[SWARM MODELS MIGRATION]

docs/applications/business-analyst-agent.md

@@ -96,7 +96,7 @@ tool_schema = QueryPlan(
 We specify the query, task specification and an appropriate system prompt.
 
 ```python
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms import Agent
 
 query = "How do we improve Nike's revenue in Q3 2024?"

examples/agents/settings/various_models/example_agent.py

@@ -4,7 +4,9 @@ import sys
 from dotenv import load_dotenv
 
 # Import the OpenAIChat model and the Agent struct
-from swarms import OpenAIChat, Agent
+from swarms import Agent
+
+from swarm_models import OpenAIChat
 
 # Load the environment variables
 load_dotenv()

examples/agents/settings/various_models/groq_agent.py

@@ -4,7 +4,7 @@ from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
 from swarms.structs.agent import Agent
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 
 # Example usage:
 api_key = os.getenv("GROQ_API_KEY")

examples/agents/tools/agent_with_basemodel_output_type.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel, Field
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms import Agent
 import os
 

examples/agents/tools/tool_agent/tool_agent_with_llm.py

@@ -3,7 +3,9 @@ import os
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 
-from swarms import OpenAIChat, ToolAgent
+from swarms import ToolAgent
+
+from swarm_models import OpenAIChat
 from swarms.tools.json_utils import base_model_to_json
 
 # Load the environment variables

examples/agents/use_cases/finance/openai_model.py

@@ -1,4 +1,6 @@
-from swarms import OpenAIChat, Agent
+from swarms import Agent
+
+from swarm_models import OpenAIChat
 import os
 
 api_key = os.getenv("OPENAI_API_KEY")

examples/collabs/swarms_example.ipynb

@@ -512,7 +512,7 @@ from swarm_models import Anthropic\n",
     "\n",
     "from dotenv import load_dotenv\n",
     "\n",
-    "from swarms import OpenAIChat, Worker, tool\n",
+    "from swarm_models import OpenAIChat, Worker, tool\n",
     "\n",
     "# Loading environment variables from .env file\n",
     "load_dotenv()\n",

examples/demos/business_analysis_swarm/business-analyst-agent.ipynb

@@ -146,7 +146,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from swarms import OpenAIChat\n",
+    "from swarm_models import OpenAIChat\n",
     "from swarms import Agent\n",
     "\n",
     "query = \"How do we improve Nike's revenue in Q3 2024?\"\n",

examples/demos/email_phiser/email_swarm.py

@@ -1,7 +1,7 @@
 import os
 
-from swarms import OpenAIChat, Agent, AgentRearrange
+from swarms import Agent, AgentRearrange
-
+from swarm_models import OpenAIChat
 
 llm = OpenAIChat(
     openai_api_key=os.getenv("OPENAI_API_KEY"),

examples/demos/society_of_agents/accountant_team.py

@@ -1,4 +1,5 @@
-from swarms import OpenAIChat, AgentRearrange, Agent
+from swarms import AgentRearrange, Agent
+from swarm_models import OpenAIChat
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )

examples/structs/swarms/concurrent_swarm/concurrent_groq 2.py

@@ -1,5 +1,6 @@
 import os
-from swarms import OpenAIChat, ConcurrentWorkflow, Agent
+from swarms import ConcurrentWorkflow, Agent
+from swarm_models import OpenAIChat
 
 TUTORIAL_WRITER_AGENT_SYS_PROMPT = """
 ### **System Prompt: Tutorial Writer AI Agent**

examples/structs/swarms/concurrent_swarm/concurrent_groq.py

@@ -1,5 +1,6 @@
 import os
-from swarms import OpenAIChat, ConcurrentWorkflow, Agent
+from swarms import ConcurrentWorkflow, Agent
+from swarm_models import OpenAIChat
 
 TUTORIAL_WRITER_AGENT_SYS_PROMPT = """
 ### **System Prompt: Tutorial Writer AI Agent**

examples/structs/swarms/multi_agent_collaboration/message_pool_example.py

@@ -1,4 +1,4 @@
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms.structs.agent import Agent
 from swarms.structs.message_pool import MessagePool
 

examples/structs/swarms/swarm_network/example_swarmnetwork.py

@@ -3,7 +3,9 @@ import os
 from dotenv import load_dotenv
 
 # Import the OpenAIChat model and the Agent struct
-from swarms import OpenAIChat, Agent, SwarmNetwork
+from swarms import Agent, SwarmNetwork
+
+from swarm_models import OpenAIChat
 
 # Load the environment variables
 load_dotenv()

examples/structs/workflows/concurrent/example_concurrentworkflow.py

@@ -1,6 +1,7 @@
 import os
 from dotenv import load_dotenv
-from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
+from swarms import Task, ConcurrentWorkflow, Agent
+from swarm_models import OpenAIChat
 
 # Load environment variables from .env file
 load_dotenv()

examples/structs/workflows/example_recursiveworkflow.py

@@ -1,6 +1,7 @@
 import os
 from dotenv import load_dotenv
-from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
+from swarms import Task, RecursiveWorkflow, Agent
+from swarm_models import OpenAIChat
 
 # Load environment variables from .env file
 load_dotenv()

examples/structs/workflows/sequential/example_sequentialworkflow.py

@@ -1,5 +1,6 @@
 import os
-from swarms import OpenAIChat, Agent, SequentialWorkflow
+from swarms import Agent, SequentialWorkflow
+from swarm_models import OpenAIChat
 from dotenv import load_dotenv
 
 load_dotenv()

multi_agent_collab_new.py

@@ -554,76 +554,76 @@ def round_robin_speaker(agents: List[Agent], iteration: int) -> Agent:
     return selected
 
 
-# # Example usage
+# Example usage
-# if __name__ == "__main__":
+if __name__ == "__main__":
-#     from swarms import OpenAIChat
+    from swarm_models import OpenAIChat
-#     from swarms.prompts.finance_agent_sys_prompt import (
+    from swarms.prompts.finance_agent_sys_prompt import (
-#         FINANCIAL_AGENT_SYS_PROMPT,
+        FINANCIAL_AGENT_SYS_PROMPT,
-#     )
+    )
-
+
-#     # Get the OpenAI API key from the environment variable
+    # Get the OpenAI API key from the environment variable
-#     api_key = os.getenv("OPENAI_API_KEY")
+    api_key = os.getenv("OPENAI_API_KEY")
-#     if not api_key:
+    if not api_key:
-#         logger.error("OpenAI API key not found in environment variables.")
+        logger.error("OpenAI API key not found in environment variables.")
-#         exit(1)
+        exit(1)
-
+
-#     # Create instances of the OpenAIChat class
+    # Create instances of the OpenAIChat class
-#     model = OpenAIChat(
+    model = OpenAIChat(
-#         api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
+        api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-#     )
+    )
-
+
-#     # Initialize agents
+    # Initialize agents
-#     agent1 = Agent(
+    agent1 = Agent(
-#         agent_name="Financial-Analysis-Agent_1",
+        agent_name="Financial-Analysis-Agent_1",
-#         system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+        system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-#         llm=model,
+        llm=model,
-#         max_loops=1,
+        max_loops=1,
-#         dynamic_temperature_enabled=True,
+        dynamic_temperature_enabled=True,
-#         saved_state_path="finance_agent_1.json",
+        saved_state_path="finance_agent_1.json",
-#         user_name="swarms_corp",
+        user_name="swarms_corp",
-#         retry_attempts=1,
+        retry_attempts=1,
-#         context_length=200000,
+        context_length=200000,
-#         return_step_meta=False,
+        return_step_meta=False,
-#     )
+    )
-
+
-#     agent2 = Agent(
+    agent2 = Agent(
-#         agent_name="Financial-Analysis-Agent_2",
+        agent_name="Financial-Analysis-Agent_2",
-#         system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+        system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-#         llm=model,
+        llm=model,
-#         max_loops=1,
+        max_loops=1,
-#         dynamic_temperature_enabled=True,
+        dynamic_temperature_enabled=True,
-#         saved_state_path="finance_agent_2.json",
+        saved_state_path="finance_agent_2.json",
-#         user_name="swarms_corp",
+        user_name="swarms_corp",
-#         retry_attempts=1,
+        retry_attempts=1,
-#         context_length=200000,
+        context_length=200000,
-#         return_step_meta=False,
+        return_step_meta=False,
-#     )
+    )
-
+
-#     agent2 = Agent(
+    agent2 = Agent(
-#         agent_name="Financial-Analysis-Agent_3",
+        agent_name="Financial-Analysis-Agent_3",
-#         system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+        system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-#         llm=model,
+        llm=model,
-#         max_loops=1,
+        max_loops=1,
-#         dynamic_temperature_enabled=True,
+        dynamic_temperature_enabled=True,
-#         saved_state_path="finance_agent_2.json",
+        saved_state_path="finance_agent_2.json",
-#         user_name="swarms_corp",
+        user_name="swarms_corp",
-#         retry_attempts=1,
+        retry_attempts=1,
-#         context_length=200000,
+        context_length=200000,
-#         return_step_meta=False,
+        return_step_meta=False,
-#     )
+    )
-
+
-#     # Initialize the MultiAgentCollaboration with the round-robin speaker function
+    # Initialize the MultiAgentCollaboration with the round-robin speaker function
-#     multi_agent_framework = MultiAgentCollaboration(
+    multi_agent_framework = MultiAgentCollaboration(
-#         agents=[agent1, agent2],
+        agents=[agent1, agent2],
-#         speaker_fn=round_robin_speaker,
+        speaker_fn=round_robin_speaker,
-#         max_loops=3,
+        max_loops=3,
-#         use_cache=True,  # Enable caching
+        use_cache=True,  # Enable caching
-#         autosave_on=True,
+        autosave_on=True,
-#     )
+    )
-
+
-#     # Run the framework with an input prompt
+    # Run the framework with an input prompt
-#     task = "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+    task = "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
-#     out = multi_agent_framework.run(task)
+    out = multi_agent_framework.run(task)
-#     print(out)
+    print(out)
-
+
-#     print(multi_agent_framework.return_output_schema_json())
+    print(multi_agent_framework.return_output_schema_json())

scripts/auto_tests_docs/auto_docs.py

@@ -6,7 +6,7 @@ import threading
 from dotenv import load_dotenv
 
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms.structs.majority_voting import MajorityVoting
 from swarms.structs.stackoverflow_swarm import StackOverflowSwarm
 from swarms.structs.task_queue_base import TaskQueueBase

scripts/auto_tests_docs/auto_docs_functions.py

@@ -6,7 +6,7 @@ import threading
 from dotenv import load_dotenv
 
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 
 load_dotenv()
 

scripts/auto_tests_docs/auto_docs_omni.py

@@ -5,7 +5,7 @@ import threading
 from dotenv import load_dotenv
 
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 
 ###########
 

scripts/auto_tests_docs/auto_tests.py

@@ -7,7 +7,7 @@ from dotenv import load_dotenv
 from swarms_memory import DictInternalMemory, DictSharedMemory
 
 from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 
 load_dotenv()
 

scripts/auto_tests_docs/auto_tests_functions.py

@@ -6,7 +6,7 @@ import threading
 from dotenv import load_dotenv
 
 from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms.utils.parse_code import extract_code_from_markdown
 
 load_dotenv()

swarms/structs/recursive_workflow.py

@@ -1,99 +0,0 @@
-import logging
-from typing import List
-
-from swarms.structs.base_structure import BaseStructure
-from swarms.structs.task import Task
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class RecursiveWorkflow(BaseStructure):
-    """
-    RecursiveWorkflow class for running a task recursively until a stopping condition is met.
-
-    Args:
-        task (Task): The task to execute.
-        stop_token (Any): The token that indicates when to stop the workflow.
-
-    Attributes:
-        task (Task): The task to execute.
-        stop_token (Any): The token that indicates when to stop the workflow.
-
-    Examples:
-    >>> from swarm_models import OpenAIChat
-    >>> from swarms.structs import RecursiveWorkflow, Task
-    >>> llm = OpenAIChat(openai_api_key="")
-    >>> task = Task(llm, "What's the weather in miami")
-    >>> workflow = RecursiveWorkflow()
-    >>> workflow.add(task)
-    >>> workflow.run()
-    """
-
-    def __init__(
-        self,
-        stop_token: str = "<DONE>",
-        stopping_conditions: callable = None,
-        max_loops: int = 1,
-        *args,
-        **kwargs,
-    ):
-        self.stop_token = stop_token
-        self.stopping_conditions = stopping_conditions
-        self.task_pool = []
-
-        assert (
-            self.stop_token is not None
-        ), "stop_token cannot be None"
-
-    def add(self, task: Task = None, tasks: List[Task] = None):
-        """Adds a task to the workflow.
-
-        Args:
-            task (Task): _description_
-            tasks (List[Task]): _description_
-        """
-        try:
-            if tasks:
-                for task in tasks:
-                    if isinstance(task, Task):
-                        self.task_pool.append(task)
-                        logger.info(
-                            "[INFO][RecursiveWorkflow] Added task"
-                            f" {task} to workflow"
-                        )
-            elif isinstance(task, Task):
-                self.task_pool.append(task)
-                logger.info(
-                    f"[INFO][RecursiveWorkflow] Added task {task} to"
-                    " workflow"
-                )
-        except Exception as error:
-            logger.warning(f"[ERROR][RecursiveWorkflow] {error}")
-            raise error
-
-    def run(self):
-        """
-        Executes the tasks in the workflow until the stop token is encountered.
-
-        Returns:
-            None
-        """
-        try:
-            loop = 0
-            while loop < self.max_loops:
-                for task in self.task_pool:
-                    while True:
-                        result = task.run()
-                        if (
-                            result is not None
-                            and self.stop_token in result
-                        ):
-                            break
-                        print(f"{result}")
-                loop += 1
-
-            return result
-        except Exception as error:
-            logger.warning(f"[ERROR][RecursiveWorkflow] {error}")
-            raise error

tests/structs/test_company.py

@@ -1,6 +1,6 @@
 import pytest
 
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms.structs.agent import Agent
 from swarms.structs.company import Company
 

tests/structs/test_message_pool.py

@@ -1,4 +1,4 @@
-from swarms import OpenAIChat
+from swarm_models import OpenAIChat
 from swarms.structs.agent import Agent
 from swarms.structs.message_pool import MessagePool