fix: update vision.md with improved agent examples and documentation

docs/swarms/concept/vision.md

@@ -15,30 +15,14 @@ Swarms aims to be the definitive and most reliable multi-agent LLM framework, of
 This example demonstrates a simple financial agent setup that responds to financial questions, such as establishing a ROTH IRA, using OpenAI's GPT-based model.
 
 ```python
-import os
+from swarms.structs.agent import Agent
-from swarms import Agent
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
-from swarm_models import OpenAIChat
-from swarms.prompts.finance_agent_sys_prompt import (
-    FINANCIAL_AGENT_SYS_PROMPT,
-)
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# Get OpenAI API key from environment
-api_key = os.getenv("OPENAI_API_KEY")
 
-# Initialize OpenAIChat model with desired parameters
+# Initialize the Financial Analysis Agent with GPT-4o-mini model
-model = OpenAIChat(
-    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-)
-
-# Initialize the Financial Analysis Agent
 agent = Agent(
     agent_name="Financial-Analysis-Agent",
     system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-    llm=model,
+    model_name="gpt-4o-mini",
     max_loops=1,
     autosave=True,
     dashboard=False,
@@ -56,7 +40,7 @@ out = agent.run(
     "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
 )
 
-# Output the agent's result
+# Output the result
 print(out)
 ```
 
@@ -64,66 +48,60 @@ print(out)
 The following example showcases how to use the `AgentRearrange` class to manage a multi-agent system. It sets up a director agent to orchestrate two workers—one to generate a transcript and another to summarize it.
 
 ```python
-from swarms import Agent, AgentRearrange
+from swarms.structs.agent import Agent
-from swarm_models import Anthropic
+from swarms.structs.rearrange import AgentRearrange  
 
-# Initialize the Director agent
+# Initialize the Director agent using Anthropic model via model_name
 director = Agent(
     agent_name="Director",
-    system_prompt="Directs the tasks for the workers",
+    system_prompt="You are a Director agent. Your role is to coordinate and direct tasks for worker agents. Break down complex tasks into clear, actionable steps.",
-    llm=Anthropic(),
+    model_name="claude-3-sonnet-20240229",
     max_loops=1,
     dashboard=False,
-    streaming_on=True,
+    streaming_on=False, 
     verbose=True,
     stopping_token="<DONE>",
     state_save_file_type="json",
     saved_state_path="director.json",
 )
 
-# Initialize Worker 1 agent (transcript generation)
+# Worker 1: transcript generation
 worker1 = Agent(
     agent_name="Worker1",
-    system_prompt="Generates a transcript for a YouTube video on what swarms are",
+    system_prompt="You are a content creator agent. Your role is to generate detailed, engaging transcripts for YouTube videos about technical topics. Focus on clarity and educational value.",
-    llm=Anthropic(),
+    model_name="claude-3-sonnet-20240229",
     max_loops=1,
     dashboard=False,
-    streaming_on=True,
+    streaming_on=False,  
     verbose=True,
     stopping_token="<DONE>",
     state_save_file_type="json",
     saved_state_path="worker1.json",
 )
 
-# Initialize Worker 2 agent (summarizes transcript)
+# Worker 2: summarization
 worker2 = Agent(
     agent_name="Worker2",
-    system_prompt="Summarizes the transcript generated by Worker1",
+    system_prompt="You are a summarization agent. Your role is to create concise, clear summaries of technical content while maintaining key information and insights.",
-    llm=Anthropic(),
+    model_name="claude-3-sonnet-20240229",
     max_loops=1,
     dashboard=False,
-    streaming_on=True,
+    streaming_on=False,  
     verbose=True,
     stopping_token="<DONE>",
     state_save_file_type="json",
     saved_state_path="worker2.json",
 )
 
-# Create a list of agents
+# Orchestrate the agents in sequence
 agents = [director, worker1, worker2]
-
-# Define the workflow pattern (sequential flow)
 flow = "Director -> Worker1 -> Worker2"
-
-# Using AgentRearrange to orchestrate the agents
 agent_system = AgentRearrange(agents=agents, flow=flow)
 
-# Running the system with a sample task
+# Run the workflow
 output = agent_system.run(
     "Create a format to express and communicate swarms of LLMs in a structured manner for YouTube"
 )
-
-# Output the result
 print(output)
 ```
 
@@ -168,5 +146,4 @@ With support for extreme third-party integration, Swarms makes it easy for devel
 Swarms abstracts the complexity of managing multiple agents with orchestration tools like `AgentRearrange`. Developers can define workflows that execute tasks concurrently or sequentially, depending on the problem at hand. This makes it easy to build and maintain large-scale automation systems.
 
 ### Conclusion:
-Swarms is not just another multi-agent framework; it's built specifically for developers who need powerful tools to automate complex, large-scale business operations. With flexible architecture, deep integration capabilities, and developer-friendly APIs, Swarms is the ultimate solution for businesses looking to streamline operations and future-proof their workflows.
+Swarms is not just another multi-agent framework; it's built specifically for developers who need powerful tools to automate complex, large-scale business operations. With flexible architecture, deep integration capabilities, and developer-friendly APIs, Swarms is the ultimate solution for businesses looking to streamline operations and future-proof their workflows.
-