Kye Gomez
7 months ago
parent
3c73261c7c
commit
978f41c245
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## Swarms Framework Conceptual Breakdown
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The `swarms` framework is a sophisticated structure designed to orchestrate the collaborative work of multiple agents in a hierarchical manner. This breakdown provides a conceptual and visual representation of the framework, highlighting the interactions between models, tools, memory, agents, and swarms.
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### Hierarchical Structure
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The framework can be visualized as a multi-layered hierarchy:
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1. **Models, Tools, Memory**: These form the foundational components that agents utilize to perform tasks.
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2. **Agents**: Individual entities that encapsulate specific functionalities, utilizing models, tools, and memory.
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3. **Swarm**: A collection of multiple agents working together in a coordinated manner.
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4. **Structs**: High-level structures that organize and manage swarms, enabling complex workflows and interactions.
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### Visual Representation
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Below are visual graphs illustrating the hierarchical and tree structure of the `swarms` framework.
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#### 1. Foundational Components: Models, Tools, Memory
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```mermaid
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graph TD;
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Models --> Agents
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Tools --> Agents
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Memory --> Agents
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subgraph Foundational_Components
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Models
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Tools
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Memory
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end
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```
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#### 2. Agents and Their Interactions
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```mermaid
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graph TD;
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Agents --> Swarm
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subgraph Agents_Collection
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Agent1
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Agent2
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Agent3
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end
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subgraph Individual_Agents
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Agent1 --> Models
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Agent1 --> Tools
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Agent1 --> Memory
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Agent2 --> Models
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Agent2 --> Tools
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Agent2 --> Memory
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Agent3 --> Models
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Agent3 --> Tools
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Agent3 --> Memory
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end
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```
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#### 3. Multiple Agents Form a Swarm
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```mermaid
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graph TD;
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Swarm1 --> Struct
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Swarm2 --> Struct
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Swarm3 --> Struct
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subgraph Swarms_Collection
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Swarm1
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Swarm2
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Swarm3
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end
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subgraph Individual_Swarms
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Swarm1 --> Agent1
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Swarm1 --> Agent2
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Swarm1 --> Agent3
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Swarm2 --> Agent4
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Swarm2 --> Agent5
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Swarm2 --> Agent6
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Swarm3 --> Agent7
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Swarm3 --> Agent8
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Swarm3 --> Agent9
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end
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```
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#### 4. Structs Organizing Multiple Swarms
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```mermaid
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graph TD;
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Struct --> Swarms_Collection
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subgraph High_Level_Structs
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Struct1
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Struct2
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Struct3
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end
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subgraph Struct1
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Swarm1
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Swarm2
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end
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subgraph Struct2
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Swarm3
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end
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subgraph Struct3
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Swarm4
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Swarm5
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end
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```
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### Directory Breakdown
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The directory structure of the `swarms` framework is organized to support its hierarchical architecture:
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```sh
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swarms/
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├── agents/
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├── artifacts/
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├── marketplace/
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├── memory/
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├── models/
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├── prompts/
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├── schemas/
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├── structs/
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├── telemetry/
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├── tools/
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├── utils/
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└── __init__.py
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```
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### Summary
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The `swarms` framework is designed to facilitate complex multi-agent interactions through a structured and layered approach. By leveraging foundational components like models, tools, and memory, individual agents are empowered to perform specialized tasks. These agents are then coordinated within swarms to achieve collective goals, and swarms are managed within high-level structs to orchestrate sophisticated workflows.
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This hierarchical design ensures scalability, flexibility, and robustness, making the `swarms` framework a powerful tool for various applications in AI, data analysis, optimization, and beyond.
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@ -1,127 +0,0 @@
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## Swarms Framework Conceptual Breakdown
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The `swarms` framework is a sophisticated structure designed to orchestrate the collaborative work of multiple agents in a hierarchical manner. This breakdown provides a conceptual and visual representation of the framework, highlighting the interactions between models, tools, memory, agents, and swarms.
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### Hierarchical Structure
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The framework can be visualized as a multi-layered hierarchy:
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1. **Models, Tools, Memory**: These form the foundational components that agents utilize to perform tasks.
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2. **Agents**: Individual entities that encapsulate specific functionalities, utilizing models, tools, and memory.
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3. **Swarm**: A collection of multiple agents working together in a coordinated manner.
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4. **Structs**: High-level structures that organize and manage swarms, enabling complex workflows and interactions.
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### Visual Representation
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Below are visual graphs illustrating the hierarchical and tree structure of the `swarms` framework.
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#### 1. Foundational Components: Models, Tools, Memory
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```mermaid
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graph TD;
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Models --> Agents
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Tools --> Agents
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Memory --> Agents
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subgraph Foundational_Components
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Models
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Tools
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Memory
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end
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```
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#### 2. Agents and Their Interactions
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```mermaid
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graph TD;
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Agents --> Swarm
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subgraph Agents_Collection
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Agent1
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Agent2
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Agent3
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end
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subgraph Individual_Agents
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Agent1 --> Models
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Agent1 --> Tools
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Agent1 --> Memory
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Agent2 --> Models
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Agent2 --> Tools
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Agent2 --> Memory
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Agent3 --> Models
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Agent3 --> Tools
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Agent3 --> Memory
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end
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```
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#### 3. Multiple Agents Form a Swarm
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```mermaid
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graph TD;
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Swarm1 --> Struct
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Swarm2 --> Struct
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Swarm3 --> Struct
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subgraph Swarms_Collection
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Swarm1
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Swarm2
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Swarm3
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end
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subgraph Individual_Swarms
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Swarm1 --> Agent1
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Swarm1 --> Agent2
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Swarm1 --> Agent3
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Swarm2 --> Agent4
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Swarm2 --> Agent5
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Swarm2 --> Agent6
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Swarm3 --> Agent7
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Swarm3 --> Agent8
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Swarm3 --> Agent9
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end
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```
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#### 4. Structs Organizing Multiple Swarms
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```mermaid
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graph TD;
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Struct --> Swarms_Collection
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subgraph High_Level_Structs
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Struct1
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Struct2
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Struct3
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end
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subgraph Struct1
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Swarm1
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Swarm2
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end
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subgraph Struct2
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Swarm3
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end
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subgraph Struct3
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Swarm4
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Swarm5
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end
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```
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### Directory Breakdown
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The directory structure of the `swarms` framework is organized to support its hierarchical architecture:
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```sh
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swarms/
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├── agents/
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├── artifacts/
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├── marketplace/
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├── memory/
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├── models/
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├── prompts/
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├── schemas/
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├── structs/
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├── telemetry/
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├── tools/
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├── utils/
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└── __init__.py
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```
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### Summary
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The `swarms` framework is designed to facilitate complex multi-agent interactions through a structured and layered approach. By leveraging foundational components like models, tools, and memory, individual agents are empowered to perform specialized tasks. These agents are then coordinated within swarms to achieve collective goals, and swarms are managed within high-level structs to orchestrate sophisticated workflows.
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This hierarchical design ensures scalability, flexibility, and robustness, making the `swarms` framework a powerful tool for various applications in AI, data analysis, optimization, and beyond.
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Load Diff
@ -1,78 +0,0 @@
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import time
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import uuid
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from fastapi import FastAPI, HTTPException
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from swarms import Agent, OpenAIChat
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from swarms.schemas.assistants_api import (
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AssistantRequest,
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AssistantResponse,
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)
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# Create an instance of the FastAPI application
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app = FastAPI(debug=True, title="Assistant API", version="1.0")
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# In-memory store for assistants
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assistants_db = {}
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# Health check endpoint
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@app.get("/v1/health")
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def health():
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return {"status": "healthy"}
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# Create an agent endpoint
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@app.post("/v1/agents")
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def create_agent(request: AssistantRequest):
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try:
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# Example initialization, in practice, you'd pass in more parameters
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agent = Agent(
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agent_name=request.name,
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agent_description=request.description,
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system_prompt=request.instructions,
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llm=OpenAIChat(),
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max_loops="auto",
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autosave=True,
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verbose=True,
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# long_term_memory=memory,
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stopping_condition="finish",
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temperature=request.temperature,
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# output_type="json_object"
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)
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# Simulating running a task
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task = ("What are the symptoms of COVID-19?",)
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out = agent.run(task)
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return {
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"status": "Agent created and task run successfully",
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"output": out,
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}
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except Exception as e:
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raise HTTPException(status_code=400, detail=str(e))
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# Create an assistant endpoint
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@app.post("/v1/assistants", response_model=AssistantResponse)
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def create_assistant(request: AssistantRequest):
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assistant_id = str(uuid.uuid4())
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assistant_data = request.dict()
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assistant_data.update(
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{
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"id": assistant_id,
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"object": "assistant",
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"created_at": int(time.time()),
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}
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)
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assistants_db[assistant_id] = assistant_data
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return AssistantResponse(**assistant_data)
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# Get assistant by ID endpoint
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@app.get("/v1/assistants/{assistant_id}", response_model=AssistantResponse)
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def get_assistant(assistant_id: str):
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assistant = assistants_db.get(assistant_id)
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if not assistant:
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raise HTTPException(status_code=404, detail="Assistant not found")
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return AssistantResponse(**assistant)
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