[CLEANUP]

README.md

@@ -434,7 +434,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
 ```python
 import os
 from dotenv import load_dotenv
-from swarms import GPT4VisionAPI, Agent
+from swarms import Agent
+
+from swarm_models import GPT4VisionAPI
 
 # Load the environment variables
 load_dotenv()
@@ -529,79 +531,6 @@ print(f"Generated data: {generated_data}")
 ```
 
 
-### `Task`
-For deeper control of your agent stack, `Task` is a simple structure for task execution with the `Agent`. Imagine zapier like LLM-based workflow automation.
-
-✅ Task is a structure for task execution with the Agent. 
-
-✅ Tasks can have descriptions, scheduling, triggers, actions, conditions, dependencies, priority, and a history. 
-
-✅ The Task structure allows for efficient workflow automation with LLM-based agents.
-
-```python
-import os
-
-from dotenv import load_dotenv
-
-from swarms import Agent, OpenAIChat, Task
-
-# Load the environment variables
-load_dotenv()
-
-
-# Define a function to be used as the action
-def my_action():
-    print("Action executed")
-
-
-# Define a function to be used as the condition
-def my_condition():
-    print("Condition checked")
-    return True
-
-
-# Create an agent
-agent = Agent(
-    llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
-    max_loops=1,
-    dashboard=False,
-)
-
-# Create a task
-task = Task(
-    description=(
-        "Generate a report on the top 3 biggest expenses for small"
-        " businesses and how businesses can save 20%"
-    ),
-    agent=agent,
-)
-
-# Set the action and condition
-task.set_action(my_action)
-task.set_condition(my_condition)
-
-# Execute the task
-print("Executing task...")
-task.run()
-
-# Check if the task is completed
-if task.is_completed():
-    print("Task completed")
-else:
-    print("Task not completed")
-
-# Output the result of the task
-print(f"Task result: {task.result}")
-```
-
----
-
-
-
-
-
-----
-
 
 # Multi-Agent Orchestration:
 Swarms was designed to facilitate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more.
@@ -623,7 +552,9 @@ In traditional swarm theory, there are many types of swarms usually for very spe
 Sequential Workflow enables you to sequentially execute tasks with `Agent` and then pass the output into the next agent and onwards until you have specified your max loops.
 
 ```python
-from swarms import Agent, SequentialWorkflow, Anthropic
+from swarms import Agent, SequentialWorkflow
+
+from swarm_models import Anthropic
 
 
 # Initialize the language model agent (e.g., GPT-3)
@@ -742,7 +673,10 @@ import os
 
 from dotenv import load_dotenv
 
-from swarms import Agent, Edge, GraphWorkflow, Node, NodeType, OpenAIChat
+
+from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
+
+from swarm_models import OpenAIChat
 
 load_dotenv()
 
@@ -1059,23 +993,10 @@ The easiest way to contribute is to pick any issue with the `good first issue` t
 
 Swarms is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the [CONTRIBUTING.md](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) to participate in Roadmap discussions!
 
-<a href="https://github.com/kyegomez/swarms/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=kyegomez/swarms" />
-</a>
-
 ----
 
 
 
-## Swarm Newsletter 🤖 🤖 🤖 📧 
-Sign up to the Swarm newsletter to receive  updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊
-
-[CLICK HERE TO SIGNUP](https://docs.google.com/forms/d/e/1FAIpQLSfqxI2ktPR9jkcIwzvHL0VY6tEIuVPd-P2fOWKnd6skT9j1EQ/viewform?usp=sf_link)
-
-## Discovery Call
-Book a discovery call to learn how Swarms can lower your operating costs by 40% with swarms of autonomous agents in lightspeed. [Click here to book a time that works for you!](https://calendly.com/swarm-corp/30min?month=2023-11)
-
-
 ## Accelerate Backlog
 Accelerate Bugs, Features, and Demos to implement by supporting us here:
 
@@ -1095,9 +1016,6 @@ Join our growing community around the world, for real-time support, ideas, and d
 
 ---
 
-# License
-Apache License
-
 # Citations
 Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you.
 

docs/swarms/structs/graph_workflow.md

@@ -139,7 +139,10 @@ import os
 
 from dotenv import load_dotenv
 
-from swarms import Agent, Edge, GraphWorkflow, Node, NodeType, OpenAIChat
+
+from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
+
+from swarm_models import OpenAIChat
 
 load_dotenv()
 

docs/swarms/structs/multi_agent_collaboration_examples.md

@@ -5,7 +5,9 @@
 Sequential Workflow enables you to sequentially execute tasks with `Agent` and then pass the output into the next agent and onwards until you have specified your max loops.
 
 ```python
-from swarms import Agent, SequentialWorkflow, Anthropic
+from swarms import Agent, SequentialWorkflow
+
+from swarm_models import Anthropic
 
 
 # Initialize the language model agent (e.g., GPT-3)
@@ -123,7 +125,10 @@ import os
 
 from dotenv import load_dotenv
 
-from swarms import Agent, Edge, GraphWorkflow, Node, NodeType, OpenAIChat
+
+from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
+
+from swarm_models import OpenAIChat
 
 load_dotenv()
 

docs/swarms/structs/sequential_workflow.md

@@ -34,7 +34,9 @@ Runs the specified task through the agents in the dynamically constructed flow.
 
 - **Usage Example:**
   ```python
-  from swarms import Agent, SequentialWorkflow, Anthropic
+  from swarms import Agent, SequentialWorkflow
+
+from swarm_models import Anthropic
 
 
     # Initialize the language model agent (e.g., GPT-3)

examples/agents/use_cases/browser/multion_examples/buy_abunch_of_cybertrucks.py

@@ -1,5 +1,6 @@
-from swarms import Agent, AgentRearrange, OpenAIChat
-from swarms.agents.multion_wrapper import MultiOnAgent
+from swarms import Agent, AgentRearrange
+
+from swarm_models import OpenAIChatfrom swarms.agents.multion_wrapper import MultiOnAgent
 
 model = MultiOnAgent(
     url="https://tesla.com",

examples/agents/use_cases/multi_modal/multi_modal_example.py

@@ -1,6 +1,8 @@
 import os
 from dotenv import load_dotenv
-from swarms import GPT4VisionAPI, Agent
+from swarms import Agent
+
+from swarm_models import GPT4VisionAPI
 
 # Load the environment variables
 load_dotenv()

examples/agents/use_cases/multi_modal/multi_modal_flow_example.py

@@ -1,4 +1,6 @@
-from swarms import GPT4VisionAPI, Agent
+from swarms import Agent
+
+from swarm_models import GPT4VisionAPI
 
 llm = GPT4VisionAPI()
 

examples/structs/swarms/multi_agent_collaboration/agent_rearrange.py

@@ -1,5 +1,6 @@
-from swarms import Agent, AgentRearrange, OpenAIChat
+from swarms import Agent, AgentRearrange
 
+from swarm_models import OpenAIChat
 
 # Initialize the director agent
 

examples/structs/swarms/multi_agent_collaboration/agent_rearrange_human_in_loop.py

@@ -1,5 +1,6 @@
-from swarms import Agent, AgentRearrange, OpenAIChat
+from swarms import Agent, AgentRearrange
 
+from swarm_models import OpenAIChat
 
 # Initialize the director agent
 

examples/structs/swarms/multi_agent_collaboration/sequential_workflow_new.py

@@ -1,4 +1,6 @@
-from swarms import Agent, SequentialWorkflow, Anthropic
+from swarms import Agent, SequentialWorkflow
+
+from swarm_models import Anthropic
 
 
 # Initialize the language model agent (e.g., GPT-3)

examples/structs/swarms/spreadsheet_swarm/spreadsheet_swarm_examples/csvs/README.md

@@ -434,7 +434,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
 ```python
 import os
 from dotenv import load_dotenv
-from swarms import GPT4VisionAPI, Agent
+from swarms import Agent
+
+from swarm_models import GPT4VisionAPI
 
 # Load the environment variables
 load_dotenv()
@@ -623,7 +625,9 @@ In traditional swarm theory, there are many types of swarms usually for very spe
 Sequential Workflow enables you to sequentially execute tasks with `Agent` and then pass the output into the next agent and onwards until you have specified your max loops.
 
 ```python
-from swarms import Agent, SequentialWorkflow, Anthropic
+from swarms import Agent, SequentialWorkflow
+
+from swarm_models import Anthropic
 
 
 # Initialize the language model agent (e.g., GPT-3)
@@ -741,7 +745,10 @@ import os
 
 from dotenv import load_dotenv
 
-from swarms import Agent, Edge, GraphWorkflow, Node, NodeType, OpenAIChat
+
+from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
+
+from swarm_models import OpenAIChat
 
 load_dotenv()
 

examples/swarms/rearrange/example.py

@@ -1,83 +1,93 @@
 import os
 import asyncio
 from dotenv import load_dotenv
+
 load_dotenv()
 
 from swarms.structs import Agent
 from swarm_models import Anthropic
 from swarms.structs.rearrange import AgentRearrange
 
-llm = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"), streaming=True)
+llm = Anthropic(
+    anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"), streaming=True
+)
+
 
 async def sequential():
 
-  agent1 = Agent(
-      agent_name="Blog generator",
-      system_prompt="Generate a blog post like stephen king",
-      llm=llm,
-      dashboard=False,
-      streaming_on=True
-  )
-
-  agent2 = Agent(
-      agent_name="Summarizer",
-      system_prompt="Summarize the blog post",
-      llm=llm,
-      dashboard=False,
-      streaming_on=True
-  )
-
-  flow = f"{agent1.name} -> {agent2.name}"
-
-  agent_rearrange = AgentRearrange(
-    [agent1, agent2], flow, verbose=False, logging=False
-  )
-
-  result = await agent_rearrange.astream(
-     "Generate a short blog post about Muhammad Ali."
-  )
-
-  # LEAVING THIS CALL BELOW FOR COMPARISON with "completion-style" .run() approach ;)
-  # await agent_rearrange.run(
-  #   "Generate a short blog post about Muhammad Ali."
-  # )
+    agent1 = Agent(
+        agent_name="Blog generator",
+        system_prompt="Generate a blog post like stephen king",
+        llm=llm,
+        dashboard=False,
+        streaming_on=True,
+    )
 
-async def parallel():
+    agent2 = Agent(
+        agent_name="Summarizer",
+        system_prompt="Summarize the blog post",
+        llm=llm,
+        dashboard=False,
+        streaming_on=True,
+    )
 
-  writer1 = Agent(
-      agent_name="Writer 1",
-      system_prompt="Generate a blog post in the style of J.K. Rowling about Muhammad Ali",
-      llm=llm,
-      dashboard=False,
-  )
+    flow = f"{agent1.name} -> {agent2.name}"
 
-  writer2 = Agent(
-      agent_name="Writer 2",
-      system_prompt="Generate a blog post in the style of Stephen King about Muhammad Ali",
-      llm=llm,
-      dashboard=False
-  )
+    agent_rearrange = AgentRearrange(
+        [agent1, agent2], flow, verbose=False, logging=False
+    )
 
-  reviewer = Agent(
-      agent_name="Reviewer",
-      system_prompt="Select the writer that wrote the best story. There can only be one best story.",
-      llm=llm,
-      dashboard=False
-  )
+    result = await agent_rearrange.astream(
+        "Generate a short blog post about Muhammad Ali."
+    )
 
-  flow = f"{writer1.name}, {writer2.name} -> {reviewer.name}"
+    # LEAVING THIS CALL BELOW FOR COMPARISON with "completion-style" .run() approach ;)
+    # await agent_rearrange.run(
+    #   "Generate a short blog post about Muhammad Ali."
+    # )
 
-  agent_rearrange = AgentRearrange(
-    [writer1, writer2, reviewer], flow, verbose=False, logging=False
-  )
 
-  result = await agent_rearrange.astream("Generate a 1 sentence story about Michael Jordan.")
+async def parallel():
+
+    writer1 = Agent(
+        agent_name="Writer 1",
+        system_prompt="Generate a blog post in the style of J.K. Rowling about Muhammad Ali",
+        llm=llm,
+        dashboard=False,
+    )
+
+    writer2 = Agent(
+        agent_name="Writer 2",
+        system_prompt="Generate a blog post in the style of Stephen King about Muhammad Ali",
+        llm=llm,
+        dashboard=False,
+    )
+
+    reviewer = Agent(
+        agent_name="Reviewer",
+        system_prompt="Select the writer that wrote the best story. There can only be one best story.",
+        llm=llm,
+        dashboard=False,
+    )
+
+    flow = f"{writer1.name}, {writer2.name} -> {reviewer.name}"
+
+    agent_rearrange = AgentRearrange(
+        [writer1, writer2, reviewer],
+        flow,
+        verbose=False,
+        logging=False,
+    )
+
+    result = await agent_rearrange.astream(
+        "Generate a 1 sentence story about Michael Jordan."
+    )
+
+    # LEAVING THIS CALL BELOW FOR COMPARISON with "completion-style" .run() approach ;)
+    # result = agent_rearrange.run(
+    #   "Generate a short blog post about Michael Jordan."
+    # )
 
-  # LEAVING THIS CALL BELOW FOR COMPARISON with "completion-style" .run() approach ;)
-  # result = agent_rearrange.run(
-  #   "Generate a short blog post about Michael Jordan."
-  # )
 
 asyncio.run(sequential())
 # asyncio.run(parallel())
-

sample_rearrange.py

@@ -1,6 +1,8 @@
 import os
 
-from swarms import Agent, AgentRearrange, OpenAIChat
+from swarms import Agent, AgentRearrange
+
+from swarm_models import OpenAIChat
 
 # Get the OpenAI API key from the environment variable
 api_key = os.getenv("OPENAI_API_KEY")

swarms/structs/dfs_search_swarm.py

@@ -1,6 +1,6 @@
 # import os
 # from swarms import Agent
-from swarm_models import OpenAIChat
+
 # from typing import List
 
 # class DepthFirstSearchSwarm:
@@ -81,7 +81,6 @@ from swarm_models import OpenAIChat
 # ####################
 # import os
 # from swarms import Agent
-from swarm_models import OpenAIChat
 
 # class DFSSwarm:
 #     def __init__(self, agents):

swarms/structs/graph_workflow.py

@@ -226,7 +226,7 @@ class GraphWorkflow(BaseModel):
 # # Example usage
 # if __name__ == "__main__":
 #     from swarms import Agent
-from swarm_models import OpenAIChat
+
 #     import os
 #     from dotenv import load_dotenv
 

swarms/structs/monte_carlo_swarm.py

@@ -201,7 +201,7 @@ def average_aggregator(results: List[float]) -> float:
 
 # import os
 # from swarms import Agent
-from swarm_models import OpenAIChat
+
 # from typing import List, Union, Callable
 # from collections import Counter
 

swarms/structs/rearrange.py

@@ -11,8 +11,6 @@ from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.omni_agent_types import AgentType
 from swarms.utils.loguru_logger import logger
-<<<<<<< HEAD
-=======
 
 
 class AgentRearrangeInput(BaseModel):
@@ -42,7 +40,6 @@ class AgentRearrangeOutput(BaseModel):
         ),
         description="The time the agent was created.",
     )
->>>>>>> ce359f5e ([5.6.8])
 
 
 class AgentRearrange(BaseSwarm):
@@ -362,7 +359,6 @@ class AgentRearrange(BaseSwarm):
             logger.error(f"An error occurred: {e}")
             return e
 
-<<<<<<< HEAD
     async def astream(
         self,
         task: str = None,
@@ -392,7 +388,9 @@ class AgentRearrange(BaseSwarm):
 
             # If custom_tasks have the agents name and tasks then combine them
             if custom_tasks is not None:
-                c_agent_name, c_task = next(iter(custom_tasks.items()))
+                c_agent_name, c_task = next(
+                    iter(custom_tasks.items())
+                )
 
                 # Find the position of the custom agent in the tasks list
                 position = tasks.index(c_agent_name)
@@ -498,7 +496,9 @@ class AgentRearrange(BaseSwarm):
                                 # print(evt) # <- useful when building/debugging
                                 if evt["event"] == "on_llm_end":
                                     result = evt["data"]["output"]
-                                    print(agent.name, "result", result)
+                                    print(
+                                        agent.name, "result", result
+                                    )
                             current_task = result
 
                 loop_count += 1
@@ -529,13 +529,13 @@ class AgentRearrange(BaseSwarm):
         if name.startswith("Human"):
             return self.human_intervention(task)
         elif name in self.sub_swarm:
-            return self.run_sub_swarm(task, name, img, *args, **kwargs)
+            return self.run_sub_swarm(
+                task, name, img, *args, **kwargs
+            )
         else:
             agent = self.agents[name]
             return agent.run(task, img, is_last, *args, **kwargs)
 
-=======
->>>>>>> ce359f5e ([5.6.8])
     def human_intervention(self, task: str) -> str:
         if self.human_in_the_loop and self.custom_human_in_the_loop:
             return self.custom_human_in_the_loop(task)