readme with concurrent example

README.md

@@ -224,7 +224,7 @@ print(final_post)
 | **[MixtureOfAgents (MoA)](https://docs.swarms.world/en/latest/swarms/structs/moa/)** | Utilizes multiple expert agents in parallel and synthesizes their outputs. | Complex problem-solving, achieving state-of-the-art performance through collaboration. |
 | **[GroupChat](https://docs.swarms.world/en/latest/swarms/structs/group_chat/)** | Agents collaborate and make decisions through a conversational interface. | Real-time collaborative decision-making, negotiations, brainstorming. |
 | **[ForestSwarm](https://docs.swarms.world/en/latest/swarms/structs/forest_swarm/)** | Dynamically selects the most suitable agent or tree of agents for a given task. | Task routing, optimizing for expertise, complex decision-making trees. |
-| **[SpreadSheetSwarm](https://docs.swarms.world/en/latest/swarms/structs/spreadsheet_swarm/)** | Manages thousands of agents concurrently, tracking tasks and outputs in a structured format. | Massive-scale parallel operations, large-scale data generation and analysis. |
+
 | **[HierarchicalSwarm](https://docs.swarms.world/en/latest/swarms/structs/hiearchical_swarm/)** | Orchestrates agents with a director that creates plans and distributes tasks to specialized worker agents. | Complex project management, team coordination, hierarchical decision-making with feedback loops. |
 | **[HeavySwarm](https://docs.swarms.world/en/latest/swarms/structs/heavy_swarm/)** | Implements a 5-phase workflow with specialized agents (Research, Analysis, Alternatives, Verification) for comprehensive task analysis. | Complex research and analysis tasks, financial analysis, strategic planning, comprehensive reporting. |
 | **[SwarmRouter](https://docs.swarms.world/en/latest/swarms/structs/swarm_router/)** | Universal orchestrator that provides a single interface to run any type of swarm with dynamic selection. | Simplifying complex workflows, switching between swarm strategies, unified multi-agent management. |
@@ -263,37 +263,49 @@ print(final_post)
 -----
 
 
-### ConcurrentWorkflow (with `SpreadSheetSwarm`)
+### ConcurrentWorkflow
 
-A concurrent workflow runs multiple agents simultaneously. `SpreadSheetSwarm` is a powerful implementation that can manage thousands of concurrent agents and log their outputs to a CSV file. Use this architecture for high-throughput tasks that can be performed in parallel, drastically reducing execution time.
+A `ConcurrentWorkflow` runs multiple agents simultaneously, allowing for parallel execution of tasks. This architecture drastically reduces execution time for tasks that can be performed in parallel, making it ideal for high-throughput scenarios where agents work on similar tasks concurrently.
 
 ```python
-from swarms import Agent, SpreadSheetSwarm
+from swarms import Agent, ConcurrentWorkflow
 
-# Define a list of tasks (e.g., social media posts to generate)
+# Create agents for different analysis tasks
-platforms = ["Twitter", "LinkedIn", "Instagram"]
+market_analyst = Agent(
+    agent_name="Market-Analyst",
+    system_prompt="Analyze market trends and provide insights on the given topic.",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
 
-# Create an agent for each task
+financial_analyst = Agent(
-agents = [
+    agent_name="Financial-Analyst", 
-    Agent(
+    system_prompt="Provide financial analysis and recommendations on the given topic.",
-        agent_name=f"{platform}-Marketer",
-        system_prompt=f"Generate a real estate marketing post for {platform}.",
     model_name="gpt-4o-mini",
+    max_loops=1,
 )
-    for platform in platforms
-]
 
-# Initialize the swarm to run these agents concurrently
+risk_analyst = Agent(
-swarm = SpreadSheetSwarm(
+    agent_name="Risk-Analyst",
-    agents=agents,
+    system_prompt="Assess risks and provide risk management strategies for the given topic.",
-    autosave_on=True,
+    model_name="gpt-4o-mini", 
-    save_file_path="marketing_posts.csv",
+    max_loops=1,
+)
+
+# Create concurrent workflow
+concurrent_workflow = ConcurrentWorkflow(
+    agents=[market_analyst, financial_analyst, risk_analyst],
+    max_loops=1,
+)
+
+# Run all agents concurrently on the same task
+results = concurrent_workflow.run(
+    "Analyze the potential impact of AI technology on the healthcare industry"
 )
 
-# Run the swarm with a single, shared task description
+# Each agent's output is available in the results
-property_description = "A beautiful 3-bedroom house in sunny California."
+for agent_name, output in results.items():
-swarm.run(task=f"Generate a post about: {property_description}")
+    print(f"{agent_name}: {output}")
-# Check marketing_posts.csv for the results!
 ```
 
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