clean up model names

Author: kyegomez

.pre-commit-config.yaml

@@ -30,7 +30,7 @@
 
 ## Initialize the workflow
 agent = Agent(
-    llm=llm,
+    model_name="gpt-4o-mini",
     name="Health and Wellness Blog",
     system_prompt="Generate a 10,000 word blog on health and wellness.",
     max_loops=4,

examples/agents/memory/agents_and_memory/agent_with_longterm_memory.py

@@ -29,7 +29,7 @@
 )
 
 ## Initialize the workflow
-agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
+agent = Agent(model_name="gpt-4o-mini", max_loops=1, autosave=True, dashboard=True)
 
 # Run the workflow on a task
 out = agent.run("Generate a 10,000 word blog on health and wellness.")

examples/agents/settings/various_models/example_agent.py

@@ -16,7 +16,7 @@
 
 ## Initialize the workflow
 agent = Agent(
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops="auto",
     autosave=True,
     dashboard=True,

examples/agents/settings/various_models/llama3_agent.py

@@ -86,7 +86,7 @@ def file_editor(file_path: str, mode: str, content: str):
         " agents. Be Helpful and Kind. Use the tools provided to"
         " assist the user. Return all code in markdown format."
     ),
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops="auto",
     autosave=True,
     dashboard=False,

examples/agents/tools/devin.py

@@ -84,7 +84,7 @@ def file_editor(file_path: str, mode: str, content: str):
         " agents. Be Helpful and Kind. Use the tools provided to"
         " assist the user. Return all code in markdown format."
     ),
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops="auto",
     autosave=True,
     dashboard=False,

examples/agents/tools/devin_agent.py

@@ -703,7 +703,7 @@
     "cost_reduction_agent = Agent(\n",
     "    agent_name=\"Cost Reduction Analyst\",\n",
     "    system_prompt=cost_reduction_analyst(),\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",
@@ -714,7 +714,7 @@
     "revenue_maximalist_agent = Agent(\n",
     "    agent_name=\"Revenue Maximization Analyst\",\n",
     "    system_prompt=revenue_maximalist_analyst(),\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",
@@ -725,7 +725,7 @@
     "cost_control_agent = Agent(\n",
     "    agent_name=\"Operational Efficiency and Cost Control Specialist\",\n",
     "    system_prompt=operational_efficiency(),\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",
@@ -736,7 +736,7 @@
     "investment_analyst_agent = Agent(\n",
     "    agent_name=\"Strategic Investment Analyst\",\n",
     "    system_prompt=strategic_investment_analyst(),\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",
@@ -746,7 +746,7 @@
     "sales_marketing_agent = Agent(\n",
     "    agent_name=\"Sales and Marketing Optimization Specialist\",\n",
     "    system_prompt=sales_marketing_agent_prompt(),\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",
@@ -757,7 +757,7 @@
     "final_agent = Agent(\n",
     "    agent_name=\"Final Agent\",\n",
     "    system_prompt=\"You are the final agent. Please summarize the findings of the previous agents and provide a comprehensive report on how NVIDIA can optimize its financial performance. When finished make a call to the phone number provided and summarize your findings to the person on the other end of the line. Summarize the points such as how to lower the costs and increase the revenue.\",\n",
-    "    llm=llm,\n",
+    "    model_name="gpt-4o-mini",\n",
     "    max_loops=1,\n",
     "    dashboard=False,\n",
     "    state_save_file_type=\"json\",\n",

examples/agents/use_cases/browser/multion/multion_example.ipynb

@@ -26,7 +26,7 @@ def browser_automation(task: str):
 agent1 = Agent(
     agent_name="CyberTruckBuyer1",
     system_prompt="Find the best deal on a Cyber Truck and provide your reasoning",
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops=1,
     # output_type=str,
     metadata="json",
@@ -39,7 +39,7 @@ def browser_automation(task: str):
 agent2 = Agent(
     agent_name="CyberTruckBuyer2",
     system_prompt="Find the best deal on a Cyber Truck and provide your reasoning",
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops=1,
     # output_type=str,
     metadata="json",
@@ -52,7 +52,7 @@ def browser_automation(task: str):
 agent3 = Agent(
     agent_name="CyberTruckBuyer3",
     system_prompt="Find the best deal on a Cyber Truck and provide your reasoning",
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops=1,
     # output_type=str,
     metadata="json",

examples/agents/use_cases/browser/multion/multion_examples/buy_abunch_of_cybertrucks.py

@@ -70,7 +70,7 @@ def execute_interpreter_tool(
     agent_name="Research Agent",
     agent_description="An agent that performs research tasks.",
     system_prompt="Perform a research task.",
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops=1,
     dashboard=True,
     # tools=[multion_tool, execute_interpreter_tool],

examples/agents/use_cases/kyle_hackathon.py

@@ -98,7 +98,7 @@ def movers_agent_system_prompt():
 agent = Agent(
     agent_name="Research Agent",
     system_prompt=research_system_prompt,
-    llm=llm,
+    model_name="gpt-4o-mini",
     max_loops="auto",
     autosave=True,
     dashboard=False,
@@ -112,7 +112,7 @@ def movers_agent_system_prompt():
 # agent = Agent(
 #     agent_name="Movers Agent",
 #     system_prompt=movers_agent_system_prompt(),
-#     llm=llm,
+#     model_name="gpt-4o-mini",
 #     max_loops=1,
 #     autosave=True,
 #     dashboard=False,