diff --git a/stacked_worker.py b/stacked_worker.py
new file mode 100644
index 00000000..9f545f1f
--- /dev/null
+++ b/stacked_worker.py
@@ -0,0 +1,89 @@
+from swarms.models import OpenAIChat
+from swarms.workers import Worker
+from swarms.tools.autogpt import tool
+from swarms.agents.hf_agents import HFAgent
+from swarms.agents.omni_modal_agent import OmniModalAgent
+
+#Initialize API Key
+api_key = ""
+
+
+# Initialize the language model,
+# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
+llm = OpenAIChat(
+ openai_api_key=api_key,
+ temperature=0.5,
+)
+
+#wrap a function with the tool decorator to make it a tool
+@tool
+def hf_agent(task: str = None):
+ """
+ An tool that uses an openai model to call and respond to a task by search for a model on huggingface
+ It first downloads the model then uses it.
+
+ Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech, etc
+
+ """
+ agent = HFAgent(model="text-davinci-003", api_key=api_key)
+ response = agent.run(task, text="¡Este es un API muy agradable!")
+ return response
+
+
+#wrap a function with the tool decorator to make it a tool
+@tool
+def omni_agent(task: str = None):
+ """
+ An tool that uses an openai Model to utilize and call huggingface models and guide them to perform a task.
+
+ Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech
+ The following tasks are what this tool should be used for:
+
+ Tasks omni agent is good for:
+ --------------
+ 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
+ """
+ agent = OmniModalAgent(llm)
+ response = agent.run(task)
+ return response
+
+# Append tools to an list
+tools = [
+ hf_agent,
+ omni_agent,
+]
+
+
+#Initialize a single Worker node with previously defined tools in addition to it's
+# predefined tools
+node = Worker(
+ llm=llm,
+ ai_name="Optimus Prime",
+ openai_api_key=api_key,
+ ai_role="Worker in a swarm",
+ external_tools=tools,
+ human_in_the_loop=False,
+ temperature=0.5,
+)
+
+#Specify task
+task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
+
+# Run the node on the task
+response = node.run(task)
+
+# Print the response
+print(response)
diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py
index 9bcb519d..4ca446ad 100644
--- a/swarms/agents/__init__.py
+++ b/swarms/agents/__init__.py
@@ -5,6 +5,8 @@
# from swarms.agents.aot import AoTAgent
# from swarms.agents.multi_modal_visual_agent import MultiModalAgent
from swarms.agents.omni_modal_agent import OmniModalAgent
+from swarms.agents.hf_agents import HFAgent
+
# utils
diff --git a/swarms/models/openai_llm.py b/swarms/agents/hf_agents.py
similarity index 99%
rename from swarms/models/openai_llm.py
rename to swarms/agents/hf_agents.py
index e0a8ae62..28c18c71 100644
--- a/swarms/models/openai_llm.py
+++ b/swarms/agents/hf_agents.py
@@ -370,7 +370,7 @@ class Agent:
return [self.generate_one(prompt, stop) for prompt in prompts]
-class OpenAIModel(Agent):
+class HFAgent(Agent):
"""
Agent that uses the openai API to generate code.
@@ -401,7 +401,7 @@ class OpenAIModel(Agent):
Example:
```py
- from swarms.models import OpenAI
+ from swarms.agents.hf_agents import HFAgent
agent = OpenAiAgent(model="text-davinci-003", api_key=xxx)
agent.run("Is the following `text` (in Spanish) positive or negative?", text="¡Este es un API muy agradable!")
diff --git a/swarms/prompts/agent_prompt_generator.py b/swarms/prompts/agent_prompt_generator.py
deleted file mode 100644
index 81e93a73..00000000
--- a/swarms/prompts/agent_prompt_generator.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import json
-from typing import List
-
-from langchain.tools.base import BaseTool
-
-FINISH_NAME = "finish"
-
-
-class PromptGenerator:
- """A class for generating custom prompt strings.
-
- Does this based on constraints, commands, resources, and performance evaluations.
- """
-
- def __init__(self) -> None:
- """Initialize the PromptGenerator object.
-
- Starts with empty lists of constraints, commands, resources,
- and performance evaluations.
- """
- self.constraints: List[str] = []
- self.commands: List[BaseTool] = []
- self.resources: List[str] = []
- self.performance_evaluation: List[str] = []
- self.response_format = {
- "thoughts": {
- "text": "thought",
- "reasoning": "reasoning",
- "plan": "- short bulleted\n- list that conveys\n- long-term plan",
- "criticism": "constructive self-criticism",
- "speak": "thoughts summary to say to user",
- },
- "command": {"name": "command name", "args": {"arg name": "value"}},
- }
-
- def add_constraint(self, constraint: str) -> None:
- """
- Add a constraint to the constraints list.
-
- Args:
- constraint (str): The constraint to be added.
- """
- self.constraints.append(constraint)
-
- def add_tool(self, tool: BaseTool) -> None:
- self.commands.append(tool)
-
- def _generate_command_string(self, tool: BaseTool) -> str:
- output = f"{tool.name}: {tool.description}"
- output += f", args json schema: {json.dumps(tool.args)}"
- return output
-
- def add_resource(self, resource: str) -> None:
- """
- Add a resource to the resources list.
-
- Args:
- resource (str): The resource to be added.
- """
- self.resources.append(resource)
-
- def add_performance_evaluation(self, evaluation: str) -> None:
- """
- Add a performance evaluation item to the performance_evaluation list.
-
- Args:
- evaluation (str): The evaluation item to be added.
- """
- self.performance_evaluation.append(evaluation)
-
- def _generate_numbered_list(self, items: list, item_type: str = "list") -> str:
- """
- Generate a numbered list from given items based on the item_type.
-
- Args:
- items (list): A list of items to be numbered.
- item_type (str, optional): The type of items in the list.
- Defaults to 'list'.
-
- Returns:
- str: The formatted numbered list.
- """
- if item_type == "command":
- command_strings = [
- f"{i + 1}. {self._generate_command_string(item)}"
- for i, item in enumerate(items)
- ]
- finish_description = (
- "use this to signal that you have finished all your objectives"
- )
- finish_args = (
- '"response": "final response to let '
- 'people know you have finished your objectives"'
- )
- finish_string = (
- f"{len(items) + 1}. {FINISH_NAME}: "
- f"{finish_description}, args: {finish_args}"
- )
- return "\n".join(command_strings + [finish_string])
- else:
- return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
-
- def generate_prompt_string(self) -> str:
- """Generate a prompt string.
-
- Returns:
- str: The generated prompt string.
- """
- formatted_response_format = json.dumps(self.response_format, indent=4)
- prompt_string = (
- f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
- f"Commands:\n"
- f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
- f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
- f"Performance Evaluation:\n"
- f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
- f"You should only respond in JSON format as described below "
- f"\nResponse Format: \n{formatted_response_format} "
- f"\nEnsure the response can be parsed by Python json.loads"
- )
-
- return prompt_string
-
-
-def get_prompt(tools: List[BaseTool]) -> str:
- """Generates a prompt string.
-
- It includes various constraints, commands, resources, and performance evaluations.
-
- Returns:
- str: The generated prompt string.
- """
-
- # Initialize the PromptGenerator object
- prompt_generator = PromptGenerator()
-
- # Add constraints to the PromptGenerator object
- prompt_generator.add_constraint(
- "~4000 word limit for short term memory. "
- "Your short term memory is short, "
- "so immediately save important information to files."
- )
- prompt_generator.add_constraint(
- "If you are unsure how you previously did something "
- "or want to recall past events, "
- "thinking about similar events will help you remember."
- )
- prompt_generator.add_constraint("No user assistance")
- prompt_generator.add_constraint(
- 'Exclusively use the commands listed in double quotes e.g. "command name"'
- )
-
- # Add commands to the PromptGenerator object
- for tool in tools:
- prompt_generator.add_tool(tool)
-
- # Add resources to the PromptGenerator object
- prompt_generator.add_resource(
- "Internet access for searches and information gathering."
- )
- prompt_generator.add_resource("Long Term memory management.")
- prompt_generator.add_resource(
- "GPT-3.5 powered Agents for delegation of simple tasks."
- )
- prompt_generator.add_resource("File output.")
-
- # Add performance evaluations to the PromptGenerator object
- prompt_generator.add_performance_evaluation(
- "Continuously review and analyze your actions "
- "to ensure you are performing to the best of your abilities."
- )
- prompt_generator.add_performance_evaluation(
- "Constructively self-criticize your big-picture behavior constantly."
- )
- prompt_generator.add_performance_evaluation(
- "Reflect on past decisions and strategies to refine your approach."
- )
- prompt_generator.add_performance_evaluation(
- "Every command has a cost, so be smart and efficient. "
- "Aim to complete tasks in the least number of steps."
- )
-
- # Generate the prompt string
- prompt_string = prompt_generator.generate_prompt_string()
-
- return prompt_string