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Kye Gomez 18 hours ago
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@ -231,9 +231,7 @@ print(final_post)
### SequentialWorkflow ### SequentialWorkflow
A `SequentialWorkflow` executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. 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.
**Description:** Ideal for processes that have clear, ordered steps. This ensures that tasks with dependencies are handled correctly.
```python ```python
from swarms import Agent, SequentialWorkflow from swarms import Agent, SequentialWorkflow
@ -259,9 +257,7 @@ print(elevator_pitch)
### ConcurrentWorkflow (with `SpreadSheetSwarm`) ### 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. 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.
**Description:** Use this for high-throughput tasks that can be performed in parallel, drastically reducing execution time.
```python ```python
from swarms import Agent, SpreadSheetSwarm from swarms import Agent, SpreadSheetSwarm
@ -296,9 +292,7 @@ swarm.run(task=f"Generate a post about: {property_description}")
### AgentRearrange ### 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/) 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.
**Description:** Perfect for orchestrating dynamic workflows where agents might work in parallel, sequence, or a combination of both.
```python ```python
from swarms import Agent, AgentRearrange from swarms import Agent, AgentRearrange
@ -451,9 +445,7 @@ print(recommendation)
### GroupChat ### 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. `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.
**Description:** Ideal for tasks that benefit from debate and multi-perspective reasoning, such as contract negotiation, brainstorming, or complex decision-making.
```python ```python
from swarms import Agent, GroupChat from swarms import Agent, GroupChat

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