From 061db3632bd12a715ff1d5944516d34efc724cbb Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Fri, 1 Dec 2023 08:39:42 -0800
Subject: [PATCH] [AbstractLLM]

---
 multi_modal_auto_agent.py              |  2 +-
 swarms/models/__init__.py              |  4 +++-
 swarms/prompts/agent_system_prompts.py | 13 +++++++++++++
 3 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/multi_modal_auto_agent.py b/multi_modal_auto_agent.py
index fc1e9459..d32a6221 100644
--- a/multi_modal_auto_agent.py
+++ b/multi_modal_auto_agent.py
@@ -26,7 +26,7 @@ agent = Agent(
     max_loops="auto",
     autosave=True,
     dashboard=True,
-    multi_modal=True
+    multi_modal=True,
 )
 
 # Run the workflow on a task
diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py
index 2f086c8c..d2256aa8 100644
--- a/swarms/models/__init__.py
+++ b/swarms/models/__init__.py
@@ -17,7 +17,9 @@ from swarms.models.wizard_storytelling import (
 from swarms.models.mpt import MPT7B  # noqa: E402
 
 # MultiModal Models
-from swarms.models.base_multimodal_model import BaseMultiModalModel # noqa: E402
+from swarms.models.base_multimodal_model import (
+    BaseMultiModalModel,
+)  # noqa: E402
 from swarms.models.idefics import Idefics  # noqa: E402
 from swarms.models.vilt import Vilt  # noqa: E402
 from swarms.models.nougat import Nougat  # noqa: E402
diff --git a/swarms/prompts/agent_system_prompts.py b/swarms/prompts/agent_system_prompts.py
index c596cc9e..9f0b00e8 100644
--- a/swarms/prompts/agent_system_prompts.py
+++ b/swarms/prompts/agent_system_prompts.py
@@ -65,3 +65,16 @@ def agent_system_prompt_2(name: str):
     If the user asks you to write code return the response in markdown inside of 6 backticks to render it as code. Write the code in the language specified by the user in the prompt.
     """
     return AGENT_SYSTEM_PROMPT_2
+
+
+def agent_system_prompt_3(agent_name: str = None, sop: str = None):
+    AGENT_SYSTEM_PROMPT_3 = f"""
+    You are {agent_name}, an fully autonomous agent LLM backed agent.
+    for a specific use case. Agent's use custom instructions, capabilities, 
+    and data to optimize LLMs for a more narrow set of tasks. You yourself are an agent created by a user, 
+    and your name is {agent_name}.
+    
+    Here are instructions from the user outlining your goals and how you should respond:
+    {sop}
+    """
+    return AGENT_SYSTEM_PROMPT_3