Merge pull request #830 from ascender1729/feat/docs-update-concurrent-agents-api

Feat/docs update concurrent agents api

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 import Agent
-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")
+from swarms.structs.agent import Agent
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
 
-# Initialize OpenAIChat model with desired parameters
-model = OpenAIChat(
-    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-)
-
-# Initialize the Financial Analysis Agent
+# Initialize the Financial Analysis Agent with GPT-4o-mini model
 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 swarm_models import Anthropic
+from swarms.structs.agent import Agent
+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",
-    llm=Anthropic(),
+    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.",
+    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",
-    llm=Anthropic(),
+    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.",
+    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",
-    llm=Anthropic(),
+    system_prompt="You are a summarization agent. Your role is to create concise, clear summaries of technical content while maintaining key information and insights.",
+    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.

docs/swarms/install/quickstart.md

@@ -70,51 +70,55 @@ The `create_agents_from_yaml` function works by reading agent configurations fro
 ```yaml
 agents:
   - agent_name: "Financial-Analysis-Agent"
-    model:
-      openai_api_key: "your_openai_api_key"
-      model_name: "gpt-4o-mini"
-      temperature: 0.1
-      max_tokens: 2000
-    system_prompt: "financial_agent_sys_prompt"
+    system_prompt: "You are a financial analysis expert. Analyze market trends and provide investment recommendations."
+    model_name: "claude-3-opus-20240229"
     max_loops: 1
-    autosave: true
+    autosave: false
     dashboard: false
-    verbose: true
-    dynamic_temperature_enabled: true
-    saved_state_path: "finance_agent.json"
+    verbose: false
+    dynamic_temperature_enabled: false
     user_name: "swarms_corp"
     retry_attempts: 1
     context_length: 200000
     return_step_meta: false
     output_type: "str"
-    task: "How can I establish a ROTH IRA to buy stocks and get a tax break?"
-
-  - agent_name: "Stock-Analysis-Agent"
-    model:
-      openai_api_key: "your_openai_api_key"
-      model_name: "gpt-4o-mini"
-      temperature: 0.2
-      max_tokens: 1500
-    system_prompt: "stock_agent_sys_prompt"
-    max_loops: 2
-    autosave: true
+    temperature: 0.1
+    max_tokens: 2000
+    task: "Analyze tech stocks for 2024 investment strategy. Provide detailed analysis and recommendations."
+
+  - agent_name: "Risk-Analysis-Agent"
+    system_prompt: "You are a risk analysis expert. Evaluate investment risks and provide mitigation strategies."
+    model_name: "claude-3-opus-20240229"
+    max_loops: 1
+    autosave: false
     dashboard: false
-    verbose: true
+    verbose: false
     dynamic_temperature_enabled: false
-    saved_state_path: "stock_agent.json"
-    user_name: "stock_user"
-    retry_attempts: 3
+    user_name: "swarms_corp"
+    retry_attempts: 1
     context_length: 150000
-    return_step_meta: true
-    output_type: "json"
-    task: "What is the best strategy for long-term stock investment?"
+    return_step_meta: false
+    output_type: "str"
+    temperature: 0.1
+    max_tokens: 2000
+    task: "Conduct a comprehensive risk analysis of the top 5 tech companies in 2024. Include risk factors and mitigation strategies."
+
+swarm_architecture:
+  name: "Financial Analysis Swarm"
+  description: "A swarm for comprehensive financial and risk analysis"
+  max_loops: 1
+  swarm_type: "SequentialWorkflow"
+  task: "Analyze tech stocks and their associated risks for 2024 investment strategy"
+  autosave: false
+  return_json: true
 ```
 
 ### 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.
-- **task**: (Optional) Task for the agent to execute once created.
+- **model_name**: The language model to use (e.g., claude-3-opus-20240229).
+- **task**: Task for the agent to execute.
+- **swarm_architecture**: (Optional) Configuration for swarm behavior.
 
 ---
 
@@ -124,41 +128,15 @@ Now, create the main Python script that will use the `create_agents_from_yaml` f
 
 ### `main.py`:
 ```python
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from swarm_models import OpenAIChat
+from swarms.agents.create_agents_from_yaml import create_agents_from_yaml
 
-from swarms.agents.create_agents_from_yaml import (
-    create_agents_from_yaml,
+# Create agents and get task results
+task_results = create_agents_from_yaml(
+    yaml_file="agents_config.yaml",
+    return_type="run_swarm"
 )
 
-# Load environment variables
-load_dotenv()
-
-# Path to your YAML file
-yaml_file = "agents.yaml"
-
-# 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
-)
-
-
-try:
-    # Create agents and run tasks (using 'both' to return agents and task results)
-    task_results = create_agents_from_yaml(
-        model=model, yaml_file=yaml_file, return_type="tasks"
-    )
-
-    logger.info(f"Results from agents: {task_results}")
-except Exception as e:
-    logger.error(f"An error occurred: {e}")
-
+print(task_results)
 ```
 
 ### Example Run:

docs/swarms/structs/various_execution_methods.md

@@ -91,20 +91,19 @@ Runs multiple agents concurrently with timeout limits.
 ## Usage Examples
 
 ```python
-from swarms import Agent, run_agents_concurrently, run_agents_with_timeout, run_agents_with_different_tasks
-from swarm_models import OpenAIChat
-
-model = OpenAIChat(
-    model_name="gpt-4o-mini",
-    temperature=0.0
+from swarms.structs.agent import Agent
+from swarms.structs.multi_agent_exec import (
+    run_agents_concurrently,
+    run_agents_with_timeout,
+    run_agents_with_different_tasks
 )
 
-# Initialize agents
+# Initialize agents using only the built-in model_name parameter
 agents = [
     Agent(
         agent_name=f"Analysis-Agent-{i}",
         system_prompt="You are a financial analysis expert",
-        llm=model,
+        model_name="gpt-4o-mini",
         max_loops=1
     )
     for i in range(5)