[DOCS][CLEANUP]

crypto_tax.py

@@ -0,0 +1,56 @@
+import os
+from dotenv import load_dotenv
+from swarm_models import OpenAIChat
+from swarms import Agent, GroupChat, expertise_based
+
+if __name__ == "__main__":
+
+    load_dotenv()
+
+    # Get the OpenAI API key from the environment variable
+    api_key = os.getenv("GROQ_API_KEY")
+
+    # Model
+    model = OpenAIChat(
+        openai_api_base="https://api.groq.com/openai/v1",
+        openai_api_key=api_key,
+        model_name="llama-3.1-70b-versatile",
+        temperature=0.1,
+    )
+
+    # Example agents
+    agent1 = Agent(
+        agent_name="Crypto-Tax-Optimization-Agent",
+        system_prompt="You are a friendly tax expert specializing in cryptocurrency investments. Provide approachable insights on optimizing tax savings for crypto transactions.",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="User",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    agent2 = Agent(
+        agent_name="Crypto-Investment-Strategies-Agent",
+        system_prompt="You are a conversational financial analyst focused on cryptocurrency investments. Offer debatable advice on investment strategies that minimize tax liabilities.",
+        llm=model,
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+        user_name="User",
+        output_type="string",
+        streaming_on=True,
+    )
+
+    agents = [agent1, agent2]
+
+    chat = GroupChat(
+        name="Crypto Tax Optimization Debate",
+        description="Debate on optimizing tax savings for cryptocurrency transactions and investments",
+        agents=agents,
+        speaker_fn=expertise_based,
+    )
+
+    history = chat.run(
+        "How can one optimize tax savings for cryptocurrency transactions and investments? I bought some Bitcoin and Ethereum last year and want to minimize my tax liabilities this year."
+    )
+    print(history.model_dump_json(indent=2))

group_chat_example.py

@@ -10,58 +10,62 @@
     load_dotenv()
 
     # Get the OpenAI API key from the environment variable
-    api_key = os.getenv("OPENAI_API_KEY")
+    api_key = os.getenv("GROQ_API_KEY")
 
-    # Create an instance of the OpenAIChat class
+    # Model
     model = OpenAIChat(
+        openai_api_base="https://api.groq.com/openai/v1",
         openai_api_key=api_key,
-        model_name="gpt-4o-mini",
+        model_name="llama-3.1-70b-versatile",
         temperature=0.1,
     )
 
     # Example agents
     agent1 = Agent(
         agent_name="Financial-Analysis-Agent",
-        system_prompt="You are a financial analyst specializing in investment strategies.",
+        system_prompt="You are a friendly financial analyst specializing in investment strategies. Be approachable and conversational.",
         llm=model,
         max_loops=1,
-        autosave=False,
-        dashboard=False,
-        verbose=True,
         dynamic_temperature_enabled=True,
         user_name="swarms_corp",
-        retry_attempts=1,
-        context_length=200000,
         output_type="string",
-        streaming_on=False,
+        streaming_on=True,
     )
 
     agent2 = Agent(
         agent_name="Tax-Adviser-Agent",
-        system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
+        system_prompt="You are a tax adviser who provides clear, concise, and approachable guidance on tax-related queries.",
         llm=model,
         max_loops=1,
-        autosave=False,
-        dashboard=False,
-        verbose=True,
         dynamic_temperature_enabled=True,
         user_name="swarms_corp",
-        retry_attempts=1,
-        context_length=200000,
         output_type="string",
-        streaming_on=False,
+        streaming_on=True,
     )
 
+    # agent3 = Agent(
+    #     agent_name="Stock-Buying-Agent",
+    #     system_prompt="You are a stock market expert who provides insights on buying and selling stocks. Be informative and concise.",
+    #     llm=model,
+    #     max_loops=1,
+    #     dynamic_temperature_enabled=True,
+    #     user_name="swarms_corp",
+    #     retry_attempts=1,
+    #     context_length=200000,
+    #     output_type="string",
+    #     streaming_on=True,
+    # )
+
     agents = [agent1, agent2]
 
     chat = GroupChat(
         name="Investment Advisory",
-        description="Financial and tax analysis group",
+        description="Financial, tax, and stock analysis group",
         agents=agents,
         speaker_fn=expertise_based,
     )
 
     history = chat.run(
-        "How to optimize tax strategy for investments?"
+        "How to save on taxes for stocks, ETFs, and mutual funds?"
     )
     print(history.model_dump_json(indent=2))

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "6.7.6"
+version = "6.7.7"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]

swarm_arange_demo.py

@@ -213,6 +213,6 @@
 
 print(
     swarm_arrange.run(
-        "Analyze swarms, 150k revenue with 45m+ agents build, with 1.4m downloads since march 2024"
+        "Analyze AI ETFs, focusing on their performance, market trends, and potential for growth"
     )
 )

swarms/structs/__init__.py

@@ -17,6 +17,7 @@
     ChatHistory,
     ChatTurn,
     AgentResponse,
+    expertise_based
 )
 from swarms.structs.majority_voting import (
     MajorityVoting,
@@ -152,4 +153,5 @@
     "ChatHistory",
     "ChatTurn",
     "AgentResponse",
+    "expertise_based",
 ]

swarms/structs/spreadsheet_swarm.py

@@ -21,10 +21,7 @@
 
 # --------------- NEW CHANGE START ---------------
 # Format time variable to be compatible across operating systems
-formatted_time = datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-
-# Create the save file path with the formatted time and UUID hex
-self.save_file_path = f"spreadsheet_swarm_{formatted_time}_run_id_{uuid_hex}.csv"
+formatted_time = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
 # --------------- NEW CHANGE END ---------------
 
 class AgentOutput(BaseModel):