Swarms was designed to faciliate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more.
In traditional swarm theory, there are many types of swarms usually for very specialized use-cases and problem sets. Such as Hiearchical and sequential are great for accounting and sales, because there is usually a boss coordinator agent that distributes a workload to other specialized agents.
| Hierarchical Swarms | A system where agents are organized in a hierarchy, with higher-level agents coordinating lower-level agents to achieve complex tasks. | [Code Link](#) | Manufacturing process optimization, multi-level sales management, healthcare resource coordination |
| Agent Rearrange | A setup where agents rearrange themselves dynamically based on the task requirements and environmental conditions. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) | Adaptive manufacturing lines, dynamic sales territory realignment, flexible healthcare staffing |
| Concurrent Workflows | Agents perform different tasks simultaneously, coordinating to complete a larger goal. | [Code Link](#) | Concurrent production lines, parallel sales operations, simultaneous patient care processes |
| Sequential Coordination | Agents perform tasks in a specific sequence, where the completion of one task triggers the start of the next. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/sequential_workflow/) | Step-by-step assembly lines, sequential sales processes, stepwise patient treatment workflows |
| Parallel Processing | Agents work on different parts of a task simultaneously to speed up the overall process. | [Code Link](#) | Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests |
### Hierarchical Swarm
**Overview:**
@ -82,124 +99,3 @@ graph TD
B3 --> A
B4 --> A
```
---
### Federated Swarm
**Overview:**
A Federated Swarm architecture involves multiple independent swarms collaborating to complete a task. Each swarm operates autonomously but can share information and results with other swarms.
**Use-Cases:**
- Distributed learning systems where data is processed across multiple nodes.
- Scenarios requiring collaboration between different teams or departments.
```mermaid
graph TD
A[Central Coordinator]
subgraph Swarm1
B1[Agent 1.1] --> B2[Agent 1.2]
B2 --> B3[Agent 1.3]
end
subgraph Swarm2
C1[Agent 2.1] --> C2[Agent 2.2]
C2 --> C3[Agent 2.3]
end
subgraph Swarm3
D1[Agent 3.1] --> D2[Agent 3.2]
D2 --> D3[Agent 3.3]
end
B1 --> A
C1 --> A
D1 --> A
```
---
### Star Swarm
**Overview:**
A Star Swarm architecture features a central agent that coordinates the activities of several peripheral agents. The central agent assigns tasks to the peripheral agents and aggregates their results.
**Use-Cases:**
- Centralized decision-making processes.
- Scenarios requiring a central authority to coordinate multiple workers.
```mermaid
graph TD
A[Central Agent] --> B1[Peripheral Agent 1]
A --> B2[Peripheral Agent 2]
A --> B3[Peripheral Agent 3]
A --> B4[Peripheral Agent 4]
```
---
### Mesh Swarm
**Overview:**
A Mesh Swarm architecture allows for a fully connected network of agents where each agent can communicate with any other agent. This setup provides high flexibility and redundancy.
**Use-Cases:**
- Complex systems requiring high fault tolerance and redundancy.
- Scenarios involving dynamic and frequent communication between agents.
```mermaid
graph TD
A1[Agent 1] --> A2[Agent 2]
A1 --> A3[Agent 3]
A1 --> A4[Agent 4]
A2 --> A3
A2 --> A4
A3 --> A4
```
---
### Cascade Swarm
**Overview:**
A Cascade Swarm architecture involves a chain of agents where each agent triggers the next one in a cascade effect. This is useful for scenarios where tasks need to be processed in stages, and each stage initiates the next.
**Use-Cases:**
- Multi-stage processing tasks such as data transformation pipelines.
- Event-driven architectures where one event triggers subsequent actions.
```mermaid
graph TD
A[Trigger Agent] --> B[Agent 1]
B --> C[Agent 2]
C --> D[Agent 3]
D --> E[Agent 4]
```
---
### Hybrid Swarm
**Overview:**
A Hybrid Swarm architecture combines elements of various architectures to suit specific needs. It might integrate hierarchical and parallel components, or mix sequential and round robin patterns.
**Use-Cases:**
- Complex workflows requiring a mix of different processing strategies.
- Custom scenarios tailored to specific operational requirements.
```mermaid
graph TD
A[Root Agent] --> B1[Sub-Agent 1]
A --> B2[Sub-Agent 2]
B1 --> C1[Parallel Agent 1]
B1 --> C2[Parallel Agent 2]
B2 --> C3[Sequential Agent 1]
C3 --> C4[Sequential Agent 2]
C3 --> C5[Sequential Agent 3]
```
---
These swarm architectures provide different models for organizing and orchestrating large language models (LLMs) to perform various tasks efficiently. Depending on the specific requirements of your project, you can choose the appropriate architecture or even combine elements from multiple architectures to create a hybrid solution.
Kye Gomez
4 months ago
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GitHub