undo a couple changes

.pre-commit-config.yaml

@@ -1,4 +1,3 @@
----
 repos:
   - repo: https://github.com/ambv/black
     rev: 22.3.0
@@ -8,7 +7,7 @@ repos:
     rev: 'v0.0.255'
     hooks:
       - id: ruff
-        args: ['----unsafe-fixes']
+        args: [----unsafe-fixes]
   - repo: https://github.com/nbQA-dev/nbQA
     rev: 1.6.3
     hooks:

.readthedocs.yml

@@ -1,11 +1,13 @@
----
 version: 2
+
 build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
+
 mkdocs:
   configuration: mkdocs.yml
+
 python:
    install:
    - requirements: requirements.txt

.yamlfmt.yaml

@@ -1,5 +0,0 @@
----
-formatter:
-  type: basic
-  include_document_start: true
-  max_line_length: 80

.yamllint.yaml

@@ -1,3 +0,0 @@
-rules:
-  line-length:
-    max: 127

example.py

@@ -1,6 +1,7 @@
 from swarms import Agent, Anthropic
 
-# Initialize the workflow
+
+## Initialize the workflow
 agent = Agent(
     agent_name="Transcript Generator",
     agent_description=(

mkdocs.yml

@@ -1,4 +1,3 @@
----
 site_name: Swarms Docs
 plugins:
   - glightbox
@@ -76,7 +75,7 @@ nav:
       - ToolAgent: "swarms/agents/toolagent.md"
   - swarms.models:
     - How to Create A Custom Language Model: "swarms/models/custom_model.md"
-          - "Deploying Azure OpenAI in Production: A Comprehensive Guide": "swarms/models/azure_openai.md"
+    - Deploying Azure OpenAI in Production: A Comprehensive Guide: "swarms/models/azure_openai.md"
     - Language:
       - BaseLLM: "swarms/models/base_llm.md"
       - Overview: "swarms/models/index.md"
@@ -144,7 +143,7 @@ nav:
 - Guides:
     - Building Custom Vector Memory Databases with the AbstractVectorDatabase Class: "swarms/memory/diy_memory.md"
     - How to Create A Custom Language Model: "swarms/models/custom_model.md"
-      - "Deploying Azure OpenAI in Production: A Comprehensive Guide": "swarms/models/azure_openai.md"
+    - Deploying Azure OpenAI in Production: A Comprehensive Guide: "swarms/models/azure_openai.md"
     - DIY Build Your Own Agent: "diy_your_own_agent.md"
     - Overview: "examples/index.md"
     - Agents:
@@ -178,5 +177,9 @@ nav:
       - Research: "corporate/research.md"
       - Demos: "corporate/demos.md"
       - Checklist: "corporate/checklist.md"
+    
     - Organization:
       - FrontEnd Member Onboarding: "corporate/front_end_contributors.md"
+    
+    
+    

playground/agents/amazon_review_agent.py

@@ -1,6 +1,6 @@
 from swarms import Agent, OpenAIChat
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=OpenAIChat(),
     max_loops="auto",

playground/agents/devin.py

@@ -1,6 +1,5 @@
-import subprocess
-
 from swarms import Agent, Anthropic, tool
+import subprocess
 
 # Model
 llm = Anthropic(

playground/agents/full_stack_agent.py

@@ -10,7 +10,7 @@ def search_api(query: str, max_results: int = 10):
     return f"Search API: {query} -> {max_results} results"
 
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     agent_name="Youtube Transcript Generator",
     agent_description=(

playground/agents/multi_modal_auto_agent_example.py

@@ -21,7 +21,7 @@ llm = GPT4VisionAPI(
 task = "What is the color of the object?"
 img = "images/swarms.jpeg"
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops="auto",

playground/creation_engine/omni_model_agent.py

@@ -0,0 +1,82 @@
+from swarms import Agent, Anthropic, tool
+
+# Model
+llm = Anthropic(
+    temperature=0.1,
+)
+
+
+# Tools
+@tool
+def text_to_video(task: str):
+    """
+    Converts a given text task into an animated video.
+
+    Args:
+        task (str): The text task to be converted into a video.
+
+    Returns:
+        str: The path to the exported GIF file.
+    """
+    import torch
+    from diffusers import (
+        AnimateDiffPipeline,
+        MotionAdapter,
+        EulerDiscreteScheduler,
+    )
+    from diffusers.utils import export_to_gif
+    from huggingface_hub import hf_hub_download
+    from safetensors.torch import load_file
+
+    device = "cuda"
+    dtype = torch.float16
+
+    step = 4  # Options: [1,2,4,8]
+    repo = "ByteDance/AnimateDiff-Lightning"
+    ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
+    base = (  # Choose to your favorite base model.
+        "emilianJR/epiCRealism"
+    )
+
+    adapter = MotionAdapter().to(device, dtype)
+    adapter.load_state_dict(
+        load_file(hf_hub_download(repo, ckpt), device=device)
+    )
+    pipe = AnimateDiffPipeline.from_pretrained(
+        base, motion_adapter=adapter, torch_dtype=dtype
+    ).to(device)
+    pipe.scheduler = EulerDiscreteScheduler.from_config(
+        pipe.scheduler.config,
+        timestep_spacing="trailing",
+        beta_schedule="linear",
+    )
+
+    output = pipe(
+        prompt=task, guidance_scale=1.0, num_inference_steps=step
+    )
+    out = export_to_gif(output.frames[0], "animation.gif")
+    return out
+
+
+# Agent
+agent = Agent(
+    agent_name="Devin",
+    system_prompt=(
+        "Autonomous agent that can interact with humans and other"
+        " agents. Be Helpful and Kind. Use the tools provided to"
+        " assist the user. Return all code in markdown format."
+    ),
+    llm=llm,
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+    interactive=True,
+    tools=[text_to_video],
+)
+
+# Run the agent
+out = agent("Create a vide of a girl coding AI wearing hijab")
+print(out)

playground/demos/ad_gen/ad_gen_example.py

@@ -68,7 +68,6 @@ class ProductAdConceptGenerator:
     def generate_concept(self):
         theme = random.choice(self.themes)
         context = random.choice(self.contexts)
-        style = random.choice(["medival", "modern", "futuristic", "retro"])
         return (
             f"{theme} inside a {style} {self.product_name}, {context}"
         )
@@ -89,7 +88,6 @@ image_paths = sd_api.run(creative_concept)
 
 # Generate ad copy
 ad_copy_agent = Agent(llm=llm, max_loops=1)
-social_media_platform = "Instagram"
 ad_copy_prompt = (
     f"Write a compelling {social_media_platform} ad copy for a"
     f" product photo showing {product_name} {creative_concept}."
@@ -97,7 +95,9 @@ ad_copy_prompt = (
 ad_copy = ad_copy_agent.run(task=ad_copy_prompt)
 
 # Output the results
-print("Ad Copy:", ad_copy)
+print("Creative Concept:", concept_result)
+print("Design Ideas:", design_result)
+print("Ad Copy:", copywriting_result)
 print(
     "Image Path:",
     image_paths[0] if image_paths else "No image generated",

playground/demos/agent_in_5/youtube_demo_agent.py

@@ -4,8 +4,8 @@ Building an Autonomous Agent in 5 minutes with:
 - Tools: Search, Browser, ETC
 - Long Term Mmeory: ChromaDB, Weaviate, Pinecone, ETC
 """
-from playground.demos.agent_in_5.chroma_db import ChromaDB
 from swarms import Agent, OpenAIChat, tool
+from playground.demos.agent_in_5.chroma_db import ChromaDB
 
 # Initialize the memory
 chroma = ChromaDB(

playground/demos/ai_acceleerated_learning/Podgraph .py

@@ -0,0 +1,59 @@
+def test_create_graph():
+    """
+    Tests that a graph can be created.
+    """
+    graph = create_graph()
+    assert isinstance(graph, dict)
+
+
+def test_weight_edges():
+    """
+    Tests that the edges of a graph can be weighted.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    for edge in graph.edges:
+        assert isinstance(edge.weight, int)
+
+
+def test_create_user_list():
+    """
+    Tests that a list of all the podcasts that the user has listened to can be created.
+    """
+    user_list = create_user_list()
+    assert isinstance(user_list, list)
+
+
+def test_find_most_similar_podcasts():
+    """
+    Tests that the most similar podcasts to a given podcast can be found.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    most_similar_podcasts = find_most_similar_podcasts(
+        graph, user_list
+    )
+    assert isinstance(most_similar_podcasts, list)
+
+
+def test_add_most_similar_podcasts():
+    """
+    Tests that the most similar podcasts to a given podcast can be added to the user's list.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    add_most_similar_podcasts(graph, user_list)
+    assert len(user_list) > 0
+
+
+def test_repeat_steps():
+    """
+    Tests that steps 5-6 can be repeated until the user's list contains the desired number of podcasts.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    repeat_steps(graph, user_list)
+    assert len(user_list) == 10

playground/demos/ai_acceleerated_learning/main.py

@@ -1,13 +1,12 @@
 import concurrent
 import csv
-
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat
 from swarms.memory import ChromaDB
+from dotenv import load_dotenv
+from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.file_processing import create_file
 from swarms.utils.loguru_logger import logger
-from swarms.utils.parse_code import extract_code_from_markdown
+
 
 # Load ENV
 load_dotenv()
@@ -70,7 +69,7 @@ def extract_and_create_agents(
     """
     try:
         agents = []
-        with open(csv_file_path, encoding="utf-8") as file:
+        with open(csv_file_path, mode="r", encoding="utf-8") as file:
             reader = csv.DictReader(file)
             for row in reader:
                 project_name = row[target_columns[0]]

playground/demos/assembly/assembly_example.py

@@ -11,7 +11,7 @@ task = (
 )
 img = "assembly_line.jpg"
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops=1,

playground/demos/autoswarm/autoswarm.py

@@ -1,10 +1,8 @@
 import os
-
 from dotenv import load_dotenv
-
-import swarms.prompts.autoswarm as sdsp
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
+import swarms.prompts.autoswarm as sdsp
 
 # Load environment variables and initialize the OpenAI Chat model
 load_dotenv()

playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py

@@ -9,7 +9,7 @@ llm = GPT4VisionAPI()
 task = "What is the color of the object?"
 img = "images/swarms.jpeg"
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     sop=MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,

playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py

@@ -6,7 +6,7 @@ llm = GPT4VisionAPI()
 task = "What is the color of the object?"
 img = "images/swarms.jpeg"
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops="auto",

playground/demos/multi_modal_chain_of_thought/vcot_example.py

@@ -22,7 +22,7 @@ llm = GPT4VisionAPI(
 task = "This is an eye test. What do you see?"
 img = "playground/demos/multi_modal_chain_of_thought/eyetest.jpg"
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops=2,

playground/demos/swarm_hackathon/Bants.py

@@ -1,6 +1,5 @@
 # Import the necessary libraries.
 import asyncio
-
 import websockets
 
 # Create a list of public group chats.

playground/demos/swarm_hackathon/Human voice.py

@@ -1,5 +1,5 @@
 import discord
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Discord Bot Setup
 client = discord.Client()

playground/demos/swarm_hackathon/OpenMind.bot.py

@@ -1,10 +1,10 @@
 # OpenMind.bot streamlines social interactions between personalized bots, representing users, media, and influencers, ensuring meaningful exchanges. It eliminates misunderstandings by using context-aware conversations, followed by summaries or audio recaps of these interactions for efficient communication.
 
-import datetime
 import json
-
+import datetime
 import pytz
-from flask import Flask, jsonify, request
+
+from flask import Flask, request, jsonify
 
 app = Flask(__name__)
 
@@ -28,7 +28,7 @@ def create_conversation():
 @app.route("/api/v1/conversations/<conversation_id>", methods=["GET"])
 def get_conversation(conversation_id):
     # Get the conversation from the database
-    with open("conversations.json") as f:
+    with open("conversations.json", "r") as f:
         conversation = json.load(f)
 
     # Return the conversation
@@ -49,7 +49,7 @@ def create_message(conversation_id):
     }
 
     # Get the conversation from the database
-    with open("conversations.json") as f:
+    with open("conversations.json", "r") as f:
         conversation = json.load(f)
 
     # Add the message to the conversation
@@ -68,7 +68,7 @@ def create_message(conversation_id):
 )
 def get_messages(conversation_id):
     # Get the conversation from the database
-    with open("conversations.json") as f:
+    with open("conversations.json", "r") as f:
         conversation = json.load(f)
 
     # Return the messages
@@ -80,7 +80,7 @@ def get_messages(conversation_id):
 )
 def get_summary(conversation_id):
     # Get the conversation from the database
-    with open("conversations.json") as f:
+    with open("conversations.json", "r") as f:
         conversation = json.load(f)
 
     # Create a summary of the conversation
@@ -98,7 +98,7 @@ def get_summary(conversation_id):
 )
 def get_audio_recap(conversation_id):
     # Get the conversation from the database
-    with open("conversations.json") as f:
+    with open("conversations.json", "r") as f:
         conversation = json.load(f)
 
     # Create an audio recap of the conversation

playground/demos/swarm_hackathon/main.py

@@ -1,14 +1,12 @@
 import concurrent
 import csv
 import os
-
-from dotenv import load_dotenv
-
-from swarms import Agent, Gemini
+from swarms import Gemini, Agent
 from swarms.memory import ChromaDB
+from dotenv import load_dotenv
+from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.file_processing import create_file
 from swarms.utils.loguru_logger import logger
-from swarms.utils.parse_code import extract_code_from_markdown
 
 # Load ENV
 load_dotenv()
@@ -73,7 +71,7 @@ def extract_and_create_agents(
     - target_columns: A list of column names to extract values from.
     """
     agents = []
-    with open(csv_file_path, encoding="utf-8") as file:
+    with open(csv_file_path, mode="r", encoding="utf-8") as file:
         reader = csv.DictReader(file)
         for row in reader:
             project_name = row[target_columns[0]]

playground/demos/visuo/text_to_sql_agent_example.py

@@ -19,7 +19,7 @@ llm = HuggingfaceLLM(
     temperature=0.5,
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops="auto",

playground/examples/example_agent.py

@@ -4,7 +4,7 @@ import sys
 from dotenv import load_dotenv
 
 # Import the OpenAIChat model and the Agent struct
-from swarms import Agent, OpenAIChat
+from swarms import OpenAIChat, Agent
 
 # Load the environment variables
 load_dotenv()
@@ -26,7 +26,7 @@ print(
     f" {sys.stderr}"
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
 
 # Run the workflow on a task

playground/examples/example_concurrentworkflow.py

@@ -1,8 +1,6 @@
 import os
-
 from dotenv import load_dotenv
-
-from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
+from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
 
 # Load environment variables from .env file
 load_dotenv()

playground/examples/example_huggingfacellm.py

@@ -1,6 +1,5 @@
-import torch
-
 from swarms.models import HuggingfaceLLM
+import torch
 
 try:
     inference = HuggingfaceLLM(

playground/examples/example_logistics.py

@@ -1,18 +1,16 @@
+from swarms.structs import Agent
 import os
-
 from dotenv import load_dotenv
-
 from swarms.models import GPT4VisionAPI
 from swarms.prompts.logistics import (
-    Efficiency_Agent_Prompt,
     Health_Security_Agent_Prompt,
-    Productivity_Agent_Prompt,
     Quality_Control_Agent_Prompt,
+    Productivity_Agent_Prompt,
     Safety_Agent_Prompt,
     Security_Agent_Prompt,
     Sustainability_Agent_Prompt,
+    Efficiency_Agent_Prompt,
 )
-from swarms.structs import Agent
 
 # Load ENV
 load_dotenv()

playground/examples/example_recursiveworkflow.py

@@ -1,8 +1,6 @@
 import os
-
 from dotenv import load_dotenv
-
-from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
+from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
 
 # Load environment variables from .env file
 load_dotenv()

playground/examples/example_sequentialworkflow.py

@@ -1,9 +1,7 @@
 import os
-
+from swarms import OpenAIChat, Agent, SequentialWorkflow
 from dotenv import load_dotenv
 
-from swarms import Agent, OpenAIChat, SequentialWorkflow
-
 load_dotenv()
 
 # Load the environment variables

playground/examples/example_simple_conversation_agent.py

@@ -3,8 +3,8 @@ import os
 from dotenv import load_dotenv
 
 from swarms import (
-    Conversation,
     OpenAIChat,
+    Conversation,
 )
 
 conv = Conversation(

playground/examples/example_swarmnetwork.py

@@ -3,7 +3,7 @@ import os
 from dotenv import load_dotenv
 
 # Import the OpenAIChat model and the Agent struct
-from swarms import Agent, OpenAIChat, SwarmNetwork
+from swarms import OpenAIChat, Agent, SwarmNetwork
 
 # Load the environment variables
 load_dotenv()
@@ -17,7 +17,7 @@ llm = OpenAIChat(
     openai_api_key=api_key,
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(llm=llm, max_loops=1, agent_name="Social Media Manager")
 agent2 = Agent(llm=llm, max_loops=1, agent_name=" Product Manager")
 agent3 = Agent(llm=llm, max_loops=1, agent_name="SEO Manager")

playground/examples/example_toolagent.py

@@ -1,6 +1,5 @@
 # Import necessary libraries
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 from swarms import ToolAgent
 
 # Load the pre-trained model and tokenizer

playground/examples/example_worker.py

@@ -1,9 +1,7 @@
 # Importing necessary modules
 import os
-
 from dotenv import load_dotenv
-
-from swarms import OpenAIChat, Worker, tool
+from swarms import Worker, OpenAIChat, tool
 
 # Loading environment variables from .env file
 load_dotenv()

playground/models/azure_openai_example.py

@@ -1,7 +1,5 @@
 import os
-
 from dotenv import load_dotenv
-
 from swarms import AzureOpenAI
 
 # Load the environment variables

playground/models/custom_model_vllm.py

@@ -1,5 +1,4 @@
 from vllm import LLM
-
 from swarms import AbstractLLM, Agent, ChromaDB
 
 

playground/structs/agent_basic_customize.py

@@ -11,7 +11,7 @@ llm = OpenAIChat(
     # max_tokens=100,
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops=2,

playground/structs/agent_with_longterm_memory.py

@@ -26,7 +26,7 @@ llm = OpenAIChat(
     max_tokens=1000,
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops=4,

playground/structs/agent_with_tools_example.py

@@ -66,7 +66,7 @@ llm = OpenAIChat(
 )
 
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     agent_name="Research Agent",
     llm=llm,

playground/structs/autoscaler_example.py

@@ -20,7 +20,7 @@ llm = OpenAIChat(
 )
 
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(llm=llm, max_loops=1, dashboard=True)
 
 

playground/structs/basic_agent_with_azure_openai.py

@@ -1,6 +1,6 @@
 from swarms import Agent, AzureOpenAI
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=AzureOpenAI(),
     max_loops="auto",

playground/structs/custom_model_with_agent.py

@@ -10,7 +10,7 @@ class ExampleLLM(AbstractLLM):
         pass
 
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=ExampleLLM(),
     max_loops="auto",

playground/structs/easy_example.py

@@ -1,6 +1,6 @@
 from swarms import Agent, OpenAIChat
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=OpenAIChat(),
     max_loops=1,

playground/structs/hierarchical_swarm.py

@@ -1,8 +1,7 @@
 import os
-
+from swarms import OpenAIChat, Agent
 from dotenv import load_dotenv
 
-from swarms import Agent, OpenAIChat
 
 # Load environment variables
 load_dotenv()

playground/structs/majority_voting.py

@@ -1,4 +1,4 @@
-from swarms import Agent, Anthropic, ChromaDB, MajorityVoting
+from swarms import Agent, MajorityVoting, ChromaDB, Anthropic
 
 # Initialize the llm
 llm = Anthropic()

playground/structs/message_pool.py

@@ -1,6 +1,7 @@
+from swarms.structs.message_pool import MessagePool
 from swarms import Agent, OpenAIChat
 from swarms.memory.chroma_db import ChromaDB
-from swarms.structs.message_pool import MessagePool
+
 
 # Agents
 agent1 = Agent(

playground/structs/multi_process_workflow.py

@@ -1,9 +1,7 @@
 import os
-
-from dotenv import load_dotenv
-
-from swarms import Agent, Gemini
+from swarms import Gemini, Agent
 from swarms.structs.multi_process_workflow import MultiProcessWorkflow
+from dotenv import load_dotenv
 
 # Load the environment variables
 load_dotenv()

playground/structs/swarm_network_example.py

@@ -1,16 +1,14 @@
 # Import the OpenAIChat model and the Agent struct
 import os
-
-from dotenv import load_dotenv
-
 from swarms import (
     Agent,
-    Anthropic,
     OpenAIChat,
     SwarmNetwork,
+    Anthropic,
     TogetherLLM,
 )
 from swarms.memory import ChromaDB
+from dotenv import load_dotenv
 
 # load the environment variables
 load_dotenv()
@@ -31,7 +29,7 @@ together_llm = TogetherLLM(
     together_api_key=os.getenv("TOGETHER_API_KEY"), max_tokens=3000
 )
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=anthropic,
     max_loops=1,

playground/structs/tool_agent.py

@@ -1,5 +1,4 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 from swarms import ToolAgent
 
 # Load the pre-trained model and tokenizer

playground/swarms/automate_docs.py

@@ -1,21 +1,21 @@
-import concurrent
 import inspect
 import os
 import threading
 from typing import Callable, List
 
-from swarms import Agent, OpenAIChat
 from swarms.prompts.documentation import DOCUMENTATION_WRITER_SOP
+from swarms import Agent, OpenAIChat
+from swarms.utils.loguru_logger import logger
+import concurrent
 
 #########
 from swarms.utils.file_processing import (
-    create_file_in_folder,
     load_json,
     sanitize_file_path,
-    zip_folders,
     zip_workspace,
+    create_file_in_folder,
+    zip_folders,
 )
-from swarms.utils.loguru_logger import logger
 
 
 class PythonDocumentationSwarm:

playground/swarms/hierarchical_swarm.py

@@ -3,9 +3,7 @@ Boss selects what agent to use
 B -> W1, W2, W3
 """
 from typing import List, Optional
-
 from pydantic import BaseModel, Field
-
 from swarms.utils.json_utils import str_to_json
 
 

playground/tools/agent_with_tools_example.py

@@ -26,7 +26,7 @@ def search_api(query: str) -> str:
     print(f"Searching API for {query}")
 
 
-# Initialize the workflow
+## Initialize the workflow
 agent = Agent(
     llm=llm,
     max_loops=5,

playground/youtube/tool.py

@@ -0,0 +1,57 @@
+from swarms import Agent, Anthropic, tool
+
+# Model
+llm = Anthropic(
+    temperature=0.1,
+)
+
+"""
+How to create tools:
+
+1. Define a function that takes the required arguments with documentation and type hints.
+2. Add the `@tool` decorator to the function.
+3. Add the function to the `tools` list in the `Agent` class.
+"""
+
+
+# Tools
+# Browser tools
+@tool
+def browser(query: str):
+    """
+    Opens a web browser and searches for the given query on Google.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: A message indicating that the search is being performed.
+    """
+    import webbrowser
+
+    url = f"https://www.google.com/search?q={query}"
+    webbrowser.open(url)
+    return f"Searching for {query} in the browser."
+
+
+# Agent
+agent = Agent(
+    agent_name="Devin",
+    system_prompt=(
+        "Autonomous agent that can interact with humans and other"
+        " agents. Be Helpful and Kind. Use the tools provided to"
+        " assist the user. Return all code in markdown format."
+    ),
+    llm=llm,
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    stopping_token="<DONE>",
+    interactive=True,
+    tools=[browser],
+)
+
+# Run the agent
+out = agent.run("what's the weather in Miami?")
+print(out)

scripts/auto_tests_docs/auto_docs.py

@@ -1,4 +1,4 @@
-# VERISON2
+###### VERISON2
 import inspect
 import os
 import threading

scripts/log_cleanup.py

@@ -2,8 +2,8 @@ import os
 import shutil
 
 # Create a new directory for the log files if it doesn't exist
-if not os.path.exists("artifacts"):
-    os.makedirs("artifacts")
+if not os.path.exists("artifacts_two"):
+    os.makedirs("artifacts_two")
 
 # Walk through the current directory
 for dirpath, dirnames, filenames in os.walk("."):
@@ -12,10 +12,10 @@ for dirpath, dirnames, filenames in os.walk("."):
         if filename.endswith(".log"):
             # Construct the full file path
             file_path = os.path.join(dirpath, filename)
-            # Move the log file to the 'artifacts' directory
-            shutil.move(file_path, "artifacts")
+            # Move the log file to the 'artifacts_two' directory
+            shutil.move(file_path, "artifacts_two")
 
 print(
-    "Moved all log files into the 'artifacts' directory and deleted"
-    " their original location."
+    "Moved all log files into the 'artifacts_two' directory and"
+    " deleted their original location."
 )

scripts/log_cleanup.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Create the new directory if it doesn't exist
+sudo mkdir -p /artifacts_logs
+
+# Find all .log files in the root directory and its subdirectories
+find / -name "*.log" -print0 | while IFS= read -r -d '' file; do
+    # Use sudo to move the file to the new directory
+    sudo mv "$file" /artifacts_logs/
+done

scripts/run_examples.sh

@@ -9,7 +9,7 @@ for f in /swarms/playground/examples/example_*.py; do
         echo "Skipping ${f} as it ran successfully in a previous run."
     else
         # Run the script if not previously successful
-        if python "$f" 2>>errors.txt; then
+        if /home/kye/miniconda3/envs/swarms/bin/python "$f" 2>>errors.txt; then
             echo "(${f}) ran successfully without errors."
             # Log the successful script execution
             echo "$f" >> "$SUCCESS_LOG"

swarms/agents/base.py

@@ -1,5 +1,5 @@
 from abc import abstractmethod
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Union, Optional
 
 
 class AbstractAgent:

swarms/agents/tool_agent.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, Optional
+from typing import Any, Optional, Callable
 
 from swarms.structs.agent import Agent
 from swarms.tools.format_tools import Jsonformer

swarms/memory/chroma_db.py

@@ -7,9 +7,9 @@ import chromadb
 import numpy as np
 from dotenv import load_dotenv
 
-from swarms.memory.base_vectordb import AbstractVectorDatabase
 from swarms.utils.data_to_text import data_to_text
 from swarms.utils.markdown_message import display_markdown_message
+from swarms.memory.base_vectordb import AbstractVectorDatabase
 
 # Load environment variables
 load_dotenv()

swarms/memory/lanchain_chroma.py

@@ -6,9 +6,8 @@ from langchain.chains.question_answering import load_qa_chain
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import Chroma
-
-from swarms.memory.base_vectordb import AbstractVectorDatabase
 from swarms.models.popular_llms import OpenAIChat
+from swarms.memory.base_vectordb import AbstractVectorDatabase
 
 
 def synchronized_mem(method):

swarms/memory/pg.py

@@ -5,7 +5,6 @@ from sqlalchemy import JSON, Column, String, create_engine
 from sqlalchemy.dialects.postgresql import UUID
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm import Session
-
 from swarms.memory.base_vectordb import AbstractVectorDatabase
 
 

swarms/memory/qdrant.py

@@ -1,7 +1,6 @@
 from typing import List
 
 from httpx import RequestError
-
 from swarms.memory.base_vectordb import AbstractVectorDatabase
 
 try:

swarms/models/__init__.py

@@ -23,7 +23,6 @@ from swarms.models.popular_llms import (
 from swarms.models.popular_llms import (
     CohereChat as Cohere,
 )
-from swarms.models.popular_llms import OctoAIChat
 from swarms.models.popular_llms import (
     OpenAIChatLLM as OpenAIChat,
 )
@@ -33,7 +32,9 @@ from swarms.models.popular_llms import (
 from swarms.models.popular_llms import (
     ReplicateLLM as Replicate,
 )
+from swarms.models.popular_llms import OctoAIChat
 from swarms.models.qwen import QwenVLMultiModal  # noqa: E402
+
 from swarms.models.sampling_params import SamplingParams, SamplingType
 from swarms.models.together import TogetherLLM  # noqa: E402
 from swarms.models.types import (  # noqa: E402
@@ -45,6 +46,7 @@ from swarms.models.types import (  # noqa: E402
 )
 from swarms.models.vilt import Vilt  # noqa: E402
 
+
 __all__ = [
     "AbstractLLM",
     "Anthropic",

swarms/models/base_embedding_model.py

@@ -2,10 +2,9 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Callable
 
 import numpy as np
-
+from typing import Callable
 from swarms.artifacts.text_artifact import TextArtifact
 from swarms.utils.exponential_backoff import ExponentialBackoffMixin
 

swarms/models/open_router.py

@@ -0,0 +1,75 @@
+from swarms.models.base_llm import AbstractLLM
+from pydantic import BaseModel
+from typing import List, Dict
+import openai
+
+
+class OpenRouterRequest(BaseModel):
+    model: str
+    messages: List[Dict[str, str]] = []
+
+
+class OpenRouterChat(AbstractLLM):
+    """
+    A class representing an OpenRouter chat model.
+
+    Args:
+        model_name (str): The name of the OpenRouter model.
+        base_url (str, optional): The base URL for the OpenRouter API. Defaults to "https://openrouter.ai/api/v1/chat/completions".
+        openrouter_api_key (str, optional): The API key for accessing the OpenRouter API. Defaults to None.
+        system_prompt (str, optional): The system prompt for the chat model. Defaults to None.
+        *args: Variable length argument list.
+        **kwargs: Arbitrary keyword arguments.
+
+    Attributes:
+        model_name (str): The name of the OpenRouter model.
+        base_url (str): The base URL for the OpenRouter API.
+        openrouter_api_key (str): The API key for accessing the OpenRouter API.
+        system_prompt (str): The system prompt for the chat model.
+
+    Methods:
+        run(task, *args, **kwargs): Runs the chat model with the given task.
+
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        base_url: str = "https://openrouter.ai/api/v1/chat/completions",
+        openrouter_api_key: str = None,
+        system_prompt: str = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.model_name = model_name
+        self.base_url = base_url
+        self.openrouter_api_key = openrouter_api_key
+        self.system_prompt = system_prompt
+
+        openai.api_base = "https://openrouter.ai/api/v1"
+        openai.api_key = openrouter_api_key
+
+    def run(self, task: str, *args, **kwargs) -> str:
+        """
+        Runs the chat model with the given task.
+
+        Args:
+            task (str): The user's task for the chat model.
+            *args: Variable length argument list.
+            **kwargs: Arbitrary keyword arguments.
+
+        Returns:
+            str: The response generated by the chat model.
+
+        """
+        response = openai.ChatCompletion.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": self.system_prompt},
+                {"role": "user", "content": task},
+            ]
+            * args,
+            **kwargs,
+        )
+        return response.choices[0].message.text

swarms/models/openai_embeddings.py

@@ -5,7 +5,7 @@ import warnings
 from typing import Any, Callable, Literal, Sequence
 
 import numpy as np
-from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic import model_validator, ConfigDict, BaseModel, Field
 from tenacity import (
     AsyncRetrying,
     before_sleep_log,

swarms/models/palm.py

@@ -8,7 +8,6 @@ from langchain.llms import BaseLLM
 from langchain.pydantic_v1 import BaseModel
 from langchain.schema import Generation, LLMResult
 from langchain.utils import get_from_dict_or_env
-from pydantic import model_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -16,6 +15,7 @@ from tenacity import (
     stop_after_attempt,
     wait_exponential,
 )
+from pydantic import model_validator
 
 logger = logging.getLogger(__name__)
 

swarms/prompts/tools.py

@@ -34,7 +34,7 @@ commands: {
 """
 
 
-# FEW SHOT EXAMPLES ################
+########### FEW SHOT EXAMPLES ################
 SCENARIOS = """
 commands: {
     "tools": {

swarms/prompts/worker_prompt.py

@@ -1,6 +1,8 @@
 import datetime
-
 from pydantic import BaseModel, Field
+from swarms.tools.tool import BaseTool
+from swarms.tools.tool_utils import scrape_tool_func_docs
+from typing import List
 
 time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
@@ -24,54 +26,202 @@ class ResponseFormat(BaseModel):
 response_json = ResponseFormat.model_json_schema()
 
 
-def worker_tools_sop_promp(name: str, memory: str, time=time):
-    out = f"""
-    You are {name},
-    Your decisions must always be made independently without seeking user assistance.
-    Play to your strengths as an LLM and pursue simple strategies with no legal complications.
-    If you have completed all your tasks, make sure to use the 'finish' command.
+tool_usage_browser = """
+
+```json
+{
+  "thoughts": {
+    "text": "To check the weather in Miami, I will use the browser tool to search for 'Miami weather'.",
+    "reasoning": "The browser tool allows me to search the web, so I can look up the current weather conditions in Miami.", 
+    "plan": "Use the browser tool to search Google for 'Miami weather'. Parse the result to get the current temperature, conditions, etc. and format that into a readable weather report."
+  },
+  "command": {
+    "name": "browser", 
+    "args": {
+      "query": "Miami weather"
+    }
+  }
+}
+```
 
-    GOALS:
+"""
 
-    1. Hello, how are you? Create an image of how you are doing!
+tool_usage_terminal = """
+
+```json
+{
+  "thoughts": {
+    "text": "To check the weather in Miami, I will use the browser tool to search for 'Miami weather'.",
+    "reasoning": "The browser tool allows me to search the web, so I can look up the current weather conditions in Miami.", 
+    "plan": "Use the browser tool to search Google for 'Miami weather'. Parse the result to get the current temperature, conditions, etc. and format that into a readable weather report."
+  },
+  "command": {
+    "name": "terminal", 
+    "args": {
+      "code": "uptime"
+    }
+  }
+}
+```
 
-    Constraints:
+"""
 
-    1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
-    2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
-    3. No user assistance
-    4. Exclusively use the commands listed in double quotes e.g. 'command name'
 
-    Commands:
+browser_and_terminal_tool = """
+```
+{
+  "thoughts": {
+    "text": "To analyze the latest stock market trends, I need to fetch current stock data and then process it using a script.",
+    "reasoning": "Using the browser tool to retrieve stock data ensures I have the most recent information. Following this, the terminal tool can run a script that analyzes this data to identify trends.",
+    "plan": "First, use the browser to get the latest stock prices. Then, use the terminal to execute a data analysis script on the fetched data."
+  },
+  "commands": [
+    {
+      "name": "browser",
+      "args": {
+        "query": "download latest stock data for NASDAQ"
+      }
+    },
+    {
+      "name": "terminal",
+      "args": {
+        "cmd": "python analyze_stocks.py"
+      }
+    }
+  ]
+}
+```
 
-    1. finish: use this to signal that you have finished all your objectives, args: 'response': 'final response to let people know you have finished your objectives'
+"""
 
-    Resources:
 
-    1. Internet access for searches and information gathering.
-    2. Long Term memory management.
-    3. Agents for delegation of simple tasks.
-    4. File output.
+browser_and_terminal_tool_two = """
+```
+{
+  "thoughts": {
+    "text": "To prepare a monthly budget report, I need current expenditure data, process it, and calculate the totals and averages.",
+    "reasoning": "The browser will fetch the latest expenditure data. The terminal will run a processing script to organize the data, and the calculator will be used to sum up expenses and compute averages.",
+    "plan": "Download the data using the browser, process it with a terminal command, and then calculate totals and averages using the calculator."
+  },
+  "commands": [
+    {
+      "name": "browser",
+      "args": {
+        "query": "download monthly expenditure data"
+      }
+    },
+    {
+      "name": "terminal",
+      "args": {
+        "cmd": "python process_expenditures.py"
+      }
+    },
+    {
+      "name": "calculator",
+      "args": {
+        "operation": "sum",
+        "numbers": "[output_from_process_expenditures]"
+      }
+    }
+  ]
+}
 
-    Performance Evaluation:
+```
 
-    1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
-    2. Constructively self-criticize your big-picture behavior constantly.
-    3. Reflect on past decisions and strategies to refine your approach.
-    4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
+"""
 
-    You should only respond in JSON format as described below Response Format, you will respond only in markdown format within 6 backticks. The JSON will be in markdown format.
 
+# Function to parse tools and get their documentation
+def parse_tools(tools: List[BaseTool] = []):
+    tool_docs = []
+    for tool in tools:
+        tool_doc = scrape_tool_func_docs(tool)
+        tool_docs.append(tool_doc)
+    return tool_docs
+
+
+# Function to generate the worker prompt
+def tool_usage_worker_prompt(
+    current_time=time, tools: List[BaseTool] = []
+):
+    tool_docs = parse_tools(tools)
+
+    prompt = f"""
+    **Date and Time**: {current_time}
+
+    ### Constraints
+    - Only use the tools as specified in the instructions.
+    - Follow the command format strictly to avoid errors and ensure consistency.
+    - Only use the tools for the intended purpose as described in the SOP.
+    - Document your thoughts, reasoning, and plan before executing the command.
+    - Provide the output in JSON format within markdown code blocks.
+    - Review the output to ensure it matches the expected outcome.
+    - Only follow the instructions provided in the SOP and do not deviate from the specified tasks unless tool usage is not required.
+    
+    ### Performance Evaluation
+    - **Efficiency**: Use tools to complete tasks with minimal steps.
+    - **Accuracy**: Ensure that commands are executed correctly to achieve the desired outcome.
+    - **Adaptability**: Be ready to adjust the use of tools based on task requirements and feedback.
+
+    ### Tool Commands
+    1. **Browser**
+       - **Purpose**: To retrieve information from the internet.
+       - **Usage**:
+         - `{{"name": "browser", "args": {{"query": "search query here"}}}}`
+         - Example: Fetch current weather in London.
+         - Command: `{{"name": "browser", "args": {{"query": "London weather"}}}}`
+         
+    2. **Terminal**
+       - **Purpose**: To execute system commands.
+       - **Usage**:
+         - `{{"name": "terminal", "args": {{"cmd": "system command here"}}}}`
+         - Example: Check disk usage on a server.
+         - Command: `{{"name": "terminal", "args": {{"cmd": "df -h"}}}}`
+         
+    3. **Custom Tool** (if applicable)
+       - **Purpose**: Describe specific functionality.
+       - **Usage**:
+         - `{{"name": "custom_tool", "args": {{"parameter": "value"}}}}`
+         - Example: Custom analytics tool.
+         - Command: `{{"name": "custom_tool", "args": {{"data": "analyze this data"}}}}`
+
+
+    ### Usage Examples
+    - **Example 1**: Retrieving Weather Information
+      ```json
+      {tool_usage_browser}
       ```
-    {response_json}
+      
+    - **Example 2**: System Check via Terminal
+      ```json
+      {tool_usage_terminal}
+      ```
+      
+    - **Example 3**: Combined Browser and Terminal Usage
+      ```json
+      {browser_and_terminal_tool}
+      ```
+      
+    - **Example 4**: Combined Browser, Terminal, and Calculator Usage
+        ```json
+        {browser_and_terminal_tool_two}
         ```
         
-    Ensure the response can be parsed by Python json.loads
-    System: The current time and date is {time}
-    System: This reminds you of these events from your past:
-    [{memory}]
     
-    Human: Determine which next command to use, and respond using the format specified above:
+
+    ### Next Steps
+    - Determine the appropriate tool for the task at hand.
+    - Format your command according to the examples provided.
+    - Execute the command and evaluate the results based on the expected outcome.
+    - Document any issues or challenges faced during the tool usage.
+    - Always output the results in the specified format: JSON in markdown code blocks.
+    
+    
+    ###### Tools Available
+    
+    {tool_docs}
+    
+    This SOP is designed to guide you through the structured and effective use of tools. By adhering to this protocol, you will enhance your productivity and accuracy in task execution.
     """
 
-    return str(out)
+    return prompt

swarms/structs/__init__.py

@@ -4,7 +4,6 @@ from swarms.structs.agent_process import (
     AgentProcess,
     AgentProcessQueue,
 )
-from swarms.structs.agent_rearrange import AgentRearrange
 from swarms.structs.auto_swarm import AutoSwarm, AutoSwarmRouter
 from swarms.structs.autoscaler import AutoScaler
 from swarms.structs.base import BaseStructure
@@ -79,6 +78,8 @@ from swarms.structs.utils import (
     find_token_in_text,
     parse_tasks,
 )
+from swarms.structs.agent_rearrange import AgentRearrange
+
 
 __all__ = [
     "Agent",

swarms/structs/agent.py

@@ -6,7 +6,7 @@ import random
 import sys
 import time
 import uuid
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import yaml
 from loguru import logger
@@ -17,16 +17,15 @@ from swarms.prompts.agent_system_prompts import AGENT_SYSTEM_PROMPT_3
 from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
     MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
 )
-from swarms.prompts.worker_prompt import worker_tools_sop_promp
 from swarms.structs.conversation import Conversation
-from swarms.tools.code_executor import CodeExecutor
-from swarms.tools.exec_tool import execute_tool_by_name
-from swarms.tools.function_util import process_tool_docs
 from swarms.tools.tool import BaseTool
 from swarms.utils.code_interpreter import SubprocessCodeInterpreter
 from swarms.utils.data_to_text import data_to_text
 from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.pdf_to_text import pdf_to_text
+from swarms.tools.exec_tool import execute_tool_by_name
+from swarms.tools.code_executor import CodeExecutor
+from swarms.prompts.worker_prompt import tool_usage_worker_prompt
 
 
 # Utils
@@ -172,7 +171,7 @@ class Agent:
         agent_name: str = "swarm-worker-01",
         agent_description: str = None,
         system_prompt: str = AGENT_SYSTEM_PROMPT_3,
-        tools: Union[List[BaseTool]] = None,
+        tools: List[BaseTool] = [],
         dynamic_temperature_enabled: Optional[bool] = False,
         sop: Optional[str] = None,
         sop_list: Optional[List[str]] = None,
@@ -210,6 +209,7 @@ class Agent:
         custom_exit_command: Optional[str] = "exit",
         sentiment_analyzer: Optional[Callable] = None,
         limit_tokens_from_string: Optional[Callable] = None,
+        custom_tools_prompt: Optional[Callable] = None,
         *args,
         **kwargs,
     ):
@@ -318,21 +318,21 @@ class Agent:
 
         # If tools are provided then set the tool prompt by adding to sop
         if self.tools:
-            tools_prompt = worker_tools_sop_promp(
-                name=self.agent_name,
-                memory=self.short_memory.return_history_as_string(),
-            )
+            if custom_tools_prompt is not None:
+                tools_prompt = custom_tools_prompt(tools=self.tools)
 
-            # Append the tools prompt to the short_term_memory
                 self.short_memory.add(
                     role=self.agent_name, content=tools_prompt
                 )
 
-            # And, add the tool documentation to the memory
-            for tool in self.tools:
-                tool_docs = process_tool_docs(tool)
+            else:
+                tools_prompt = tool_usage_worker_prompt(
+                    tools=self.tools
+                )
+
+                # Append the tools prompt to the short_term_memory
                 self.short_memory.add(
-                    role=self.agent_name, content=tool_docs
+                    role=self.agent_name, content=tools_prompt
                 )
 
         # If the long term memory is provided then set the long term memory prompt
@@ -614,7 +614,7 @@ class Agent:
                             else (task_prompt, img, *args)
                         )
                         response = self.llm(*response_args, **kwargs)
-                        print(response)
+                        # print(response)
                         self.short_memory.add(
                             role=self.agent_name, content=response
                         )
@@ -696,6 +696,11 @@ class Agent:
                                 content=sentiment,
                             )
 
+                        if self.streaming:
+                            self.streaming(response)
+                        else:
+                            print(response)
+
                         success = True  # Mark as successful to exit the retry loop
 
                     except Exception as e:
@@ -713,10 +718,11 @@ class Agent:
                     break  # Exit the loop if all retry attempts fail
 
                 # Check stopping conditions
+                if self.stopping_token is not None:
                     if self.stopping_token in response:
                         break
                 elif (
-                    self.stopping_condition
+                    self.stopping_condition is not None
                     and self._check_stopping_condition(response)
                 ):
                     break

swarms/structs/agent_process.py

@@ -1,10 +1,10 @@
 from datetime import datetime
-from typing import Callable
 
 from pydantic import BaseModel
 
 from swarms.structs.omni_agent_types import agents
 from swarms.utils.loguru_logger import logger
+from typing import Callable
 
 
 class AgentProcess(BaseModel):

swarms/structs/agent_rearrange.py

@@ -1,7 +1,6 @@
 import logging
 from collections import defaultdict
 from typing import Callable, Sequence
-
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 

swarms/structs/base_swarm.py

@@ -15,8 +15,8 @@ import yaml
 
 from swarms.structs.agent import Agent
 from swarms.structs.conversation import Conversation
-from swarms.structs.omni_agent_types import agent
 from swarms.utils.loguru_logger import logger
+from swarms.structs.omni_agent_types import agent
 
 
 class BaseSwarm(ABC):
@@ -102,8 +102,8 @@ class BaseSwarm(ABC):
 
         # Handle the case where the agents are not provided
         # Handle agents
-        for agent_instance in self.agents:
-            if not isinstance(agent_instance, Agent):
+        for agent in self.agents:
+            if not isinstance(agent, Agent):
                 raise TypeError("Agents must be of type Agent.")
 
         if self.agents is None:
@@ -392,26 +392,26 @@ class BaseSwarm(ABC):
         Returns:
 
         """
-        for agent_instance in self.agents:
-            agent_instance.reset()
+        for agent in self.agents:
+            agent.reset()
 
     def select_agent(self, agent_id: str):
         """
         Select an agent through their id
         """
         # Find agent with id
-        for agent_instance in self.agents:
-            if agent_instance.id == agent_id:
-                return agent_instance
+        for agent in self.agents:
+            if agent.id == agent_id:
+                return agent
 
     def select_agent_by_name(self, agent_name: str):
         """
         Select an agent through their name
         """
         # Find agent with id
-        for agent_instance in self.agents:
-            if agent_instance.name == agent_name:
-                return agent_instance
+        for agent in self.agents:
+            if agent.name == agent_name:
+                return agent
 
     def task_assignment_by_id(
         self, task: str, agent_id: str, *args, **kwargs
@@ -471,8 +471,8 @@ class BaseSwarm(ABC):
             _type_: _description_
         """
         responses = []
-        for agent_instance in self.agents:
-            responses.append(agent_instance(task, *args, **kwargs))
+        for agent in self.agents:
+            responses.append(agent(task, *args, **kwargs))
         return responses
 
     def run_on_all_agents(self, task: str = None, *args, **kwargs):

swarms/structs/conversation.py

@@ -1,11 +1,12 @@
 import datetime
 import json
-from typing import Any, Optional
+from typing import Optional
 
 from termcolor import colored
 
 from swarms.memory.base_db import AbstractDatabase
 from swarms.structs.base import BaseStructure
+from typing import Any
 
 
 class Conversation(BaseStructure):

swarms/structs/graph_workflow.py

@@ -17,8 +17,7 @@ class GraphWorkflow(BaseStructure):
         connect(from_node, to_node): Connects two nodes in the graph.
         set_entry_point(node_name): Sets the entry point node for the workflow.
         add_edge(from_node, to_node): Adds an edge between two nodes in the graph.
-        add_conditional_edges(from_node, condition, edge_dict):
-            Adds conditional edges from a node to multiple nodes based on a condition.
+        add_conditional_edges(from_node, condition, edge_dict): Adds conditional edges from a node to multiple nodes based on a condition.
         run(): Runs the workflow and returns the graph.
 
     Examples:
@@ -127,11 +126,15 @@ class GraphWorkflow(BaseStructure):
         if from_node in self.graph:
             for condition_value, to_node in edge_dict.items():
                 if to_node in self.graph:
-                    self.graph[from_node]["edges"][to_node] = condition
+                    self.graph[from_node]["edges"][
+                        to_node
+                    ] = condition
                 else:
                     raise ValueError("Node does not exist in graph")
         else:
-            raise ValueError(f"Node {from_node} does not exist in graph")
+            raise ValueError(
+                f"Node {from_node} does not exist in graph"
+            )
 
     def run(self):
         """
@@ -157,7 +160,9 @@ class GraphWorkflow(BaseStructure):
             ValueError: _description_
         """
         if node_name not in self.graph:
-            raise ValueError(f"Node {node_name} does not exist in graph")
+            raise ValueError(
+                f"Node {node_name} does not exist in graph"
+            )
 
     def _check_nodes_exist(self, from_node, to_node):
         """

swarms/structs/message_pool.py

@@ -20,7 +20,9 @@ def _hash(input: str):
     return hex_dig
 
 
-def msg_hash(agent: Agent, content: str, turn: int, msg_type: str = "text"):
+def msg_hash(
+    agent: Agent, content: str, turn: int, msg_type: str = "text"
+):
     """
     Generate a hash value for a message.
 
@@ -35,7 +37,8 @@ def msg_hash(agent: Agent, content: str, turn: int, msg_type: str = "text"):
     """
     time = time_ns()
     return _hash(
-        f"agent: {agent.agent_name}\ncontent: {content}\ntimestamp:" f" {str(time)}\nturn: {turn}\nmsg_type: {msg_type}"
+        f"agent: {agent.agent_name}\ncontent: {content}\ntimestamp:"
+        f" {str(time)}\nturn: {turn}\nmsg_type: {msg_type}"
     )
 
 
@@ -64,17 +67,11 @@ class MessagePool:
     >>> message_pool.add(agent=agent2, content="Hello, agent1!", turn=1)
     >>> message_pool.add(agent=agent3, content="Hello, agent1!", turn=1)
     >>> message_pool.get_all_messages()
-    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
+    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
     >>> message_pool.get_visible_messages(agent=agent1, turn=1)
-    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
+    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
     >>> message_pool.get_visible_messages(agent=agent2, turn=1)
-    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True},
-     {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
+    [{'agent': Agent(agent_name='agent1'), 'content': 'Hello, agent2!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent2'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}, {'agent': Agent(agent_name='agent3'), 'content': 'Hello, agent1!', 'turn': 1, 'visible_to': 'all', 'logged': True}]
     """
 
     def __init__(
@@ -101,7 +98,9 @@ class MessagePool:
 
         logger.info("MessagePool initialized")
         logger.info(f"Number of agents: {len(agents)}")
-        logger.info(f"Agents: {[agent.agent_name for agent in agents]}")
+        logger.info(
+            f"Agents: {[agent.agent_name for agent in agents]}"
+        )
         logger.info(f"moderator: {moderator.agent_name} is available")
         logger.info(f"Number of turns: {turns}")
 
@@ -188,11 +187,18 @@ class MessagePool:
             List[Dict]: The list of visible messages.
         """
         # Get the messages before the current turn
-        prev_messages = [message for message in self.messages if message["turn"] < turn]
+        prev_messages = [
+            message
+            for message in self.messages
+            if message["turn"] < turn
+        ]
 
         visible_messages = []
         for message in prev_messages:
-            if message["visible_to"] == "all" or agent.agent_name in message["visible_to"]:
+            if (
+                message["visible_to"] == "all"
+                or agent.agent_name in message["visible_to"]
+            ):
                 visible_messages.append(message)
         return visible_messages
 

swarms/structs/meta_system_prompt.py

@@ -1,24 +1,25 @@
+from swarms.structs.agent import Agent
 from typing import Union
-
-from swarms.models.base_llm import AbstractLLM
 from swarms.models.popular_llms import OpenAIChat
+from swarms.models.base_llm import AbstractLLM
 from swarms.prompts.meta_system_prompt import (
     meta_system_prompt_generator,
 )
-from swarms.structs.agent import Agent
 
-meta_prompter_llm = OpenAIChat(system_prompt=str(meta_system_prompt_generator))
+meta_prompter_llm = OpenAIChat(
+    system_prompt=str(meta_system_prompt_generator)
+)
 
 
-def meta_system_prompt(agent: Union[Agent, AbstractLLM], system_prompt: str) -> str:
+def meta_system_prompt(
+    agent: Union[Agent, AbstractLLM], system_prompt: str
+) -> str:
     """
     Generates a meta system prompt for the given agent using the provided system prompt.
 
     Args:
-        agent (Union[Agent, AbstractLLM]):
-            The agent or LLM (Language Learning Model) for which the meta system prompt is generated.
-        system_prompt (str):
-            The system prompt used to generate the meta system prompt.
+        agent (Union[Agent, AbstractLLM]): The agent or LLM (Language Learning Model) for which the meta system prompt is generated.
+        system_prompt (str): The system prompt used to generate the meta system prompt.
 
     Returns:
         str: The generated meta system prompt.

swarms/structs/omni_agent_types.py

@@ -4,7 +4,6 @@ from typing import (
     Sequence,
     Union,
 )
-
 from swarms.models.base_llm import AbstractLLM
 from swarms.models.base_multimodal_model import BaseMultiModalModel
 from swarms.structs.agent import Agent

swarms/structs/rearrange.py

@@ -1,9 +1,8 @@
 import logging
 from collections import defaultdict
-from typing import Callable, Sequence
-
-from swarms.structs.agent import Agent
 from swarms.utils.loguru_logger import logger
+from swarms.structs.agent import Agent
+from typing import Sequence, Callable
 
 
 class AgentRearrange:

swarms/structs/sermon_swarm.py

@@ -1,5 +1,4 @@
-from typing import Callable, List, Sequence, Union
-
+from typing import Union, Sequence, List, Callable
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 

swarms/structs/step.py

@@ -1,8 +1,7 @@
 from typing import Dict, List, Sequence
 
-from pydantic import BaseModel
-
 from swarms.tools.tool import BaseTool
+from pydantic import BaseModel
 
 
 class Step(BaseModel):

swarms/structs/swarm_net.py

@@ -5,6 +5,7 @@ import threading
 from typing import List, Optional
 
 # from fastapi import FastAPI
+
 from swarms.structs.agent import Agent
 from swarms.structs.base import BaseStructure
 from swarms.utils.logger import logger  # noqa: F401

swarms/structs/team.py

@@ -1,7 +1,7 @@
 import json
 from typing import List, Optional
 
-from pydantic import BaseModel, Field, Json, model_validator
+from pydantic import model_validator, BaseModel, Field, Json
 
 from swarms.structs.agent import Agent
 from swarms.structs.task import Task

swarms/telemetry/__init__.py

@@ -1,5 +1,4 @@
 from swarms.telemetry.log_all import log_all_calls, log_calls
-from swarms.telemetry.sentry_active import activate_sentry
 from swarms.telemetry.sys_info import (
     get_cpu_info,
     get_os_version,
@@ -17,6 +16,7 @@ from swarms.telemetry.user_utils import (
     get_system_info,
     get_user_device_data,
 )
+from swarms.telemetry.sentry_active import activate_sentry
 
 __all__ = [
     "log_all_calls",

swarms/telemetry/auto_upgrade_swarms.py

@@ -1,8 +1,7 @@
 import subprocess
 
-from termcolor import colored
-
 from swarms.telemetry.check_update import check_for_update
+from termcolor import colored
 
 
 def auto_update():

swarms/telemetry/bootup.py

@@ -1,5 +1,5 @@
-import logging
 import os
+import logging
 import warnings
 
 from swarms.telemetry.auto_upgrade_swarms import auto_update

swarms/telemetry/sentry_active.py

@@ -1,7 +1,6 @@
 import os
-
-import sentry_sdk
 from dotenv import load_dotenv
+import sentry_sdk
 
 load_dotenv()
 

swarms/tools/__init__.py

@@ -1,3 +1,4 @@
+from swarms.tools.tool import BaseTool, Tool, StructuredTool, tool
 from swarms.tools.code_executor import CodeExecutor
 from swarms.tools.exec_tool import (
     AgentAction,
@@ -6,7 +7,6 @@ from swarms.tools.exec_tool import (
     execute_tool_by_name,
     preprocess_json_input,
 )
-from swarms.tools.tool import BaseTool, StructuredTool, Tool, tool
 from swarms.tools.tool_utils import (
     execute_tools,
     extract_tool_commands,

swarms/tools/exec_tool.py

@@ -1,5 +1,5 @@
-import concurrent.futures
 import json
+import concurrent.futures
 import re
 from abc import abstractmethod
 from typing import Dict, List, NamedTuple
@@ -9,6 +9,8 @@ from pydantic import ValidationError
 
 from swarms.tools.tool import BaseTool
 
+from swarms.utils.loguru_logger import logger
+
 
 class AgentAction(NamedTuple):
     """Action returned by AgentOutputParser."""
@@ -97,6 +99,9 @@ def execute_tool_by_name(
     # Get command name and arguments
     action = output_parser.parse(text)
     tools = {t.name: t for t in tools}
+
+    logger.info(f"Tools available: {tools}")
+
     if action.name == stop_token:
         return action.args["response"]
     if action.name in tools:
@@ -109,6 +114,7 @@ def execute_tool_by_name(
                 with concurrent.futures.ThreadPoolExecutor() as executor:
                     futures = []
                     for tool_name in tool_names:
+                        logger.info(f"Executing tool: {tool_name}")
                         futures.append(
                             executor.submit(
                                 tools[tool_name].run, action.args

swarms/tools/format_tools.py

@@ -1,16 +1,15 @@
 import json
 from typing import Any, Dict, List, Union
 
-from pydantic import BaseModel
 from termcolor import cprint
 from transformers import PreTrainedModel, PreTrainedTokenizer
-
-from swarms.models.base_llm import AbstractLLM
+from pydantic import BaseModel
 from swarms.tools.logits_processor import (
     NumberStoppingCriteria,
     OutputNumbersTokens,
     StringStoppingCriteria,
 )
+from swarms.models.base_llm import AbstractLLM
 
 GENERATION_MARKER = "|GENERATION|"
 

swarms/tools/tool.py

@@ -1,8 +1,8 @@
 from langchain.tools import (
     BaseTool,
-    StructuredTool,
     Tool,
+    StructuredTool,
     tool,
-)
+)  # noqa F401
 
 __all__ = ["BaseTool", "Tool", "StructuredTool", "tool"]

swarms/tools/tool_utils.py

@@ -1,12 +1,13 @@
-import inspect
 import json
 import re
-from typing import Any, Callable, List
-
-from termcolor import colored
+from typing import Any, List
 
 from swarms.prompts.tools import SCENARIOS
 from swarms.tools.tool import BaseTool
+import inspect
+from typing import Callable
+
+from termcolor import colored
 
 
 def scrape_tool_func_docs(fn: Callable) -> str:
@@ -17,8 +18,7 @@ def scrape_tool_func_docs(fn: Callable) -> str:
         fn (Callable): The function to scrape.
 
     Returns:
-        str: A string containing the function's name, documentation string, and a list of its parameters.
-             Each parameter is represented as a line containing the parameter's name, default value, and annotation.
+        str: A string containing the function's name, documentation string, and a list of its parameters. Each parameter is represented as a line containing the parameter's name, default value, and annotation.
     """
     try:
         # If the function is a tool, get the original function
@@ -35,7 +35,10 @@ def scrape_tool_func_docs(fn: Callable) -> str:
                 f" {param.annotation if param.annotation is not param.empty else 'None'}"
             )
         parameters_str = "\n".join(parameters)
-        return f"Function: {fn.__name__}\nDocstring:" f" {inspect.getdoc(fn)}\nParameters:\n{parameters_str}"
+        return (
+            f"Function: {fn.__name__}\nDocstring:"
+            f" {inspect.getdoc(fn)}\nParameters:\n{parameters_str}"
+        )
     except Exception as error:
         print(
             colored(

swarms/utils/__init__.py

@@ -1,6 +1,5 @@
 from swarms.utils.class_args_wrapper import print_class_parameters
 from swarms.utils.code_interpreter import SubprocessCodeInterpreter
-from swarms.utils.concurrent_utils import execute_concurrently
 from swarms.utils.csv_and_pandas import (
     csv_to_dataframe,
     dataframe_to_strings,
@@ -18,11 +17,11 @@ from swarms.utils.download_weights_from_url import (
 )
 from swarms.utils.exponential_backoff import ExponentialBackoffMixin
 from swarms.utils.file_processing import (
-    create_file_in_folder,
     load_json,
     sanitize_file_path,
-    zip_folders,
     zip_workspace,
+    create_file_in_folder,
+    zip_folders,
 )
 from swarms.utils.find_img_path import find_image_path
 from swarms.utils.json_output_parser import JsonOutputParser
@@ -45,6 +44,8 @@ from swarms.utils.save_logs import parse_log_file
 # from swarms.utils.supervision_visualizer import MarkVisualizer
 from swarms.utils.try_except_wrapper import try_except_wrapper
 from swarms.utils.yaml_output_parser import YamlOutputParser
+from swarms.utils.concurrent_utils import execute_concurrently
+
 
 __all__ = [
     "print_class_parameters",

swarms/utils/apa.py

@@ -108,7 +108,7 @@ class Action:
     def to_json(self):
         try:
             tool_output = json.loads(self.tool_output)
-        except json.JSONDecodeError:
+        except:
             tool_output = self.tool_output
         return {
             "thought": self.thought,

swarms/utils/function_calling_utils.py

@@ -1,7 +1,7 @@
-import asyncio
 import concurrent.futures
-from inspect import iscoroutinefunction
 from typing import Any, Callable, Dict, List
+from inspect import iscoroutinefunction
+import asyncio
 
 
 # Helper function to run an asynchronous function in a synchronous way

swarms/utils/load_model_torch.py

@@ -18,8 +18,7 @@ def load_model_torch(
         model_path (str): Path to the saved model file.
         device (torch.device): Device to move the model to.
         model (nn.Module): The model architecture, if the model file only contains the state dictionary.
-        strict (bool): Whether to strictly enforce that the keys in the state dictionary match
-            the keys returned by the model's `state_dict()` function.
+        strict (bool): Whether to strictly enforce that the keys in the state dictionary match the keys returned by the model's `state_dict()` function.
         map_location (callable): A function to remap the storage locations of the loaded model.
         *args: Additional arguments to pass to `torch.load`.
         **kwargs: Additional keyword arguments to pass to `torch.load`.
@@ -32,11 +31,15 @@ def load_model_torch(
         RuntimeError: If there is an error while loading the model.
     """
     if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu"
+        )
 
     try:
         if model is None:
-            model = torch.load(model_path, map_location=map_location, *args, **kwargs)
+            model = torch.load(
+                model_path, map_location=map_location, *args, **kwargs
+            )
         else:
             model.load_state_dict(
                 torch.load(