diff --git a/README.md b/README.md index d11cbcac..94af41d3 100644 --- a/README.md +++ b/README.md @@ -231,9 +231,7 @@ print(final_post) ### SequentialWorkflow -A `SequentialWorkflow` executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. - -**Description:** Ideal for processes that have clear, ordered steps. This ensures that tasks with dependencies are handled correctly. +A `SequentialWorkflow` executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. `SequentialWorkflow` is Ideal for processes that have clear, ordered steps. This ensures that tasks with dependencies are handled correctly. ```python from swarms import Agent, SequentialWorkflow @@ -259,9 +257,7 @@ print(elevator_pitch) ### ConcurrentWorkflow (with `SpreadSheetSwarm`) -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. - -**Description:** Use this for high-throughput tasks that can be performed in parallel, drastically reducing execution time. +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. ```python from swarms import Agent, SpreadSheetSwarm @@ -296,9 +292,7 @@ swarm.run(task=f"Generate a post about: {property_description}") ### AgentRearrange -Inspired by `einsum`, `AgentRearrange` lets you define complex, non-linear relationships between agents using a simple string-based syntax. [Learn more](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) - -**Description:** Perfect for orchestrating dynamic workflows where agents might work in parallel, sequence, or a combination of both. +Inspired by `einsum`, `AgentRearrange` lets you define complex, non-linear relationships between agents using a simple string-based syntax. [Learn more](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/). This architecture is Perfect for orchestrating dynamic workflows where agents might work in parallel, sequence, or a combination of both. ```python from swarms import Agent, AgentRearrange @@ -451,9 +445,7 @@ print(recommendation) ### GroupChat -`GroupChat` creates a conversational environment where multiple agents can interact, discuss, and collaboratively solve a problem. You can define the speaking order or let it be determined dynamically. - -**Description:** Ideal for tasks that benefit from debate and multi-perspective reasoning, such as contract negotiation, brainstorming, or complex decision-making. +`GroupChat` creates a conversational environment where multiple agents can interact, discuss, and collaboratively solve a problem. You can define the speaking order or let it be determined dynamically. This architecture is ideal for tasks that benefit from debate and multi-perspective reasoning, such as contract negotiation, brainstorming, or complex decision-making. ```python from swarms import Agent, GroupChat