From 9c58a314bb6fde743eb830a158a02d614cc5dcab Mon Sep 17 00:00:00 2001
From: Pavan Kumar <66913595+ascender1729@users.noreply.github.com>
Date: Mon, 26 May 2025 14:16:06 +0000
Subject: [PATCH] update: README and examples removing usage of swarms.models
 and directly integrating models into agents using LiteLLM

---
 README.md | 37 +++++++++++--------------------------
 1 file changed, 11 insertions(+), 26 deletions(-)

diff --git a/README.md b/README.md
index 05b0cbdb..f4f85753 100644
--- a/README.md
+++ b/README.md
@@ -1386,10 +1386,7 @@ SpreadSheetSwarm manages thousands of agents concurrently for efficient task pro
 [Learn more:](https://docs.swarms.world/en/latest/swarms/structs/spreadsheet_swarm/)
 
 ```python
-import os
 from swarms import Agent, SpreadSheetSwarm
-from swarm_models import OpenAIChat
-
 # Define custom system prompts for each social media platform
 TWITTER_AGENT_SYS_PROMPT = """
 You are a Twitter marketing expert specializing in real estate. Your task is to create engaging, concise tweets to promote properties, analyze trends to maximize engagement, and use appropriate hashtags and timing to reach potential buyers.
@@ -1416,7 +1413,7 @@ agents = [
     Agent(
         agent_name="Twitter-RealEstate-Agent",
         system_prompt=TWITTER_AGENT_SYS_PROMPT,
-        model_name="gpt-4o",
+        model_name="gpt-4o-mini",
         max_loops=1,
         dynamic_temperature_enabled=True,
         saved_state_path="twitter_realestate_agent.json",
@@ -1426,7 +1423,7 @@ agents = [
     Agent(
         agent_name="Instagram-RealEstate-Agent",
         system_prompt=INSTAGRAM_AGENT_SYS_PROMPT,
-        model_name="gpt-4o",
+        model_name="gpt-4o-mini",
         max_loops=1,
         dynamic_temperature_enabled=True,
         saved_state_path="instagram_realestate_agent.json",
@@ -1436,7 +1433,7 @@ agents = [
     Agent(
         agent_name="Facebook-RealEstate-Agent",
         system_prompt=FACEBOOK_AGENT_SYS_PROMPT,
-        model_name="gpt-4o",
+        model_name="gpt-4o-mini",
         max_loops=1,
         dynamic_temperature_enabled=True,
         saved_state_path="facebook_realestate_agent.json",
@@ -1446,7 +1443,7 @@ agents = [
     Agent(
         agent_name="LinkedIn-RealEstate-Agent",
         system_prompt=LINKEDIN_AGENT_SYS_PROMPT,
-        model_name="gpt-4o",
+        model_name="gpt-4o-mini",
         max_loops=1,
         dynamic_temperature_enabled=True,
         saved_state_path="linkedin_realestate_agent.json",
@@ -1456,7 +1453,7 @@ agents = [
     Agent(
         agent_name="Email-RealEstate-Agent",
         system_prompt=EMAIL_AGENT_SYS_PROMPT,
-        model_name="gpt-4o",
+        model_name="gpt-4o-mini",
         max_loops=1,
         dynamic_temperature_enabled=True,
         saved_state_path="email_realestate_agent.json",
@@ -1920,32 +1917,18 @@ A production-grade multi-agent system enabling sophisticated group conversations
 
 
 ```python
-
-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("OPENAI_API_KEY")
-
-    # Create an instance of the OpenAIChat class
-    model = OpenAIChat(
-        openai_api_key=api_key,
-        model_name="gpt-4o-mini",
-        temperature=0.1,
-    )
 
     # Example agents
     agent1 = Agent(
         agent_name="Financial-Analysis-Agent",
         system_prompt="You are a financial analyst specializing in investment strategies.",
-        llm=model,
+        model_name="gpt-4o-mini",
+        temperature=0.1,        
         max_loops=1,
         autosave=False,
         dashboard=False,
@@ -1961,7 +1944,8 @@ if __name__ == "__main__":
     agent2 = Agent(
         agent_name="Tax-Adviser-Agent",
         system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
-        llm=model,
+        model_name="gpt-4o-mini",
+        temperature=0.1,           
         max_loops=1,
         autosave=False,
         dashboard=False,
@@ -1986,7 +1970,8 @@ if __name__ == "__main__":
     history = chat.run(
         "How to optimize tax strategy for investments?"
     )
-    print(history.model_dump_json(indent=2))
+    print(history)
+
 
 ```