From 9db340b7605ec575d225a26256e27f78dde75cf3 Mon Sep 17 00:00:00 2001
From: Kye Gomez <kye@swarms.world>
Date: Mon, 23 Dec 2024 10:44:35 -0800
Subject: [PATCH] [6.7.6]

---
 README.md             | 80 +++++++++++++++++++++++++++++++++++++++++++
 group_chat_example.py | 67 ++++++++++++++++++++++++++++++++++++
 2 files changed, 147 insertions(+)
 create mode 100644 group_chat_example.py

diff --git a/README.md b/README.md
index 70b31e6e..09020e0d 100644
--- a/README.md
+++ b/README.md
@@ -1840,6 +1840,86 @@ result = mixture_router.run("Evaluate the potential acquisition of TechStartup I
 ```
 
 
+-------
+
+## GroupChat
+
+A production-grade multi-agent system enabling sophisticated group conversations between AI agents with customizable speaking patterns, parallel processing capabilities, and comprehensive conversation tracking.
+
+
+````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,
+        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,
+    )
+
+    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,
+        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,
+    )
+
+    agents = [agent1, agent2]
+
+    chat = GroupChat(
+        name="Investment Advisory",
+        description="Financial and tax analysis group",
+        agents=agents,
+        speaker_fn=expertise_based,
+    )
+
+    history = chat.run(
+        "How to optimize tax strategy for investments?"
+    )
+    print(history.model_dump_json(indent=2))
+
+```
+
+--------
+
 
 ----------
 
diff --git a/group_chat_example.py b/group_chat_example.py
new file mode 100644
index 00000000..dbe508bb
--- /dev/null
+++ b/group_chat_example.py
@@ -0,0 +1,67 @@
+
+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,
+        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,
+    )
+
+    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,
+        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,
+    )
+
+    agents = [agent1, agent2]
+
+    chat = GroupChat(
+        name="Investment Advisory",
+        description="Financial and tax analysis group",
+        agents=agents,
+        speaker_fn=expertise_based,
+    )
+
+    history = chat.run(
+        "How to optimize tax strategy for investments?"
+    )
+    print(history.model_dump_json(indent=2))