refactor: update docs and examples to use model_name parameter instead of swarm_models

docs/swarms/install/quickstart.md

@@ -20,35 +20,27 @@ $ pip install -U swarms
 
 ### **Usage Example: Single Agent**
 
-Here’s a simple example of creating a financial analysis agent powered by OpenAI’s GPT-4 model. This agent will analyze financial queries like how to set up a ROTH IRA.
+Here's a simple example of creating a financial analysis agent powered by OpenAI's GPT-4o-mini model. This agent will analyze financial queries like how to set up a ROTH IRA.
 
 ```python
-import os
+from swarms.structs.agent import Agent
-from swarms import Agent
-from swarm_models import OpenAIChat
-from dotenv import load_dotenv
-
-load_dotenv()
 
-# Initialize OpenAI model
+# Initialize the agent with GPT-4o-mini model
-model = OpenAIChat(
-    openai_api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1
-)
-
-# Initialize the agent
 agent = Agent(
     agent_name="Financial-Analysis-Agent",
     system_prompt="Analyze financial situations and provide advice...",
-    llm=model,
     max_loops=1,
     autosave=True,
     dashboard=False,
     verbose=True,
-    saved_state_path="finance_agent.json"
+    saved_state_path="finance_agent.json",
+    model_name="gpt-4o-mini",
 )
 
-# Run the agent on a financial query
+# Run your query
-out = agent.run("How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?")
+out = agent.run(
+    "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
+)
 print(out)
 ```
 
@@ -57,13 +49,13 @@ print(out)
 - **Attributes:**
   - `agent_name`: Name of the agent.
   - `system_prompt`: System-level instruction guiding the agent's behavior.
-  - `llm`: Language model used by the agent (e.g., GPT, Anthropic).
+  - `model_name`: Name of the model to use (e.g., "gpt-4o-mini").
   - `max_loops`: Max iterations for a task.
   - `autosave`: Auto-saves the state after each iteration.
   
 - **Methods:**
-  - `run(task: str)`: Executes the agent’s task.
+  - `run(task: str)`: Executes the agent's task.
-  - `ingest_docs(doc_path: str)`: Ingests documents into the agent’s knowledge base.
+  - `ingest_docs(doc_path: str)`: Ingests documents into the agent's knowledge base.
   - `filtered_run(task: str)`: Runs agent with a filtered system prompt.
 
 -----
@@ -121,7 +113,7 @@ agents:
 ### Key Configuration Fields:
 - **agent_name**: Name of the agent.
 - **model**: Defines the language model settings (e.g., API key, model name, temperature, and max tokens).
-- **system_prompt**: The system prompt used to guide the agent’s behavior.
+- **system_prompt**: The system prompt used to guide the agent's behavior.
 - **task**: (Optional) Task for the agent to execute once created.
 
 ---
@@ -248,7 +240,7 @@ Steps:
 
 For example, here's an example on how to create an agent from griptape.
 
-Here’s how you can create a custom **Griptape** agent that integrates with the **Swarms** framework by inheriting from the `Agent` class in **Swarms** and overriding the `run(task: str) -> str` method.
+Here's how you can create a custom **Griptape** agent that integrates with the **Swarms** framework by inheriting from the `Agent` class in **Swarms** and overriding the `run(task: str) -> str` method.
 
 
 ```python
@@ -420,7 +412,7 @@ print(output)
 
 ### 5. **Spreadsheet Swarm**
 
-**Overview**: `SpreadSheetSwarm` enables the management of thousands of agents simultaneously, where each agent operates on its own thread. It’s ideal for overseeing large-scale agent outputs.
+**Overview**: `SpreadSheetSwarm` enables the management of thousands of agents simultaneously, where each agent operates on its own thread. It's ideal for overseeing large-scale agent outputs.
 
 #### Mermaid Graph:
 
@@ -466,7 +458,7 @@ These are the key swarm architectures available in the **Swarms Framework**. Eac
 #### **Workflow Classes**
 
 - **SequentialWorkflow:**
-  - Chains agents, where one agent's output becomes the next agent’s input.
+  - Chains agents, where one agent's output becomes the next agent's input.
   
 - **AgentRearrange:**
   - Dynamically rearranges agent tasks either in parallel or sequentially based on defined flow.

example.py

@@ -1,45 +1,17 @@
-from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
+from swarms.structs.agent import Agent
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
 
-# Initialize agents with model_name parameter
+# Initialize the agent
-agent1 = Agent(
+agent = Agent(
-    agent_name="Agent1",
+    agent_name="Financial-Analysis-Agent",
-    model_name="openai/gpt-4o-mini",  # Using provider prefix
+    agent_description="Personal finance advisor agent",
-    temperature=0.5,
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-    max_tokens=4000,
+    max_loops=2,
-    max_loops=1,
+    model_name="gpt-4o-mini",
-    autosave=True,
+    dynamic_temperature_enabled=True,
-    dashboard=True,
+    interactive=False,
 )
 
-agent2 = Agent(
+agent.run("Conduct an analysis of the best real undervalued ETFs")
-    agent_name="Agent2",
-    model_name="openai/gpt-4o-mini",  # Using provider prefix
-    temperature=0.5,
-    max_tokens=4000,
-    max_loops=1,
-    autosave=True,
-    dashboard=True,
-)
-
-def sample_task():
-    print("Running sample task")
-    return "Task completed"
-
-wf_graph = GraphWorkflow()
-wf_graph.add_node(Node(id="agent1", type=NodeType.AGENT, agent=agent1))
-wf_graph.add_node(Node(id="agent2", type=NodeType.AGENT, agent=agent2))
-wf_graph.add_node(Node(id="task1", type=NodeType.TASK, callable=sample_task))
-
-wf_graph.add_edge(Edge(source="agent1", target="task1"))
-wf_graph.add_edge(Edge(source="agent2", target="task1"))
-
-wf_graph.set_entry_points(["agent1", "agent2"])
-wf_graph.set_end_points(["task1"])
-
-print(wf_graph.visualize())
-
-results = wf_graph.run()
-print("Execution results:", results)

examples/async_agent.py

@@ -2,19 +2,17 @@ from swarms import Agent
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
-from swarm_models import OpenAIChat
 
-model = OpenAIChat(model_name="gpt-4o")
+# Initialize the agent (no swarm_models import needed)
-
-
-# Initialize the agent
 agent = Agent(
     agent_name="Financial-Analysis-Agent",
     agent_description="Personal finance advisor agent",
-    system_prompt=FINANCIAL_AGENT_SYS_PROMPT
+    system_prompt=(
-    + "Output the <DONE> token when you're done creating a portfolio of etfs, index, funds, and more for AI",
+        FINANCIAL_AGENT_SYS_PROMPT
+        + " Output the <DONE> token when you're done creating a portfolio"
+    ),
     max_loops=1,
-    llm=model,
+    model_name="gpt-4o",
     dynamic_temperature_enabled=True,
     user_name="Kye",
     retry_attempts=3,
@@ -33,7 +31,8 @@ agent = Agent(
 
 async def run_agent():
     await agent.arun(
-        "Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
+        "Create a table of super high-growth AI investment opportunities "
+        "with $40k in ETFs, index funds, etc., and output it in Markdown.",
         all_cores=True,
     )
 

examples/example_async_vs_multithread.py

@@ -1,30 +1,14 @@
-import os
 import asyncio
-from swarms import Agent
-from swarm_models import OpenAIChat
 import time
 import psutil
 
-from swarms.prompts.finance_agent_sys_prompt import (
+from swarms.structs.agent import Agent
-    FINANCIAL_AGENT_SYS_PROMPT,
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
-)
-from dotenv import load_dotenv
-
-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
+# Initialize the agent (no external imports or env lookups needed here)
-model = OpenAIChat(
-    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-)
-
-# Initialize the agent
 agent = Agent(
     agent_name="Financial-Analysis-Agent",
     system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-    llm=model,
     max_loops=1,
     autosave=True,
     dashboard=False,
@@ -37,39 +21,35 @@ agent = Agent(
     return_step_meta=False,
     output_type="string",
     streaming_on=False,
+    model_name="gpt-4o-mini",
 )
 
-
+# Helper decorator to measure time and memory usage
-# Function to measure time and memory usage
 def measure_time_and_memory(func):
     def wrapper(*args, **kwargs):
-        start_time = time.time()
+        start = time.time()
         result = func(*args, **kwargs)
-        end_time = time.time()
+        elapsed = time.time() - start
-        memory_usage = psutil.Process().memory_info().rss / 1024**2
+        mem_mb = psutil.Process().memory_info().rss / 1024**2
-        print(f"Time taken: {end_time - start_time} seconds")
+        print(f"[{func.__name__}] Time: {elapsed:.2f}s | Memory: {mem_mb:.2f} MB")
-        print(f"Memory used: {memory_usage} MB")
         return result
-
     return wrapper
 
-
+# Async wrapper using asyncio.to_thread for the blocking call
-# Function to run the agent asynchronously
 @measure_time_and_memory
 async def run_agent_async():
-    await asyncio.gather(
+    return await asyncio.to_thread(
-        agent.run(
+        agent.run,
-            "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+        "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
-        )
     )
 
-
+# Threaded wrapper simply runs the async version in the event loop again
-# Function to run the agent on another thread
 @measure_time_and_memory
-def run_agent_thread():
+def run_agent_in_thread():
     asyncio.run(run_agent_async())
 
-
+if __name__ == "__main__":
-# Run the agent asynchronously and on another thread to test the speed
+    # 1) Run asynchronously
-asyncio.run(run_agent_async())
+    asyncio.run(run_agent_async())
-run_agent_thread()
+    # 2) Then run again via the threaded wrapper
+    run_agent_in_thread()