector_store_id,
                    file_ids=file_ids
                )
        else:
            raise Exception("Invalid tool resource.")

    def get_settings_path(self):
        return self.settings_path

    def _read_instructions(self):
        class_instructions_path = os.path.normpath(os.path.join(self.get_class_folder_path(), self.instructions))
        if os.path.isfile(class_instructions_path):
            with open(class_instructions_path, 'r') as f:
                self.instructions = f.read()
        elif os.path.isfile(self.instructions):
            with open(self.instructions, 'r') as f:
                self.instructions = f.read()
        elif "./instructions.md" in self.instructions or "./instructions.txt" in self.instructions:
            raise Exception("Instructions file not found.")

    def get_class_folder_path(self):
        try:
            # First, try to use the __file__ attribute of the module
            return os.path.abspath(os.path.dirname(self.__module__.__file__))
        except (TypeError, OSError, AttributeError) as e:
            # If that fails, fall back to inspect
            try:
                class_file = inspect.getfile(self.__class__)
            except (TypeError, OSError, AttributeError) as e:
                return "./"
            return os.path.abspath(os.path.realpath(os.path.dirname(class_file)))

    def add_shared_instructions(self, instructions: str):
        if not instructions:
            return

        if self._shared_instructions is None:
            self._shared_instructions = instructions
        else:
            self.instructions = self.instructions.replace(self._shared_instructions, "")
            self.instructions = self.instructions.strip().strip("\n")
            self._shared_instructions = instructions

        self.instructions = self._shared_instructions + "\n\n" + self.instructions

    # --- Cleanup Methods ---
    def delete(self):
        """Deletes assistant, all vector stores, and all files associated with the agent."""
        self._delete_assistant()
        self._delete_files()
        self._delete_settings()

    def _delete_files(self):
        if not self.tool_resources:
            return

        file_ids = []
        if self.tool_resources.get('code_interpreter'):
            file_ids = self.tool_resources['code_interpreter'].get('file_ids', [])

        if self.tool_resources.get('file_search'):
            file_search_vector_store_ids = self.tool_resources['file_search'].get('vector_store_ids', [])
            for vector_store_id in file_search_vector_store_ids:
                files = self.client.beta.vector_stores.files.list(vector_store_id=vector_store_id, limit=100)
                for file in files:
                    file_ids.append(file.id)

                self.client.beta.vector_stores.delete(vector_store_id)

        for file_id in file_ids:
            self.client.files.delete(file_id)

    def _delete_assistant(self):
        self.client.beta.assistants.delete(self.id)
        self._delete_settings()

    def _delete_settings(self):
        path = self.get_settings_path()
        # check if settings.json exists
        if os.path.isfile(path):
            settings = []
            with open(path, 'r') as f:
                settings = json.load(f)
                for i, assistant_settings in enumerate(settings):
                    if assistant_settings['id'] == self.id:
                        settings.pop(i)
                        break
            with open(path, 'w') as f:
                json.dump(settings, f, indent=4)

__init__(id=None, name=None, description=None, instructions='', tools=None, tool_resources=None, temperature=None, top_p=None, response_format='auto', tools_folder=None, files_folder=None, schemas_folder=None, api_headers=None, api_params=None, file_ids=None, metadata=None, model='gpt-4o-2024-08-06', validation_attempts=1, max_prompt_tokens=None, max_completion_tokens=None, truncation_strategy=None, examples=None, file_search=None, parallel_tool_calls=True, refresh_from_id=True)

Initializes an Agent with specified attributes, tools, and OpenAI client.

Parameters:

Name	Type	Description	Default
id	str	Loads the assistant from OpenAI assistant ID. Assistant will be created or loaded from settings if ID is not provided. Defaults to None.	None
name	str	Name of the agent. Defaults to the class name if not provided.	None
description	str	A brief description of the agent's purpose. Defaults to None.	None
instructions	str	Path to a file containing specific instructions for the agent. Defaults to an empty string.	''
tools	List[Union[Type[BaseTool], Type[Retrieval], Type[CodeInterpreter]]]	A list of tools (as classes) that the agent can use. Defaults to an empty list.	None
tool_resources	ToolResources	A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs. Defaults to None.	None
temperature	float	The temperature parameter for the OpenAI API. Defaults to None.	None
top_p	float	The top_p parameter for the OpenAI API. Defaults to None.	None
response_format	Union[str, Dict, type]	The response format for the OpenAI API. If BaseModel is provided, it will be converted to a response format. Defaults to None.	'auto'
tools_folder	str	Path to a directory containing tools associated with the agent. Each tool must be defined in a separate file. File must be named as the class name of the tool. Defaults to None.	None
files_folder	Union[List[str], str]	Path or list of paths to directories containing files associated with the agent. Defaults to None.	None
schemas_folder	Union[List[str], str]	Path or list of paths to directories containing OpenAPI schemas associated with the agent. Defaults to None.	None
api_headers	Dict[str, Dict[str, str]]	Headers to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.	None
api_params	Dict[str, Dict[str, str]]	Extra params to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.	None
metadata	Dict[str, str]	Metadata associated with the agent. Defaults to an empty dictionary.	None
model	str	The model identifier for the OpenAI API. Defaults to "gpt-4o".	'gpt-4o-2024-08-06'
validation_attempts	int	Number of attempts to validate the response with response_validator function. Defaults to 1.	1
max_prompt_tokens	int	Maximum number of tokens allowed in the prompt. Defaults to None.	None
max_completion_tokens	int	Maximum number of tokens allowed in the completion. Defaults to None.	None
truncation_strategy	TruncationStrategy	Truncation strategy for the OpenAI API. Defaults to None.	None
examples	List[Dict]	A list of example messages for the agent. Defaults to None.	None
file_search	FileSearchConfig	A dictionary containing the file search tool configuration. Defaults to None.	None
parallel_tool_calls	bool	Whether to enable parallel function calling during tool use. Defaults to True.	True
refresh_from_id	bool	Whether to load and update the agent from the OpenAI assistant ID when provided. Defaults to True.	True

This constructor sets up the agent with its unique properties, initializes the OpenAI client, reads instructions if provided, and uploads any associated files.

Source code in agency_swarm/agents/agent.py

def __init__(
        self,
        id: str = None,
        name: str = None,
        description: str = None,
        instructions: str = "",
        tools: List[Union[Type[BaseTool], Type[FileSearch], Type[CodeInterpreter], type[Retrieval]]] = None,
        tool_resources: ToolResources = None,
        temperature: float = None,
        top_p: float = None,
        response_format: Union[str, dict, type] = "auto",
        tools_folder: str = None,
        files_folder: Union[List[str], str] = None,
        schemas_folder: Union[List[str], str] = None,
        api_headers: Dict[str, Dict[str, str]] = None,
        api_params: Dict[str, Dict[str, str]] = None,
        file_ids: List[str] = None,
        metadata: Dict[str, str] = None,
        model: str = "gpt-4o-2024-08-06",
        validation_attempts: int = 1,
        max_prompt_tokens: int = None,
        max_completion_tokens: int = None,
        truncation_strategy: dict = None,
        examples: List[ExampleMessage] = None,
        file_search: FileSearchConfig = None,
        parallel_tool_calls: bool = True,
        refresh_from_id: bool = True,
):
    """
    Initializes an Agent with specified attributes, tools, and OpenAI client.

    Parameters:
        id (str, optional): Loads the assistant from OpenAI assistant ID. Assistant will be created or loaded from settings if ID is not provided. Defaults to None.
        name (str, optional): Name of the agent. Defaults to the class name if not provided.
        description (str, optional): A brief description of the agent's purpose. Defaults to None.
        instructions (str, optional): Path to a file containing specific instructions for the agent. Defaults to an empty string.
        tools (List[Union[Type[BaseTool], Type[Retrieval], Type[CodeInterpreter]]], optional): A list of tools (as classes) that the agent can use. Defaults to an empty list.
        tool_resources (ToolResources, optional): A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs. Defaults to None.
        temperature (float, optional): The temperature parameter for the OpenAI API. Defaults to None.
        top_p (float, optional): The top_p parameter for the OpenAI API. Defaults to None.
        response_format (Union[str, Dict, type], optional): The response format for the OpenAI API. If BaseModel is provided, it will be converted to a response format. Defaults to None.
        tools_folder (str, optional): Path to a directory containing tools associated with the agent. Each tool must be defined in a separate file. File must be named as the class name of the tool. Defaults to None.
        files_folder (Union[List[str], str], optional): Path or list of paths to directories containing files associated with the agent. Defaults to None.
        schemas_folder (Union[List[str], str], optional): Path or list of paths to directories containing OpenAPI schemas associated with the agent. Defaults to None.
        api_headers (Dict[str,Dict[str, str]], optional): Headers to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.
        api_params (Dict[str, Dict[str, str]], optional): Extra params to be used for the openapi requests. Each key must be a full filename from schemas_folder. Defaults to an empty dictionary.
        metadata (Dict[str, str], optional): Metadata associated with the agent. Defaults to an empty dictionary.
        model (str, optional): The model identifier for the OpenAI API. Defaults to "gpt-4o".
        validation_attempts (int, optional): Number of attempts to validate the response with response_validator function. Defaults to 1.
        max_prompt_tokens (int, optional): Maximum number of tokens allowed in the prompt. Defaults to None.
        max_completion_tokens (int, optional): Maximum number of tokens allowed in the completion. Defaults to None.
        truncation_strategy (TruncationStrategy, optional): Truncation strategy for the OpenAI API. Defaults to None.
        examples (List[Dict], optional): A list of example messages for the agent. Defaults to None.
        file_search (FileSearchConfig, optional): A dictionary containing the file search tool configuration. Defaults to None.
        parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.
        refresh_from_id (bool, optional): Whether to load and update the agent from the OpenAI assistant ID when provided. Defaults to True.

    This constructor sets up the agent with its unique properties, initializes the OpenAI client, reads instructions if provided, and uploads any associated files.
    """
    # public attributes
    self.id = id
    self.name = name if name else self.__class__.__name__
    self.description = description
    self.instructions = instructions
    self.tools = tools[:] if tools is not None else []
    self.tools = [tool for tool in self.tools if tool.__name__ != "ExampleTool"]
    self.tool_resources = tool_resources
    self.temperature = temperature
    self.top_p = top_p
    self.response_format = response_format
    # use structured outputs if response_format is a BaseModel
    if isinstance(self.response_format, type):
        self.response_format = type_to_response_format_param(self.response_format)
    self.tools_folder = tools_folder
    self.files_folder = files_folder if files_folder else []
    self.schemas_folder = schemas_folder if schemas_folder else []
    self.api_headers = api_headers if api_headers else {}
    self.api_params = api_params if api_params else {}
    self.metadata = metadata if metadata else {}
    self.model = model
    self.validation_attempts = validation_attempts
    self.max_prompt_tokens = max_prompt_tokens
    self.max_completion_tokens = max_completion_tokens
    self.truncation_strategy = truncation_strategy
    self.examples = examples
    self.file_search = file_search
    self.parallel_tool_calls = parallel_tool_calls
    self.refresh_from_id = refresh_from_id

    self.settings_path = './settings.json'

    # private attributes
    self._assistant: Any = None
    self._shared_instructions = None

    # init methods
    self.client = get_openai_client()
    self._read_instructions()

    # upload files
    self._upload_files()
    if file_ids:
        print("Warning: 'file_ids' parameter is deprecated. Please use 'tool_resources' parameter instead.")
        self.add_file_ids(file_ids, "file_search")

    self._parse_schemas()
    self._parse_tools_folder()

delete()

Deletes assistant, all vector stores, and all files associated with the agent.

Source code in agency_swarm/agents/agent.py

def delete(self):
    """Deletes assistant, all vector stores, and all files associated with the agent."""
    self._delete_assistant()
    self._delete_files()
    self._delete_settings()

get_openapi_schema(url)

Get openapi schema that contains all tools from the agent as different api paths. Make sure to call this after agency has been initialized.

Source code in agency_swarm/agents/agent.py

def get_openapi_schema(self, url):
    """Get openapi schema that contains all tools from the agent as different api paths. Make sure to call this after agency has been initialized."""
    if self.assistant is None:
        raise Exception(
            "Assistant is not initialized. Please initialize the agency first, before using this method")

    return ToolFactory.get_openapi_schema(self.tools, url)

init_oai()

Initializes the OpenAI assistant for the agent.

This method handles the initialization and potential updates of the agent's OpenAI assistant. It loads the assistant based on a saved ID, updates the assistant if necessary, or creates a new assistant if it doesn't exist. After initialization or update, it saves the assistant's settings.

Output

self: Returns the agent instance for chaining methods or further processing.

Source code in agency_swarm/agents/agent.py

def init_oai(self):
    """
    Initializes the OpenAI assistant for the agent.

    This method handles the initialization and potential updates of the agent's OpenAI assistant. It loads the assistant based on a saved ID, updates the assistant if necessary, or creates a new assistant if it doesn't exist. After initialization or update, it saves the assistant's settings.

    Output:
        self: Returns the agent instance for chaining methods or further processing.
    """

    # check if settings.json exists
    path = self.get_settings_path()

    # load assistant from id
    if self.id:
        if not self.refresh_from_id:
            return self

        self.assistant = self.client.beta.assistants.retrieve(self.id)
        # Assign attributes to self if they are None
        self.instructions = self.instructions or self.assistant.instructions
        self.name = self.name if self.name != self.__class__.__name__ else self.assistant.name
        self.description = self.description or self.assistant.description
        self.temperature = self.assistant.temperature if self.temperature is None else self.temperature
        self.top_p = self.top_p or self.assistant.top_p
        self.response_format = self.response_format or self.assistant.response_format
        if not isinstance(self.response_format, str):
            self.response_format = self.response_format or self.response_format.model_dump()
        else:
            self.response_format = self.response_format or self.assistant.response_format
        self.tool_resources = self.tool_resources or self.assistant.tool_resources.model_dump()
        self.metadata = self.metadata or self.assistant.metadata
        self.model = self.model or self.assistant.model
        self.tool_resources = self.tool_resources or self.assistant.tool_resources.model_dump()

        for tool in self.assistant.tools:
            # update assistants created with v1
            if tool.type == "retrieval":
                self.client.beta.assistants.update(self.id, tools=self.get_oai_tools())

        # update assistant if parameters are different
        if not self._check_parameters(self.assistant.model_dump()):
            self._update_assistant()

        return self

    # load assistant from settings
    if os.path.exists(path):
        with open(path, 'r') as f:
            settings = json.load(f)
            # iterate settings and find the assistant with the same name
            for assistant_settings in settings:
                if assistant_settings['name'] == self.name:
                    try:
                        self.assistant = self.client.beta.assistants.retrieve(assistant_settings['id'])
                        self.id = assistant_settings['id']

                        # update assistant if parameters are different
                        if not self._check_parameters(self.assistant.model_dump()):
                            print("Updating agent... " + self.name)
                            self._update_assistant()

                        if self.assistant.tool_resources:
                            self.tool_resources = self.assistant.tool_resources.model_dump()

                        self._update_settings()
                        return self
                    except NotFoundError:
                        continue

    # create assistant if settings.json does not exist or assistant with the same name does not exist
    self.assistant = self.client.beta.assistants.create(
        model=self.model,
        name=self.name,
        description=self.description,
        instructions=self.instructions,
        tools=self.get_oai_tools(),
        tool_resources=self.tool_resources,
        metadata=self.metadata,
        temperature=self.temperature,
        top_p=self.top_p,
        response_format=self.response_format,
    )

    if self.assistant.tool_resources:
        self.tool_resources = self.assistant.tool_resources.model_dump()

    self.id = self.assistant.id

    self._save_settings()

    return self

response_validator(message)

Validates the response from the agent. If the response is invalid, it must raise an exception with instructions for the caller agent on how to proceed.

Parameters:

Name	Type	Description	Default
message	str	The response from the agent.	required

Returns:

Name	Type	Description
str	str	The validated response.

Source code in agency_swarm/agents/agent.py

def response_validator(self, message: str | list) -> str:
    """
    Validates the response from the agent. If the response is invalid, it must raise an exception with instructions
    for the caller agent on how to proceed.

    Parameters:
        message (str): The response from the agent.

    Returns:
        str: The validated response.
    """
    return message

Agency

Source code in agency_swarm/agency/agency.py

class Agency:
    def __init__(self,
                 agency_chart: List,
                 shared_instructions: str = "",
                 shared_files: Union[str, List[str]] = None,
                 async_mode: Literal['threading', "tools_threading"] = None,
                 send_message_tool_class: Type[SendMessageBase] = SendMessage,
                 settings_path: str = "./settings.json",
                 settings_callbacks: SettingsCallbacks = None,
                 threads_callbacks: ThreadsCallbacks = None,
                 temperature: float = 0.3,
                 top_p: float = 1.0,
                 max_prompt_tokens: int = None,
                 max_completion_tokens: int = None,
                 truncation_strategy: dict = None,
                 ):
        """
        Initializes the Agency object, setting up agents, threads, and core functionalities.

        Parameters:
            agency_chart: The structure defining the hierarchy and interaction of agents within the agency.
            shared_instructions (str, optional): A path to a file containing shared instructions for all agents. Defaults to an empty string.
            shared_files (Union[str, List[str]], optional): A path to a folder or a list of folders containing shared files for all agents. Defaults to None.
            async_mode (str, optional): Specifies the mode for asynchronous processing. In "threading" mode, all sub-agents run in separate threads. In "tools_threading" mode, all tools run in separate threads, but agents do not. Defaults to None.
            send_message_tool_class (Type[SendMessageBase], optional): The class to use for the send_message tool. For async communication, use `SendMessageAsyncThreading`. Defaults to SendMessage.
            settings_path (str, optional): The path to the settings file for the agency. Must be json. If file does not exist, it will be created. Defaults to None.
            settings_callbacks (SettingsCallbacks, optional): A dictionary containing functions to load and save settings for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.
            threads_callbacks (ThreadsCallbacks, optional): A dictionary containing functions to load and save threads for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.
            temperature (float, optional): The temperature value to use for the agents. Agent-specific values will override this. Defaults to 0.3.
            top_p (float, optional): The top_p value to use for the agents. Agent-specific values will override this. Defaults to None.
            max_prompt_tokens (int, optional): The maximum number of tokens allowed in the prompt for each agent. Agent-specific values will override this. Defaults to None.
            max_completion_tokens (int, optional): The maximum number of tokens allowed in the completion for each agent. Agent-specific values will override this. Defaults to None.
            truncation_strategy (dict, optional): The truncation strategy to use for the completion for each agent. Agent-specific values will override this. Defaults to None.

        This constructor initializes various components of the Agency, including CEO, agents, threads, and user interactions. It parses the agency chart to set up the organizational structure and initializes the messaging tools, agents, and threads necessary for the operation of the agency. Additionally, it prepares a main thread for user interactions.
        """
        self.ceo = None
        self.user = User()
        self.agents = []
        self.agents_and_threads = {}
        self.main_recipients = []
        self.main_thread = None
        self.recipient_agents = None  # for autocomplete
        self.shared_files = shared_files if shared_files else []
        self.async_mode = async_mode
        self.send_message_tool_class = send_message_tool_class
        self.settings_path = settings_path
        self.settings_callbacks = settings_callbacks
        self.threads_callbacks = threads_callbacks
        self.temperature = temperature
        self.top_p = top_p
        self.max_prompt_tokens = max_prompt_tokens
        self.max_completion_tokens = max_completion_tokens
        self.truncation_strategy = truncation_strategy

        # set thread type based send_message_tool_class async mode
        if hasattr(send_message_tool_class.ToolConfig, "async_mode") and send_message_tool_class.ToolConfig.async_mode:
            self._thread_type = ThreadAsync
        else:
            self._thread_type = Thread  

        if self.async_mode == "threading":
            from agency_swarm.tools.send_message import SendMessageAsyncThreading
            print("Warning: 'threading' mode is deprecated. Please use send_message_tool_class = SendMessageAsyncThreading to use async communication.")
            self.send_message_tool_class = SendMessageAsyncThreading
        elif self.async_mode == "tools_threading":
            Thread.async_mode = "tools_threading"
            print("Warning: 'tools_threading' mode is deprecated. Use tool.ToolConfig.async_mode = 'threading' instead.")
        elif self.async_mode is None:
            pass
        else:
            raise Exception("Please select async_mode = 'threading' or 'tools_threading'.")

        if os.path.isfile(os.path.join(self._get_class_folder_path(), shared_instructions)):
            self._read_instructions(os.path.join(self._get_class_folder_path(), shared_instructions))
        elif os.path.isfile(shared_instructions):
            self._read_instructions(shared_instructions)
        else:
            self.shared_instructions = shared_instructions

        self.shared_state = SharedState()

        self._parse_agency_chart(agency_chart)
        self._init_threads()
        self._create_special_tools()
        self._init_agents()

    def get_completion(self, message: str,
                       message_files: List[str] = None,
                       yield_messages: bool = False,
                       recipient_agent: Agent = None,
                       additional_instructions: str = None,
                       attachments: List[dict] = None,
                       tool_choice: dict = None,
                       verbose: bool = False,
                       response_format: dict = None):
        """
        Retrieves the completion for a given message from the main thread.

        Parameters:
            message (str): The message for which completion is to be retrieved.
            message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
            yield_messages (bool, optional): Flag to determine if intermediate messages should be yielded. Defaults to True.
            recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
            additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
            attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
            tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
            parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.
            verbose (bool, optional): Whether to print the intermediary messages in console. Defaults to False.
            response_format (dict, optional): The response format to use for the completion.

        Returns:
            Generator or final response: Depending on the 'yield_messages' flag, this method returns either a generator yielding intermediate messages or the final response from the main thread.
        """
        if verbose and yield_messages:
            raise Exception("Verbose mode is not compatible with yield_messages=True")

        res = self.main_thread.get_completion(message=message,
                                               message_files=message_files,
                                               attachments=attachments,
                                               recipient_agent=recipient_agent,
                                               additional_instructions=additional_instructions,
                                               tool_choice=tool_choice,
                                               yield_messages=yield_messages or verbose,
                                               response_format=response_format)

        if not yield_messages or verbose:
            while True:
                try:
                    message = next(res)
                    if verbose:
                        message.cprint()
                except StopIteration as e:
                    return e.value

        return res


    def get_completion_stream(self,
                              message: str,
                              event_handler: type(AgencyEventHandler),
                              message_files: List[str] = None,
                              recipient_agent: Agent = None,
                              additional_instructions: str = None,
                              attachments: List[dict] = None,
                              tool_choice: dict = None,
                              response_format: dict = None):
        """
        Generates a stream of completions for a given message from the main thread.

        Parameters:
            message (str): The message for which completion is to be retrieved.
            event_handler (type(AgencyEventHandler)): The event handler class to handle the completion stream. https://github.com/openai/openai-python/blob/main/helpers.md
            message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
            recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
            additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
            attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
            tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
            parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.

        Returns:
            Final response: Final response from the main thread.
        """
        if not inspect.isclass(event_handler):
            raise Exception("Event handler must not be an instance.")

        res = self.main_thread.get_completion_stream(message=message,
                                                      message_files=message_files,
                                                      event_handler=event_handler,
                                                      attachments=attachments,
                                                      recipient_agent=recipient_agent,
                                                      additional_instructions=additional_instructions,
                                                      tool_choice=tool_choice,
                                                      response_format=response_format)

        while True:
            try:
                next(res)
            except StopIteration as e:
                event_handler.on_all_streams_end()

                return e.value

    def get_completion_parse(self, message: str,
                             response_format: Type[T],
                             message_files: List[str] = None,
                             recipient_agent: Agent = None,
                             additional_instructions: str = None,
                             attachments: List[dict] = None,
                             tool_choice: dict = None,
                             verbose: bool = False) -> T:
        """
        Retrieves the completion for a given message from the main thread and parses the response using the provided pydantic model.

        Parameters:
            message (str): The message for which completion is to be retrieved.
            response_format (type(BaseModel)): The response format to use for the completion. 
            message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
            recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
            additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
            attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
            tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
            verbose (bool, optional): Whether to print the intermediary messages in console. Defaults to False.

        Returns:
            Final response: The final response from the main thread, parsed using the provided pydantic model.
        """
        response_model = None
        if isinstance(response_format, type):
            response_model = response_format
            response_format = type_to_response_format_param(response_format)

        res = self.get_completion(message=message,
                            message_files=message_files,
                            recipient_agent=recipient_agent,
                            additional_instructions=additional_instructions,
                            attachments=attachments,
                            tool_choice=tool_choice,
                            response_format=response_format,
                            verbose=verbose)

        try:
            return response_model.model_validate_json(res)
        except:
            parsed_res = json.loads(res)
            if 'refusal' in parsed_res:
                raise RefusalError(parsed_res['refusal'])
            else:
                raise Exception("Failed to parse response: " + res)

    def demo_gradio(self, height=450, dark_mode=True, **kwargs):
        """
        Launches a Gradio-based demo interface for the agency chatbot.

        Parameters:
            height (int, optional): The height of the chatbot widget in the Gradio interface. Default is 600.
            dark_mode (bool, optional): Flag to determine if the interface should be displayed in dark mode. Default is True.
            **kwargs: Additional keyword arguments to be passed to the Gradio interface.
        This method sets up and runs a Gradio interface, allowing users to interact with the agency's chatbot. It includes a text input for the user's messages and a chatbot interface for displaying the conversation. The method handles user input and chatbot responses, updating the interface dynamically.
        """

        try:
            import gradio as gr
        except ImportError:
            raise Exception("Please install gradio: pip install gradio")

        js = """function () {
          gradioURL = window.location.href
          if (!gradioURL.endsWith('?__theme={theme}')) {
            window.location.replace(gradioURL + '?__theme={theme}');
          }
        }"""

        if dark_mode:
            js = js.replace("{theme}", "dark")
        else:
            js = js.replace("{theme}", "light")

        attachments = []
        images = []
        message_file_names = None
        uploading_files = False
        recipient_agent_names = [agent.name for agent in self.main_recipients]
        recipient_agent = self.main_recipients[0]

        with gr.Blocks(js=js) as demo:
            chatbot_queue = queue.Queue()
            chatbot = gr.Chatbot(height=height)
            with gr.Row():
                with gr.Column(scale=9):
                    dropdown = gr.Dropdown(label="Recipient Agent", choices=recipient_agent_names,
                                           value=recipient_agent.name)
                    msg = gr.Textbox(label="Your Message", lines=4)
                with gr.Column(scale=1):
                    file_upload = gr.Files(label="OpenAI Files", type="filepath")
            button = gr.Button(value="Send", variant="primary")

            def handle_dropdown_change(selected_option):
                nonlocal recipient_agent
                recipient_agent = self._get_agent_by_name(selected_option)

            def handle_file_upload(file_list):
                nonlocal attachments
                nonlocal message_file_names
                nonlocal uploading_files
                nonlocal images
                uploading_files = True
                attachments = []
                message_file_names = []
                if file_list:
                    try:
                        for file_obj in file_list:
                            purpose = get_file_purpose(file_obj.name)

                            with open(file_obj.name, 'rb') as f:
                                # Upload the file to OpenAI
                                file = self.main_thread.client.files.create(
                                    file=f,
                                    purpose=purpose
                                )

                            if purpose == "vision":
                                images.append({
                                    "type": "image_file",
                                    "image_file": {"file_id": file.id}
                                })
                            else:
                                attachments.append({
                                    "file_id": file.id,
                                    "tools": get_tools(file.filename)
                                })

                            message_file_names.append(file.filename)
                            print(f"Uploaded file ID: {file.id}")
                        return attachments
                    except Exception as e:
                        print(f"Error: {e}")
                        return str(e)
                    finally:
                        uploading_files = False

                uploading_files = False
                return "No files uploaded"

            def user(user_message, history):
                if not user_message.strip():
                    return user_message, history

                nonlocal message_file_names
                nonlocal uploading_files
                nonlocal images
                nonlocal attachments
                nonlocal recipient_agent

                # Check if attachments contain file search or code interpreter types
                def check_and_add_tools_in_attachments(attachments, recipient_agent):
                    for attachment in attachments:
                        for tool in attachment.get("tools", []):
                            if tool["type"] == "file_search":
                                if not any(isinstance(t, FileSearch) for t in recipient_agent.tools):
                                    # Add FileSearch tool if it does not exist
                                    recipient_agent.tools.append(FileSearch)
                                    recipient_agent.client.beta.assistants.update(recipient_agent.id, tools=recipient_agent.get_oai_tools())
                                    print("Added FileSearch tool to recipient agent to analyze the file.")
                            elif tool["type"] == "code_interpreter":
                                if not any(isinstance(t, CodeInterpreter) for t in recipient_agent.tools):
                                    # Add CodeInterpreter tool if it does not exist
                                    recipient_agent.tools.append(CodeInterpreter)
                                    recipient_agent.client.beta.assistants.update(recipient_agent.id, tools=recipient_agent.get_oai_tools())
                                    print("Added CodeInterpreter tool to recipient agent to analyze the file.")
                    return None

                check_and_add_tools_in_attachments(attachments, recipient_agent)

                if history is None:
                    history = []

                original_user_message = user_message

                # Append the user message with a placeholder for bot response
                if recipient_agent:
                    user_message = f"👤 User 🗣️ @{recipient_agent.name}:\n" + user_message.strip()
                else:
                    user_message = f"👤 User:" + user_message.strip()

                nonlocal message_file_names
                if message_file_names:
                    user_message += "\n\n📎 Files:\n" + "\n".join(message_file_names)

                return original_user_message, history + [[user_message, None]]

            class GradioEventHandler(AgencyEventHandler):
                message_output = None

                @classmethod
                def change_recipient_agent(cls, recipient_agent_name):
                    nonlocal chatbot_queue
                    chatbot_queue.put("[change_recipient_agent]")
                    chatbot_queue.put(recipient_agent_name)

                @override
                def on_message_created(self, message: Message) -> None:
                    if message.role == "user":
                        full_content = ""
                        for content in message.content:
                            if content.type == "image_file":
                                full_content += f"🖼️ Image File: {content.image_file.file_id}\n"
                                continue

                            if content.type == "image_url":
                                full_content += f"\n{content.image_url.url}\n"
                                continue

                            if content.type == "text":
                                full_content += content.text.value + "\n"


                        self.message_output = MessageOutput("text", self.agent_name, self.recipient_agent_name,
                                                            full_content)

                    else:
                        self.message_output = MessageOutput("text", self.recipient_agent_name, self.agent_name,
                                                            "")

                    chatbot_queue.put("[new_message]")
                    chatbot_queue.put(self.message_output.get_formatted_content())

                @override
                def on_text_delta(self, delta, snapshot):
                    chatbot_queue.put(delta.value)

                @override
                def on_tool_call_created(self, tool_call: ToolCall):
                    if isinstance(tool_call, dict):
                        if "type" not in tool_call:
                            tool_call["type"] = "function"

                        if tool_call["type"] == "function":
                            tool_call = FunctionToolCall(**tool_call)
                        elif tool_call["type"] == "code_interpreter":
                            tool_call = CodeInterpreterToolCall(**tool_call)
                        elif tool_call["type"] == "file_search" or tool_call["type"] == "retrieval":
                            tool_call = FileSearchToolCall(**tool_call)
                        else:
                            raise ValueError("Invalid tool call type: " + tool_call["type"])

                    # TODO: add support for code interpreter and retrieval tools
                    if tool_call.type == "function":
                        chatbot_queue.put("[new_message]")
                        self.message_output = MessageOutput("function", self.recipient_agent_name, self.agent_name,
                                                            str(tool_call.function))
                        chatbot_queue.put(self.message_output.get_formatted_header() + "\n")

                @override
                def on_tool_call_done(self, snapshot: ToolCall):
                    if isinstance(snapshot, dict):
                        if "type" not in snapshot:
                            snapshot["type"] = "function"

                        if snapshot["type"] == "function":
                            snapshot = FunctionToolCall(**snapshot)
                        elif snapshot["type"] == "code_interpreter":
                            snapshot = CodeInterpreterToolCall(**snapshot)
                        elif snapshot["type"] == "file_search":
                            snapshot = FileSearchToolCall(**snapshot)
                        else:
                            raise ValueError("Invalid tool call type: " + snapshot["type"])

                    self.message_output = None

                    # TODO: add support for code interpreter and retrieval tools
                    if snapshot.type != "function":
                        return

                    chatbot_queue.put(str(snapshot.function))

                    if snapshot.function.name == "SendMessage":
                        try:
                            args = eval(snapshot.function.arguments)
                            recipient = args["recipient"]
                            self.message_output = MessageOutput("text", self.recipient_agent_name, recipient,
                                                                args["message"])

                            chatbot_queue.put("[new_message]")
                            chatbot_queue.put(self.message_output.get_formatted_content())
                        except Exception as e:
                            pass

                    self.message_output = None

                @override
                def on_run_step_done(self, run_step: RunStep) -> None:
                    if run_step.type == "tool_calls":
                        for tool_call in run_step.step_details.tool_calls:
                            if tool_call.type != "function":
                                continue

                            if tool_call.function.name == "SendMessage":
                                continue

                            self.message_output = None
                            chatbot_queue.put("[new_message]")

                            self.message_output = MessageOutput("function_output", tool_call.function.name,
                                                                self.recipient_agent_name,
                                                                tool_call.function.output)

                            chatbot_queue.put(self.message_output.get_formatted_header() + "\n")
                            chatbot_queue.put(tool_call.function.output)

                @override
                @classmethod
                def on_all_streams_end(cls):
                    cls.message_output = None
                    chatbot_queue.put("[end]")

            def bot(original_message, history):
                nonlocal attachments
                nonlocal message_file_names
                nonlocal recipient_agent
                nonlocal recipient_agent_names
                nonlocal images
                nonlocal uploading_files

                if not original_message:
                    return "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))

                if uploading_files:
                    history.append([None, "Uploading files... Please wait."])
                    yield "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))
                    return "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))

                print("Message files: ", attachments)
                print("Images: ", images)

                if images and len(images) > 0:
                    original_message = [
                        {
                            "type": "text",
                            "text": original_message,
                        },
                        *images
                    ]


                completion_thread = threading.Thread(target=self.get_completion_stream, args=(
                    original_message, GradioEventHandler, [], recipient_agent, "", attachments, None))
                completion_thread.start()

                attachments = []
                message_file_names = []
                images = []
                uploading_files = False

                new_message = True
                while True:
                    try:
                        bot_message = chatbot_queue.get(block=True)

                        if bot_message == "[end]":
                            completion_thread.join()
                            break

                        if bot_message == "[new_message]":
                            new_message = True
                            continue

                        if bot_message == "[change_recipient_agent]":
                            new_agent_name = chatbot_queue.get(block=True)
                            recipient_agent = self._get_agent_by_name(new_agent_name)
                            yield "", history, gr.update(value=new_agent_name, choices=set([*recipient_agent_names, recipient_agent.name]))
                            continue

                        if new_message:
                            history.append([None, bot_message])
                            new_message = False
                        else:
                            history[-1][1] += bot_message

                        yield "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))
                    except queue.Empty:
                        break

            button.click(
                user,
                inputs=[msg, chatbot],
                outputs=[msg, chatbot]
            ).then(
                bot, [msg, chatbot, dropdown], [msg, chatbot, dropdown]
            )
            dropdown.change(handle_dropdown_change, dropdown)
            file_upload.change(handle_file_upload, file_upload)
            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
                bot, [msg, chatbot], [msg, chatbot, dropdown]
            )

            # Enable queuing for streaming intermediate outputs
            demo.queue(default_concurrency_limit=10)

        # Launch the demo
        demo.launch(**kwargs)
        return demo

    def _recipient_agent_completer(self, text, state):
        """
        Autocomplete completer for recipient agent names.
        """
        options = [agent for agent in self.recipient_agents if agent.lower().startswith(text.lower())]
        if state < len(options):
            return options[state]
        else:
            return None

    def _setup_autocomplete(self):
        """
        Sets up readline with the completer function.
        """
        try:
            import readline
        except ImportError:
            # Attempt to import pyreadline for Windows compatibility
            try:
                import pyreadline as readline
            except ImportError:
                print(
                    "Module 'readline' not found. Autocomplete will not work. If you are using Windows, try installing 'pyreadline3'.")
                return

        if not readline:
            return

        try:
            readline.set_completer(self._recipient_agent_completer)
            readline.parse_and_bind('tab: complete')
        except Exception as e:
            print(f"Error setting up autocomplete for agents in terminal: {e}. Autocomplete will not work.")

    def run_demo(self):
        """
        Executes agency in the terminal with autocomplete for recipient agent names.
        """
        outer_self = self
        from agency_swarm import AgencyEventHandler
        class TermEventHandler(AgencyEventHandler):
            message_output = None

            @override
            def on_message_created(self, message: Message) -> None:
                if message.role == "user":
                    self.message_output = MessageOutputLive("text", self.agent_name, self.recipient_agent_name,
                                                            "")
                    self.message_output.cprint_update(message.content[0].text.value)
                else:
                    self.message_output = MessageOutputLive("text", self.recipient_agent_name, self.agent_name, "")

            @override
            def on_message_done(self, message: Message) -> None:
                self.message_output = None

            @override
            def on_text_delta(self, delta, snapshot):
                self.message_output.cprint_update(snapshot.value)

            @override
            def on_tool_call_created(self, tool_call):
                if isinstance(tool_call, dict):
                    if "type" not in tool_call:
                        tool_call["type"] = "function"

                    if tool_call["type"] == "function":
                        tool_call = FunctionToolCall(**tool_call)
                    elif tool_call["type"] == "code_interpreter":
                        tool_call = CodeInterpreterToolCall(**tool_call)
                    elif tool_call["type"] == "file_search" or tool_call["type"] == "retrieval":
                        tool_call = FileSearchToolCall(**tool_call)
                    else:
                        raise ValueError("Invalid tool call type: " + tool_call["type"])

                # TODO: add support for code interpreter and retirieval tools

                if tool_call.type == "function":
                    self.message_output = MessageOutputLive("function", self.recipient_agent_name, self.agent_name,
                                                            str(tool_call.function))

            @override
            def on_tool_call_delta(self, delta, snapshot):
                if isinstance(snapshot, dict):
                    if "type" not in snapshot:
                        snapshot["type"] = "function"

                    if snapshot["type"] == "function":
                        snapshot = FunctionToolCall(**snapshot)
                    elif snapshot["type"] == "code_interpreter":
                        snapshot = CodeInterpreterToolCall(**snapshot)
                    elif snapshot["type"] == "file_search":
                        snapshot = FileSearchToolCall(**snapshot)
                    else:
                        raise ValueError("Invalid tool call type: " + snapshot["type"])

                self.message_output.cprint_update(str(snapshot.function))

            @override
            def on_tool_call_done(self, snapshot):
                self.message_output = None

                # TODO: add support for code interpreter and retrieval tools
                if snapshot.type != "function":
                    return

                if snapshot.function.name == "SendMessage" and not (hasattr(outer_self.send_message_tool_class.ToolConfig, 'output_as_result') and outer_self.send_message_tool_class.ToolConfig.output_as_result):
                    try:
                        args = eval(snapshot.function.arguments)
                        recipient = args["recipient"]
                        self.message_output = MessageOutputLive("text", self.recipient_agent_name, recipient,
                                                                "")

                        self.message_output.cprint_update(args["message"])
                    except Exception as e:
                        pass

                self.message_output = None

            @override
            def on_run_step_done(self, run_step: RunStep) -> None:
                if run_step.type == "tool_calls":
                    for tool_call in run_step.step_details.tool_calls:
                        if tool_call.type != "function":
                            continue

                        if tool_call.function.name == "SendMessage":
                            continue

                        self.message_output = None
                        self.message_output = MessageOutputLive("function_output", tool_call.function.name,
                                                                self.recipient_agent_name, tool_call.function.output)
                        self.message_output.cprint_update(tool_call.function.output)

                    self.message_output = None

            @override
            def on_end(self):
                self.message_output = None

        self.recipient_agents = [str(agent.name) for agent in self.main_recipients]

        self._setup_autocomplete()  # Prepare readline for autocomplete

        while True:
            console.rule()
            text = input("👤 USER: ")

            if not text:
                continue

            if text.lower() == "exit":
                break

            recipient_agent = None
            if "@" in text:
                recipient_agent = text.split("@")[1].split(" ")[0]
                text = text.replace(f"@{recipient_agent}", "").strip()
                try:
                    recipient_agent = \
                        [agent for agent in self.recipient_agents if agent.lower() == recipient_agent.lower()][0]
                    recipient_agent = self._get_agent_by_name(recipient_agent)
                except Exception as e:
                    print(f"Recipient agent {recipient_agent} not found.")
                    continue

            self.get_completion_stream(message=text, event_handler=TermEventHandler, recipient_agent=recipient_agent)

    def get_customgpt_schema(self, url: str):
        """Returns the OpenAPI schema for the agency from the CEO agent, that you can use to integrate with custom gpts.

        Parameters:
            url (str): Your server url where the api will be hosted.
        """

        return self.ceo.get_openapi_schema(url)

    def plot_agency_chart(self):
        pass

    def _init_agents(self):
        """
        Initializes all agents in the agency with unique IDs, shared instructions, and OpenAI models.

        This method iterates through each agent in the agency, assigns a unique ID, adds shared instructions, and initializes the OpenAI models for each agent.

        There are no input parameters.

        There are no output parameters as this method is used for internal initialization purposes within the Agency class.
        """
        if self.settings_callbacks:
            loaded_settings = self.settings_callbacks["load"]()
            with open(self.settings_path, 'w') as f:
                json.dump(loaded_settings, f, indent=4)

        for agent in self.agents:
            if "temp_id" in agent.id:
                agent.id = None

            agent.add_shared_instructions(self.shared_instructions)
            agent.settings_path = self.settings_path

            if self.shared_files:
                if isinstance(self.shared_files, str):
                    self.shared_files = [self.shared_files]

                if isinstance(agent.files_folder, str):
                    agent.files_folder = [agent.files_folder]
                    agent.files_folder += self.shared_files
                elif isinstance(agent.files_folder, list):
                    agent.files_folder += self.shared_files

            if self.temperature is not None and agent.temperature is None:
                agent.temperature = self.temperature
            if self.top_p and agent.top_p is None:
                agent.top_p = self.top_p
            if self.max_prompt_tokens is not None and agent.max_prompt_tokens is None:
                agent.max_prompt_tokens = self.max_prompt_tokens
            if self.max_completion_tokens is not None and agent.max_completion_tokens is None:
                agent.max_completion_tokens = self.max_completion_tokens
            if self.truncation_strategy is not None and agent.truncation_strategy is None:
                agent.truncation_strategy = self.truncation_strategy

            if not agent.shared_state:
                agent.shared_state = self.shared_state

            agent.init_oai()

        if self.settings_callbacks:
            with open(self.agents[0].get_settings_path(), 'r') as f:
                settings = f.read()
            settings = json.loads(settings)
            self.settings_callbacks["save"](settings)

    def _init_threads(self):
        """
        Initializes threads for communication between agents within the agency.

        This method creates Thread objects for each pair of interacting agents as defined in the agents_and_threads attribute of the Agency. Each thread facilitates communication and task execution between an agent and its designated recipient agent.

        No input parameters.

        Output Parameters:
            This method does not return any value but updates the agents_and_threads attribute with initialized Thread objects.
        """
        self.main_thread = Thread(self.user, self.ceo)

        # load thread ids
        loaded_thread_ids = {}
        if self.threads_callbacks:
            loaded_thread_ids = self.threads_callbacks["load"]()
            if "main_thread" in loaded_thread_ids and loaded_thread_ids["main_thread"]:
                self.main_thread.id = loaded_thread_ids["main_thread"]
            else:
                self.main_thread.init_thread()

        # Save main_thread into agents_and_threads
        self.agents_and_threads["main_thread"] = self.main_thread

        # initialize threads
        for agent_name, threads in self.agents_and_threads.items():
            if agent_name == "main_thread":
                continue
            for other_agent, items in threads.items():
                # create thread class
                self.agents_and_threads[agent_name][other_agent] = self._thread_type(
                    self._get_agent_by_name(items["agent"]),
                    self._get_agent_by_name(
                        items["recipient_agent"]))

                # load thread id if available
                if agent_name in loaded_thread_ids and other_agent in loaded_thread_ids[agent_name]:
                    self.agents_and_threads[agent_name][other_agent].id = loaded_thread_ids[agent_name][other_agent]
                # init threads if threre are threads callbacks so the ids are saved for later use
                elif self.threads_callbacks:
                    self.agents_and_threads[agent_name][other_agent].init_thread()

        # save thread ids
        if self.threads_callbacks:
            loaded_thread_ids = {}
            for agent_name, threads in self.agents_and_threads.items():
                if agent_name == "main_thread":
                    continue
                loaded_thread_ids[agent_name] = {}
                for other_agent, thread in threads.items():
                    loaded_thread_ids[agent_name][other_agent] = thread.id

            loaded_thread_ids["main_thread"] = self.main_thread.id

            self.threads_callbacks["save"](loaded_thread_ids)

    def _parse_agency_chart(self, agency_chart):
        """
        Parses the provided agency chart to initialize and organize agents within the agency.

        Parameters:
            agency_chart: A structure representing the hierarchical organization of agents within the agency.
                    It can contain Agent objects and lists of Agent objects.

        This method iterates through each node in the agency chart. If a node is an Agent, it is set as the CEO if not already assigned.
        If a node is a list, it iterates through the agents in the list, adding them to the agency and establishing communication
        threads between them. It raises an exception if the agency chart is invalid or if multiple CEOs are defined.
        """
        if not isinstance(agency_chart, list):
            raise Exception("Invalid agency chart.")

        if len(agency_chart) == 0:
            raise Exception("Agency chart cannot be empty.")

        for node in agency_chart:
            if isinstance(node, Agent):
                if not self.ceo:
                    self.ceo = node
                    self._add_agent(self.ceo)
                else:
                    self._add_agent(node)
                self._add_main_recipient(node)

            elif isinstance(node, list):
                for i, agent in enumerate(node):
                    if not isinstance(agent, Agent):
                        raise Exception("Invalid agency chart.")

                    index = self._add_agent(agent)

                    if i == len(node) - 1:
                        continue

                    if agent.name not in self.agents_and_threads.keys():
                        self.agents_and_threads[agent.name] = {}

                    if i < len(node) - 1:
                        other_agent = node[i + 1]
                        if other_agent.name == agent.name:
                            continue
                        if other_agent.name not in self.agents_and_threads[agent.name].keys():
                            self.agents_and_threads[agent.name][other_agent.name] = {
                                "agent": agent.name,
                                "recipient_agent": other_agent.name,
                            }
            else:
                raise Exception("Invalid agency chart.")

    def _add_agent(self, agent):
        """
        Adds an agent to the agency, assigning a temporary ID if necessary.

        Parameters:
            agent (Agent): The agent to be added to the agency.

        Returns:
            int: The index of the added agent within the agency's agents list.

        This method adds an agent to the agency's list of agents. If the agent does not have an ID, it assigns a temporary unique ID. It checks for uniqueness of the agent's name before addition. The method returns the index of the agent in the agency's agents list, which is used for referencing the agent within the agency.
        """
        if not agent.id:
            # assign temp id
            agent.id = "temp_id_" + str(uuid.uuid4())
        if agent.id not in self._get_agent_ids():
            if agent.name in self._get_agent_names():
                raise Exception("Agent names must be unique.")
            self.agents.append(agent)
            return len(self.agents) - 1
        else:
            return self._get_agent_ids().index(agent.id)

    def _add_main_recipient(self, agent):
        """
        Adds an agent to the agency's list of main recipients.

        Parameters:
            agent (Agent): The agent to be added to the agency's list of main recipients.

        This method adds an agent to the agency's list of main recipients. These are agents that can be directly contacted by the user.
        """
        main_recipient_ids = [agent.id for agent in self.main_recipients]

        if agent.id not in main_recipient_ids:
            self.main_recipients.append(agent)

    def _read_instructions(self, path):
        """
        Reads shared instructions from a specified file and stores them in the agency.

        Parameters:
            path (str): The file path from which to read the shared instructions.

        This method opens the file located at the given path, reads its contents, and stores these contents in the 'shared_instructions' attribute of the agency. This is used to provide common guidelines or instructions to all agents within the agency.
        """
        path = path
        with open(path, 'r') as f:
            self.shared_instructions = f.read()

    def _create_special_tools(self):
        """
        Creates and assigns 'SendMessage' tools to each agent based on the agency's structure.

        This method iterates through the agents and threads in the agency, creating SendMessage tools for each agent. These tools enable agents to send messages to other agents as defined in the agency's structure. The SendMessage tools are tailored to the specific recipient agents that each agent can communicate with.

        No input parameters.

        No output parameters; this method modifies the agents' toolset internally.
        """
        for agent_name, threads in self.agents_and_threads.items():
            if agent_name == "main_thread":
                continue
            recipient_names = list(threads.keys())
            recipient_agents = self._get_agents_by_names(recipient_names)
            if len(recipient_agents) == 0:
                continue
            agent = self._get_agent_by_name(agent_name)
            agent.add_tool(self._create_send_message_tool(agent, recipient_agents))
            if self._thread_type == ThreadAsync:
                agent.add_tool(self._create_get_response_tool(agent, recipient_agents))

    def _create_send_message_tool(self, agent: Agent, recipient_agents: List[Agent]):
        """
        Creates a SendMessage tool to enable an agent to send messages to specified recipient agents.


        Parameters:
            agent (Agent): The agent who will be sending messages.
            recipient_agents (List[Agent]): A list of recipient agents who can receive messages.

        Returns:
            SendMessage: A SendMessage tool class that is dynamically created and configured for the given agent and its recipient agents. This tool allows the agent to send messages to the specified recipients, facilitating inter-agent communication within the agency.
        """
        recipient_names = [agent.name for agent in recipient_agents]
        recipients = Enum("recipient", {name: name for name in recipient_names})

        agent_descriptions = ""
        for recipient_agent in recipient_agents:
            if not recipient_agent.description:
                continue
            agent_descriptions += recipient_agent.name + ": "
            agent_descriptions += recipient_agent.description + "\n"

        class SendMessage(self.send_message_tool_class):
            recipient: recipients = Field(..., description=agent_descriptions)

            @field_validator('recipient')
            @classmethod
            def check_recipient(cls, value):
                if value.value not in recipient_names:
                    raise ValueError(f"Recipient {value} is not valid. Valid recipients are: {recipient_names}")
                return value

        SendMessage._caller_agent = agent
        SendMessage._agents_and_threads = self.agents_and_threads

        return SendMessage

    def _create_get_response_tool(self, agent: Agent, recipient_agents: List[Agent]):
        """
        Creates a CheckStatus tool to enable an agent to check the status of a task with a specified recipient agent.
        """
        recipient_names = [agent.name for agent in recipient_agents]
        recipients = Enum("recipient", {name: name for name in recipient_names})

        outer_self = self

        class GetResponse(BaseTool):
            """This tool allows you to check the status of a task or get a response from a specified recipient agent, if the task has been completed. You must always use 'SendMessage' tool with the designated agent first."""
            recipient: recipients = Field(...,
                                          description=f"Recipient agent that you want to check the status of. Valid recipients are: {recipient_names}")

            @field_validator('recipient')
            def check_recipient(cls, value):
                if value.value not in recipient_names:
                    raise ValueError(f"Recipient {value} is not valid. Valid recipients are: {recipient_names}")
                return value

            def run(self):
                thread = outer_self.agents_and_threads[self._caller_agent.name][self.recipient.value]

                return thread.check_status()

        GetResponse._caller_agent = agent

        return GetResponse

    def _get_agent_by_name(self, agent_name):
        """
        Retrieves an agent from the agency based on the agent's name.

        Parameters:
            agent_name (str): The name of the agent to be retrieved.

        Returns:
            Agent: The agent object with the specified name.

        Raises:
            Exception: If no agent with the given name is found in the agency.
        """
        for agent in self.agents:
            if agent.name == agent_name:
                return agent
        raise Exception(f"Agent {agent_name} not found.")

    def _get_agents_by_names(self, agent_names):
        """
        Retrieves a list of agent objects based on their names.

        Parameters:
            agent_names: A list of strings representing the names of the agents to be retrieved.

        Returns:
            A list of Agent objects corresponding to the given names.
        """
        return [self._get_agent_by_name(agent_name) for agent_name in agent_names]

    def _get_agent_ids(self):
        """
        Retrieves the IDs of all agents currently in the agency.

        Returns:
            List[str]: A list containing the unique IDs of all agents.
        """
        return [agent.id for agent in self.agents]

    def _get_agent_names(self):
        """
        Retrieves the names of all agents in the agency.

        Returns:
            List[str]: A list of names of all agents currently part of the agency.
        """
        return [agent.name for agent in self.agents]

    def _get_class_folder_path(self):
        """
        Retrieves the absolute path of the directory containing the class file.

        Returns:
            str: The absolute path of the directory where the class file is located.
        """
        return os.path.abspath(os.path.dirname(inspect.getfile(self.__class__)))

    def delete(self):
        """
        This method deletes the agency and all its agents, cleaning up any files and vector stores associated with each agent.
        """
        for agent in self.agents:
            agent.delete()

__init__(agency_chart, shared_instructions='', shared_files=None, async_mode=None, send_message_tool_class=SendMessage, settings_path='./settings.json', settings_callbacks=None, threads_callbacks=None, temperature=0.3, top_p=1.0, max_prompt_tokens=None, max_completion_tokens=None, truncation_strategy=None)

Initializes the Agency object, setting up agents, threads, and core functionalities.

Parameters:

Name	Type	Description	Default
agency_chart	List	The structure defining the hierarchy and interaction of agents within the agency.	required
shared_instructions	str	A path to a file containing shared instructions for all agents. Defaults to an empty string.	''
shared_files	Union[str, List[str]]	A path to a folder or a list of folders containing shared files for all agents. Defaults to None.	None
async_mode	str	Specifies the mode for asynchronous processing. In "threading" mode, all sub-agents run in separate threads. In "tools_threading" mode, all tools run in separate threads, but agents do not. Defaults to None.	None
send_message_tool_class	Type[SendMessageBase]	The class to use for the send_message tool. For async communication, use SendMessageAsyncThreading. Defaults to SendMessage.	SendMessage
settings_path	str	The path to the settings file for the agency. Must be json. If file does not exist, it will be created. Defaults to None.	'./settings.json'
settings_callbacks	SettingsCallbacks	A dictionary containing functions to load and save settings for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.	None
threads_callbacks	ThreadsCallbacks	A dictionary containing functions to load and save threads for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.	None
temperature	float	The temperature value to use for the agents. Agent-specific values will override this. Defaults to 0.3.	0.3
top_p	float	The top_p value to use for the agents. Agent-specific values will override this. Defaults to None.	1.0
max_prompt_tokens	int	The maximum number of tokens allowed in the prompt for each agent. Agent-specific values will override this. Defaults to None.	None
max_completion_tokens	int	The maximum number of tokens allowed in the completion for each agent. Agent-specific values will override this. Defaults to None.	None
truncation_strategy	dict	The truncation strategy to use for the completion for each agent. Agent-specific values will override this. Defaults to None.	None

This constructor initializes various components of the Agency, including CEO, agents, threads, and user interactions. It parses the agency chart to set up the organizational structure and initializes the messaging tools, agents, and threads necessary for the operation of the agency. Additionally, it prepares a main thread for user interactions.

Source code in agency_swarm/agency/agency.py

def __init__(self,
             agency_chart: List,
             shared_instructions: str = "",
             shared_files: Union[str, List[str]] = None,
             async_mode: Literal['threading', "tools_threading"] = None,
             send_message_tool_class: Type[SendMessageBase] = SendMessage,
             settings_path: str = "./settings.json",
             settings_callbacks: SettingsCallbacks = None,
             threads_callbacks: ThreadsCallbacks = None,
             temperature: float = 0.3,
             top_p: float = 1.0,
             max_prompt_tokens: int = None,
             max_completion_tokens: int = None,
             truncation_strategy: dict = None,
             ):
    """
    Initializes the Agency object, setting up agents, threads, and core functionalities.

    Parameters:
        agency_chart: The structure defining the hierarchy and interaction of agents within the agency.
        shared_instructions (str, optional): A path to a file containing shared instructions for all agents. Defaults to an empty string.
        shared_files (Union[str, List[str]], optional): A path to a folder or a list of folders containing shared files for all agents. Defaults to None.
        async_mode (str, optional): Specifies the mode for asynchronous processing. In "threading" mode, all sub-agents run in separate threads. In "tools_threading" mode, all tools run in separate threads, but agents do not. Defaults to None.
        send_message_tool_class (Type[SendMessageBase], optional): The class to use for the send_message tool. For async communication, use `SendMessageAsyncThreading`. Defaults to SendMessage.
        settings_path (str, optional): The path to the settings file for the agency. Must be json. If file does not exist, it will be created. Defaults to None.
        settings_callbacks (SettingsCallbacks, optional): A dictionary containing functions to load and save settings for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.
        threads_callbacks (ThreadsCallbacks, optional): A dictionary containing functions to load and save threads for the agency. The keys must be "load" and "save". Both values must be defined. Defaults to None.
        temperature (float, optional): The temperature value to use for the agents. Agent-specific values will override this. Defaults to 0.3.
        top_p (float, optional): The top_p value to use for the agents. Agent-specific values will override this. Defaults to None.
        max_prompt_tokens (int, optional): The maximum number of tokens allowed in the prompt for each agent. Agent-specific values will override this. Defaults to None.
        max_completion_tokens (int, optional): The maximum number of tokens allowed in the completion for each agent. Agent-specific values will override this. Defaults to None.
        truncation_strategy (dict, optional): The truncation strategy to use for the completion for each agent. Agent-specific values will override this. Defaults to None.

    This constructor initializes various components of the Agency, including CEO, agents, threads, and user interactions. It parses the agency chart to set up the organizational structure and initializes the messaging tools, agents, and threads necessary for the operation of the agency. Additionally, it prepares a main thread for user interactions.
    """
    self.ceo = None
    self.user = User()
    self.agents = []
    self.agents_and_threads = {}
    self.main_recipients = []
    self.main_thread = None
    self.recipient_agents = None  # for autocomplete
    self.shared_files = shared_files if shared_files else []
    self.async_mode = async_mode
    self.send_message_tool_class = send_message_tool_class
    self.settings_path = settings_path
    self.settings_callbacks = settings_callbacks
    self.threads_callbacks = threads_callbacks
    self.temperature = temperature
    self.top_p = top_p
    self.max_prompt_tokens = max_prompt_tokens
    self.max_completion_tokens = max_completion_tokens
    self.truncation_strategy = truncation_strategy

    # set thread type based send_message_tool_class async mode
    if hasattr(send_message_tool_class.ToolConfig, "async_mode") and send_message_tool_class.ToolConfig.async_mode:
        self._thread_type = ThreadAsync
    else:
        self._thread_type = Thread  

    if self.async_mode == "threading":
        from agency_swarm.tools.send_message import SendMessageAsyncThreading
        print("Warning: 'threading' mode is deprecated. Please use send_message_tool_class = SendMessageAsyncThreading to use async communication.")
        self.send_message_tool_class = SendMessageAsyncThreading
    elif self.async_mode == "tools_threading":
        Thread.async_mode = "tools_threading"
        print("Warning: 'tools_threading' mode is deprecated. Use tool.ToolConfig.async_mode = 'threading' instead.")
    elif self.async_mode is None:
        pass
    else:
        raise Exception("Please select async_mode = 'threading' or 'tools_threading'.")

    if os.path.isfile(os.path.join(self._get_class_folder_path(), shared_instructions)):
        self._read_instructions(os.path.join(self._get_class_folder_path(), shared_instructions))
    elif os.path.isfile(shared_instructions):
        self._read_instructions(shared_instructions)
    else:
        self.shared_instructions = shared_instructions

    self.shared_state = SharedState()

    self._parse_agency_chart(agency_chart)
    self._init_threads()
    self._create_special_tools()
    self._init_agents()

delete()

This method deletes the agency and all its agents, cleaning up any files and vector stores associated with each agent.

Source code in agency_swarm/agency/agency.py

def delete(self):
    """
    This method deletes the agency and all its agents, cleaning up any files and vector stores associated with each agent.
    """
    for agent in self.agents:
        agent.delete()

demo_gradio(height=450, dark_mode=True, **kwargs)

Launches a Gradio-based demo interface for the agency chatbot.

Parameters:

Name	Type	Description	Default
height	int	The height of the chatbot widget in the Gradio interface. Default is 600.	450
dark_mode	bool	Flag to determine if the interface should be displayed in dark mode. Default is True.	True
**kwargs		Additional keyword arguments to be passed to the Gradio interface.	{}

This method sets up and runs a Gradio interface, allowing users to interact with the agency's chatbot. It includes a text input for the user's messages and a chatbot interface for displaying the conversation. The method handles user input and chatbot responses, updating the interface dynamically.

Source code in agency_swarm/agency/agency.py

def demo_gradio(self, height=450, dark_mode=True, **kwargs):
    """
    Launches a Gradio-based demo interface for the agency chatbot.

    Parameters:
        height (int, optional): The height of the chatbot widget in the Gradio interface. Default is 600.
        dark_mode (bool, optional): Flag to determine if the interface should be displayed in dark mode. Default is True.
        **kwargs: Additional keyword arguments to be passed to the Gradio interface.
    This method sets up and runs a Gradio interface, allowing users to interact with the agency's chatbot. It includes a text input for the user's messages and a chatbot interface for displaying the conversation. The method handles user input and chatbot responses, updating the interface dynamically.
    """

    try:
        import gradio as gr
    except ImportError:
        raise Exception("Please install gradio: pip install gradio")

    js = """function () {
      gradioURL = window.location.href
      if (!gradioURL.endsWith('?__theme={theme}')) {
        window.location.replace(gradioURL + '?__theme={theme}');
      }
    }"""

    if dark_mode:
        js = js.replace("{theme}", "dark")
    else:
        js = js.replace("{theme}", "light")

    attachments = []
    images = []
    message_file_names = None
    uploading_files = False
    recipient_agent_names = [agent.name for agent in self.main_recipients]
    recipient_agent = self.main_recipients[0]

    with gr.Blocks(js=js) as demo:
        chatbot_queue = queue.Queue()
        chatbot = gr.Chatbot(height=height)
        with gr.Row():
            with gr.Column(scale=9):
                dropdown = gr.Dropdown(label="Recipient Agent", choices=recipient_agent_names,
                                       value=recipient_agent.name)
                msg = gr.Textbox(label="Your Message", lines=4)
            with gr.Column(scale=1):
                file_upload = gr.Files(label="OpenAI Files", type="filepath")
        button = gr.Button(value="Send", variant="primary")

        def handle_dropdown_change(selected_option):
            nonlocal recipient_agent
            recipient_agent = self._get_agent_by_name(selected_option)

        def handle_file_upload(file_list):
            nonlocal attachments
            nonlocal message_file_names
            nonlocal uploading_files
            nonlocal images
            uploading_files = True
            attachments = []
            message_file_names = []
            if file_list:
                try:
                    for file_obj in file_list:
                        purpose = get_file_purpose(file_obj.name)

                        with open(file_obj.name, 'rb') as f:
                            # Upload the file to OpenAI
                            file = self.main_thread.client.files.create(
                                file=f,
                                purpose=purpose
                            )

                        if purpose == "vision":
                            images.append({
                                "type": "image_file",
                                "image_file": {"file_id": file.id}
                            })
                        else:
                            attachments.append({
                                "file_id": file.id,
                                "tools": get_tools(file.filename)
                            })

                        message_file_names.append(file.filename)
                        print(f"Uploaded file ID: {file.id}")
                    return attachments
                except Exception as e:
                    print(f"Error: {e}")
                    return str(e)
                finally:
                    uploading_files = False

            uploading_files = False
            return "No files uploaded"

        def user(user_message, history):
            if not user_message.strip():
                return user_message, history

            nonlocal message_file_names
            nonlocal uploading_files
            nonlocal images
            nonlocal attachments
            nonlocal recipient_agent

            # Check if attachments contain file search or code interpreter types
            def check_and_add_tools_in_attachments(attachments, recipient_agent):
                for attachment in attachments:
                    for tool in attachment.get("tools", []):
                        if tool["type"] == "file_search":
                            if not any(isinstance(t, FileSearch) for t in recipient_agent.tools):
                                # Add FileSearch tool if it does not exist
                                recipient_agent.tools.append(FileSearch)
                                recipient_agent.client.beta.assistants.update(recipient_agent.id, tools=recipient_agent.get_oai_tools())
                                print("Added FileSearch tool to recipient agent to analyze the file.")
                        elif tool["type"] == "code_interpreter":
                            if not any(isinstance(t, CodeInterpreter) for t in recipient_agent.tools):
                                # Add CodeInterpreter tool if it does not exist
                                recipient_agent.tools.append(CodeInterpreter)
                                recipient_agent.client.beta.assistants.update(recipient_agent.id, tools=recipient_agent.get_oai_tools())
                                print("Added CodeInterpreter tool to recipient agent to analyze the file.")
                return None

            check_and_add_tools_in_attachments(attachments, recipient_agent)

            if history is None:
                history = []

            original_user_message = user_message

            # Append the user message with a placeholder for bot response
            if recipient_agent:
                user_message = f"👤 User 🗣️ @{recipient_agent.name}:\n" + user_message.strip()
            else:
                user_message = f"👤 User:" + user_message.strip()

            nonlocal message_file_names
            if message_file_names:
                user_message += "\n\n📎 Files:\n" + "\n".join(message_file_names)

            return original_user_message, history + [[user_message, None]]

        class GradioEventHandler(AgencyEventHandler):
            message_output = None

            @classmethod
            def change_recipient_agent(cls, recipient_agent_name):
                nonlocal chatbot_queue
                chatbot_queue.put("[change_recipient_agent]")
                chatbot_queue.put(recipient_agent_name)

            @override
            def on_message_created(self, message: Message) -> None:
                if message.role == "user":
                    full_content = ""
                    for content in message.content:
                        if content.type == "image_file":
                            full_content += f"🖼️ Image File: {content.image_file.file_id}\n"
                            continue

                        if content.type == "image_url":
                            full_content += f"\n{content.image_url.url}\n"
                            continue

                        if content.type == "text":
                            full_content += content.text.value + "\n"


                    self.message_output = MessageOutput("text", self.agent_name, self.recipient_agent_name,
                                                        full_content)

                else:
                    self.message_output = MessageOutput("text", self.recipient_agent_name, self.agent_name,
                                                        "")

                chatbot_queue.put("[new_message]")
                chatbot_queue.put(self.message_output.get_formatted_content())

            @override
            def on_text_delta(self, delta, snapshot):
                chatbot_queue.put(delta.value)

            @override
            def on_tool_call_created(self, tool_call: ToolCall):
                if isinstance(tool_call, dict):
                    if "type" not in tool_call:
                        tool_call["type"] = "function"

                    if tool_call["type"] == "function":
                        tool_call = FunctionToolCall(**tool_call)
                    elif tool_call["type"] == "code_interpreter":
                        tool_call = CodeInterpreterToolCall(**tool_call)
                    elif tool_call["type"] == "file_search" or tool_call["type"] == "retrieval":
                        tool_call = FileSearchToolCall(**tool_call)
                    else:
                        raise ValueError("Invalid tool call type: " + tool_call["type"])

                # TODO: add support for code interpreter and retrieval tools
                if tool_call.type == "function":
                    chatbot_queue.put("[new_message]")
                    self.message_output = MessageOutput("function", self.recipient_agent_name, self.agent_name,
                                                        str(tool_call.function))
                    chatbot_queue.put(self.message_output.get_formatted_header() + "\n")

            @override
            def on_tool_call_done(self, snapshot: ToolCall):
                if isinstance(snapshot, dict):
                    if "type" not in snapshot:
                        snapshot["type"] = "function"

                    if snapshot["type"] == "function":
                        snapshot = FunctionToolCall(**snapshot)
                    elif snapshot["type"] == "code_interpreter":
                        snapshot = CodeInterpreterToolCall(**snapshot)
                    elif snapshot["type"] == "file_search":
                        snapshot = FileSearchToolCall(**snapshot)
                    else:
                        raise ValueError("Invalid tool call type: " + snapshot["type"])

                self.message_output = None

                # TODO: add support for code interpreter and retrieval tools
                if snapshot.type != "function":
                    return

                chatbot_queue.put(str(snapshot.function))

                if snapshot.function.name == "SendMessage":
                    try:
                        args = eval(snapshot.function.arguments)
                        recipient = args["recipient"]
                        self.message_output = MessageOutput("text", self.recipient_agent_name, recipient,
                                                            args["message"])

                        chatbot_queue.put("[new_message]")
                        chatbot_queue.put(self.message_output.get_formatted_content())
                    except Exception as e:
                        pass

                self.message_output = None

            @override
            def on_run_step_done(self, run_step: RunStep) -> None:
                if run_step.type == "tool_calls":
                    for tool_call in run_step.step_details.tool_calls:
                        if tool_call.type != "function":
                            continue

                        if tool_call.function.name == "SendMessage":
                            continue

                        self.message_output = None
                        chatbot_queue.put("[new_message]")

                        self.message_output = MessageOutput("function_output", tool_call.function.name,
                                                            self.recipient_agent_name,
                                                            tool_call.function.output)

                        chatbot_queue.put(self.message_output.get_formatted_header() + "\n")
                        chatbot_queue.put(tool_call.function.output)

            @override
            @classmethod
            def on_all_streams_end(cls):
                cls.message_output = None
                chatbot_queue.put("[end]")

        def bot(original_message, history):
            nonlocal attachments
            nonlocal message_file_names
            nonlocal recipient_agent
            nonlocal recipient_agent_names
            nonlocal images
            nonlocal uploading_files

            if not original_message:
                return "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))

            if uploading_files:
                history.append([None, "Uploading files... Please wait."])
                yield "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))
                return "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))

            print("Message files: ", attachments)
            print("Images: ", images)

            if images and len(images) > 0:
                original_message = [
                    {
                        "type": "text",
                        "text": original_message,
                    },
                    *images
                ]


            completion_thread = threading.Thread(target=self.get_completion_stream, args=(
                original_message, GradioEventHandler, [], recipient_agent, "", attachments, None))
            completion_thread.start()

            attachments = []
            message_file_names = []
            images = []
            uploading_files = False

            new_message = True
            while True:
                try:
                    bot_message = chatbot_queue.get(block=True)

                    if bot_message == "[end]":
                        completion_thread.join()
                        break

                    if bot_message == "[new_message]":
                        new_message = True
                        continue

                    if bot_message == "[change_recipient_agent]":
                        new_agent_name = chatbot_queue.get(block=True)
                        recipient_agent = self._get_agent_by_name(new_agent_name)
                        yield "", history, gr.update(value=new_agent_name, choices=set([*recipient_agent_names, recipient_agent.name]))
                        continue

                    if new_message:
                        history.append([None, bot_message])
                        new_message = False
                    else:
                        history[-1][1] += bot_message

                    yield "", history, gr.update(value=recipient_agent.name, choices=set([*recipient_agent_names, recipient_agent.name]))
                except queue.Empty:
                    break

        button.click(
            user,
            inputs=[msg, chatbot],
            outputs=[msg, chatbot]
        ).then(
            bot, [msg, chatbot, dropdown], [msg, chatbot, dropdown]
        )
        dropdown.change(handle_dropdown_change, dropdown)
        file_upload.change(handle_file_upload, file_upload)
        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
            bot, [msg, chatbot], [msg, chatbot, dropdown]
        )

        # Enable queuing for streaming intermediate outputs
        demo.queue(default_concurrency_limit=10)

    # Launch the demo
    demo.launch(**kwargs)
    return demo

get_completion(message, message_files=None, yield_messages=False, recipient_agent=None, additional_instructions=None, attachments=None, tool_choice=None, verbose=False, response_format=None)

Retrieves the completion for a given message from the main thread.

Parameters:

Name	Type	Description	Default
message	str	The message for which completion is to be retrieved.	required
message_files	list	A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter. Defaults to None.	None
yield_messages	bool	Flag to determine if intermediate messages should be yielded. Defaults to True.	False
recipient_agent	Agent	The agent to which the message should be sent. Defaults to the first agent in the agency chart.	None
additional_instructions	str	Additional instructions to be sent with the message. Defaults to None.	None
attachments	List[dict]	A list of attachments to be sent with the message, following openai format. Defaults to None.	None
tool_choice	dict	The tool choice for the recipient agent to use. Defaults to None.	None
parallel_tool_calls	bool	Whether to enable parallel function calling during tool use. Defaults to True.	required
verbose	bool	Whether to print the intermediary messages in console. Defaults to False.	False
response_format	dict	The response format to use for the completion.	None

Returns:

Type	Description
	Generator or final response: Depending on the 'yield_messages' flag, this method returns either a generator yielding intermediate messages or the final response from the main thread.

Source code in agency_swarm/agency/agency.py

def get_completion(self, message: str,
                   message_files: List[str] = None,
                   yield_messages: bool = False,
                   recipient_agent: Agent = None,
                   additional_instructions: str = None,
                   attachments: List[dict] = None,
                   tool_choice: dict = None,
                   verbose: bool = False,
                   response_format: dict = None):
    """
    Retrieves the completion for a given message from the main thread.

    Parameters:
        message (str): The message for which completion is to be retrieved.
        message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
        yield_messages (bool, optional): Flag to determine if intermediate messages should be yielded. Defaults to True.
        recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
        additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
        attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
        tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
        parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.
        verbose (bool, optional): Whether to print the intermediary messages in console. Defaults to False.
        response_format (dict, optional): The response format to use for the completion.

    Returns:
        Generator or final response: Depending on the 'yield_messages' flag, this method returns either a generator yielding intermediate messages or the final response from the main thread.
    """
    if verbose and yield_messages:
        raise Exception("Verbose mode is not compatible with yield_messages=True")

    res = self.main_thread.get_completion(message=message,
                                           message_files=message_files,
                                           attachments=attachments,
                                           recipient_agent=recipient_agent,
                                           additional_instructions=additional_instructions,
                                           tool_choice=tool_choice,
                                           yield_messages=yield_messages or verbose,
                                           response_format=response_format)

    if not yield_messages or verbose:
        while True:
            try:
                message = next(res)
                if verbose:
                    message.cprint()
            except StopIteration as e:
                return e.value

    return res

get_completion_parse(message, response_format, message_files=None, recipient_agent=None, additional_instructions=None, attachments=None, tool_choice=None, verbose=False)

Retrieves the completion for a given message from the main thread and parses the response using the provided pydantic model.

Parameters:

Name	Type	Description	Default
message	str	The message for which completion is to be retrieved.	required
response_format	type(BaseModel	The response format to use for the completion.	required
message_files	list	A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter. Defaults to None.	None
recipient_agent	Agent	The agent to which the message should be sent. Defaults to the first agent in the agency chart.	None
additional_instructions	str	Additional instructions to be sent with the message. Defaults to None.	None
attachments	List[dict]	A list of attachments to be sent with the message, following openai format. Defaults to None.	None
tool_choice	dict	The tool choice for the recipient agent to use. Defaults to None.	None
verbose	bool	Whether to print the intermediary messages in console. Defaults to False.	False

Returns:

Type	Description
T	Final response: The final response from the main thread, parsed using the provided pydantic model.

Source code in agency_swarm/agency/agency.py

def get_completion_parse(self, message: str,
                         response_format: Type[T],
                         message_files: List[str] = None,
                         recipient_agent: Agent = None,
                         additional_instructions: str = None,
                         attachments: List[dict] = None,
                         tool_choice: dict = None,
                         verbose: bool = False) -> T:
    """
    Retrieves the completion for a given message from the main thread and parses the response using the provided pydantic model.

    Parameters:
        message (str): The message for which completion is to be retrieved.
        response_format (type(BaseModel)): The response format to use for the completion. 
        message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
        recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
        additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
        attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
        tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
        verbose (bool, optional): Whether to print the intermediary messages in console. Defaults to False.

    Returns:
        Final response: The final response from the main thread, parsed using the provided pydantic model.
    """
    response_model = None
    if isinstance(response_format, type):
        response_model = response_format
        response_format = type_to_response_format_param(response_format)

    res = self.get_completion(message=message,
                        message_files=message_files,
                        recipient_agent=recipient_agent,
                        additional_instructions=additional_instructions,
                        attachments=attachments,
                        tool_choice=tool_choice,
                        response_format=response_format,
                        verbose=verbose)

    try:
        return response_model.model_validate_json(res)
    except:
        parsed_res = json.loads(res)
        if 'refusal' in parsed_res:
            raise RefusalError(parsed_res['refusal'])
        else:
            raise Exception("Failed to parse response: " + res)

get_completion_stream(message, event_handler, message_files=None, recipient_agent=None, additional_instructions=None, attachments=None, tool_choice=None, response_format=None)

Generates a stream of completions for a given message from the main thread.

Parameters:

Name	Type	Description	Default
message	str	The message for which completion is to be retrieved.	required
event_handler	type(AgencyEventHandler	The event handler class to handle the completion stream. https://github.com/openai/openai-python/blob/main/helpers.md	required
message_files	list	A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter. Defaults to None.	None
recipient_agent	Agent	The agent to which the message should be sent. Defaults to the first agent in the agency chart.	None
additional_instructions	str	Additional instructions to be sent with the message. Defaults to None.	None
attachments	List[dict]	A list of attachments to be sent with the message, following openai format. Defaults to None.	None
tool_choice	dict	The tool choice for the recipient agent to use. Defaults to None.	None
parallel_tool_calls	bool	Whether to enable parallel function calling during tool use. Defaults to True.	required

Returns:

Type	Description
	Final response: Final response from the main thread.

Source code in agency_swarm/agency/agency.py

def get_completion_stream(self,
                          message: str,
                          event_handler: type(AgencyEventHandler),
                          message_files: List[str] = None,
                          recipient_agent: Agent = None,
                          additional_instructions: str = None,
                          attachments: List[dict] = None,
                          tool_choice: dict = None,
                          response_format: dict = None):
    """
    Generates a stream of completions for a given message from the main thread.

    Parameters:
        message (str): The message for which completion is to be retrieved.
        event_handler (type(AgencyEventHandler)): The event handler class to handle the completion stream. https://github.com/openai/openai-python/blob/main/helpers.md
        message_files (list, optional): A list of file ids to be sent as attachments with the message. When using this parameter, files will be assigned both to file_search and code_interpreter tools if available. It is recommended to assign files to the most sutiable tool manually, using the attachments parameter.  Defaults to None.
        recipient_agent (Agent, optional): The agent to which the message should be sent. Defaults to the first agent in the agency chart.
        additional_instructions (str, optional): Additional instructions to be sent with the message. Defaults to None.
        attachments (List[dict], optional): A list of attachments to be sent with the message, following openai format. Defaults to None.
        tool_choice (dict, optional): The tool choice for the recipient agent to use. Defaults to None.
        parallel_tool_calls (bool, optional): Whether to enable parallel function calling during tool use. Defaults to True.

    Returns:
        Final response: Final response from the main thread.
    """
    if not inspect.isclass(event_handler):
        raise Exception("Event handler must not be an instance.")

    res = self.main_thread.get_completion_stream(message=message,
                                                  message_files=message_files,
                                                  event_handler=event_handler,
                                                  attachments=attachments,
                                                  recipient_agent=recipient_agent,
                                                  additional_instructions=additional_instructions,
                                                  tool_choice=tool_choice,
                                                  response_format=response_format)

    while True:
        try:
            next(res)
        except StopIteration as e:
            event_handler.on_all_streams_end()

            return e.value

get_customgpt_schema(url)

Returns the OpenAPI schema for the agency from the CEO agent, that you can use to integrate with custom gpts.

Parameters:

Name	Type	Description	Default
url	str	Your server url where the api will be hosted.	required

Source code in agency_swarm/agency/agency.py

def get_customgpt_schema(self, url: str):
    """Returns the OpenAPI schema for the agency from the CEO agent, that you can use to integrate with custom gpts.

    Parameters:
        url (str): Your server url where the api will be hosted.
    """

    return self.ceo.get_openapi_schema(url)

run_demo()

Executes agency in the terminal with autocomplete for recipient agent names.

Source code in agency_swarm/agency/agency.py

def run_demo(self):
    """
    Executes agency in the terminal with autocomplete for recipient agent names.
    """
    outer_self = self
    from agency_swarm import AgencyEventHandler
    class TermEventHandler(AgencyEventHandler):
        message_output = None

        @override
        def on_message_created(self, message: Message) -> None:
            if message.role == "user":
                self.message_output = MessageOutputLive("text", self.agent_name, self.recipient_agent_name,
                                                        "")
                self.message_output.cprint_update(message.content[0].text.value)
            else:
                self.message_output = MessageOutputLive("text", self.recipient_agent_name, self.agent_name, "")

        @override
        def on_message_done(self, message: Message) -> None:
            self.message_output = None

        @override
        def on_text_delta(self, delta, snapshot):
            self.message_output.cprint_update(snapshot.value)

        @override
        def on_tool_call_created(self, tool_call):
            if isinstance(tool_call, dict):
                if "type" not in tool_call:
                    tool_call["type"] = "function"

                if tool_call["type"] == "function":
                    tool_call = FunctionToolCall(**tool_call)
                elif tool_call["type"] == "code_interpreter":
                    tool_call = CodeInterpreterToolCall(**tool_call)
                elif tool_call["type"] == "file_search" or tool_call["type"] == "retrieval":
                    tool_call = FileSearchToolCall(**tool_call)
                else:
                    raise ValueError("Invalid tool call type: " + tool_call["type"])

            # TODO: add support for code interpreter and retirieval tools

            if tool_call.type == "function":
                self.message_output = MessageOutputLive("function", self.recipient_agent_name, self.agent_name,
                                                        str(tool_call.function))

        @override
        def on_tool_call_delta(self, delta, snapshot):
            if isinstance(snapshot, dict):
                if "type" not in snapshot:
                    snapshot["type"] = "function"

                if snapshot["type"] == "function":
                    snapshot = FunctionToolCall(**snapshot)
                elif snapshot["type"] == "code_interpreter":
                    snapshot = CodeInterpreterToolCall(**snapshot)
                elif snapshot["type"] == "file_search":
                    snapshot = FileSearchToolCall(**snapshot)
                else:
                    raise ValueError("Invalid tool call type: " + snapshot["type"])

            self.message_output.cprint_update(str(snapshot.function))

        @override
        def on_tool_call_done(self, snapshot):
            self.message_output = None

            # TODO: add support for code interpreter and retrieval tools
            if snapshot.type != "function":
                return

            if snapshot.function.name == "SendMessage" and not (hasattr(outer_self.send_message_tool_class.ToolConfig, 'output_as_result') and outer_self.send_message_tool_class.ToolConfig.output_as_result):
                try:
                    args = eval(snapshot.function.arguments)
                    recipient = args["recipient"]
                    self.message_output = MessageOutputLive("text", self.recipient_agent_name, recipient,
                                                            "")

                    self.message_output.cprint_update(args["message"])
                except Exception as e:
                    pass

            self.message_output = None

        @override
        def on_run_step_done(self, run_step: RunStep) -> None:
            if run_step.type == "tool_calls":
                for tool_call in run_step.step_details.tool_calls:
                    if tool_call.type != "function":
                        continue

                    if tool_call.function.name == "SendMessage":
                        continue

                    self.message_output = None
                    self.message_output = MessageOutputLive("function_output", tool_call.function.name,
                                                            self.recipient_agent_name, tool_call.function.output)
                    self.message_output.cprint_update(tool_call.function.output)

                self.message_output = None

        @override
        def on_end(self):
            self.message_output = None

    self.recipient_agents = [str(agent.name) for agent in self.main_recipients]

    self._setup_autocomplete()  # Prepare readline for autocomplete

    while True:
        console.rule()
        text = input("👤 USER: ")

        if not text:
            continue

        if text.lower() == "exit":
            break

        recipient_agent = None
        if "@" in text:
            recipient_agent = text.split("@")[1].split(" ")[0]
            text = text.replace(f"@{recipient_agent}", "").strip()
            try:
                recipient_agent = \
                    [agent for agent in self.recipient_agents if agent.lower() == recipient_agent.lower()][0]
                recipient_agent = self._get_agent_by_name(recipient_agent)
            except Exception as e:
                print(f"Recipient agent {recipient_agent} not found.")
                continue

        self.get_completion_stream(message=text, event_handler=TermEventHandler, recipient_agent=recipient_agent)

ToolFactory

Source code in agency_swarm/tools/ToolFactory.py

class ToolFactory:

    @staticmethod
    def from_langchain_tools(tools: List) -> List[Type[BaseTool]]:
        """
        Converts a list of langchain tools into a list of BaseTools.

        Parameters:
            tools: The langchain tools to convert.

        Returns:
            A list of BaseTools.
        """
        converted_tools = []
        for tool in tools:
            converted_tools.append(ToolFactory.from_langchain_tool(tool))

        return converted_tools

    @staticmethod
    def from_langchain_tool(tool) -> Type[BaseTool]:
        """
        Converts a langchain tool into a BaseTool.

        Parameters:
            tool: The langchain tool to convert.

        Returns:
            A BaseTool.
        """
        try:
            from langchain.tools import format_tool_to_openai_function
        except ImportError:
            raise ImportError("You must install langchain to use this method.")

        if inspect.isclass(tool):
            tool = tool()

        def callback(self):
            tool_input = self.model_dump()
            try:
                return tool.run(tool_input)
            except TypeError:
                if len(tool_input) == 1:
                    return tool.run(list(tool_input.values())[0])
                else:
                    raise TypeError(f"Error parsing input for tool '{tool.__class__.__name__}' Please open an issue "
                                    f"on github.")

        return ToolFactory.from_openai_schema(
            format_tool_to_openai_function(tool),
            callback
        )


    @staticmethod
    def from_openai_schema(schema: Dict[str, Any], callback: Any) -> Type[BaseTool]:
        """
        Converts an OpenAI schema into a BaseTool.

        Parameters:
            schema: The OpenAI schema to convert.
            callback: The function to run when the tool is called.

        Returns:
            A BaseTool.
        """
        data_model_types = get_data_model_types(
            DataModelType.PydanticV2BaseModel,
            target_python_version=PythonVersion.PY_37
        )

        parser = JsonSchemaParser(
            json.dumps(schema['parameters']),
            data_model_type=data_model_types.data_model,
            data_model_root_type=data_model_types.root_model,
            data_model_field_type=data_model_types.field_model,
            data_type_manager_type=data_model_types.data_type_manager,
            dump_resolve_reference_action=data_model_types.dump_resolve_reference_action,
            use_schema_description=True,
            validation=False,
            class_name='Model',
            # custom_template_dir=Path('/Users/vrsen/Projects/agency-swarm/agency-swarm/agency_swarm/tools/data_schema_templates')
        )

        result = parser.parse()

        # # Execute the result to extract the model
        exec_globals = {}
        exec(result, exec_globals)
        model = exec_globals.get('Model')

        if not model:
            raise ValueError(f"Could not extract model from schema {schema['name']}")

        class ToolConfig:
            strict: bool = schema.get("strict", False)

        tool = type(schema['name'], (BaseTool, model), {
            "__doc__": schema.get('description', ""),
            "run": callback,
        })

        tool.ToolConfig = ToolConfig

        return tool

    @staticmethod
    def from_openapi_schema(schema: Union[str, dict], headers: Dict[str, str] = None, params: Dict[str, Any] = None, strict: bool = False) \
            -> List[Type[BaseTool]]:
        """
        Converts an OpenAPI schema into a list of BaseTools.

        Parameters:
            schema: The OpenAPI schema to convert.
            headers: The headers to use for requests.
            params: The parameters to use for requests.
            strict: Whether to use strict OpenAI mode.
        Returns:
            A list of BaseTools.
        """
        if isinstance(schema, dict):
            openapi_spec = schema
            openapi_spec = jsonref.JsonRef.replace_refs(openapi_spec)
        else:
            openapi_spec = jsonref.loads(schema)
        tools = []
        headers = headers or {}
        headers = {k: v for k, v in headers.items() if v is not None}
        for path, methods in openapi_spec["paths"].items():
            for method, spec_with_ref in methods.items():
                async def callback(self):
                    url = openapi_spec["servers"][0]["url"] + path
                    parameters = self.model_dump().get('parameters', {})
                    # replace all parameters in url
                    for param, value in parameters.items():
                        if "{" + str(param) + "}" in url:
                            url = url.replace(f"{{{param}}}", str(value))
                            parameters[param] = None
                    url = url.rstrip("/")
                    parameters = {k: v for k, v in parameters.items() if v is not None}
                    parameters = {**parameters, **params} if params else parameters
                    async with httpx.AsyncClient(timeout=90) as client:  # Set custom read timeout to 10 seconds
                        if method == "get":
                            response = await client.get(url, params=parameters, headers=headers)
                        elif method == "post":
                            response = await client.post(url,
                                                         params=parameters,
                                                         json=self.model_dump().get('requestBody', None),
                                                         headers=headers)
                        elif method == "put":
                            response = await client.put(url,
                                                        params=parameters,
                                                        json=self.model_dump().get('requestBody', None),
                                                        headers=headers)
                        elif method == "delete":
                            response = await client.delete(url,
                                                           params=parameters,
                                                           json=self.model_dump().get('requestBody', None),
                                                           headers=headers)
                        return response.json()

                # 1. Resolve JSON references.
                spec = jsonref.replace_refs(spec_with_ref)

                # 2. Extract a name for the functions.
                function_name = spec.get("operationId")

                # 3. Extract a description and parameters.
                desc = spec.get("description") or spec.get("summary", "")

                schema = {"type": "object", "properties": {}}

                req_body = (
                    spec.get("requestBody", {})
                    .get("content", {})
                    .get("application/json", {})
                    .get("schema")
                )
                if req_body:
                    schema["properties"]["requestBody"] = req_body

                spec_params = spec.get("parameters", [])
                if spec_params:
                    param_properties = {}
                    required_params = []
                    for param in spec_params:
                        if "schema" not in param and "type" in param:
                            param["schema"] = {"type": param["type"]}
                        param_properties[param["name"]] = param["schema"]
                        if "description" in param:
                            param_properties[param["name"]]["description"] = param["description"]
                        if "required" in param and param["required"]:
                            required_params.append(param["name"])
                        if "example" in param:
                            param_properties[param["name"]]["example"] = param["example"]
                        if "examples" in param:
                            param_properties[param["name"]]["examples"] = param["examples"]

                    schema["properties"]["parameters"] = {
                        "type": "object",
                        "properties": param_properties,
                        "required": required_params
                    }

                function = {
                    "name": function_name,
                    "description": desc,
                    "parameters": schema,
                    "strict": strict
                }

                tools.append(ToolFactory.from_openai_schema(function, callback))

        return tools

    @staticmethod
    def from_file(file_path: str) -> Type[BaseTool]:
        """Dynamically imports a BaseTool class from a Python file within a package structure.

        Parameters:
            file_path: The file path to the Python file containing the BaseTool class.

        Returns:
            The imported BaseTool class.
        """
        file_path = os.path.relpath(file_path)
        # Normalize the file path to be absolute and extract components
        directory, file_name = os.path.split(file_path)
        import_path = os.path.splitext(file_path)[0].replace(os.sep, ".")
        class_name = os.path.splitext(file_name)[0]

        exec_globals = globals()

        # importing from agency_swarm package
        if "agency_swarm" in import_path:
            import_path = import_path.lstrip(".")
            exec(f"from {import_path} import {class_name}", exec_globals)
        # importing from current working directory
        else:
            current_working_directory = os.getcwd()
            sys.path.append(current_working_directory)
            exec(f"from {import_path} import {class_name}", exec_globals)



        imported_class = exec_globals.get(class_name)
        if not imported_class:
            raise ImportError(f"Could not import {class_name} from {import_path}")

        # Check if the imported class is a subclass of BaseTool
        if not issubclass(imported_class, BaseTool):
            raise TypeError(f"Class {class_name} must be a subclass of BaseTool")

        return imported_class

    @staticmethod
    def get_openapi_schema(tools: List[Type[BaseTool]], url: str, title="Agent Tools",
                           description="A collection of tools.") -> str:
        """
        Generates an OpenAPI schema from a list of BaseTools.

        Parameters:
            tools: BaseTools to generate the schema from.
            url: The base URL for the schema.
            title: The title of the schema.
            description: The description of the schema.

        Returns:
            A JSON string representing the OpenAPI schema with all the tools combined as separate endpoints.
        """
        schema = {
            "openapi": "3.1.0",
            "info": {
                "title": title,
                "description": description,
                "version": "v1.0.0"
            },
            "servers": [
                {
                    "url": url,
                }
            ],
            "paths": {},
            "components": {
                "schemas": {},
                "securitySchemes": {
                    "apiKey": {
                        "type": "apiKey"
                    }
                }
            },
        }

        for tool in tools:
            if not issubclass(tool, BaseTool):
                continue

            openai_schema = tool.openai_schema
            defs = {}
            if '$defs' in openai_schema['parameters']:
                defs = openai_schema['parameters']['$defs']
                del openai_schema['parameters']['$defs']

            schema['paths']["/" + openai_schema['name']] = {
                "post": {
                    "description": openai_schema['description'],
                    "operationId": openai_schema['name'],
                    "x-openai-isConsequential": False,
                    "parameters": [],
                    "requestBody": {
                        "content": {
                            "application/json": {
                                "schema": openai_schema['parameters']
                            }
                        }
                    },
                }
            }

            schema['components']['schemas'].update(defs)

        schema = json.dumps(schema, indent=2).replace("#/$defs/", "#/components/schemas/")

        return schema

from_file(file_path) staticmethod

Dynamically imports a BaseTool class from a Python file within a package structure.

Parameters:

Name	Type	Description	Default
file_path	str	The file path to the Python file containing the BaseTool class.	required

Returns:

Type	Description
Type[BaseTool]	The imported BaseTool class.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def from_file(file_path: str) -> Type[BaseTool]:
    """Dynamically imports a BaseTool class from a Python file within a package structure.

    Parameters:
        file_path: The file path to the Python file containing the BaseTool class.

    Returns:
        The imported BaseTool class.
    """
    file_path = os.path.relpath(file_path)
    # Normalize the file path to be absolute and extract components
    directory, file_name = os.path.split(file_path)
    import_path = os.path.splitext(file_path)[0].replace(os.sep, ".")
    class_name = os.path.splitext(file_name)[0]

    exec_globals = globals()

    # importing from agency_swarm package
    if "agency_swarm" in import_path:
        import_path = import_path.lstrip(".")
        exec(f"from {import_path} import {class_name}", exec_globals)
    # importing from current working directory
    else:
        current_working_directory = os.getcwd()
        sys.path.append(current_working_directory)
        exec(f"from {import_path} import {class_name}", exec_globals)



    imported_class = exec_globals.get(class_name)
    if not imported_class:
        raise ImportError(f"Could not import {class_name} from {import_path}")

    # Check if the imported class is a subclass of BaseTool
    if not issubclass(imported_class, BaseTool):
        raise TypeError(f"Class {class_name} must be a subclass of BaseTool")

    return imported_class

from_langchain_tool(tool) staticmethod

Converts a langchain tool into a BaseTool.

Parameters:

Name	Type	Description	Default
tool		The langchain tool to convert.	required

Returns:

Type	Description
Type[BaseTool]	A BaseTool.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def from_langchain_tool(tool) -> Type[BaseTool]:
    """
    Converts a langchain tool into a BaseTool.

    Parameters:
        tool: The langchain tool to convert.

    Returns:
        A BaseTool.
    """
    try:
        from langchain.tools import format_tool_to_openai_function
    except ImportError:
        raise ImportError("You must install langchain to use this method.")

    if inspect.isclass(tool):
        tool = tool()

    def callback(self):
        tool_input = self.model_dump()
        try:
            return tool.run(tool_input)
        except TypeError:
            if len(tool_input) == 1:
                return tool.run(list(tool_input.values())[0])
            else:
                raise TypeError(f"Error parsing input for tool '{tool.__class__.__name__}' Please open an issue "
                                f"on github.")

    return ToolFactory.from_openai_schema(
        format_tool_to_openai_function(tool),
        callback
    )

from_langchain_tools(tools) staticmethod

Converts a list of langchain tools into a list of BaseTools.

Parameters:

Name	Type	Description	Default
tools	List	The langchain tools to convert.	required

Returns:

Type	Description
List[Type[BaseTool]]	A list of BaseTools.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def from_langchain_tools(tools: List) -> List[Type[BaseTool]]:
    """
    Converts a list of langchain tools into a list of BaseTools.

    Parameters:
        tools: The langchain tools to convert.

    Returns:
        A list of BaseTools.
    """
    converted_tools = []
    for tool in tools:
        converted_tools.append(ToolFactory.from_langchain_tool(tool))

    return converted_tools

from_openai_schema(schema, callback) staticmethod

Converts an OpenAI schema into a BaseTool.

Parameters:

Name	Type	Description	Default
schema	Dict[str, Any]	The OpenAI schema to convert.	required
callback	Any	The function to run when the tool is called.	required

Returns:

Type	Description
Type[BaseTool]	A BaseTool.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def from_openai_schema(schema: Dict[str, Any], callback: Any) -> Type[BaseTool]:
    """
    Converts an OpenAI schema into a BaseTool.

    Parameters:
        schema: The OpenAI schema to convert.
        callback: The function to run when the tool is called.

    Returns:
        A BaseTool.
    """
    data_model_types = get_data_model_types(
        DataModelType.PydanticV2BaseModel,
        target_python_version=PythonVersion.PY_37
    )

    parser = JsonSchemaParser(
        json.dumps(schema['parameters']),
        data_model_type=data_model_types.data_model,
        data_model_root_type=data_model_types.root_model,
        data_model_field_type=data_model_types.field_model,
        data_type_manager_type=data_model_types.data_type_manager,
        dump_resolve_reference_action=data_model_types.dump_resolve_reference_action,
        use_schema_description=True,
        validation=False,
        class_name='Model',
        # custom_template_dir=Path('/Users/vrsen/Projects/agency-swarm/agency-swarm/agency_swarm/tools/data_schema_templates')
    )

    result = parser.parse()

    # # Execute the result to extract the model
    exec_globals = {}
    exec(result, exec_globals)
    model = exec_globals.get('Model')

    if not model:
        raise ValueError(f"Could not extract model from schema {schema['name']}")

    class ToolConfig:
        strict: bool = schema.get("strict", False)

    tool = type(schema['name'], (BaseTool, model), {
        "__doc__": schema.get('description', ""),
        "run": callback,
    })

    tool.ToolConfig = ToolConfig

    return tool

from_openapi_schema(schema, headers=None, params=None, strict=False) staticmethod

Converts an OpenAPI schema into a list of BaseTools.

Parameters:

Name	Type	Description	Default
schema	Union[str, dict]	The OpenAPI schema to convert.	required
headers	Dict[str, str]	The headers to use for requests.	None
params	Dict[str, Any]	The parameters to use for requests.	None
strict	bool	Whether to use strict OpenAI mode.	False

Returns: A list of BaseTools.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def from_openapi_schema(schema: Union[str, dict], headers: Dict[str, str] = None, params: Dict[str, Any] = None, strict: bool = False) \
        -> List[Type[BaseTool]]:
    """
    Converts an OpenAPI schema into a list of BaseTools.

    Parameters:
        schema: The OpenAPI schema to convert.
        headers: The headers to use for requests.
        params: The parameters to use for requests.
        strict: Whether to use strict OpenAI mode.
    Returns:
        A list of BaseTools.
    """
    if isinstance(schema, dict):
        openapi_spec = schema
        openapi_spec = jsonref.JsonRef.replace_refs(openapi_spec)
    else:
        openapi_spec = jsonref.loads(schema)
    tools = []
    headers = headers or {}
    headers = {k: v for k, v in headers.items() if v is not None}
    for path, methods in openapi_spec["paths"].items():
        for method, spec_with_ref in methods.items():
            async def callback(self):
                url = openapi_spec["servers"][0]["url"] + path
                parameters = self.model_dump().get('parameters', {})
                # replace all parameters in url
                for param, value in parameters.items():
                    if "{" + str(param) + "}" in url:
                        url = url.replace(f"{{{param}}}", str(value))
                        parameters[param] = None
                url = url.rstrip("/")
                parameters = {k: v for k, v in parameters.items() if v is not None}
                parameters = {**parameters, **params} if params else parameters
                async with httpx.AsyncClient(timeout=90) as client:  # Set custom read timeout to 10 seconds
                    if method == "get":
                        response = await client.get(url, params=parameters, headers=headers)
                    elif method == "post":
                        response = await client.post(url,
                                                     params=parameters,
                                                     json=self.model_dump().get('requestBody', None),
                                                     headers=headers)
                    elif method == "put":
                        response = await client.put(url,
                                                    params=parameters,
                                                    json=self.model_dump().get('requestBody', None),
                                                    headers=headers)
                    elif method == "delete":
                        response = await client.delete(url,
                                                       params=parameters,
                                                       json=self.model_dump().get('requestBody', None),
                                                       headers=headers)
                    return response.json()

            # 1. Resolve JSON references.
            spec = jsonref.replace_refs(spec_with_ref)

            # 2. Extract a name for the functions.
            function_name = spec.get("operationId")

            # 3. Extract a description and parameters.
            desc = spec.get("description") or spec.get("summary", "")

            schema = {"type": "object", "properties": {}}

            req_body = (
                spec.get("requestBody", {})
                .get("content", {})
                .get("application/json", {})
                .get("schema")
            )
            if req_body:
                schema["properties"]["requestBody"] = req_body

            spec_params = spec.get("parameters", [])
            if spec_params:
                param_properties = {}
                required_params = []
                for param in spec_params:
                    if "schema" not in param and "type" in param:
                        param["schema"] = {"type": param["type"]}
                    param_properties[param["name"]] = param["schema"]
                    if "description" in param:
                        param_properties[param["name"]]["description"] = param["description"]
                    if "required" in param and param["required"]:
                        required_params.append(param["name"])
                    if "example" in param:
                        param_properties[param["name"]]["example"] = param["example"]
                    if "examples" in param:
                        param_properties[param["name"]]["examples"] = param["examples"]

                schema["properties"]["parameters"] = {
                    "type": "object",
                    "properties": param_properties,
                    "required": required_params
                }

            function = {
                "name": function_name,
                "description": desc,
                "parameters": schema,
                "strict": strict
            }

            tools.append(ToolFactory.from_openai_schema(function, callback))

    return tools

get_openapi_schema(tools, url, title='Agent Tools', description='A collection of tools.') staticmethod

Generates an OpenAPI schema from a list of BaseTools.

Parameters:

Name	Type	Description	Default
tools	List[Type[BaseTool]]	BaseTools to generate the schema from.	required
url	str	The base URL for the schema.	required
title		The title of the schema.	'Agent Tools'
description		The description of the schema.	'A collection of tools.'

Returns:

Type	Description
str	A JSON string representing the OpenAPI schema with all the tools combined as separate endpoints.

Source code in agency_swarm/tools/ToolFactory.py

@staticmethod
def get_openapi_schema(tools: List[Type[BaseTool]], url: str, title="Agent Tools",
                       description="A collection of tools.") -> str:
    """
    Generates an OpenAPI schema from a list of BaseTools.

    Parameters:
        tools: BaseTools to generate the schema from.
        url: The base URL for the schema.
        title: The title of the schema.
        description: The description of the schema.

    Returns:
        A JSON string representing the OpenAPI schema with all the tools combined as separate endpoints.
    """
    schema = {
        "openapi": "3.1.0",
        "info": {
            "title": title,
            "description": description,
            "version": "v1.0.0"
        },
        "servers": [
            {
                "url": url,
            }
        ],
        "paths": {},
        "components": {
            "schemas": {},
            "securitySchemes": {
                "apiKey": {
                    "type": "apiKey"
                }
            }
        },
    }

    for tool in tools:
        if not issubclass(tool, BaseTool):
            continue

        openai_schema = tool.openai_schema
        defs = {}
        if '$defs' in openai_schema['parameters']:
            defs = openai_schema['parameters']['$defs']
            del openai_schema['parameters']['$defs']

        schema['paths']["/" + openai_schema['name']] = {
            "post": {
                "description": openai_schema['description'],
                "operationId": openai_schema['name'],
                "x-openai-isConsequential": False,
                "parameters": [],
                "requestBody": {
                    "content": {
                        "application/json": {
                            "schema": openai_schema['parameters']
                        }
                    }
                },
            }
        }

        schema['components']['schemas'].update(defs)

    schema = json.dumps(schema, indent=2).replace("#/$defs/", "#/components/schemas/")

    return schema