[CLEANUP]

docs/mkdocs.yml

@@ -1,8 +1,8 @@
 docs_dir: '.'  # replace with the correct path if your documentation files are not in the same directory as mkdocs.yml
-site_name: Swarms Documentation
-site_url: https://swarms.apac.ai
+site_name: Swarms
+site_url: https://docs.swarms.world
 site_author: Swarms
-site_description: Orchestrate Swarms of Agents From Any Framework Like OpenAI, Langchain, and Etc for Real World Workflow Automation.
+site_description: The Enterprise-Grade Production-Ready Multi-Agent Framework
 repo_name: kyegomez/swarms
 repo_url: https://github.com/kyegomez/swarms
 edit_uri: https://github.com/kyegomez/swarms/tree/main/docs

swarms/agents/__init__.py

@@ -1,7 +1,5 @@
 from swarms.agents.agent_wrapper import agent_wrapper
 from swarms.agents.base import AbstractAgent
-from swarms.agents.omni_modal_agent import OmniModalAgent
-from swarms.agents.simple_agent import SimpleAgent
 from swarms.agents.stopping_conditions import (
     check_cancelled,
     check_complete,

swarms/agents/developer_agents.py

@@ -1,136 +0,0 @@
-from swarms.prompts.documentation import DOCUMENTATION_WRITER_SOP
-from swarms.prompts.tests import TEST_WRITER_SOP_PROMPT
-from swarms.structs.agent import Agent
-
-
-class UnitTesterAgent:
-    """
-    This class represents a unit testing agent responsible for generating unit tests for the swarms package.
-
-    Attributes:
-    - llm: The low-level model used by the agent.
-    - agent_name (str): The name of the agent.
-    - agent_description (str): The description of the agent.
-    - max_loops (int): The maximum number of loops the agent can run.
-    - SOP_PROMPT: The system output prompt used by the agent.
-    - agent: The underlying agent object used for running tasks.
-
-    Methods:
-    - run(task: str, *args, **kwargs) -> str: Run the agent with the given task and return the response.
-    """
-
-    def __init__(
-        self,
-        llm,
-        agent_name: str = "Unit Testing Agent",
-        agent_description: str = "This agent is responsible for generating unit tests for the swarms package.",
-        max_loops: int = 1,
-        sop: str = None,
-        module: str = None,
-        path: str = None,
-        autosave: bool = True,
-        *args,
-        **kwargs,
-    ):
-        super().__init__()
-        self.llm = llm
-        self.agent_name = agent_name
-        self.agent_description = agent_description
-        self.max_loops = max_loops
-        self.sop = sop
-        self.module = module
-        self.path = path
-        self.autosave = autosave
-
-        self.agent = Agent(
-            llm=llm,
-            agent_name=agent_name,
-            agent_description=agent_description,
-            autosave=self.autosave,
-            system_prompt=agent_description,
-            max_loops=max_loops,
-            *args,
-            **kwargs,
-        )
-
-    def run(self, task: str, module: str, path: str, *args, **kwargs):
-        """
-        Run the agent with the given task.
-
-        Args:
-        - task (str): The task to run the agent with.
-
-        Returns:
-        - str: The response from the agent.
-        """
-        return self.agent.run(
-            TEST_WRITER_SOP_PROMPT(task, self.module, self.path),
-            *args,
-            **kwargs,
-        )
-
-
-class DocumentorAgent:
-    """
-    This class represents a documentor agent responsible for generating unit tests for the swarms package.
-
-    Attributes:
-    - llm: The low-level model used by the agent.
-    - agent_name (str): The name of the agent.
-    - agent_description (str): The description of the agent.
-    - max_loops (int): The maximum number of loops the agent can run.
-    - SOP_PROMPT: The system output prompt used by the agent.
-    - agent: The underlying agent object used for running tasks.
-
-    Methods:
-    - run(task: str, *args, **kwargs) -> str: Run the agent with the given task and return the response.
-    """
-
-    def __init__(
-        self,
-        llm,
-        agent_name: str = "Documentor Agent",
-        agent_description: str = "This agent is responsible for generating unit tests for the swarms package.",
-        max_loops: int = 1,
-        sop: str = None,
-        module: str = None,
-        path: str = None,
-        autosave: bool = True,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.llm = llm
-        self.agent_name = agent_name
-        self.agent_description = agent_description
-        self.max_loops = max_loops
-        self.sop = sop
-        self.module = module
-        self.path = path
-        self.autosave = autosave
-
-        self.agent = Agent(
-            llm=llm,
-            agent_name=agent_name,
-            agent_description=agent_description,
-            autosave=self.autosave,
-            system_prompt=agent_description,
-            max_loops=max_loops,
-            *args,
-            **kwargs,
-        )
-
-    def run(self, task: str, module: str, path: str, *args, **kwargs):
-        """
-        Run the agent with the given task.
-
-        Args:
-        - task (str): The task to run the agent with.
-
-        Returns:
-        - str: The response from the agent.
-        """
-        return self.agent.run(
-            DOCUMENTATION_WRITER_SOP(task, self.module) * args,
-            **kwargs,
-        )

swarms/agents/omni_modal_agent.py

@@ -1,100 +0,0 @@
-from langchain.base_language import BaseLanguageModel
-from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import (
-    load_response_generator,
-)
-from langchain_experimental.autonomous_agents.hugginggpt.task_executor import (
-    TaskExecutor,
-)
-from langchain_experimental.autonomous_agents.hugginggpt.task_planner import (
-    load_chat_planner,
-)
-from transformers import load_tool
-
-from swarms.structs.agent import Agent
-from swarms.utils.loguru_logger import logger
-
-
-class OmniModalAgent(Agent):
-    """
-    OmniModalAgent
-    LLM -> Plans -> Tasks -> Tools -> Response
-
-    Architecture:
-    1. LLM: Language Model
-    2. Chat Planner: Plans
-    3. Task Executor: Tasks
-    4. Tools: Tools
-
-    Args:
-        llm (BaseLanguageModel): Language Model
-        tools (List[BaseTool]): List of tools
-
-    Returns:
-        str: response
-
-    Usage:
-    from swarms import OmniModalAgent, OpenAIChat,
-
-    llm = OpenAIChat()
-    agent = OmniModalAgent(llm)
-    response = agent.run("Hello, how are you? Create an image of how your are doing!")
-    """
-
-    def __init__(
-        self,
-        llm: BaseLanguageModel,
-        verbose: bool = False,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(llm=llm, *args, **kwargs)
-        self.llm = llm
-        self.verbose = verbose
-
-        print("Loading tools...")
-        self.tools = [
-            load_tool(tool_name)
-            for tool_name in [
-                "document-question-answering",
-                "image-captioning",
-                "image-question-answering",
-                "image-segmentation",
-                "speech-to-text",
-                "summarization",
-                "text-classification",
-                "text-question-answering",
-                "translation",
-                "huggingface-tools/text-to-image",
-                "huggingface-tools/text-to-video",
-                "text-to-speech",
-                "huggingface-tools/text-download",
-                "huggingface-tools/image-transformation",
-            ]
-        ]
-
-        # Load the chat planner and response generator
-        self.chat_planner = load_chat_planner(llm)
-        self.response_generator = load_response_generator(llm)
-        self.task_executor = TaskExecutor
-        self.history = []
-
-    def run(self, task: str) -> str:
-        """Run the OmniAgent"""
-        try:
-            plan = self.chat_planner.plan(
-                inputs={
-                    "input": task,
-                    "hf_tools": self.tools,
-                }
-            )
-            self.task_executor = TaskExecutor(plan)
-            self.task_executor.run()
-
-            response = self.response_generator.generate(
-                {"task_execution": self.task_executor}
-            )
-
-            return response
-        except Exception as error:
-            logger.error(f"Error running the agent: {error}")
-            return f"Error running the agent: {error}"

swarms/agents/simple_agent.py

@@ -1,105 +0,0 @@
-import importlib
-import pkgutil
-from typing import Any
-
-import swarms.models
-from swarms.models.base_llm import BaseLLM
-from swarms.structs.conversation import Conversation
-
-
-def get_llm_by_name(name: str):
-    """
-    Searches all the modules exported from the 'swarms.models' path for a class with the given name.
-
-    Args:
-        name (str): The name of the class to search for.
-
-    Returns:
-        type: The class with the given name, or None if no such class is found.
-    """
-    for importer, modname, ispkg in pkgutil.iter_modules(
-        swarms.models.__path__
-    ):
-        module = importlib.import_module(f"swarms.models.{modname}")
-        if hasattr(module, name):
-            return getattr(module, name)
-    return None
-
-
-# Run the language model in a loop for n iterations
-def SimpleAgent(
-    llm: BaseLLM = None, iters: Any = "automatic", *args, **kwargs
-):
-    """
-    A simple agent that interacts with a language model.
-
-    Args:
-        llm (BaseLLM): The language model to use for generating responses.
-        iters (Any): The number of iterations or "automatic" to run indefinitely.
-        *args: Additional positional arguments to pass to the language model.
-        **kwargs: Additional keyword arguments to pass to the language model.
-
-    Raises:
-        Exception: If the language model is not defined or cannot be found.
-
-    Returns:
-        None
-    """
-    try:
-        if llm is None:
-            raise Exception("Language model not defined")
-
-        if isinstance(llm, str):
-            llm = get_llm_by_name(llm)
-            if llm is None:
-                raise Exception(f"Language model {llm} not found")
-            llm = llm(*args, **kwargs)
-    except Exception as error:
-        print(f"[ERROR][SimpleAgent] {error}")
-        raise error
-
-    try:
-        conv = Conversation(*args, **kwargs)
-        if iters == "automatic":
-            i = 0
-            while True:
-                user_input = input("\033[91mUser:\033[0m ")
-                conv.add("user", user_input)
-                if user_input.lower() == "quit":
-                    break
-                task = (
-                    conv.return_history_as_string()
-                )  # Get the conversation history
-                out = llm(task, *args, **kwargs)
-                conv.add("assistant", out)
-                print(
-                    f"\033[94mAssistant:\033[0m {out}",
-                )
-                conv.display_conversation()
-                conv.export_conversation("conversation.txt")
-                i += 1
-        else:
-            for i in range(iters):
-                user_input = input("\033[91mUser:\033[0m ")
-                conv.add("user", user_input)
-                if user_input.lower() == "quit":
-                    break
-                task = (
-                    conv.return_history_as_string()
-                )  # Get the conversation history
-                out = llm(task, *args, **kwargs)
-                conv.add("assistant", out)
-                print(
-                    f"\033[94mAssistant:\033[0m {out}",
-                )
-                conv.display_conversation()
-                conv.export_conversation("conversation.txt")
-
-    except Exception as error:
-        print(f"[ERROR][SimpleAgentConversation] {error}")
-        raise error
-
-    except KeyboardInterrupt:
-        print("[INFO][SimpleAgentConversation] Keyboard interrupt")
-        conv.export_conversation("conversation.txt")
-        raise KeyboardInterrupt