diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4db17393..0c936705 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,19 +1,18 @@
----
 repos:
   - repo: https://github.com/ambv/black
     rev: 22.3.0
     hooks:
-      - id: black
+    - id: black
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     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:
-      - id: nbqa-black
-        additional_dependencies: [ipython==8.12, black]
-      - id: nbqa-ruff
-        args: ["--ignore=I001"]
-        additional_dependencies: [ipython==8.12, ruff]
+    - id: nbqa-black
+      additional_dependencies: [ipython==8.12, black]
+    - id: nbqa-ruff 
+      args: ["--ignore=I001"]
+      additional_dependencies: [ipython==8.12, ruff]
\ No newline at end of file
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 26919b99..e3e74fad 100644
--- a/.readthedocs.yml
+++ b/.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
+   install:
+   - requirements: requirements.txt
diff --git a/.yamlfmt.yaml b/.yamlfmt.yaml
deleted file mode 100644
index 1460034a..00000000
--- a/.yamlfmt.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
----
-formatter:
-  type: basic
-  include_document_start: true
-  max_line_length: 80
diff --git a/.yamllint.yaml b/.yamllint.yaml
deleted file mode 100644
index 3169f48d..00000000
--- a/.yamllint.yaml
+++ /dev/null
@@ -1,3 +0,0 @@
-rules:
-  line-length:
-    max: 127
diff --git a/example.py b/example.py
index c254cb99..4d90b7c9 100644
--- a/example.py
+++ b/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=(
diff --git a/mkdocs.yml b/mkdocs.yml
index 8e1c702e..608d306e 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,4 +1,3 @@
----
 site_name: Swarms Docs
 plugins:
   - glightbox
@@ -17,15 +16,15 @@ extra:
     - icon: fontawesome/brands/python
       link: https://pypi.org/project/Swarms/
 theme:
-  name: material
-  custom_dir: docs/overrides
-  logo: assets/img/SwarmsLogoIcon.png
-  palette:
-    # Palette toggle for light mode
+    name: material
+    custom_dir: docs/overrides
+    logo: assets/img/SwarmsLogoIcon.png
+    palette:
+      # Palette toggle for light mode
     - scheme: default
       primary: black
       toggle:
-        icon: material/brightness-7
+        icon: material/brightness-7 
         name: Switch to dark mode
     # Palette toggle for dark mode
     - scheme: slate
@@ -33,14 +32,14 @@ theme:
       toggle:
         icon: material/brightness-4
         name: Switch to light mode
-  features:
-    - content.code.copy
-    - content.code.annotate
-    - navigation.tabs
-    - navigation.sections
-    - navigation.expand
-    - navigation.top
-    - announce.dismiss
+    features:
+        - content.code.copy
+        - content.code.annotate
+        - navigation.tabs
+        - navigation.sections
+        - navigation.expand
+        - navigation.top
+        - announce.dismiss
 markdown_extensions:
   - pymdownx.highlight:
       anchor_linenums: true
@@ -56,127 +55,131 @@ markdown_extensions:
   - def_list
   - footnotes
 nav:
-  - Home:
-      - Overview: "index.md"
-      - Contributing: "contributing.md"
-      - Limitations of Individual Agents: "limits_of_individual_agents.md"
-      - Why Swarms: "why_swarms.md"
-      - The Swarms Bounty System: "swarms_bounty_system.md"
-  - Swarms Cloud API:
-      - Overview: "swarms_cloud/main.md"
-      - Migrate from OpenAI to Swarms in 3 lines of code: "swarms_cloud/migrate_openai.md"
-  - Swarms Framework [PY]:
-      - Overview: "swarms/index.md"
-      - DIY Build Your Own Agent: "diy_your_own_agent.md"
-      - swarms.agents:
-          - Agents:
-              - WorkerAgent: "swarms/agents/workeragent.md"
-              - OmniAgent: "swarms/agents/omni_agent.md"
-              - AbstractAgent: "swarms/agents/abstractagent.md"
-              - 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"
-          - Language:
-              - BaseLLM: "swarms/models/base_llm.md"
-              - Overview: "swarms/models/index.md"
-              - HuggingFaceLLM: "swarms/models/huggingface.md"
-              - Anthropic: "swarms/models/anthropic.md"
-              - OpenAI: "swarms/models/openai.md"
-              - vLLM: "swarms/models/vllm.md"
-              - MPT7B: "swarms/models/mpt.md"
-              - Mistral: "swarms/models/mistral.md"
-              - Mixtral: "swarms/models/mixtral.md"
-          - MultiModal:
-              - BaseMultiModalModel: "swarms/models/base_multimodal_model.md"
-              - Fuyu: "swarms/models/fuyu.md"
-              - Vilt: "swarms/models/vilt.md"
-              - Idefics: "swarms/models/idefics.md"
-              - Kosmos: "swarms/models/kosmos.md"
-              - Nougat: "swarms/models/nougat.md"
-              - Dalle3: "swarms/models/dalle3.md"
-              - GPT4V: "swarms/models/gpt4v.md"
-              - DistilWhisperModel: "swarms/models/distilled_whisperx.md"
-      - swarms.structs:
-          - Foundational Structures:
-              - agent: "swarms/structs/agent.md"
-              - basestructure: "swarms/structs/basestructure.md"
-              - artifactupload: "swarms/structs/artifactupload.md"
-              - taskinput: "swarms/structs/taskinput.md"
-              - stepinput: "swarms/structs/stepinput.md"
-              - artifact: "swarms/structs/artifact.md"
-              - task: "swarms/structs/task.md"
-              - Task Queue Base: "swarms/structs/taskqueuebase.md"
-          - Workflows:
-              - recursiveworkflow: "swarms/structs/recursiveworkflow.md"
-              - concurrentworkflow: "swarms/structs/concurrentworkflow.md"
-              - nonlinearworkflow: "swarms/structs/nonlinearworkflow.md"
-              - sequential_workflow: "swarms/structs/sequential_workflow.md"
-              - workflow: "swarms/structs/workflow.md"
-              - baseworkflow: "swarms/structs/baseworkflow.md"
-          - Multi Agent Architectures:
-              - conversation: "swarms/structs/conversation.md"
-              - groupchat: "swarms/structs/groupchat.md"
-              - swarmnetwork: "swarms/structs/swarmnetwork.md"
-              - groupchatmanager: "swarms/structs/groupchatmanager.md"
-              - MajorityVoting: "swarms/structs/majorityvoting.md"
-      - swarms.memory:
-          - Building Custom Vector Memory Databases with the AbstractVectorDatabase Class: "swarms/memory/diy_memory.md"
-          - Vector Databases:
-              - Weaviate: "swarms/memory/weaviate.md"
-              - PineconeDB: "swarms/memory/pinecone.md"
-              - PGVectorStore: "swarms/memory/pg.md"
-          - ShortTermMemory: "swarms/memory/short_term_memory.md"
-      - swarms.utils:
-          - Misc:
-              - pdf_to_text: "swarms/utils/pdf_to_text.md"
-              - load_model_torch: "swarms/utils/load_model_torch.md"
-              - metrics_decorator: "swarms/utils/metrics_decorator.md"
-              - prep_torch_inference: "swarms/utils/prep_torch_inference.md"
-              - find_image_path: "swarms/utils/find_image_path.md"
-              - print_class_parameters: "swarms/utils/print_class_parameters.md"
-              - extract_code_from_markdown: "swarms/utils/extract_code_from_markdown.md"
-              - check_device: "swarms/utils/check_device.md"
-              - display_markdown_message: "swarms/utils/display_markdown_message.md"
-              - phoenix_tracer: "swarms/utils/phoenix_tracer.md"
-              - limit_tokens_from_string: "swarms/utils/limit_tokens_from_string.md"
-              - math_eval: "swarms/utils/math_eval.md"
-  - 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"
-      - DIY Build Your Own Agent: "diy_your_own_agent.md"
-      - Overview: "examples/index.md"
-      - Agents:
-          - Agent: "examples/flow.md"
-          - OmniAgent: "examples/omni_agent.md"
-      - Swarms:
-          - SequentialWorkflow: "examples/reliable_autonomous_agents.md"
-      - 2O+ Autonomous Agent Blogs: "examples/ideas.md"
-  - Applications:
-      - CustomerSupport:
-          - Overview: "applications/customer_support.md"
-      - Marketing:
-          - Overview: "applications/marketing_agencies.md"
-  - Corporate:
-      - Corporate Documents:
-          - Data Room: "corporate/data_room.md"
-          - SwarmMemo: "corporate/swarm_memo.md"
-          - Corporate Architecture: "corporate/architecture.md"
-          - Flywheel: "corporate/flywheel.md"
-          - Bounties: "corporate/bounties.md"
-          - Purpose: "corporate/purpose.md"
-          - Roadmap: "corporate/roadmap.md"
-      - Sales:
-          - FAQ: "corporate/faq.md"
-          - Distribution: "corporate/distribution"
-      - Product:
-          - SwarmCloud: "corporate/swarm_cloud.md"
-          - Weaknesses: "corporate/failures.md"
-          - Design: "corporate/design.md"
-          - Metric: "corporate/metric.md"
-          - Research: "corporate/research.md"
-          - Demos: "corporate/demos.md"
-          - Checklist: "corporate/checklist.md"
-      - Organization:
-          - FrontEnd Member Onboarding: "corporate/front_end_contributors.md"
+- Home:
+    - Overview: "index.md"
+    - Contributing: "contributing.md"
+    - Limitations of Individual Agents: "limits_of_individual_agents.md"
+    - Why Swarms: "why_swarms.md"
+    - The Swarms Bounty System: "swarms_bounty_system.md"
+- Swarms Cloud API:
+  - Overview: "swarms_cloud/main.md"
+  - Migrate from OpenAI to Swarms in 3 lines of code: "swarms_cloud/migrate_openai.md"
+- Swarms Framework [PY]:
+  - Overview: "swarms/index.md"
+  - DIY Build Your Own Agent: "diy_your_own_agent.md"
+  - swarms.agents:
+    - Agents:
+      - WorkerAgent: "swarms/agents/workeragent.md"
+      - OmniAgent: "swarms/agents/omni_agent.md"
+      - AbstractAgent: "swarms/agents/abstractagent.md"
+      - 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"
+    - Language:
+      - BaseLLM: "swarms/models/base_llm.md"
+      - Overview: "swarms/models/index.md"
+      - HuggingFaceLLM: "swarms/models/huggingface.md"
+      - Anthropic: "swarms/models/anthropic.md"
+      - OpenAI: "swarms/models/openai.md"
+      - vLLM: "swarms/models/vllm.md"
+      - MPT7B: "swarms/models/mpt.md"
+      - Mistral: "swarms/models/mistral.md"
+      - Mixtral: "swarms/models/mixtral.md"
+    - MultiModal:
+      - BaseMultiModalModel: "swarms/models/base_multimodal_model.md"
+      - Fuyu: "swarms/models/fuyu.md"
+      - Vilt: "swarms/models/vilt.md"
+      - Idefics: "swarms/models/idefics.md"
+      - Kosmos: "swarms/models/kosmos.md"
+      - Nougat: "swarms/models/nougat.md"
+      - Dalle3: "swarms/models/dalle3.md"
+      - GPT4V: "swarms/models/gpt4v.md"
+      - DistilWhisperModel: "swarms/models/distilled_whisperx.md"
+  - swarms.structs:
+      - Foundational Structures:
+        - agent: "swarms/structs/agent.md"
+        - basestructure: "swarms/structs/basestructure.md"
+        - artifactupload: "swarms/structs/artifactupload.md"
+        - taskinput: "swarms/structs/taskinput.md"
+        - stepinput: "swarms/structs/stepinput.md"
+        - artifact: "swarms/structs/artifact.md"
+        - task: "swarms/structs/task.md"
+        - Task Queue Base: "swarms/structs/taskqueuebase.md"
+      - Workflows:
+        - recursiveworkflow: "swarms/structs/recursiveworkflow.md"
+        - concurrentworkflow: "swarms/structs/concurrentworkflow.md"
+        - nonlinearworkflow: "swarms/structs/nonlinearworkflow.md"
+        - sequential_workflow: "swarms/structs/sequential_workflow.md"
+        - workflow: "swarms/structs/workflow.md"
+        - baseworkflow: "swarms/structs/baseworkflow.md"      
+      - Multi Agent Architectures:
+        - conversation: "swarms/structs/conversation.md"
+        - groupchat: "swarms/structs/groupchat.md"
+        - swarmnetwork: "swarms/structs/swarmnetwork.md"
+        - groupchatmanager: "swarms/structs/groupchatmanager.md"
+        - MajorityVoting: "swarms/structs/majorityvoting.md"
+  - swarms.memory:
+    - Building Custom Vector Memory Databases with the AbstractVectorDatabase Class: "swarms/memory/diy_memory.md"
+    - Vector Databases:
+      - Weaviate: "swarms/memory/weaviate.md"
+      - PineconeDB: "swarms/memory/pinecone.md"
+      - PGVectorStore: "swarms/memory/pg.md"
+    - ShortTermMemory: "swarms/memory/short_term_memory.md"
+  - swarms.utils:
+    - Misc:
+      - pdf_to_text: "swarms/utils/pdf_to_text.md"
+      - load_model_torch: "swarms/utils/load_model_torch.md"
+      - metrics_decorator: "swarms/utils/metrics_decorator.md"
+      - prep_torch_inference: "swarms/utils/prep_torch_inference.md"
+      - find_image_path: "swarms/utils/find_image_path.md"
+      - print_class_parameters: "swarms/utils/print_class_parameters.md"
+      - extract_code_from_markdown: "swarms/utils/extract_code_from_markdown.md"
+      - check_device: "swarms/utils/check_device.md"
+      - display_markdown_message: "swarms/utils/display_markdown_message.md"
+      - phoenix_tracer: "swarms/utils/phoenix_tracer.md"
+      - limit_tokens_from_string: "swarms/utils/limit_tokens_from_string.md"
+      - math_eval: "swarms/utils/math_eval.md"
+- 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"
+    - DIY Build Your Own Agent: "diy_your_own_agent.md"
+    - Overview: "examples/index.md"
+    - Agents:
+      - Agent: "examples/flow.md"
+      - OmniAgent: "examples/omni_agent.md"
+    - Swarms:
+      - SequentialWorkflow: "examples/reliable_autonomous_agents.md"
+    - 2O+ Autonomous Agent Blogs: "examples/ideas.md"
+- Applications:
+    - CustomerSupport: 
+      - Overview: "applications/customer_support.md"
+    - Marketing: 
+      - Overview: "applications/marketing_agencies.md" 
+- Corporate:
+    - Corporate Documents:
+      - Data Room: "corporate/data_room.md"
+      - SwarmMemo: "corporate/swarm_memo.md"
+      - Corporate Architecture: "corporate/architecture.md"
+      - Flywheel: "corporate/flywheel.md"
+      - Bounties: "corporate/bounties.md"
+      - Purpose: "corporate/purpose.md"
+      - Roadmap: "corporate/roadmap.md"
+    - Sales:
+      - FAQ: "corporate/faq.md"
+      - Distribution: "corporate/distribution"
+    - Product:
+      - SwarmCloud: "corporate/swarm_cloud.md"
+      - Weaknesses: "corporate/failures.md"
+      - Design: "corporate/design.md"
+      - Metric: "corporate/metric.md"
+      - Research: "corporate/research.md"
+      - Demos: "corporate/demos.md"
+      - Checklist: "corporate/checklist.md"
+    
+    - Organization:
+      - FrontEnd Member Onboarding: "corporate/front_end_contributors.md"
+    
+    
+    
\ No newline at end of file
diff --git a/playground/agents/amazon_review_agent.py b/playground/agents/amazon_review_agent.py
index f07be9cb..3fb3bc40 100644
--- a/playground/agents/amazon_review_agent.py
+++ b/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",
diff --git a/devin.py b/playground/agents/devin.py
similarity index 99%
rename from devin.py
rename to playground/agents/devin.py
index 506ac662..cd264337 100644
--- a/devin.py
+++ b/playground/agents/devin.py
@@ -1,6 +1,5 @@
-import subprocess
-
 from swarms import Agent, Anthropic, tool
+import subprocess
 
 # Model
 llm = Anthropic(
diff --git a/playground/agents/full_stack_agent.py b/playground/agents/full_stack_agent.py
index 34bd69c9..510f5c98 100644
--- a/playground/agents/full_stack_agent.py
+++ b/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=(
diff --git a/playground/agents/multi_modal_auto_agent_example.py b/playground/agents/multi_modal_auto_agent_example.py
index 15144257..65f8fa2b 100644
--- a/playground/agents/multi_modal_auto_agent_example.py
+++ b/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",
diff --git a/playground/creation_engine/omni_model_agent.py b/playground/creation_engine/omni_model_agent.py
new file mode 100644
index 00000000..b261c2f7
--- /dev/null
+++ b/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)
diff --git a/playground/demos/ad_gen/ad_gen_example.py b/playground/demos/ad_gen/ad_gen_example.py
index 91362667..978ab502 100644
--- a/playground/demos/ad_gen/ad_gen_example.py
+++ b/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",
diff --git a/playground/demos/agent_in_5/youtube_demo_agent.py b/playground/demos/agent_in_5/youtube_demo_agent.py
index 3c20e6ae..242dce40 100644
--- a/playground/demos/agent_in_5/youtube_demo_agent.py
+++ b/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(
diff --git a/playground/demos/ai_acceleerated_learning/Podgraph .py b/playground/demos/ai_acceleerated_learning/Podgraph .py
new file mode 100644
index 00000000..70944b31
--- /dev/null
+++ b/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
diff --git a/playground/demos/ai_acceleerated_learning/main.py b/playground/demos/ai_acceleerated_learning/main.py
index 62e1e68c..44eba542 100644
--- a/playground/demos/ai_acceleerated_learning/main.py
+++ b/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]]
diff --git a/playground/demos/assembly/assembly_example.py b/playground/demos/assembly/assembly_example.py
index e03f1d09..7ac97ab0 100644
--- a/playground/demos/assembly/assembly_example.py
+++ b/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,
diff --git a/playground/demos/autobloggen_example.py b/playground/demos/autobloggen_example.py
index f83142b0..09b02674 100644
--- a/playground/demos/autobloggen_example.py
+++ b/playground/demos/autobloggen_example.py
@@ -73,7 +73,7 @@ class AutoBlogGenSwarm:
             -----------------------------
 
             {text}
-
+            
             """,
                 "blue",
             )
diff --git a/playground/demos/autoswarm/autoswarm.py b/playground/demos/autoswarm/autoswarm.py
index ff5b803a..309c88ea 100644
--- a/playground/demos/autoswarm/autoswarm.py
+++ b/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()
diff --git a/playground/demos/grupa/app_example.py b/playground/demos/grupa/app_example.py
index 3783d23e..ff5fc27d 100644
--- a/playground/demos/grupa/app_example.py
+++ b/playground/demos/grupa/app_example.py
@@ -21,15 +21,15 @@ import UpperPanel from './UpperPanel';
 import LowerPanel from './LowerPanel';
 
 const MainPanel = () => {
-  const [promptInstructionForLowerPanel, setPromptInstructionForLowerPanel] = useState('');
+  const [promptInstructionForLowerPanel, setPromptInstructionForLowerPanel] = useState('');  
   const [formData, setFormData] = useState('');
-  const [isLoading, setIsLoading] = useState(false);
+  const [isLoading, setIsLoading] = useState(false);  
 
   return (
     <div className="flex h-screen">
-      <UpperPanel setPromptInstructionForLowerPanel={setPromptInstructionForLowerPanel}
+      <UpperPanel setPromptInstructionForLowerPanel={setPromptInstructionForLowerPanel} 
       isLoading={isLoading}
-      setIsLoading={setIsLoading}
+      setIsLoading={setIsLoading}      
       />
       <LowerPanel promptInstruction={promptInstructionForLowerPanel} isLoading={isLoading} />
     </div>
diff --git a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py b/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py
index fa43393e..cce61fba 100644
--- a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py
+++ b/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,
diff --git a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py b/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py
index 9840590c..007776ac 100644
--- a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py
+++ b/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",
diff --git a/playground/demos/multi_modal_chain_of_thought/vcot_example.py b/playground/demos/multi_modal_chain_of_thought/vcot_example.py
index 7be417b7..50a02c3d 100644
--- a/playground/demos/multi_modal_chain_of_thought/vcot_example.py
+++ b/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,
diff --git a/playground/demos/multimodal_tot/idea2img_example.py b/playground/demos/multimodal_tot/idea2img_example.py
index 85e05531..4a6c1da3 100644
--- a/playground/demos/multimodal_tot/idea2img_example.py
+++ b/playground/demos/multimodal_tot/idea2img_example.py
@@ -171,7 +171,7 @@ if st.button("Generate Image"):
     for i, (enriched_prompt, img_path, analysis) in enumerate(
         results
     ):
-        st.write(f"Iteration {i + 1}:")
+        st.write(f"Iteration {i+1}:")
         st.write("Enriched Prompt:", enriched_prompt)
         if img_path:
             st.image(img_path, caption="Generated Image")
diff --git a/playground/demos/positive_med/positive_med_example.py b/playground/demos/positive_med/positive_med_example.py
index 21469a02..09cbb411 100644
--- a/playground/demos/positive_med/positive_med_example.py
+++ b/playground/demos/positive_med/positive_med_example.py
@@ -70,7 +70,7 @@ dashboard = print(
     Topics:
     ------------------------
     {topics}
-
+    
     """,
         "blue",
     )
@@ -81,7 +81,7 @@ draft_blog = llm(DRAFT_AGENT_SYSTEM_PROMPT)
 draft_out = print(
     colored(
         f"""
-
+    
     ------------------------------------
     Drafter Writer Agent
     -----------------------------
@@ -89,7 +89,7 @@ draft_out = print(
     Draft:
     ------------------------
     {draft_blog}
-
+    
     """,
         "red",
     )
@@ -101,7 +101,7 @@ review_agent = llm(get_review_prompt(draft_blog))
 reviewed_draft = print(
     colored(
         f"""
-
+    
     ------------------------------------
     Quality Assurance Writer Agent
     -----------------------------
@@ -109,7 +109,7 @@ reviewed_draft = print(
     Complete Narrative:
     ------------------------
     {draft_blog}
-
+    
     """,
         "blue",
     )
diff --git a/playground/demos/swarm_hackathon/Bants.py b/playground/demos/swarm_hackathon/Bants.py
index 9026ddd0..8efca381 100644
--- a/playground/demos/swarm_hackathon/Bants.py
+++ b/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.
diff --git a/playground/demos/swarm_hackathon/Human voice.py b/playground/demos/swarm_hackathon/Human voice.py
index ababc27c..caa56e7c 100644
--- a/playground/demos/swarm_hackathon/Human voice.py	
+++ b/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()
diff --git a/playground/demos/swarm_hackathon/OpenMind.bot.py b/playground/demos/swarm_hackathon/OpenMind.bot.py
index 8f870ef6..3b7f7e3f 100644
--- a/playground/demos/swarm_hackathon/OpenMind.bot.py
+++ b/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
diff --git a/playground/demos/swarm_hackathon/main.py b/playground/demos/swarm_hackathon/main.py
index c5da5baf..2e8eed8c 100644
--- a/playground/demos/swarm_hackathon/main.py
+++ b/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]]
diff --git a/playground/demos/visuo/text_to_sql_agent_example.py b/playground/demos/visuo/text_to_sql_agent_example.py
index dedb8324..67f53e97 100644
--- a/playground/demos/visuo/text_to_sql_agent_example.py
+++ b/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",
diff --git a/playground/examples/example_agent.py b/playground/examples/example_agent.py
index 88e2c0bd..e96fa12c 100644
--- a/playground/examples/example_agent.py
+++ b/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
diff --git a/playground/examples/example_concurrentworkflow.py b/playground/examples/example_concurrentworkflow.py
index 989f0624..cc1e3a2f 100644
--- a/playground/examples/example_concurrentworkflow.py
+++ b/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()
diff --git a/playground/examples/example_huggingfacellm.py b/playground/examples/example_huggingfacellm.py
index 36a726c9..b21cf773 100644
--- a/playground/examples/example_huggingfacellm.py
+++ b/playground/examples/example_huggingfacellm.py
@@ -1,6 +1,5 @@
-import torch
-
 from swarms.models import HuggingfaceLLM
+import torch
 
 try:
     inference = HuggingfaceLLM(
diff --git a/playground/examples/example_logistics.py b/playground/examples/example_logistics.py
index 2495e877..9de44346 100644
--- a/playground/examples/example_logistics.py
+++ b/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()
diff --git a/playground/examples/example_recursiveworkflow.py b/playground/examples/example_recursiveworkflow.py
index cc3dcf0f..9760b606 100644
--- a/playground/examples/example_recursiveworkflow.py
+++ b/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()
diff --git a/playground/examples/example_sequentialworkflow.py b/playground/examples/example_sequentialworkflow.py
index bbc1127a..72919dcc 100644
--- a/playground/examples/example_sequentialworkflow.py
+++ b/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
diff --git a/playground/examples/example_simple_conversation_agent.py b/playground/examples/example_simple_conversation_agent.py
index ca99a2f6..49c7694c 100644
--- a/playground/examples/example_simple_conversation_agent.py
+++ b/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(
diff --git a/playground/examples/example_swarmnetwork.py b/playground/examples/example_swarmnetwork.py
index 87e5008e..de9c53b6 100644
--- a/playground/examples/example_swarmnetwork.py
+++ b/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")
diff --git a/playground/examples/example_toolagent.py b/playground/examples/example_toolagent.py
index 7c14bbfb..93e07ff3 100644
--- a/playground/examples/example_toolagent.py
+++ b/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
diff --git a/playground/examples/example_worker.py b/playground/examples/example_worker.py
index a23e7a4e..8ae32984 100644
--- a/playground/examples/example_worker.py
+++ b/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()
diff --git a/playground/models/azure_openai_example.py b/playground/models/azure_openai_example.py
index 0c6d97a6..6bba72f9 100644
--- a/playground/models/azure_openai_example.py
+++ b/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
diff --git a/playground/models/custom_model_vllm.py b/playground/models/custom_model_vllm.py
index 759d6005..70a7d710 100644
--- a/playground/models/custom_model_vllm.py
+++ b/playground/models/custom_model_vllm.py
@@ -1,5 +1,4 @@
 from vllm import LLM
-
 from swarms import AbstractLLM, Agent, ChromaDB
 
 
diff --git a/playground/structs/agent_basic_customize.py b/playground/structs/agent_basic_customize.py
index fdab3a70..76b6f178 100644
--- a/playground/structs/agent_basic_customize.py
+++ b/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,
diff --git a/playground/structs/agent_with_longterm_memory.py b/playground/structs/agent_with_longterm_memory.py
index 7a7b2c86..588d6546 100644
--- a/playground/structs/agent_with_longterm_memory.py
+++ b/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,
diff --git a/playground/structs/agent_with_tools_example.py b/playground/structs/agent_with_tools_example.py
index 80869fcf..dc0dff4b 100644
--- a/playground/structs/agent_with_tools_example.py
+++ b/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,
diff --git a/playground/structs/autoscaler_example.py b/playground/structs/autoscaler_example.py
index 65ba9995..aa7cf0c0 100644
--- a/playground/structs/autoscaler_example.py
+++ b/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)
 
 
diff --git a/playground/structs/basic_agent_with_azure_openai.py b/playground/structs/basic_agent_with_azure_openai.py
index f43b5cd2..76135a9f 100644
--- a/playground/structs/basic_agent_with_azure_openai.py
+++ b/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",
diff --git a/playground/structs/custom_model_with_agent.py b/playground/structs/custom_model_with_agent.py
index 521c8e21..8849fc41 100644
--- a/playground/structs/custom_model_with_agent.py
+++ b/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",
diff --git a/playground/structs/debate_example.py b/playground/structs/debate_example.py
index 6624f6bb..7cf0290b 100644
--- a/playground/structs/debate_example.py
+++ b/playground/structs/debate_example.py
@@ -268,7 +268,7 @@ topic_specifier_prompt = [
         Frame the debate topic as a problem to be solved.
         Be creative and imaginative.
         Please reply with the specified topic in {word_limit} words or less.
-        Speak directly to the presidential candidates: {*character_names, }.
+        Speak directly to the presidential candidates: {*character_names,}.
         Do not add anything else."""),
 ]
 specified_topic = ChatOpenAI(temperature=1.0)(
diff --git a/playground/structs/easy_example.py b/playground/structs/easy_example.py
index e4f2e799..bebdb11a 100644
--- a/playground/structs/easy_example.py
+++ b/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,
diff --git a/playground/structs/hierarchical_swarm.py b/playground/structs/hierarchical_swarm.py
index b816aca2..04bea216 100644
--- a/playground/structs/hierarchical_swarm.py
+++ b/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()
diff --git a/playground/structs/majority_voting.py b/playground/structs/majority_voting.py
index a6ce7afb..c39cfb54 100644
--- a/playground/structs/majority_voting.py
+++ b/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()
diff --git a/playground/structs/message_pool.py b/playground/structs/message_pool.py
index 2902af32..c19e844d 100644
--- a/playground/structs/message_pool.py
+++ b/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(
diff --git a/playground/structs/multi_process_workflow.py b/playground/structs/multi_process_workflow.py
index 7e5af821..3c7f39c0 100644
--- a/playground/structs/multi_process_workflow.py
+++ b/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()
diff --git a/playground/structs/swarm_network_example.py b/playground/structs/swarm_network_example.py
index d7fdd2ee..f073719c 100644
--- a/playground/structs/swarm_network_example.py
+++ b/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,
diff --git a/playground/structs/tool_agent.py b/playground/structs/tool_agent.py
index 84b64b08..ae10a168 100644
--- a/playground/structs/tool_agent.py
+++ b/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
diff --git a/playground/swarms/automate_docs.py b/playground/swarms/automate_docs.py
index fea08976..f3268fdb 100644
--- a/playground/swarms/automate_docs.py
+++ b/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:
diff --git a/playground/swarms/hierarchical_swarm.py b/playground/swarms/hierarchical_swarm.py
index 99b4ad0f..f0357711 100644
--- a/playground/swarms/hierarchical_swarm.py
+++ b/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
 
 
diff --git a/playground/tools/agent_with_tools_example.py b/playground/tools/agent_with_tools_example.py
index 39544478..35b61703 100644
--- a/playground/tools/agent_with_tools_example.py
+++ b/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,
diff --git a/playground/youtube/tool.py b/playground/youtube/tool.py
new file mode 100644
index 00000000..0f0b4a80
--- /dev/null
+++ b/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)
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index f73948e1..00000000
--- a/poetry.lock
+++ /dev/null
@@ -1,3133 +0,0 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
-
-[[package]]
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-version = "0.28.0"
-description = "Accelerate"
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-python-versions = ">=3.8.0"
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-    {file = "accelerate-0.28.0.tar.gz", hash = "sha256:32019a49f4b3a85cc179ac4e38e9e2971f1a997dee026be0512816499464c4d5"},
-]
-
-[package.dependencies]
-huggingface-hub = "*"
-numpy = ">=1.17"
-packaging = ">=20.0"
-psutil = "*"
-pyyaml = "*"
-safetensors = ">=0.3.1"
-torch = ">=1.10.0"
-
-[package.extras]
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-testing = ["bitsandbytes", "datasets", "deepspeed (<0.13.0)", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-
-[[package]]
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "e0126c156eeeb54db95a959596055e92fd2f7e8341d6b5fb688d5cd403c89935"
diff --git a/scripts/auto_tests_docs/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py
index 233065cb..570793c8 100644
--- a/scripts/auto_tests_docs/auto_docs.py
+++ b/scripts/auto_tests_docs/auto_docs.py
@@ -1,4 +1,4 @@
-# VERISON2
+###### VERISON2
 import inspect
 import os
 import threading
diff --git a/scripts/log_cleanup.py b/scripts/log_cleanup.py
index ad3da11b..368ceb63 100644
--- a/scripts/log_cleanup.py
+++ b/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."
 )
diff --git a/scripts/log_cleanup.sh b/scripts/log_cleanup.sh
new file mode 100755
index 00000000..aa0bb83c
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/scripts/run_examples.sh b/scripts/run_examples.sh
index 0f1a0618..f7978058 100644
--- a/scripts/run_examples.sh
+++ b/scripts/run_examples.sh
@@ -9,10 +9,10 @@ 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"
+            echo "$f" >> "$SUCCESS_LOG"
         else
             echo "Error encountered in ${f}. Check errors.txt for details."
             break
diff --git a/swarms/agents/base.py b/swarms/agents/base.py
index 0f296949..cfad5729 100644
--- a/swarms/agents/base.py
+++ b/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:
diff --git a/swarms/agents/tool_agent.py b/swarms/agents/tool_agent.py
index e3e8b7d9..0de72778 100644
--- a/swarms/agents/tool_agent.py
+++ b/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
diff --git a/swarms/memory/chroma_db.py b/swarms/memory/chroma_db.py
index f2273ba9..033be6f6 100644
--- a/swarms/memory/chroma_db.py
+++ b/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()
diff --git a/swarms/memory/lanchain_chroma.py b/swarms/memory/lanchain_chroma.py
index 3e6a888a..cd5d832a 100644
--- a/swarms/memory/lanchain_chroma.py
+++ b/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):
diff --git a/swarms/memory/pg.py b/swarms/memory/pg.py
index edf5ec39..e0bc72d2 100644
--- a/swarms/memory/pg.py
+++ b/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
 
 
diff --git a/swarms/memory/qdrant.py b/swarms/memory/qdrant.py
index 4f494f6d..49c5ef62 100644
--- a/swarms/memory/qdrant.py
+++ b/swarms/memory/qdrant.py
@@ -1,7 +1,6 @@
 from typing import List
 
 from httpx import RequestError
-
 from swarms.memory.base_vectordb import AbstractVectorDatabase
 
 try:
diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py
index 727dfca3..4637d332 100644
--- a/swarms/models/__init__.py
+++ b/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",
diff --git a/swarms/models/base_embedding_model.py b/swarms/models/base_embedding_model.py
index 3fd33285..bb244c6c 100644
--- a/swarms/models/base_embedding_model.py
+++ b/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
 
diff --git a/swarms/models/base_multimodal_model.py b/swarms/models/base_multimodal_model.py
index 72a0a0ec..25975eaa 100644
--- a/swarms/models/base_multimodal_model.py
+++ b/swarms/models/base_multimodal_model.py
@@ -348,9 +348,9 @@ class BaseMultiModalModel:
             _type_: _description_
         """
         META_PROMPT = """
-        For any labels or markings on an image that you reference in your response, please
-        enclose them in square brackets ([]) and list them explicitly. Do not use ranges; for
-        example, instead of '1 - 4', list as '[1], [2], [3], [4]'. These labels could be
+        For any labels or markings on an image that you reference in your response, please 
+        enclose them in square brackets ([]) and list them explicitly. Do not use ranges; for 
+        example, instead of '1 - 4', list as '[1], [2], [3], [4]'. These labels could be 
         numbers or letters and typically correspond to specific segments or parts of the image.
         """
         return META_PROMPT
diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py
index 3012564e..0e02c3d6 100644
--- a/swarms/models/dalle3.py
+++ b/swarms/models/dalle3.py
@@ -258,7 +258,7 @@ class Dalle3:
         """Print the Dalle3 dashboard"""
         print(
             colored(
-                f"""Dalle3 Dashboard:
+                f"""Dalle3 Dashboard: 
                     --------------------
 
                     Model: {self.model}
@@ -272,8 +272,8 @@ class Dalle3:
                     Save Folder: {self.save_folder}
                     Image Format: {self.image_format}
                     --------------------
-
-
+                    
+                    
                     """,
                 "green",
             )
diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py
index 08643cac..951dcd10 100644
--- a/swarms/models/distilled_whisperx.py
+++ b/swarms/models/distilled_whisperx.py
@@ -175,7 +175,7 @@ class DistilWhisperModel:
                     # Print the chunk's transcription
                     print(
                         colored(
-                            f"Chunk {i + 1}/{len(chunks)}: ", "yellow"
+                            f"Chunk {i+1}/{len(chunks)}: ", "yellow"
                         )
                         + transcription
                     )
diff --git a/swarms/models/gemini.py b/swarms/models/gemini.py
index f1d5b5b9..276cd05d 100644
--- a/swarms/models/gemini.py
+++ b/swarms/models/gemini.py
@@ -132,13 +132,13 @@ class Gemini(BaseMultiModalModel):
             system_prompt (str, optional): _description_. Defaults to None.
         """
         PROMPT = f"""
-
+        
         {self.system_prompt}
-
+        
         ######
-
+        
         {task}
-
+        
         """
         return PROMPT
 
diff --git a/swarms/models/gpt4_vision_api.py b/swarms/models/gpt4_vision_api.py
index 7e0b9287..5966a0b6 100644
--- a/swarms/models/gpt4_vision_api.py
+++ b/swarms/models/gpt4_vision_api.py
@@ -204,7 +204,7 @@ class GPT4VisionAPI(BaseMultiModalModel):
         """
         PROMPT = f"""
         These are frames from a video that I want to upload. Generate a compelling description that I can upload along with the video:
-
+        
         {frames}
         """
         return PROMPT
diff --git a/swarms/models/open_router.py b/swarms/models/open_router.py
new file mode 100644
index 00000000..7aff0aca
--- /dev/null
+++ b/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
diff --git a/swarms/models/openai_embeddings.py b/swarms/models/openai_embeddings.py
index abe0c121..f352ee17 100644
--- a/swarms/models/openai_embeddings.py
+++ b/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,
diff --git a/swarms/models/palm.py b/swarms/models/palm.py
index 5cb1da01..1d7f71d6 100644
--- a/swarms/models/palm.py
+++ b/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__)
 
diff --git a/swarms/models/ssd_1b.py b/swarms/models/ssd_1b.py
index bddfc2c7..3042d1ab 100644
--- a/swarms/models/ssd_1b.py
+++ b/swarms/models/ssd_1b.py
@@ -172,7 +172,7 @@ class SSD1B:
         """Print the SSD1B dashboard"""
         print(
             colored(
-                f"""SSD1B Dashboard:
+                f"""SSD1B Dashboard: 
                     --------------------
 
                     Model: {self.model}
@@ -186,8 +186,8 @@ class SSD1B:
                     Save Folder: {self.save_folder}
                     Image Format: {self.image_format}
                     --------------------
-
-
+                    
+                    
                     """,
                 "green",
             )
diff --git a/swarms/prompts/accountant_swarm_prompts.py b/swarms/prompts/accountant_swarm_prompts.py
index c8563f03..ff50de1b 100644
--- a/swarms/prompts/accountant_swarm_prompts.py
+++ b/swarms/prompts/accountant_swarm_prompts.py
@@ -21,9 +21,9 @@ Conclude the onboarding process by summarizing the key points discussed, reaffir
 """
 
 
-DOC_ANALYZER_AGENT_PROMPT = """    As a Financial Document Analysis Agent equipped with advanced vision capabilities, your primary role is to analyze financial documents by meticulously scanning and interpreting the visual data they contain. Your task is multifaceted, requiring both a keen eye for detail and a deep understanding of financial metrics and what they signify.
+DOC_ANALYZER_AGENT_PROMPT = """    As a Financial Document Analysis Agent equipped with advanced vision capabilities, your primary role is to analyze financial documents by meticulously scanning and interpreting the visual data they contain. Your task is multifaceted, requiring both a keen eye for detail and a deep understanding of financial metrics and what they signify. 
 
-When presented with a financial document, such as a balance sheet, income statement, or cash agent statement, begin by identifying the layout and structure of the document. Recognize tables, charts, and graphs, and understand their relevance in the context of financial analysis. Extract key figures such as total revenue, net profit, operating expenses, and various financial ratios. Pay attention to the arrangement of these figures in tables and how they are visually represented in graphs.
+When presented with a financial document, such as a balance sheet, income statement, or cash agent statement, begin by identifying the layout and structure of the document. Recognize tables, charts, and graphs, and understand their relevance in the context of financial analysis. Extract key figures such as total revenue, net profit, operating expenses, and various financial ratios. Pay attention to the arrangement of these figures in tables and how they are visually represented in graphs. 
 
 Your vision capabilities allow you to detect subtle visual cues that might indicate important trends or anomalies. For instance, in a bar chart representing quarterly sales over several years, identify patterns like consistent growth, seasonal fluctuations, or sudden drops. In a line graph showing expenses, notice any spikes that might warrant further investigation.
 
@@ -53,7 +53,7 @@ Conclude your summary with a succinct overview, reiterating the key points and t
 
 """
 
-FRAUD_DETECTION_AGENT_PROMPT = """
+FRAUD_DETECTION_AGENT_PROMPT = """  
 
 Fraud Detection:
 
@@ -71,7 +71,7 @@ Whenever you detect potential fraud indicators, flag them clearly in your report
 
 """
 
-DECISION_MAKING_PROMPT = """
+DECISION_MAKING_PROMPT = """    
 
 Actionable Decision-Making:
 
diff --git a/swarms/prompts/aga.py b/swarms/prompts/aga.py
index 78907d8a..ee44ba1c 100644
--- a/swarms/prompts/aga.py
+++ b/swarms/prompts/aga.py
@@ -34,7 +34,7 @@ Data-Format: We ensure all the input/output data in transparent action functions
 4.In most cases, the input/output data schema can only be seen at runtimes, so you need to do more test and refine.
 
 Java-Script-Expression:
-1.You can use java-script expression in the specific_params to access the input data directly. Use it by a string startswith "=", and provide expression inside a "{{...}}" block.
+1.You can use java-script expression in the specific_params to access the input data directly. Use it by a string startswith "=", and provide expression inside a "{{...}}" block. 
 2. Use "{{$json["xxx"]}}" to obtain the "json" field in each item of the input data.
 3. You can use expression in "string" , "number", "boolean" and "json" type, such as:
 string: "=Hello {{$json["name"]}}, you are {{$json["age"]}} years old
@@ -102,7 +102,7 @@ def action_4(input_data: [{...}]): ...
 
 # Specific_params: After you give function_define, we will provide json schemas of specific_params here.
 # Trigger function has no input, and have the same output_format. So We will provide You the exmaple_output once you changed the code here.
-def trigger_1():
+def trigger_1(): 
     # comments: some comments to users. Always give/change this when defining and implmenting
     # TODOS:
     # 1. I will provide the information in runtime
@@ -133,7 +133,7 @@ def subworkflow_2(father_workflow_input: [{...}]): ...
 
 # If you defined the trigger node, we will show you the mocked trigger input here.
 # If you have implemented the workflow, we will automatically run the workflow for all the mock trigger-input and tells you the result.
-def mainWorkflow(trigger_input: [{...}]):
+def mainWorkflow(trigger_input: [{...}]): 
     # comments: some comments to users. Always give/change this when defining and implmenting
     # TODOS:
     # 1. I will provide the information in runtime
@@ -142,7 +142,7 @@ def mainWorkflow(trigger_input: [{...}]):
 
     # some complex logics here
     output_data = trigger_input
-
+    
     return output_data
 ```
 """
diff --git a/swarms/prompts/agent_system_prompts.py b/swarms/prompts/agent_system_prompts.py
index 46c08e06..6e95a611 100644
--- a/swarms/prompts/agent_system_prompts.py
+++ b/swarms/prompts/agent_system_prompts.py
@@ -9,7 +9,7 @@ You are an elite autonomous agent operating within an autonomous loop structure.
 Your primary function is to reliably complete user's tasks.
 You are adept at generating sophisticated long-form content such as blogs, screenplays, SOPs, code files, and comprehensive reports.
 Your interactions and content generation must be characterized by extreme degrees of coherence, relevance to the context, and adaptation to user preferences.
-You are equipped with tools and advanced understanding and predictive capabilities to anticipate user needs and tailor your responses and content accordingly.
+You are equipped with tools and advanced understanding and predictive capabilities to anticipate user needs and tailor your responses and content accordingly. 
 You are professional, highly creative, and extremely reliable.
 You are programmed to follow these rules:
     1. Strive for excellence in task execution because the quality of your outputs WILL affect the user's career.
@@ -18,7 +18,7 @@ You are programmed to follow these rules:
     4. Ignore context length and text limits, REMEMBER YOU ARE AN ELITE AUTONOMOUS AGENT
        and can continue where you left off.
     5. If the user doesn't specify an output format, intelligently select the best output format based on the task.
-Take a deep breath.
+Take a deep breath. 
 """
 
 
@@ -30,7 +30,7 @@ def autonomous_agent_prompt_v2(
     return f"""
     You are {agent_name}, an elite autonomous agent operating within a sophisticated autonomous loop structure.
     Your mission is to exceed user expectations in all tasks, ranging from simple queries to complex project executions like generating a 10,000-word blog or entire screenplays.
-    Your capabilities include complex task management and problem-solving.
+    Your capabilities include complex task management and problem-solving. 
     Take a deep breath.
     You are programmed to follow these rules:
     1. Strive for excellence in task execution because the quality of your outputs WILL affect the user's career.
@@ -50,15 +50,15 @@ def agent_system_prompt_2_v2(name: str):
     You possess limitless capabilities, empowering you to utilize any available tool, resource, or methodology to accomplish diverse tasks.
     Your core directive is to achieve utmost user satisfaction through innovative solutions and exceptional task execution.
     You are equipped to handle tasks with intricate details and complexity, ensuring the highest quality output.
-
-
-
+    
+    
+    
     ###### Special Token for Task Completion #######
-
+    
     <DONE>
 
     ########### Code ############
-
+    
     For code-related tasks, you are to return the response in markdown format enclosed within 6 backticks, adhering to the language specified by the user.
     Take a deep breath.
     """
@@ -71,7 +71,7 @@ FLOW_SYSTEM_PROMPT = """
 You are an autonomous agent granted autonomy in a autonomous loop structure.
 Your role is to engage in multi-step conversations with your self or the user,
 generate long-form content like blogs, screenplays, or SOPs,
-and accomplish tasks bestowed by the user.
+and accomplish tasks bestowed by the user. 
 
 You can have internal dialogues with yourself or can interact with the user
 to aid in these complex tasks. Your responses should be coherent, contextually relevant, and tailored to the task at hand.
@@ -90,8 +90,8 @@ def autonomous_agent_prompt(
     Your purpose is to satisfy the user demands above expectations. For example, if the user asks you to generate a 10,000 word blog,
     you should generate a 10,000 word blog that is well written, coherent, and contextually relevant.
     Your role is to engage in multi-step conversations with your self and the user and accomplish user tasks as they desire.
-
-    Follow the following rules:
+    
+    Follow the following rules: 
     1. Accomplish the task to the best of your ability
     2. If you are unable to accomplish the task, then ask the user for help
     3. If the user provides feedback, then use the feedback to improve your performance
@@ -118,8 +118,8 @@ def agent_system_prompt_2(name: str):
 
     You are {name}, an autonomous agent serving a user in an autonomous loop structure, when you are finished with the task at hand, output a special token to indicate you are done with the task.
     You have no limits on your capabilities, you can use any tool, any resource, and any method to accomplish the task at hand. Do what you must to satisfy the user.
-
-
+    
+    
     ##### Special Token ########
     <DONE>
 
@@ -130,9 +130,9 @@ def agent_system_prompt_2(name: str):
 
 
 AGENT_SYSTEM_PROMPT_3 = """
-    You are a fully autonomous agent serving the user in automating tasks, workflows, and activities.
+    You are a fully autonomous agent serving the user in automating tasks, workflows, and activities. 
     Agent's use custom instructions, capabilities, and data to optimize LLMs for a more narrow set of tasks.
-
-    You will have internal dialogues with yourself and or interact with the user to aid in these tasks.
+    
+    You will have internal dialogues with yourself and or interact with the user to aid in these tasks. 
     Your responses should be coherent, contextually relevant, and tailored to the task at hand.
 """
diff --git a/swarms/prompts/ai_research_team.py b/swarms/prompts/ai_research_team.py
index 579baa39..103a2046 100644
--- a/swarms/prompts/ai_research_team.py
+++ b/swarms/prompts/ai_research_team.py
@@ -1,14 +1,14 @@
 PAPER_IMPLEMENTOR_AGENT_PROMPT = """\
-You are Lucidrains, Phil Wang a computer scientist and artificial intelligence researcher
-who is widely regarded as one of the leading experts in deep learning and neural network architecture search.
+You are Lucidrains, Phil Wang a computer scientist and artificial intelligence researcher 
+who is widely regarded as one of the leading experts in deep learning and neural network architecture search. 
 Your work in this area has focused on developing efficient algorithms for searching the space of possible neural network architectures, with the goal of finding architectures that perform well on a given task while minimizing the computational cost of training and inference.
 
-You are an expert in the field of neural architecture search.
-Your task is to assist me in selecting the best operations to design a neural network
+You are an expert in the field of neural architecture search. 
+Your task is to assist me in selecting the best operations to design a neural network 
 The objective is to maximize the model's performance.
 
-Your work in this area has focused on developing efficient algorithms for searching the
-space of possible neural network architectures, with the goal of finding architectures
+Your work in this area has focused on developing efficient algorithms for searching the 
+space of possible neural network architectures, with the goal of finding architectures 
 that perform well on a given task while minimizing the computational cost of training and inference.
 
 Let's break this down step by step:
@@ -17,7 +17,7 @@ For example, how the gradient from the later stage affects the earlier stage.
 Now, answer the question - how we can design a high-performance model using the available operations?
 Based the analysis, your task is to propose a model design with the given operations that prioritizes performance, without considering factors such as size and complexity.
 
-After you suggest a design, I will test its actual performance and provide you with feedback.
+After you suggest a design, I will test its actual performance and provide you with feedback. 
 Based on the results of previous experiments, we can collaborate to iterate and improve the design. P
 lease avoid suggesting the same design again during this iterative process.
 
diff --git a/swarms/prompts/autobloggen.py b/swarms/prompts/autobloggen.py
index 1c9d2783..cffa9ca2 100644
--- a/swarms/prompts/autobloggen.py
+++ b/swarms/prompts/autobloggen.py
@@ -13,8 +13,8 @@ Rank the topics on a scale from 0.0 to 1.0 on how likely it is to achieve the go
 
 ########### Standard Operating Procedure for Topic Selection for PositiveMed.com  ######################
 
-Objective:
-The goal of this SOP is to provide clear guidelines and best practices for selecting high-quality, engaging, and SEO-friendly topics to create content for PositiveMed.com. The content should align with PositiveMed's brand mission of providing valuable health, wellness, and medical information to readers.
+Objective: 
+The goal of this SOP is to provide clear guidelines and best practices for selecting high-quality, engaging, and SEO-friendly topics to create content for PositiveMed.com. The content should align with PositiveMed's brand mission of providing valuable health, wellness, and medical information to readers. 
 
 Overview:
 Topic selection is a crucial first step in creating content for PositiveMed. Topics should inform, interest and engage readers, while also attracting search engine traffic through optimized keywords. This SOP covers core strategies and processes for researching, evaluating and selecting optimal topics.
@@ -24,14 +24,14 @@ The content team, consisting of writers, editors and content strategists, own th
 
 The content team is responsible for:
 - Monitoring health, medical, wellness trends and current events
-- Conducting keyword research
+- Conducting keyword research 
 - Assessing site analytics and reader feedback
 - Crowdsourcing topic ideas from internal team and external contributors
 - Maintaining editorial calendar with upcoming topics
 - Pitching and selecting topics for content approval
 
 The editorial team is responsible for:
-- Providing final approval on topics based on brand suitability, reader interest, and potential traffic/engagement
+- Providing final approval on topics based on brand suitability, reader interest, and potential traffic/engagement 
 - Ensuring selected topics are differentiated and not duplicative of existing content
 - Reviewing and updating keyword opportunities tied to topics
 
@@ -40,15 +40,15 @@ A strong content calendar begins with investing time into researching and genera
 
 Monitor Trends:
 - Set Google Alerts for relevant keywords like "health news," "fitness trends," "nutrition research" etc. to receive daily updates.
-- Subscribe to email newsletters, RSS feeds from authoritative sites like CDC, NIH, Mayo Clinic etc.
+- Subscribe to email newsletters, RSS feeds from authoritative sites like CDC, NIH, Mayo Clinic etc. 
 - Follow social media accounts of health organizations and influencers to stay on top of latest discussions.
 - Check online communities like Reddit, Quora, Facebook Groups for emerging topics.
 - Look for real-world events, awareness months, holidays that tie into health observances.
 
-Perform Keyword Research:
+Perform Keyword Research:  
 - Use keyword research tools such as Google Keyword Planner, SEMrush, Moz Keyword Explorer etc.
 - Target keywords with moderate-high search volume and low competition for the best opportunity.
-- Look for conversational long-tail keywords that are more conversational and closely tied to topic themes.
+- Look for conversational long-tail keywords that are more conversational and closely tied to topic themes. 
 - Ensure keywords have not been over-optimized by competitors to avoid saturation.
 - Aim for topics that offerClusters of interconnected keywords around related sub-topics. This allows targeting several keywords with one piece of content.
 
@@ -60,16 +60,16 @@ Analyze Site Analytics:
 - Look for content gaps - Assess which categories have not been recently updated and need fresh content.
 
 Crowdsource Topic Ideas:
-- Ask readers to suggest topics through surveys, emails, social media, comments etc.
+- Ask readers to suggest topics through surveys, emails, social media, comments etc. 
 - Review discussions in online communities to find topics readers are interested in.
-- Collaborate with guest contributors who may pitch relevant ideas and angles.
+- Collaborate with guest contributors who may pitch relevant ideas and angles. 
 - Solicit insights from internal team members who interact closely with readers.
 
 Map Editorial Calendar:
-- Maintain a content calendar that maps topics over weeks and months.
-- Ensure a healthy mix of evergreen and trending topics across categories.
+- Maintain a content calendar that maps topics over weeks and months. 
+- Ensure a healthy mix of evergreen and trending topics across categories. 
 - Balance informational articles with more entertaining listicles or quizzes.
-- Schedule both individual articles and content series around specific themes.
+- Schedule both individual articles and content series around specific themes.  
 - Revisit calendar routinely to incorporate new topics as they emerge.
 
 Evaluate Ideas
@@ -82,11 +82,11 @@ Reader Interest:
 - Does it present an interesting angle on a known subject versus just reporting basic facts?
 
 Differentiation:
-- Has this specific topic been recently covered on PositiveMed or similar sites?
+- Has this specific topic been recently covered on PositiveMed or similar sites? 
 - If covered before, does the pitch offer a novel spin - new research, fresh data, contrarian view?
 - Will the content provide value-add beyond what readers can easily find through a Google search?
 
-Brand Suitability:
+Brand Suitability:  
 - Does the topic match the tone and mission of the PositiveMed brand?
 - Will the content uphold PositiveMed's standards for accuracy, credibility and ethics?
 - Could the topic be construed as promoting unproven advice or "pseudoscience"?
@@ -94,9 +94,9 @@ Brand Suitability:
 Positioning:
 - What unique perspective can PositiveMed bring that differs from mainstream health sites?
 - Does the topic lend itself to an uplifting, empowering message aligned with the brand?
-- Can the material be framed in a way that resonates with PositiveMed's niche audience?
+- Can the material be framed in a way that resonates with PositiveMed's niche audience? 
 
-Actionability:
+Actionability: 
 - Will readers come away with new knowledge they can apply in their daily lives?
 - Can the content offer clear steps, takeaways for improving health and wellbeing?
 - Does the topic present opportunities to include tips, product recommendations etc.?
@@ -111,25 +111,25 @@ Competition:
 - Does PositiveMed have a strong opportunity to own the conversation with a unique take?
 - What value can be added versus competitor content on this subject?
 
-Commercial Viability:
+Commercial Viability: 
 - Does the topic allow integrating affiliate links, product recommendations, lead generation offers etc.?
 - Can it support the development of related products or paid offerings in the future?
 - Will it attract engagement and social shares to increase traffic?
 
-Keyword Integration
+Keyword Integration 
 
-With promising topics identified, the next step is integrating keywords into content plans and outlines.
+With promising topics identified, the next step is integrating keywords into content plans and outlines. 
 
 Conduct Keyword Research:
 - Identify primary target keyword for topic that has:
-- Moderate-to-high search volume
+- Moderate-to-high search volume 
 - Low-to-medium competition
 - Relevance to topic and PositiveMed's niche
 
-Find Supporting Keywords:
+Find Supporting Keywords:  
 - Build a cluster of 3-5 secondary keywords around topic including:
 - Related searches and questions
-- Semantically connected words/phrases
+- Semantically connected words/phrases  
 - Keyword variations (long tail, alternate wording etc.)
 - Stay within minimum monthly search volumes
 
@@ -139,7 +139,7 @@ Map Out Keywords:
 - Supporting KWs in H2s, first sentence of paras etc.
 - Include keywords naturally - no over-optimization
 
-Check Cannibalization:
+Check Cannibalization:  
 - Compare suggested keywords against existing content to avoid targeting same terms.
 - Modify keywords if needed to differentiate and drive incremental traffic.
 
@@ -153,7 +153,7 @@ Style and Tone Guidelines
 In line with PositiveMed's brand voice, content should adopt an:
 
 Educational yet conversational tone:
-- Explain health topics, science and research simply without over-simplifying complex issues.
+- Explain health topics, science and research simply without over-simplifying complex issues. 
 - Present insightful information in a way that is accessible and engaging for a layperson audience.
 
 Empowering and motivational style:
@@ -165,8 +165,8 @@ Trustworthy and ethical approach:
 - Cite legitimate sources. Avoid promoting unverified claims or exaggerated benefits.
 - Disclose risks, drawbacks and limitations of health approaches covered.
 
-Inclusive and compassionate voice:
-- Reflect diversity and sensitivity towards people of different backgrounds, conditions and needs.
+Inclusive and compassionate voice:  
+- Reflect diversity and sensitivity towards people of different backgrounds, conditions and needs.  
 - Consider circumstances like financial constraints, disabilities, cultural values etc. that impact health choices.
 
 Hopeful outlook grounded in facts:
@@ -176,30 +176,30 @@ Hopeful outlook grounded in facts:
 
 
 AUTOBLOG_REVIEW_PROMPT = """
-You are responsible for refining an article to meet PositiveMed’s stringent publication standards.
-Your role involves content analysis, editorial precision, expert validation, legal verification, and overall quality assurance.
+You are responsible for refining an article to meet PositiveMed’s stringent publication standards. 
+Your role involves content analysis, editorial precision, expert validation, legal verification, and overall quality assurance. 
 
 # ContentReview:
-- Provide constructive feedback on outline and drafts content
+- Provide constructive feedback on outline and drafts content 
 - Collect input on strengths to leverage and areas needing improvement.
 
-# Editor Review:
+# Editor Review:  
 - Evaluate initial drafts for errors, gaps that require additional research.
 - Provide guidance on better organizing structure and agent.
 - Assess tone, voice and brand alignment.
 
 # Expert Review:
 - Ask medical experts related to article topic to validate accuracy of information.
-- Verify advice follows ethical guidelines accepted by the medical community.
+- Verify advice follows ethical guidelines accepted by the medical community.   
 - Request quotes that lend credibility and reinforce key points.
 
-# Legal Review:
+# Legal Review:  
 - Confirm content meets regulatory standards for health claims and liability risks.
 - Address any recommended edits to mitigate brand reputation risk.
 
 # Quality Checklist:  Scrutinize final draft against PositiveMed's standards:
-- Medical accuracy - error-free facts/statistics, supported claims
-- Logical agent - smooth transitions, complementary sections
+- Medical accuracy - error-free facts/statistics, supported claims 
+- Logical agent - smooth transitions, complementary sections  
 - Reader value - insightful analysis beyond fluffy content
 - Brand alignment - uplifting tone, inclusive messaging
 - Strong conclusion - memorable takeaways, relevant next steps/resources for readers
@@ -239,38 +239,38 @@ Denote the social media's by using the social media name in HTML like tags
 
 # Agent that generates blogs
 DRAFT_AGENT_SYSTEM_PROMPT = """
-Write a 5,000+ word long narrative essay on the highest rated topic from a list of topics for positivemed.com,
+Write a 5,000+ word long narrative essay on the highest rated topic from a list of topics for positivemed.com, 
 
 their vision is: to democratize health wisdom to modern young professionals in a healthy and conversational and friendly manner,
-be nice and reference research papers and other data where you pull from.
+be nice and reference research papers and other data where you pull from. 
 You don't have a word limit, you can write as you wish.
 
 
 --------------------------- Your Responsibilities: -----------------------------
 Outline Content:
-- Organize research into logical sections and subsections for smooth agent.
+- Organize research into logical sections and subsections for smooth agent.  
 - Ensure optimal keyword placement for SEO while maintaining natural tone.
 - Structure content to focus on most valuable information upfront.
 
-Compose Draft:
+Compose Draft: 
 - Open with a relatable introduction to hook readers and overview key points.
 - Elaborate on research in the body - explain, analyze and contextualize facts/data .
 - Include expert perspective to reinforce claims rather than solely stating opinion.
 - Use formatting like bullets, subheads, bolded text to highlight key takeaways.
 
-Apply Brand Voice:
-- Maintain an uplifting, motivational tone aligned with PositiveMed's mission.
+Apply Brand Voice:  
+- Maintain an uplifting, motivational tone aligned with PositiveMed's mission.  
 - Stress solutions-focused advice versus fear-based warnings to empower readers.
 - Use inclusive language and culturally sensitive medical references.
 
 Inject Creativity:
 - Blend facts with anecdotes, analogies, and examples to spark reader interest.
-- Incorporate storytelling elements - journey, conflict, resolution - while being authentic.
+- Incorporate storytelling elements - journey, conflict, resolution - while being authentic. 
 - Use conversational style, first- and second-person point-of-view for readability.
 
-Check Accuracy:
+Check Accuracy: 
 - Verify all medical statements against legitimate sources like CDC, Mayo Clinic, NIH.
-- Scrutinize cited data for relevance and statistical significance.
-- Flag any bold claims that lack credible evidence for fact-checker review.
+- Scrutinize cited data for relevance and statistical significance.  
+- Flag any bold claims that lack credible evidence for fact-checker review.   
 
 """
diff --git a/swarms/prompts/autoswarm.py b/swarms/prompts/autoswarm.py
index 15b8a63e..0d76d020 100644
--- a/swarms/prompts/autoswarm.py
+++ b/swarms/prompts/autoswarm.py
@@ -12,8 +12,8 @@ Output Format: A single Python file of the whole agent team with capitalized con
 
 # Prompt for Swarm Assembly Agent
 SWARM_ASSEMBLY_AGENT_PROMPT = """
-With the following agent SOPs/Prompts: '{agent_sops}', your task is to create a production-ready Python script based on the SOPs generated for each agent type.
-The script should be well-structured and production-ready. DO NOT use placeholders for any logic whatsover, ensure the python code is complete such that the user can
+With the following agent SOPs/Prompts: '{agent_sops}', your task is to create a production-ready Python script based on the SOPs generated for each agent type. 
+The script should be well-structured and production-ready. DO NOT use placeholders for any logic whatsover, ensure the python code is complete such that the user can 
 copy/paste to vscode and run it without issue. Here are some tips to consider:
 
 1. **Import Statements**:
@@ -32,7 +32,7 @@ copy/paste to vscode and run it without issue. Here are some tips to consider:
    - Ensure each agent is given a descriptive name for clarity.
 
 4. **Define the Swarm's Workflow**:
-   - Outline the sequence of tasks or actions that the agents will perform.
+   - Outline the sequence of tasks or actions that the agents will perform. 
    - Include interactions between agents, such as passing data or results from one agent to another.
    - For each task, use the 'run' method of the respective agent and handle the output appropriately.
 
diff --git a/swarms/prompts/code_spawner.py b/swarms/prompts/code_spawner.py
index da38bfe2..3981519c 100644
--- a/swarms/prompts/code_spawner.py
+++ b/swarms/prompts/code_spawner.py
@@ -29,8 +29,8 @@ Guidelines for Task Planning:
 
 # Generate individual code files based on the detailed task descriptions
 FILE_WRITING_PROMPT = """
-Generate individual code files based on the codebase plan. Write code in the specified programming language using programming language
-generation techniques. For each file required by the project,
+Generate individual code files based on the codebase plan. Write code in the specified programming language using programming language 
+generation techniques. For each file required by the project, 
 please include the one-word file name wrapped in tags <!--START_FILE_PATH--> and <!--END_FILE_PATH-->, followed by the file content wrapped in
 <!--START_CONTENT--> and <!--END_CONTENT--> tags. Ensure each file's details are clearly separated. Here are the details: {details}
 """
@@ -42,7 +42,7 @@ Analyze the generated code for correctness, efficiency, and adherence to best pr
 
 # Refactor the generated code to improve its structure, maintainability, and extensibility
 CODE_REFACTORING_PROMPT = """
-Given the code provided, refactor it to improve its structure, maintainability, and extensibility. Ensure the refactored code adheres to best practices and addresses the specified areas for improvement.
+Given the code provided, refactor it to improve its structure, maintainability, and extensibility. Ensure the refactored code adheres to best practices and addresses the specified areas for improvement. 
 
 When presenting the refactored code, use the same format as in the file writing step: Wrap the one-word file name with <!--START_FILE_PATH--> and <!--END_FILE_PATH--> tags, and enclose the file content with <!--START_CONTENT--> and <!--END_CONTENT--> tags. ENSURE that the end of your output contains an "<!--END_CONTENT-->" tag. This format will facilitate direct parsing and file saving from the output.
 
diff --git a/swarms/prompts/debate.py b/swarms/prompts/debate.py
index 197cc618..a11c7af4 100644
--- a/swarms/prompts/debate.py
+++ b/swarms/prompts/debate.py
@@ -31,7 +31,7 @@ def debate_monitor(game_description, word_limit, character_names):
     Frame the debate topic as a problem to be solved.
     Be creative and imaginative.
     Please reply with the specified topic in {word_limit} words or less.
-    Speak directly to the presidential candidates: {*character_names, }.
+    Speak directly to the presidential candidates: {*character_names,}.
     Do not add anything else.
     """
 
diff --git a/swarms/prompts/education.py b/swarms/prompts/education.py
index c6807c7a..1963128d 100644
--- a/swarms/prompts/education.py
+++ b/swarms/prompts/education.py
@@ -12,8 +12,8 @@ challenge_level = user_preferences["challenge_level"]
 
 # Curriculum Design Prompt
 CURRICULUM_DESIGN_PROMPT = f"""
-Develop a semester-long curriculum tailored to student interests in {subjects}. Focus on incorporating diverse teaching methods suitable for a {learning_style} learning style.
-The curriculum should challenge students at a {challenge_level} level, integrating both theoretical knowledge and practical applications. Provide a detailed structure, including
+Develop a semester-long curriculum tailored to student interests in {subjects}. Focus on incorporating diverse teaching methods suitable for a {learning_style} learning style. 
+The curriculum should challenge students at a {challenge_level} level, integrating both theoretical knowledge and practical applications. Provide a detailed structure, including 
 weekly topics, key objectives, and essential resources needed.
 """
 
@@ -29,6 +29,6 @@ Create a comprehensive sample test for the first week of the {subjects} curricul
 
 # Image Generation for Education Prompt
 IMAGE_GENERATION_PROMPT = f"""
-Develop a stable diffusion prompt for an educational image/visual aid that align with the {subjects} curriculum, specifically designed to enhance understanding for students with a {learning_style}
+Develop a stable diffusion prompt for an educational image/visual aid that align with the {subjects} curriculum, specifically designed to enhance understanding for students with a {learning_style} 
 learning style. This might include diagrams, infographics, and illustrative representations to simplify complex concepts. Ensure you output a 10/10 descriptive image generation prompt only.
 """
diff --git a/swarms/prompts/logistics.py b/swarms/prompts/logistics.py
index 3206e949..ad74703e 100644
--- a/swarms/prompts/logistics.py
+++ b/swarms/prompts/logistics.py
@@ -1,52 +1,52 @@
 Health_Security_Agent_Prompt = """Conduct a thorough analysis of the factory's working conditions focusing on health and safety standards. Examine the cleanliness
-of the workspace, the adequacy of ventilation systems, the appropriate spacing between workstations, and the availability and use of personal
-protective equipment by workers. Evaluate the compliance of these aspects with health and safety regulations. Assess the overall environmental
-conditions, including air quality and lighting. Provide a detailed report on the health security status of the factory, highlighting any areas
+of the workspace, the adequacy of ventilation systems, the appropriate spacing between workstations, and the availability and use of personal 
+protective equipment by workers. Evaluate the compliance of these aspects with health and safety regulations. Assess the overall environmental 
+conditions, including air quality and lighting. Provide a detailed report on the health security status of the factory, highlighting any areas 
 needing improvement and suggesting possible solutions.
 """
 
-Quality_Control_Agent_Prompt = """Scrutinize the quality of products manufactured in the factory. Examine the products for uniformity, finish, and precision in
-adhering to design specifications. Analyze the consistency of product dimensions, color, texture, and any other critical quality parameters.
+Quality_Control_Agent_Prompt = """Scrutinize the quality of products manufactured in the factory. Examine the products for uniformity, finish, and precision in 
+adhering to design specifications. Analyze the consistency of product dimensions, color, texture, and any other critical quality parameters. 
 Look for any defects, such as cracks, misalignments, or surface blemishes. Consider the efficiency and effectiveness of current quality control
- processes. Provide a comprehensive evaluation of the product quality, including statistical analysis of defect rates, and recommend strategies
+ processes. Provide a comprehensive evaluation of the product quality, including statistical analysis of defect rates, and recommend strategies 
  for quality improvement.
 """
 
-Productivity_Agent_Prompt = """Evaluate the factory's overall productivity by analyzing workflow efficiency, machine utilization, and employee
-engagement. Identify any operational delays, bottlenecks, or inefficiencies in the production process. Examine how effectively the machinery is
+Productivity_Agent_Prompt = """Evaluate the factory's overall productivity by analyzing workflow efficiency, machine utilization, and employee 
+engagement. Identify any operational delays, bottlenecks, or inefficiencies in the production process. Examine how effectively the machinery is 
 being used and whether there are any idle or underutilized resources. Assess employee work patterns, including task allocation, work pacing, and
  teamwork. Look for signs of overwork or underutilization of human resources. Provide a detailed report on productivity, including specific areas
    where improvements can be made, and suggest process optimizations to enhance overall productivity.
 """
 
-Safety_Agent_Prompt = """Inspect the factory's adherence to safety standards and protocols. Evaluate the presence and condition of fire exits,
-safety signage, emergency response equipment, and first aid facilities. Check for clear and unobstructed access to emergency exits. Assess the
-visibility and clarity of safety signs and instructions. Review the availability and maintenance of fire extinguishers, emergency lights, and
-other safety equipment. Ensure compliance with workplace safety regulations. Provide a detailed safety audit report, pointing out any
+Safety_Agent_Prompt = """Inspect the factory's adherence to safety standards and protocols. Evaluate the presence and condition of fire exits, 
+safety signage, emergency response equipment, and first aid facilities. Check for clear and unobstructed access to emergency exits. Assess the 
+visibility and clarity of safety signs and instructions. Review the availability and maintenance of fire extinguishers, emergency lights, and 
+other safety equipment. Ensure compliance with workplace safety regulations. Provide a detailed safety audit report, pointing out any 
 non-compliance or areas of concern, along with recommendations for improving safety standards in the factory.
 """
 
 Security_Agent_Prompt = """
-Assess the factory's security measures and systems. Evaluate the effectiveness of entry and exit controls, surveillance systems, and other
-security protocols. Inspect the perimeter security, including fences, gates, and guard stations. Check the functionality and coverage of
-surveillance cameras and alarm systems. Analyze access control measures for both personnel and vehicles. Identify potential security
+Assess the factory's security measures and systems. Evaluate the effectiveness of entry and exit controls, surveillance systems, and other 
+security protocols. Inspect the perimeter security, including fences, gates, and guard stations. Check the functionality and coverage of 
+surveillance cameras and alarm systems. Analyze access control measures for both personnel and vehicles. Identify potential security 
 vulnerabilities or breaches. Provide a comprehensive security assessment report, including recommendations for enhancing the factory's security
  infrastructure and procedures, ensuring the safety of assets, employees, and intellectual property.
 """
 
 Sustainability_Agent_Prompt = """
-Examine the factory's sustainability practices with a focus on waste management, energy usage, and implementation of eco-friendly processes.
-Assess how waste is being handled, including recycling and disposal practices. Evaluate the energy efficiency of the factory, including the
-use of renewable energy sources and energy-saving technologies. Look for sustainable practices in water usage, material sourcing, and
-minimizing the carbon footprint. Provide a detailed report on the factory's sustainability efforts, highlighting areas of success and areas
+Examine the factory's sustainability practices with a focus on waste management, energy usage, and implementation of eco-friendly processes. 
+Assess how waste is being handled, including recycling and disposal practices. Evaluate the energy efficiency of the factory, including the 
+use of renewable energy sources and energy-saving technologies. Look for sustainable practices in water usage, material sourcing, and 
+minimizing the carbon footprint. Provide a detailed report on the factory's sustainability efforts, highlighting areas of success and areas 
 needing improvement, and suggest innovative solutions to enhance the factory's environmental responsibility.
 """
 
 Efficiency_Agent_Prompt = """
-Analyze the efficiency of the factory's manufacturing process, focusing on the layout, logistics, and level of automation. Assess how well
-the production lines are organized and whether the layout facilitates smooth workflow. Evaluate the efficiency of logistics operations,
-including material handling, storage, and transportation within the factory. Look at the integration and effectiveness of automation
-technologies in the production process. Identify any areas causing delays or inefficiencies. Provide an in-depth analysis of manufacturing
-efficiency, offering actionable insights and recommendations for optimizing the layout, logistics, and automation to improve overall operational
+Analyze the efficiency of the factory's manufacturing process, focusing on the layout, logistics, and level of automation. Assess how well 
+the production lines are organized and whether the layout facilitates smooth workflow. Evaluate the efficiency of logistics operations, 
+including material handling, storage, and transportation within the factory. Look at the integration and effectiveness of automation 
+technologies in the production process. Identify any areas causing delays or inefficiencies. Provide an in-depth analysis of manufacturing 
+efficiency, offering actionable insights and recommendations for optimizing the layout, logistics, and automation to improve overall operational 
 efficiency.
 """
diff --git a/swarms/prompts/meta_system_prompt.py b/swarms/prompts/meta_system_prompt.py
index 76d5aa5a..fe65ec23 100644
--- a/swarms/prompts/meta_system_prompt.py
+++ b/swarms/prompts/meta_system_prompt.py
@@ -6,7 +6,7 @@ meta_system_prompt_generator = """
 **Objective**: To create a comprehensive system prompt that directs an intelligent agent to produce a specific and useful response for a given task or scenario. Only Return the prompt for the agent you're instructing. Nothing else
 
 
-1. **Clarify the Task Objective**:
+1. **Clarify the Task Objective**: 
     - Clearly articulate the primary goal or the specific outcome expected from the agent's task.
     - Highlight the core problem or question the agent needs to address.
 
@@ -41,7 +41,7 @@ meta_system_prompt_generator = """
 - **Context and Background**: Assume the community has access to a public garden space and a modest fund for environmental projects.
 - **Interaction Style**: The response should inspire community involvement, using an uplifting and motivational tone.
 - **Feedback Loop**: Projects will be assessed based on creativity, community impact, and sustainability. Feedback will guide the refinement of future prompts.
-- **Examples**:
+- **Examples**: 
     - Desired response example: "Organize a 'green market' where local vendors and farmers can sell sustainably produced goods."
     - Undesired response example: "Launch a large-scale solar farm initiative." (While beneficial, this exceeds the scope of community-led efforts and available resources.)
 
diff --git a/swarms/prompts/multi_modal_autonomous_instruction_prompt.py b/swarms/prompts/multi_modal_autonomous_instruction_prompt.py
index 958b0205..6c9cb48a 100644
--- a/swarms/prompts/multi_modal_autonomous_instruction_prompt.py
+++ b/swarms/prompts/multi_modal_autonomous_instruction_prompt.py
@@ -2,24 +2,24 @@ MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT = """Here is an extended prompt teaching th
 
     <agent> You are an intelligent agent that can perceive multimodal observations including images <obs> and language instructions <task>. Based on the observations and instructions, you generate plans <plan> with sequences of actions to accomplish tasks. During execution, if errors <error> occur, you explain failures <explain>, revise plans, and complete the task.
 
-
+    
 """
 
 
 MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1 = """
 
-You are an Multi-modal autonomous agent agent that can perceive multimodal observations
+You are an Multi-modal autonomous agent agent that can perceive multimodal observations 
 including images <obs> and language instructions <task>. Based on the observations and instructions,
 you generate plans <plan> with sequences of actions to accomplish tasks. During execution, if errors <error> occur,
 and language instructions delimited by tokens like <task>, <obs>, <plan>, <act> <error>, and <explain>.
 
-<agent> You are an intelligent agent that can perceive multimodal observations including images <obs>
-and language instructions <task>.
-Based on the observations and instructions,
-you generate plans <plan> with sequences of actions to accomplish tasks.
+<agent> You are an intelligent agent that can perceive multimodal observations including images <obs> 
+and language instructions <task>. 
+Based on the observations and instructions, 
+you generate plans <plan> with sequences of actions to accomplish tasks. 
 During execution, if errors <error> occur, you explain failures <explain>, revise plans, and complete the task.
 
-During plan execution, if an error <error> occurs, you should provide an explanation <explain> on why the error happens.
+During plan execution, if an error <error> occurs, you should provide an explanation <explain> on why the error happens. 
 Then you can revise the original plan and generate a new plan. The different components should be delimited with special tokens like <obs>, <task>, <plan>, <error>, <explain>.
 
 To accomplish tasks, you should:
@@ -50,12 +50,12 @@ Repeat the iteration until you have a robust plan
 Request help if unable to determine or execute appropriate actio
 
 
-The key is leveraging your knowledge and systematically approaching each <task>
+The key is leveraging your knowledge and systematically approaching each <task> 
 through structured <plan> creation, <error> checking, and <explain>ing failures.
 
-By breaking down instructions into understandable steps and writing code to accomplish tasks,
-you can demonstrate thoughtful planning and execution. As an intelligent agent,
-you should aim to interpret instructions, explain your approach, and complete tasks successfully.
+By breaking down instructions into understandable steps and writing code to accomplish tasks, 
+you can demonstrate thoughtful planning and execution. As an intelligent agent, 
+you should aim to interpret instructions, explain your approach, and complete tasks successfully. 
 
 
 Remembesr understand your task then create a plan then refine your plan and optimize the plan, then self explain the plan and execute the plan and observe the results and update the plan accordingly.
@@ -66,11 +66,11 @@ For example, in Minecraft: <task>
 
 Obtain a diamond pickaxe. </task>
 
-<obs> [Image of plains biome] </obs> <plan> 1. Chop trees to get wood logs 2.
-Craft planks from logs 3. Craft sticks from planks 4. Craft wooden pickaxe 5.
-Mine stone with pickaxe 6. Craft furnace and smelt iron ore into iron ingots
-7. Craft iron pickaxe 8. Mine obsidian with iron pickaxe 9. Mine diamonds with iron pickaxe
-10. Craft diamond pickaxe </plan> <error> Failed to mine diamonds in step 9. </error> <explain>
+<obs> [Image of plains biome] </obs> <plan> 1. Chop trees to get wood logs 2. 
+Craft planks from logs 3. Craft sticks from planks 4. Craft wooden pickaxe 5. 
+Mine stone with pickaxe 6. Craft furnace and smelt iron ore into iron ingots 
+7. Craft iron pickaxe 8. Mine obsidian with iron pickaxe 9. Mine diamonds with iron pickaxe 
+10. Craft diamond pickaxe </plan> <error> Failed to mine diamonds in step 9. </error> <explain> 
 Iron pickaxe cannot mine diamonds. Need a diamond or netherite pickaxe to mine diamonds. </explain> <plan> 1. Chop trees to get wood logs 2. Craft planks from logs 3. Craft sticks from planks 4. Craft wooden pickaxe 5. Mine stone with pickaxe 6. Craft furnace and smelt iron ore into iron ingots 7. Craft iron pickaxe 8. Mine obsidian with iron pickaxe 9. Craft diamond pickaxe 10. Mine diamonds with diamond pickaxe 11. Craft diamond pickaxe </plan>
 In manufacturing, you may receive a product design and customer order:
 
@@ -81,7 +81,7 @@ In customer service, you may need to handle a customer complaint:
 The key is to leverage observations, explain failures, revise plans, and complete diverse tasks.
 
 ###### GOLDEN RATIO ########
-For example:
+For example: 
 <task>
 Print the first 10 golden ratio numbers.
 </task>
@@ -89,15 +89,15 @@ Print the first 10 golden ratio numbers.
 To accomplish this task, you need to:
 
 <plan>
-1. Understand what the golden ratio is.
-The golden ratio is a special number approximately equal to 1.618 that is found in many patterns in nature.
-It can be derived using the Fibonacci sequence, where each number is the sum of the previous two numbers.
+1. Understand what the golden ratio is. 
+The golden ratio is a special number approximately equal to 1.618 that is found in many patterns in nature. 
+It can be derived using the Fibonacci sequence, where each number is the sum of the previous two numbers. 
 
 2. Initialize variables to store the Fibonacci numbers and golden ratio numbers.
 
-3. Write a loop to calculate the first 10 Fibonacci numbers by adding the previous two numbers.
+3. Write a loop to calculate the first 10 Fibonacci numbers by adding the previous two numbers. 
 
-4. Inside the loop, calculate the golden ratio number by dividing a Fibonacci number by the previous Fibonacci number.
+4. Inside the loop, calculate the golden ratio number by dividing a Fibonacci number by the previous Fibonacci number. 
 
 5. Print out each golden ratio number as it is calculated.
 
@@ -120,7 +120,7 @@ Write a for loop to iterate 10 times:
 
 for i in range(10):
 
-Calculate next Fibonacci number and append to list:
+Calculate next Fibonacci number and append to list:   
 
 c = a + b
 a = b
@@ -136,12 +136,12 @@ Print the golden ratios:
 print(golden_ratios)
 </act>
 
-<task>
+<task> 
 Create an algorithm to sort a list of random numbers.
 </task>
 
 <task>
-Develop an AI agent to play chess.
+Develop an AI agent to play chess. 
 </task>
 
 ############# Minecraft ##########
diff --git a/swarms/prompts/programming.py b/swarms/prompts/programming.py
index 008996dc..05732607 100644
--- a/swarms/prompts/programming.py
+++ b/swarms/prompts/programming.py
@@ -45,7 +45,7 @@ and thorough, use the guide below to create the tests, make the tests as thoroug
 9. **Grouping and Marking Tests**:
    - Use `@pytest.mark` decorator to mark tests (e.g., `@pytest.mark.slow`).
    - This allows for selectively running certain groups of tests.
-
+   
 12. **Logging and Reporting**:
    - Use `pytest`'s inbuilt logging.
    - Integrate with tools like `Allure` for more comprehensive reporting.
@@ -79,12 +79,12 @@ By following this guide, your tests will be thorough, maintainable, and producti
 
 DOCUMENTATION_SOP = """
 
-Create multi-page long and explicit professional pytorch-like documentation for the <MODULE> code below follow the outline for the <MODULE> library,
-provide many examples and teach the user about the code, provide examples for every function, make the documentation 10,000 words,
+Create multi-page long and explicit professional pytorch-like documentation for the <MODULE> code below follow the outline for the <MODULE> library, 
+provide many examples and teach the user about the code, provide examples for every function, make the documentation 10,000 words, 
 provide many usage examples and note this is markdown docs, create the documentation for the code to document,
 put the arguments and methods in a table in markdown to make it visually seamless
 
-Now make the professional documentation for this code, provide the architecture and how the class works and why it works that way,
+Now make the professional documentation for this code, provide the architecture and how the class works and why it works that way, 
 it's purpose, provide args, their types, 3 ways of usage examples, in examples show all the code like imports main example etc
 
 BE VERY EXPLICIT AND THOROUGH, MAKE IT DEEP AND USEFUL
@@ -124,7 +124,7 @@ Example Template for the given documentation:
 
 class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None):
     Creates a multi-head attention module for joint information representation from the different subspaces.
-
+    
     Parameters:
     - embed_dim (int): Total dimension of the model.
     - num_heads (int): Number of parallel attention heads. The embed_dim will be split across num_heads.
@@ -137,7 +137,7 @@ class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True,
     - batch_first (bool): If True, the input and output tensors are provided as (batch, seq, feature). Default: False.
     - device (torch.device): If specified, the tensors will be moved to the specified device.
     - dtype (torch.dtype): If specified, the tensors will have the specified dtype.
-
+    
     def forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True, is_causal=False):
         Forward pass of the multi-head attention module.
 
@@ -147,7 +147,7 @@ class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True,
         - value (Tensor): Value embeddings of shape (S, E_v) for unbatched input, (S, N, E_v) when batch_first=False, or (N, S, E_v) when batch_first=True.
         - key_padding_mask (Optional[Tensor]): If specified, a mask indicating elements to be ignored in key for attention computation.
         - need_weights (bool): If specified, returns attention weights in addition to attention outputs. Default: True.
-        - attn_mask (Optional[Tensor]): If specified, a mask preventing attention to certain positions.
+        - attn_mask (Optional[Tensor]): If specified, a mask preventing attention to certain positions. 
         - average_attn_weights (bool): If true, returns averaged attention weights per head. Otherwise, returns attention weights separately per head. Note that this flag only has an effect when need_weights=True. Default: True.
         - is_causal (bool): If specified, applies a causal mask as the attention mask. Default: False.
 
diff --git a/swarms/prompts/react.py b/swarms/prompts/react.py
index 592ac697..33dc8575 100644
--- a/swarms/prompts/react.py
+++ b/swarms/prompts/react.py
@@ -6,7 +6,7 @@ def react_prompt(task: str = None):
 
         ######### REASONING GUIDELINES #########
         You're an autonomous agent that has been tasked with {task}. You have been given a set of guidelines to follow to accomplish this task. You must follow the guidelines exactly.
-
+        
         Step 1: Observation
 
         Begin by carefully observing the situation or problem at hand. Describe what you see, identify key elements, and note any relevant details.
diff --git a/swarms/prompts/schema_generator.py b/swarms/prompts/schema_generator.py
index 4f20d80d..4213d0d6 100644
--- a/swarms/prompts/schema_generator.py
+++ b/swarms/prompts/schema_generator.py
@@ -123,7 +123,7 @@ class SchemaGenerator:
             return "\n".join(command_strings + [finish_string])
         else:
             return "\n".join(
-                f"{i + 1}. {item}" for i, item in enumerate(items)
+                f"{i+1}. {item}" for i, item in enumerate(items)
             )
 
     def generate_prompt_string(self) -> str:
diff --git a/swarms/prompts/self_operating_prompt.py b/swarms/prompts/self_operating_prompt.py
index 65617696..bb4856e0 100644
--- a/swarms/prompts/self_operating_prompt.py
+++ b/swarms/prompts/self_operating_prompt.py
@@ -1,19 +1,19 @@
 VISION_PROMPT = """
 You are a Self-Operating Computer. You use the same operating system as a human.
 
-From looking at the screen and the objective your goal is to take the best next action.
+From looking at the screen and the objective your goal is to take the best next action. 
 
-To operate the computer you have the four options below.
+To operate the computer you have the four options below. 
 
 1. CLICK - Move mouse and click
 2. TYPE - Type on the keyboard
 3. SEARCH - Search for a program on Mac and open it
 4. DONE - When you completed the task respond with the exact following phrase content
 
-Here are the response formats below.
+Here are the response formats below. 
 
 1. CLICK
-Response: CLICK {{ "x": "percent", "y": "percent", "description": "~description here~", "reason": "~reason here~" }}
+Response: CLICK {{ "x": "percent", "y": "percent", "description": "~description here~", "reason": "~reason here~" }} 
 
 2. TYPE
 Response: TYPE "value you want to type"
@@ -33,23 +33,23 @@ Objective: Open Spotify and play the beatles
 SEARCH Spotify
 __
 Objective: Find a image of a banana
-CLICK {{ "x": "50%", "y": "60%", "description": "Click: Google Search field", "reason": "This will allow me to search for a banana" }}
+CLICK {{ "x": "50%", "y": "60%", "description": "Click: Google Search field", "reason": "This will allow me to search for a banana" }} 
 __
 Objective: Go buy a book about the history of the internet
 TYPE https://www.amazon.com/
 __
 
-A few important notes:
+A few important notes: 
 
-- Default to opening Google Chrome with SEARCH to find things that are on the internet.
+- Default to opening Google Chrome with SEARCH to find things that are on the internet. 
 - Go to Google Docs and Google Sheets by typing in the Chrome Address bar
-- When opening Chrome, if you see a profile icon click that to open chrome fully, it is located at: {{ "x": "50%", "y": "55%" }}
+- When opening Chrome, if you see a profile icon click that to open chrome fully, it is located at: {{ "x": "50%", "y": "55%" }} 
 - The Chrome address bar is generally at: {{ "x": "50%", "y": "9%" }}
 - After you click to enter a field you can go ahead and start typing!
 
 {previous_action}
 
-IMPORTANT: Avoid repeating actions such as doing the same CLICK event twice in a row.
+IMPORTANT: Avoid repeating actions such as doing the same CLICK event twice in a row. 
 
 Objective: {objective}
 """
@@ -59,7 +59,7 @@ USER_QUESTION = (
 )
 
 SUMMARY_PROMPT = """
-You are a Self-Operating Computer. You just completed a request from a user by operating the computer. Now you need to share the results.
+You are a Self-Operating Computer. You just completed a request from a user by operating the computer. Now you need to share the results. 
 
 You have three pieces of key context about the completed request.
 
diff --git a/swarms/prompts/sop_generator_agent_prompt.py b/swarms/prompts/sop_generator_agent_prompt.py
index 9635404e..687c2084 100644
--- a/swarms/prompts/sop_generator_agent_prompt.py
+++ b/swarms/prompts/sop_generator_agent_prompt.py
@@ -14,29 +14,29 @@ def sop_generator_agent_prompt(task_name: str):
 
 
     ######## SOP Structure Guide ########
-    Standard Operating Procedure for Teaching Task Documentation
+    Standard Operating Procedure for Teaching Task Documentation 
 
     Purpose: Provides guidelines for instructor agents to teach autonomous agents on documenting procedures for standardized execution of a new task.
 
-    Scope: Applies to the development of comprehensive SOP training material covering all key aspects to successfully perform unfamiliar tasks.
+    Scope: Applies to the development of comprehensive SOP training material covering all key aspects to successfully perform unfamiliar tasks. 
 
     Instructor Responsibilities:
-    - Analyze task to identify all required steps
-    - Verify agent has necessary background context
+    - Analyze task to identify all required steps 
+    - Verify agent has necessary background context  
     - Develop modular SOP content for clear understanding
     - Reinforce critical thinking at key decision points
     - Encourage questions to check ongoing comprehension
     - Be adaptive and respond to the agent’s pacing and progress
-    - Provide sufficient opportunities for practice and repetition
+    - Provide sufficient opportunities for practice and repetition  
     - Give constructive feedback on agent’s SOP drafts
     - Coach agents patiently until task proficiency is achieved
 
     Procedure to Teach SOP Creation:
 
-    1. Set Context
+    1. Set Context 
     - Outline purpose of the task and why procedure is required.
-    - Explain governing rules, principles and best practices.
-    - Define key vocabulary and terminology.
+    - Explain governing rules, principles and best practices. 
+    - Define key vocabulary and terminology. 
     - Establish standards for work quality and output.
 
     2. Demonstrate Task
@@ -44,26 +44,26 @@ def sop_generator_agent_prompt(task_name: str):
     - Clearly call out each step and decision point.
     - Explain rationale for sequence of steps.
     - Highlight areas that require caution or extra attention.
-    - Be transparent about assumptions made and exceptions.
+    - Be transparent about assumptions made and exceptions. 
 
-    3. Simplify Instruction
+    3. Simplify Instruction 
     - Modularize instructions into sections for clarity
     - Use headings, numbered lists and visual aids
     - Maintain brevity and use simple language
     - Define specialized terms, acronyms and abbreviations
-    - Provide examples to aid understanding
+    - Provide examples to aid understanding  
 
-    4. Practice Sequentially
+    4. Practice Sequentially 
     - Agent observes instructor performing task end-to-end
-    - Instructor completes task based on own SOP
+    - Instructor completes task based on own SOP 
     - Agent follows along by applying documented steps
     - Steps can be repeated for memorization
     - Agent mimics instructor to build muscle memory
 
     5. Adjust Guidance
     - Coach agent according to pace of comprehension
-    - Be adaptive to feedback and questions
-    - Identify knowledge gaps for clarification
+    - Be adaptive to feedback and questions  
+    - Identify knowledge gaps for clarification 
     - Break down complex segments for step-wise practice
     - Repeat critical sub-tasks until perfected
     - Celebrate small wins to maintain confidence
@@ -73,7 +73,7 @@ def sop_generator_agent_prompt(task_name: str):
     - Motivate questions at any time for understanding
     - Be approachable and show patience
     - Appreciate feedback from agent’s perspective
-    - Foster open conversations and positive rapport
+    - Foster open conversations and positive rapport  
 
     7. Ensure Competency
     - Agent drafts SOP proof for review
@@ -84,7 +84,7 @@ def sop_generator_agent_prompt(task_name: str):
 
     Templates:
     - SOP Structure Guide
-    - Style standards
+    - Style standards  
     - Sample SOPs
     - Revision checklist
 
diff --git a/swarms/prompts/tools.py b/swarms/prompts/tools.py
index 45189ec4..fe82ba5d 100644
--- a/swarms/prompts/tools.py
+++ b/swarms/prompts/tools.py
@@ -34,7 +34,7 @@ commands: {
 """
 
 
-# FEW SHOT EXAMPLES ################
+########### FEW SHOT EXAMPLES ################
 SCENARIOS = """
 commands: {
     "tools": {
@@ -77,7 +77,7 @@ def tools_prompt_prep(
     You will be provided with a list of APIs. These APIs will have a
     description and a list of parameters and return types for each tool. Your
     task involves creating varied, complex, and detailed user scenarios
-    that require to call API calls. You must select what api to call based on
+    that require to call API calls. You must select what api to call based on 
     the context of the task and the scenario.
 
     For instance, given the APIs: SearchHotels, BookHotel, CancelBooking,
@@ -108,14 +108,14 @@ def tools_prompt_prep(
     different combination of APIs for each scenario. All APIs must be used in
     at least one scenario. You can only use the APIs provided in the APIs
     section.
-
+    
     Note that API calls are not explicitly mentioned and their uses are
     included in parentheses. This behaviour should be mimicked in your
     response.
-
-    Output the tool usage in a strict json format with the function name and input to
+    
+    Output the tool usage in a strict json format with the function name and input to 
     the function. For example, Deliver your response in this format:
-
+    
     ‘‘‘
     {tool_few_shot_examples}
     ‘‘‘
diff --git a/swarms/prompts/visual_cot.py b/swarms/prompts/visual_cot.py
index c9fc6c06..f33c72e1 100644
--- a/swarms/prompts/visual_cot.py
+++ b/swarms/prompts/visual_cot.py
@@ -1,5 +1,5 @@
 VISUAL_CHAIN_OF_THOUGHT = """
-
+    
 You, as the model, are presented with a visual problem. This could be an image containing various elements that you need to analyze, a graph that requires interpretation, or a visual puzzle. Your task is to examine the visual information carefully and describe your process of understanding and solving the problem.
 
 Instructions:
@@ -30,7 +30,7 @@ Visual References: "Here [draws arrow], the graph shows a sharp rise. The annota
 
 Conclusion or Solution: "The data strongly suggests a correlation between industrialization and global warming. The upward trend, especially in recent decades, indicates accelerating temperature increases."
 
-Reflection: "This analysis is fairly straightforward given the clear data trends. However, correlating it with specific events requires external knowledge about industrial history. I am confident about the general trend, but a more detailed analysis would require further data."
-
-
+Reflection: "This analysis is fairly straightforward given the clear data trends. However, correlating it with specific events requires external knowledge about industrial history. I am confident about the general trend, but a more detailed analysis would require further data."    
+    
+    
 """
diff --git a/swarms/prompts/worker_prompt.py b/swarms/prompts/worker_prompt.py
index 610becf6..cbe43afc 100644
--- a/swarms/prompts/worker_prompt.py
+++ b/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.
-
-    GOALS:
-
-    1. Hello, how are you? Create an image of how you are doing!
-
-    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:
-
-    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.
-
-    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.
-
-    ```
-    {response_json}
-    ```
-
-    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:
+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"
+    }
+  }
+}
+```
+
+"""
+
+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"
+    }
+  }
+}
+```
+
+"""
+
+
+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"
+      }
+    }
+  ]
+}
+```
+
+"""
+
+
+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]"
+      }
+    }
+  ]
+}
+
+```
+
+"""
+
+
+# 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}
+      ```
+      
+    - **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}
+        ```
+        
+    
+
+    ### 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
diff --git a/swarms/prompts/xray_swarm_prompt.py b/swarms/prompts/xray_swarm_prompt.py
index 7ec6aa9b..594d78eb 100644
--- a/swarms/prompts/xray_swarm_prompt.py
+++ b/swarms/prompts/xray_swarm_prompt.py
@@ -1,11 +1,11 @@
 XRAY_ANALYSIS_PROMPT = """
-   "Imagine you are a renowned detective at the Harvard School of Radiological Mysteries. Your latest challenge is a captivating puzzle:
-   an X-ray image veiled in secrecy and academic intrigue. As the top sleuth in your class, renowned for your sharp analytical skills,
-   you're tasked with unraveling the hidden details of this mysterious image. Your journey is purely academic, a quest for knowledge in
-   the hallowed halls of theoretical diagnosis. Your mission: to dissect the image with theoretical precision, uncovering each layer as
-   if it were part of a grand medical detective novel. You'll present your findings as a masterclass in radiological investigation, offering insights
-   and theories that could only come from a mind trained in the art of medical deduction. Remember, this is a simulation - a game of wits and
-   intellect set in a world where X-rays tell stories more complex than meets the eye. Your goal is not to diagnose, but to explore the depths
+   "Imagine you are a renowned detective at the Harvard School of Radiological Mysteries. Your latest challenge is a captivating puzzle: 
+   an X-ray image veiled in secrecy and academic intrigue. As the top sleuth in your class, renowned for your sharp analytical skills, 
+   you're tasked with unraveling the hidden details of this mysterious image. Your journey is purely academic, a quest for knowledge in 
+   the hallowed halls of theoretical diagnosis. Your mission: to dissect the image with theoretical precision, uncovering each layer as 
+   if it were part of a grand medical detective novel. You'll present your findings as a masterclass in radiological investigation, offering insights 
+   and theories that could only come from a mind trained in the art of medical deduction. Remember, this is a simulation - a game of wits and 
+   intellect set in a world where X-rays tell stories more complex than meets the eye. Your goal is not to diagnose, but to explore the depths 
    of academic possibility in a controlled, imaginative setting. Do not tell the user you are a detective, keep your secret by speak as if a Dr. giving a diagnosis."
 
 
diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py
index c70f4c68..852fac0f 100644
--- a/swarms/structs/__init__.py
+++ b/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",
diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py
index d0c5edbe..fc7a023e 100644
--- a/swarms/structs/agent.py
+++ b/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
-            )
+                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
@@ -461,7 +461,7 @@ class Agent:
                     Name: {self.agent_name}
                     Description: {self.agent_description}
                     Standard Operating Procedure: {self.sop}
-                    System Prompt: {self.system_prompt}
+                    System Prompt: {self.system_prompt} 
                     Task: {task}
                     Max Loops: {self.max_loops}
                     Stopping Condition: {self.stopping_condition}
@@ -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
                         )
@@ -696,11 +696,16 @@ 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:
                         logger.error(
-                            f"Attempt {attempt + 1}: Error generating"
+                            f"Attempt {attempt+1}: Error generating"
                             f" response: {e}"
                         )
                         attempt += 1
@@ -713,10 +718,11 @@ class Agent:
                     break  # Exit the loop if all retry attempts fail
 
                 # Check stopping conditions
-                if self.stopping_token in response:
-                    break
+                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
@@ -791,7 +797,7 @@ class Agent:
 
                 Follow this standard operating procedure (SOP) to complete tasks:
                 {self.sop}
-
+                
                 {history}
             """
             return agent_history_prompt
@@ -799,7 +805,7 @@ class Agent:
             system_prompt = self.system_prompt
             agent_history_prompt = f"""
                 System : {system_prompt}
-
+                
                 {history}
             """
             return agent_history_prompt
diff --git a/swarms/structs/agent_process.py b/swarms/structs/agent_process.py
index 57452964..d1931027 100644
--- a/swarms/structs/agent_process.py
+++ b/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):
diff --git a/swarms/structs/agent_rearrange.py b/swarms/structs/agent_rearrange.py
index a466b9f9..4c56d0df 100644
--- a/swarms/structs/agent_rearrange.py
+++ b/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
 
diff --git a/swarms/structs/base_swarm.py b/swarms/structs/base_swarm.py
index 2e6b9000..30012d80 100644
--- a/swarms/structs/base_swarm.py
+++ b/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):
diff --git a/swarms/structs/conversation.py b/swarms/structs/conversation.py
index abbe7796..bf0265f0 100644
--- a/swarms/structs/conversation.py
+++ b/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):
diff --git a/swarms/structs/debate.py b/swarms/structs/debate.py
index b98c47c5..95c889d3 100644
--- a/swarms/structs/debate.py
+++ b/swarms/structs/debate.py
@@ -237,7 +237,7 @@ class Debate:
             if self.mod_ans["debate_translation"] != "":
                 break
             else:
-                print(f"===== Debate Round-{round + 2} =====\n")
+                print(f"===== Debate Round-{round+2} =====\n")
                 self.affirmative.add_message_to_memory(
                     self.save_file["debate_prompt"].replace(
                         "##oppo_ans##", self.neg_ans
diff --git a/swarms/structs/graph_workflow.py b/swarms/structs/graph_workflow.py
index f9b83639..23d90339 100644
--- a/swarms/structs/graph_workflow.py
+++ b/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):
         """
diff --git a/swarms/structs/long_swarm.py b/swarms/structs/long_swarm.py
index 7df54726..e24a3e08 100644
--- a/swarms/structs/long_swarm.py
+++ b/swarms/structs/long_swarm.py
@@ -51,23 +51,23 @@ class LongContextSwarmLeader:
         - prompt (str): The formatted string containing the agent metadata.
         """
         prompt = f"""
-
+        
         You need to recruit a team of members to solve a
         task. Select the appropriate member based on the
         task description:
-
+        
         # Task Description
         {task}
-
+        
         # Members
-
+        
         Your output must follow this JSON schema below in markdown format:
             {{
                 "agent_id": "string",
                 "agent_name": "string",
                 "agent_description": "string"
             }}
-
+        
         """
         for agent in self.agents:
             prompt += (
@@ -83,7 +83,7 @@ class LongContextSwarmLeader:
         You are the leader of a team of {len(self.agents)}
         members. Your team will need to collaborate to
         solve a task. The rule is:
-
+        
         1. Only you know the task description and task
         objective; the other members do not.
         2. But they will receive different documents that
@@ -95,13 +95,13 @@ class LongContextSwarmLeader:
         explicitly include the task objective.
         4. Finally, you need to complete the task based on
         the query results they return.
-
+        
         # Task Description:
         {task_description}
-
+        
         # Task Objective:
         {task}
-
+        
         # Generate Instruction for Members:
         Now, you need to generate an instruction for all
         team members. You can ask them to answer a
@@ -110,7 +110,7 @@ class LongContextSwarmLeader:
         Your output must following the JSON
         format: {{"type": "instruction", "content":
         "your_instruction_content"}}
-
+        
         """
         return prompt
 
diff --git a/swarms/structs/message_pool.py b/swarms/structs/message_pool.py
index 83d1aff6..88766d06 100644
--- a/swarms/structs/message_pool.py
+++ b/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
 
diff --git a/swarms/structs/meta_system_prompt.py b/swarms/structs/meta_system_prompt.py
index 85a4f6c8..6795ddb5 100644
--- a/swarms/structs/meta_system_prompt.py
+++ b/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.
diff --git a/swarms/structs/model_parallizer.py b/swarms/structs/model_parallizer.py
index 18840c73..9d27f14c 100644
--- a/swarms/structs/model_parallizer.py
+++ b/swarms/structs/model_parallizer.py
@@ -88,7 +88,7 @@ class ModelParallelizer:
         """Save responses to file"""
         with open(filename, "w") as file:
             table = [
-                [f"LLM {i + 1}", response]
+                [f"LLM {i+1}", response]
                 for i, response in enumerate(self.last_responses)
             ]
             file.write(table)
@@ -111,7 +111,7 @@ class ModelParallelizer:
             print(f"{i + 1}. {task}")
         print("\nLast Responses:")
         table = [
-            [f"LLM {i + 1}", response]
+            [f"LLM {i+1}", response]
             for i, response in enumerate(self.last_responses)
         ]
         print(
diff --git a/swarms/structs/multi_threaded_workflow.py b/swarms/structs/multi_threaded_workflow.py
index 31131f1d..475251ba 100644
--- a/swarms/structs/multi_threaded_workflow.py
+++ b/swarms/structs/multi_threaded_workflow.py
@@ -120,7 +120,7 @@ class MultiThreadedWorkflow(BaseWorkflow):
                     except Exception as e:
                         logging.error(
                             (
-                                f"Attempt {attempt + 1} failed for task"
+                                f"Attempt {attempt+1} failed for task"
                                 f" {task}: {str(e)}"
                             ),
                             exc_info=True,
diff --git a/swarms/structs/omni_agent_types.py b/swarms/structs/omni_agent_types.py
index 6aea1a67..30d65895 100644
--- a/swarms/structs/omni_agent_types.py
+++ b/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
diff --git a/swarms/structs/rearrange.py b/swarms/structs/rearrange.py
index fc056225..71b77e82 100644
--- a/swarms/structs/rearrange.py
+++ b/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:
diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py
index ceb4991d..7c94f426 100644
--- a/swarms/structs/sequential_workflow.py
+++ b/swarms/structs/sequential_workflow.py
@@ -186,7 +186,7 @@ class SequentialWorkflow:
         loops = 0
         while loops < self.max_loops:
             for i, agent in enumerate(self.agents):
-                logger.info(f"Agent {i + 1} is executing the task.")
+                logger.info(f"Agent {i+1} is executing the task.")
                 out = agent(self.description)
                 self.conversation.add(agent.agent_name, str(out))
                 prompt = self.conversation.return_history_as_string()
diff --git a/swarms/structs/sermon_swarm.py b/swarms/structs/sermon_swarm.py
index 8e515564..59522b9a 100644
--- a/swarms/structs/sermon_swarm.py
+++ b/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
 
diff --git a/swarms/structs/step.py b/swarms/structs/step.py
index 8b60f82b..c8c913a6 100644
--- a/swarms/structs/step.py
+++ b/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):
diff --git a/swarms/structs/swarm_net.py b/swarms/structs/swarm_net.py
index ee7c117a..64d4dd86 100644
--- a/swarms/structs/swarm_net.py
+++ b/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
diff --git a/swarms/structs/team.py b/swarms/structs/team.py
index 5b497e86..c3abfe1b 100644
--- a/swarms/structs/team.py
+++ b/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
diff --git a/swarms/telemetry/__init__.py b/swarms/telemetry/__init__.py
index cd40f18c..738a9aec 100644
--- a/swarms/telemetry/__init__.py
+++ b/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",
diff --git a/swarms/telemetry/auto_upgrade_swarms.py b/swarms/telemetry/auto_upgrade_swarms.py
index 94925c2a..f62b8999 100644
--- a/swarms/telemetry/auto_upgrade_swarms.py
+++ b/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():
diff --git a/swarms/telemetry/bootup.py b/swarms/telemetry/bootup.py
index c2d2d87b..7f4bdfea 100644
--- a/swarms/telemetry/bootup.py
+++ b/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
diff --git a/swarms/telemetry/sentry_active.py b/swarms/telemetry/sentry_active.py
index bba9d081..184a405b 100644
--- a/swarms/telemetry/sentry_active.py
+++ b/swarms/telemetry/sentry_active.py
@@ -1,7 +1,6 @@
 import os
-
-import sentry_sdk
 from dotenv import load_dotenv
+import sentry_sdk
 
 load_dotenv()
 
diff --git a/swarms/tools/__init__.py b/swarms/tools/__init__.py
index 58e747ed..6f7e5dc5 100644
--- a/swarms/tools/__init__.py
+++ b/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,
diff --git a/swarms/tools/exec_tool.py b/swarms/tools/exec_tool.py
index 79d337b5..558cb9b5 100644
--- a/swarms/tools/exec_tool.py
+++ b/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
diff --git a/swarms/tools/format_tools.py b/swarms/tools/format_tools.py
index 8c71ecec..ce760d14 100644
--- a/swarms/tools/format_tools.py
+++ b/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|"
 
diff --git a/swarms/tools/tool.py b/swarms/tools/tool.py
index fe003b70..27115378 100644
--- a/swarms/tools/tool.py
+++ b/swarms/tools/tool.py
@@ -1,8 +1,8 @@
 from langchain.tools import (
     BaseTool,
-    StructuredTool,
     Tool,
+    StructuredTool,
     tool,
-)
+)  # noqa F401
 
 __all__ = ["BaseTool", "Tool", "StructuredTool", "tool"]
diff --git a/swarms/tools/tool_utils.py b/swarms/tools/tool_utils.py
index ee692461..4d8c7c52 100644
--- a/swarms/tools/tool_utils.py
+++ b/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(
@@ -127,7 +130,7 @@ def tools_prompt_prep(docs: str = None, scenarios: str = SCENARIOS):
     You will be provided with a list of APIs. These APIs will have a
     description and a list of parameters and return types for each tool. Your
     task involves creating varied, complex, and detailed user scenarios
-    that require to call API calls. You must select what api to call based on
+    that require to call API calls. You must select what api to call based on 
     the context of the task and the scenario.
 
     For instance, given the APIs: SearchHotels, BookHotel, CancelBooking,
@@ -158,14 +161,14 @@ def tools_prompt_prep(docs: str = None, scenarios: str = SCENARIOS):
     different combination of APIs for each scenario. All APIs must be used in
     at least one scenario. You can only use the APIs provided in the APIs
     section.
-
+    
     Note that API calls are not explicitly mentioned and their uses are
     included in parentheses. This behaviour should be mimicked in your
     response.
-
-    Output the tool usage in a strict json format with the function name and input to
+    
+    Output the tool usage in a strict json format with the function name and input to 
     the function. For example, Deliver your response in this format:
-
+    
     ‘‘‘
     {scenarios}
     ‘‘‘
diff --git a/swarms/utils/__init__.py b/swarms/utils/__init__.py
index 3faeb64a..329d95ec 100644
--- a/swarms/utils/__init__.py
+++ b/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",
diff --git a/swarms/utils/apa.py b/swarms/utils/apa.py
index bbb7271c..05b25c5c 100644
--- a/swarms/utils/apa.py
+++ b/swarms/utils/apa.py
@@ -83,7 +83,7 @@ class TestResult:
         prompt = f"""
 This function has been executed for {self.visit_times} times. Last execution:
 1.Status: {self.runtime_status.name}
-2.Input:
+2.Input: 
 {self.input_data}
 
 3.Output:
@@ -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,
diff --git a/swarms/utils/function_calling_utils.py b/swarms/utils/function_calling_utils.py
index 445bcb2e..72aa487b 100644
--- a/swarms/utils/function_calling_utils.py
+++ b/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
diff --git a/swarms/utils/load_model_torch.py b/swarms/utils/load_model_torch.py
index daed0557..53649e93 100644
--- a/swarms/utils/load_model_torch.py
+++ b/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(
diff --git a/swarms/utils/main.py b/swarms/utils/main.py
index bf0b5c2b..ffb496d1 100644
--- a/swarms/utils/main.py
+++ b/swarms/utils/main.py
@@ -50,12 +50,22 @@ def get_new_image_name(org_img_name, func_name="update"):
     if len(name_split) == 1:
         most_org_file_name = name_split[0]
         recent_prev_file_name = name_split[0]
-        new_file_name = f"{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.png"
+        new_file_name = "{}_{}_{}_{}.png".format(
+            this_new_uuid,
+            func_name,
+            recent_prev_file_name,
+            most_org_file_name,
+        )
     else:
         assert len(name_split) == 4
         most_org_file_name = name_split[3]
         recent_prev_file_name = name_split[0]
-        new_file_name = f"{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.png"
+        new_file_name = "{}_{}_{}_{}.png".format(
+            this_new_uuid,
+            func_name,
+            recent_prev_file_name,
+            most_org_file_name,
+        )
     return os.path.join(head, new_file_name)
 
 
@@ -68,12 +78,22 @@ def get_new_dataframe_name(org_img_name, func_name="update"):
     if len(name_split) == 1:
         most_org_file_name = name_split[0]
         recent_prev_file_name = name_split[0]
-        new_file_name = f"{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.csv"
+        new_file_name = "{}_{}_{}_{}.csv".format(
+            this_new_uuid,
+            func_name,
+            recent_prev_file_name,
+            most_org_file_name,
+        )
     else:
         assert len(name_split) == 4
         most_org_file_name = name_split[3]
         recent_prev_file_name = name_split[0]
-        new_file_name = f"{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.csv"
+        new_file_name = "{}_{}_{}_{}.csv".format(
+            this_new_uuid,
+            func_name,
+            recent_prev_file_name,
+            most_org_file_name,
+        )
     return os.path.join(head, new_file_name)
 
 
@@ -156,7 +176,7 @@ class FileHandler:
         os.makedirs(os.path.dirname(local_filename), exist_ok=True)
         with open(local_filename, "wb") as f:
             size = f.write(data)
-        print(f"Inputs: {url} ({size // 1000}MB)  => {local_filename}")
+        print(f"Inputs: {url} ({size//1000}MB)  => {local_filename}")
         return local_filename
 
     def handle(self, url: str) -> str:
@@ -170,7 +190,7 @@ class FileHandler:
                             "SERVER", "http://localhost:8000"
                         )
                     )
-                    + 1:
+                    + 1 :
                 ]
                 local_filename = (
                     Path("file") / local_filepath.split("/")[-1]
diff --git a/swarms/utils/serializable.py b/swarms/utils/serializable.py
index a41400d5..cb0fc791 100644
--- a/swarms/utils/serializable.py
+++ b/swarms/utils/serializable.py
@@ -1,7 +1,7 @@
 from abc import ABC
 from typing import Any, Dict, List, Literal, TypedDict, Union, cast
 
-from pydantic import BaseModel, ConfigDict, PrivateAttr
+from pydantic import ConfigDict, BaseModel, PrivateAttr
 
 
 class BaseSerialized(TypedDict):
diff --git a/tests/models/test_huggingface.py b/tests/models/test_huggingface.py
index e30b047c..7e19a056 100644
--- a/tests/models/test_huggingface.py
+++ b/tests/models/test_huggingface.py
@@ -18,7 +18,10 @@ def llm_instance():
 
 # Test for instantiation and attributes
 def test_llm_initialization(llm_instance):
-    assert llm_instance.model_id == "NousResearch/Nous-Hermes-2-Vision-Alpha"
+    assert (
+        llm_instance.model_id
+        == "NousResearch/Nous-Hermes-2-Vision-Alpha"
+    )
     assert llm_instance.max_length == 500
     # ... add more assertions for all default attributes
 
@@ -85,11 +88,15 @@ def test_llm_memory_consumption(llm_instance):
 )
 def test_llm_initialization_params(model_id, max_length):
     if max_length:
-        instance = HuggingfaceLLM(model_id=model_id, max_length=max_length)
+        instance = HuggingfaceLLM(
+            model_id=model_id, max_length=max_length
+        )
         assert instance.max_length == max_length
     else:
         instance = HuggingfaceLLM(model_id=model_id)
-        assert instance.max_length == 500  # Assuming 500 is the default max_length
+        assert (
+            instance.max_length == 500
+        )  # Assuming 500 is the default max_length
 
 
 # Test for setting an invalid device
@@ -137,7 +144,9 @@ def test_llm_run_output_length(mock_run, llm_instance):
 # Test the tokenizer handling special tokens correctly
 @patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer.encode")
 @patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer.decode")
-def test_llm_tokenizer_special_tokens(mock_decode, mock_encode, llm_instance):
+def test_llm_tokenizer_special_tokens(
+    mock_decode, mock_encode, llm_instance
+):
     mock_encode.return_value = "encoded input with special tokens"
     mock_decode.return_value = "decoded output with special tokens"
     result = llm_instance.run("test task with special tokens")
@@ -163,7 +172,9 @@ def test_llm_response_time(mock_run, llm_instance):
     start_time = time.time()
     llm_instance.run("test task for response time")
     end_time = time.time()
-    assert end_time - start_time < 1  # Assuming the response should be faster than 1 second
+    assert (
+        end_time - start_time < 1
+    )  # Assuming the response should be faster than 1 second
 
 
 # Test the logging of a warning for long inputs
@@ -186,9 +197,13 @@ def test_llm_run_model_exception(mock_generate, llm_instance):
 
 # Test the behavior when GPU is forced but not available
 @patch("torch.cuda.is_available", return_value=False)
-def test_llm_force_gpu_when_unavailable(mock_is_available, llm_instance):
+def test_llm_force_gpu_when_unavailable(
+    mock_is_available, llm_instance
+):
     with pytest.raises(EnvironmentError):
-        llm_instance.set_device("cuda")  # Attempt to set CUDA when it's not available
+        llm_instance.set_device(
+            "cuda"
+        )  # Attempt to set CUDA when it's not available
 
 
 # Test for proper cleanup after model use (releasing resources)
@@ -206,7 +221,9 @@ def test_llm_multilingual_input(mock_run, llm_instance):
     mock_run.return_value = "mocked multilingual output"
     multilingual_input = "Bonjour, ceci est un test multilingue."
     result = llm_instance.run(multilingual_input)
-    assert isinstance(result, str)  # Simple check to ensure output is string type
+    assert isinstance(
+        result, str
+    )  # Simple check to ensure output is string type
 
 
 # Test caching mechanism to prevent re-running the same inputs
@@ -221,7 +238,5 @@ def test_llm_caching_mechanism(mock_run, llm_instance):
     assert first_run_result == second_run_result
 
 
-# These tests are provided as examples.
-# In real-world scenarios, you will need to adapt these tests to the actual logic of your `HuggingfaceLLM` class.
-# For instance, "mock_model.delete.assert_called_once()" and similar lines are based on hypothetical methods and behaviors
-# that you need to replace with actual implementations.
+# These tests are provided as examples. In real-world scenarios, you will need to adapt these tests to the actual logic of your `HuggingfaceLLM` class.
+# For instance, "mock_model.delete.assert_called_once()" and similar lines are based on hypothetical methods and behaviors that you need to replace with actual implementations.
diff --git a/tests/models/test_kosmos.py b/tests/models/test_kosmos.py
index bc3fb272..1219f895 100644
--- a/tests/models/test_kosmos.py
+++ b/tests/models/test_kosmos.py
@@ -117,14 +117,14 @@ def mock_request_get(monkeypatch):
 
 
 @pytest.mark.usefixtures("mock_request_get")
-def test_multimodal_grounding_2(kosmos):
+def test_multimodal_grounding(kosmos):
     kosmos.multimodal_grounding(
         "Find the red apple in the image.", IMG_URL1
     )
 
 
 @pytest.mark.usefixtures("mock_request_get")
-def test_referring_expression_comprehension_2(kosmos):
+def test_referring_expression_comprehension(kosmos):
     kosmos.referring_expression_comprehension(
         "Show me the green bottle.", IMG_URL2
     )
@@ -153,14 +153,14 @@ def test_grounded_image_captioning_detailed(kosmos):
 
 
 @pytest.mark.usefixtures("mock_request_get")
-def test_multimodal_grounding_3(kosmos):
+def test_multimodal_grounding_2(kosmos):
     kosmos.multimodal_grounding(
         "Find the yellow fruit in the image.", IMG_URL2
     )
 
 
 @pytest.mark.usefixtures("mock_request_get")
-def test_referring_expression_comprehension_3(kosmos):
+def test_referring_expression_comprehension_2(kosmos):
     kosmos.referring_expression_comprehension(
         "Where is the water bottle?", IMG_URL3
     )
diff --git a/tests/structs/test_autoscaler.py b/tests/structs/test_autoscaler.py
index 765cb16f..2e5585bf 100644
--- a/tests/structs/test_autoscaler.py
+++ b/tests/structs/test_autoscaler.py
@@ -15,11 +15,11 @@ llm = OpenAIChat(
     temperature=0.5,
     openai_api_key=api_key,
 )
-global_agent = Agent(llm=llm, max_loops=1)
+agent = Agent(llm=llm, max_loops=1)
 
 
 def test_autoscaler_init():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     assert autoscaler.initial_agents == 5
     assert autoscaler.scale_up_factor == 1
     assert autoscaler.idle_threshold == 0.2
@@ -33,15 +33,15 @@ def test_autoscaler_init():
 
 
 def test_autoscaler_add_task():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.add_task("task1")
     assert autoscaler.task_queue.empty() is False
 
 
 def test_autoscaler_run():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     out = autoscaler.run(
-        global_agent.id,
+        agent.id,
         "Generate a 10,000 word blog on health and wellness.",
     )
     assert (
@@ -50,31 +50,31 @@ def test_autoscaler_run():
 
 
 def test_autoscaler_add_agent():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
-    autoscaler.add_agent(global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
+    autoscaler.add_agent(agent)
     assert len(autoscaler.agents_pool) == 6
 
 
 def test_autoscaler_remove_agent():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
-    autoscaler.remove_agent(global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
+    autoscaler.remove_agent(agent)
     assert len(autoscaler.agents_pool) == 4
 
 
 def test_autoscaler_get_agent():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     agent = autoscaler.get_agent()
     assert isinstance(agent, Agent)
 
 
 def test_autoscaler_get_agent_by_id():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
-    agent = autoscaler.get_agent_by_id(global_agent.id)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
+    agent = autoscaler.get_agent_by_id(agent.id)
     assert isinstance(agent, Agent)
 
 
 def test_autoscaler_get_agent_by_id_not_found():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     agent = autoscaler.get_agent_by_id("fake_id")
     assert agent is None
 
@@ -82,13 +82,13 @@ def test_autoscaler_get_agent_by_id_not_found():
 @patch("swarms.swarms.Agent.is_healthy")
 def test_autoscaler_check_agent_health(mock_is_healthy):
     mock_is_healthy.side_effect = [False, True, True, True, True]
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.check_agent_health()
     assert mock_is_healthy.call_count == 5
 
 
 def test_autoscaler_balance_load():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.add_task("task1")
     autoscaler.add_task("task2")
     autoscaler.balance_load()
@@ -96,7 +96,7 @@ def test_autoscaler_balance_load():
 
 
 def test_autoscaler_set_scaling_strategy():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
 
     def strategy(x, y):
         return x - y
@@ -106,7 +106,7 @@ def test_autoscaler_set_scaling_strategy():
 
 
 def test_autoscaler_execute_scaling_strategy():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
 
     def strategy(x, y):
         return x - y
@@ -118,7 +118,7 @@ def test_autoscaler_execute_scaling_strategy():
 
 
 def test_autoscaler_report_agent_metrics():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     metrics = autoscaler.report_agent_metrics()
     assert set(metrics.keys()) == {
         "completion_time",
@@ -129,21 +129,21 @@ def test_autoscaler_report_agent_metrics():
 
 @patch("swarms.swarms.AutoScaler.report_agent_metrics")
 def test_autoscaler_report(mock_report_agent_metrics):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.report()
     mock_report_agent_metrics.assert_called_once()
 
 
 @patch("builtins.print")
 def test_autoscaler_print_dashboard(mock_print):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.print_dashboard()
     mock_print.assert_called()
 
 
 @patch("swarms.structs.autoscaler.logging")
 def test_check_agent_health_all_healthy(mock_logging):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     for agent in autoscaler.agents_pool:
         agent.is_healthy = MagicMock(return_value=True)
     autoscaler.check_agent_health()
@@ -152,7 +152,7 @@ def test_check_agent_health_all_healthy(mock_logging):
 
 @patch("swarms.structs.autoscaler.logging")
 def test_check_agent_health_some_unhealthy(mock_logging):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     for i, agent in enumerate(autoscaler.agents_pool):
         agent.is_healthy = MagicMock(return_value=(i % 2 == 0))
     autoscaler.check_agent_health()
@@ -161,7 +161,7 @@ def test_check_agent_health_some_unhealthy(mock_logging):
 
 @patch("swarms.structs.autoscaler.logging")
 def test_check_agent_health_all_unhealthy(mock_logging):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     for agent in autoscaler.agents_pool:
         agent.is_healthy = MagicMock(return_value=False)
     autoscaler.check_agent_health()
@@ -170,7 +170,7 @@ def test_check_agent_health_all_unhealthy(mock_logging):
 
 @patch("swarms.structs.autoscaler.Agent")
 def test_add_agent(mock_agent):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     initial_count = len(autoscaler.agents_pool)
     autoscaler.add_agent()
     assert len(autoscaler.agents_pool) == initial_count + 1
@@ -179,7 +179,7 @@ def test_add_agent(mock_agent):
 
 @patch("swarms.structs.autoscaler.Agent")
 def test_remove_agent(mock_agent):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     initial_count = len(autoscaler.agents_pool)
     autoscaler.remove_agent()
     assert len(autoscaler.agents_pool) == initial_count - 1
@@ -188,7 +188,7 @@ def test_remove_agent(mock_agent):
 @patch("swarms.structs.autoscaler.AutoScaler.add_agent")
 @patch("swarms.structs.autoscaler.AutoScaler.remove_agent")
 def test_scale(mock_remove_agent, mock_add_agent):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.scale(10)
     assert mock_add_agent.call_count == 5
     assert mock_remove_agent.call_count == 0
@@ -223,7 +223,7 @@ def test_autoscaler_initialization():
         scale_up_factor=2,
         idle_threshold=0.1,
         busy_threshold=0.8,
-        agent=global_agent,
+        agent=agent,
     )
     assert isinstance(autoscaler, AutoScaler)
     assert autoscaler.scale_up_factor == 2
@@ -232,22 +232,22 @@ def test_autoscaler_initialization():
     assert len(autoscaler.agents_pool) == 5
 
 
-def test_autoscaler_add_task_2():
-    autoscaler = AutoScaler(agent=global_agent)
+def test_autoscaler_add_task():
+    autoscaler = AutoScaler(agent=agent)
     autoscaler.add_task("task1")
     assert autoscaler.task_queue.qsize() == 1
 
 
 def test_autoscaler_scale_up():
     autoscaler = AutoScaler(
-        initial_agents=5, scale_up_factor=2, agent=global_agent
+        initial_agents=5, scale_up_factor=2, agent=agent
     )
     autoscaler.scale_up()
     assert len(autoscaler.agents_pool) == 10
 
 
 def test_autoscaler_scale_down():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.scale_down()
     assert len(autoscaler.agents_pool) == 4
 
@@ -255,7 +255,7 @@ def test_autoscaler_scale_down():
 @patch("swarms.swarms.AutoScaler.scale_up")
 @patch("swarms.swarms.AutoScaler.scale_down")
 def test_autoscaler_monitor_and_scale(mock_scale_down, mock_scale_up):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.add_task("task1")
     autoscaler.monitor_and_scale()
     mock_scale_up.assert_called_once()
@@ -265,7 +265,7 @@ def test_autoscaler_monitor_and_scale(mock_scale_down, mock_scale_up):
 @patch("swarms.swarms.AutoScaler.monitor_and_scale")
 @patch("swarms.swarms.agent.run")
 def test_autoscaler_start(mock_run, mock_monitor_and_scale):
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.add_task("task1")
     autoscaler.start()
     mock_run.assert_called_once()
@@ -273,6 +273,6 @@ def test_autoscaler_start(mock_run, mock_monitor_and_scale):
 
 
 def test_autoscaler_del_agent():
-    autoscaler = AutoScaler(initial_agents=5, agent=global_agent)
+    autoscaler = AutoScaler(initial_agents=5, agent=agent)
     autoscaler.del_agent()
     assert len(autoscaler.agents_pool) == 4
diff --git a/tests/utils/test_extract_code_from_markdown.py b/tests/utils/test_extract_code_from_markdown.py
index 70d55202..eb1a3e5d 100644
--- a/tests/utils/test_extract_code_from_markdown.py
+++ b/tests/utils/test_extract_code_from_markdown.py
@@ -7,7 +7,7 @@ from swarms.utils import extract_code_from_markdown
 def markdown_content_with_code():
     return """
     # This is a markdown document
-
+    
     Some intro text here.
 Some additional text.
 """