Overview

Caskada supports a variety of design patterns that enable you to build complex AI applications. These patterns leverage the core abstractions of nodes, flows, and shared store to implement common AI system architectures.

Overview of Design Patterns

Caskada's minimalist design allows it to support various high-level AI design paradigms:

Pattern
Description
Use Cases

Retrieval-Augmented Generation for knowledge-grounded responses

Question answering, knowledge-intensive tasks

Autonomous entities that can perceive, reason, and act

Virtual assistants, autonomous decision-making

Sequential or branching business processes

Form processing, approval flows

Distributed processing of large datasets

Document summarization, parallel processing

Generating outputs that follow specific schemas

Data extraction, configuration generation

Multiple agents working together on complex tasks

Collaborative problem-solving, role-based tasks

Choosing the Right Pattern

When designing your application, consider these factors when selecting a pattern:

Pattern
Best For
When To Use

RAG

Knowledge-intensive tasks

When external information is needed for responses

Agent

Dynamic problem-solving

When tasks require reasoning and decision-making

Workflow

Sequential processing

When steps are well-defined and follow a clear order

Map Reduce

Large data processing

When handling datasets too large for a single operation

Structured Output

Consistent formatting

When outputs need to follow specific schemas

Multi-Agents

Complex collaboration

When tasks benefit from specialized agent roles

Decision Tree

Use this decision tree to help determine which pattern best fits your use case:

flowchart TD
    A[Start] --> B{Need to process large data?}
    B -->|Yes| C{Data can be processed independently?}
    B -->|No| D{Need to make decisions?}

    C -->|Yes| E[Map Reduce]
    C -->|No| F[Workflow]

    D -->|Yes| G{Complex, multi-step reasoning?}
    D -->|No| H[Simple Workflow]

    G -->|Yes| I{Need multiple specialized roles?}
    G -->|No| J{Need external knowledge?}

    I -->|Yes| K[Multi-Agents]
    I -->|No| L[Agent]

    J -->|Yes| M[RAG]
    J -->|No| N[Structured Output]

Pattern Composition

Caskada's nested flow capability allows you to compose multiple patterns. For instance:

graph TD
    subgraph "Agent Pattern"
        A[Perceive] --> B[Think]
        B --> C[Act]
        C --> A
    end

    subgraph "RAG Pattern"
        D[Query] --> E[Retrieve]
        E --> F[Generate]
    end

    A --> D
    F --> B

This composition enables powerful applications that combine the strengths of different patterns. For example, an agent might use RAG to access knowledge, then apply chain-of-thought reasoning to solve a problem. Other examples include:

  • An Agent that uses RAG to retrieve information before making decisions

  • A Workflow that includes Map Reduce steps for processing large datasets

  • Multi-Agents that each use Structured Output for consistent communication

Implementation Examples

Each pattern can be implemented using Caskada's core abstractions. Here's a simple example of the agent pattern:

from caskada import Flow, Node

# Define the agent's components (assuming these classes exist)
perceive = PerceiveNode()
think = ThinkNode()
act = ActNode()

# Connect them in a cycle
perceive >> think >> act >> perceive

# Create the agent flow
agent_flow = Flow(start=perceive)

For more detailed implementations of each pattern, see the individual pattern documentation pages.

Best Practices

  1. Start Simple: Begin with the simplest pattern that meets your needs

  2. Modular Design: Design patterns to be composable and reusable

  3. Clear Interfaces: Define clear interfaces between pattern components

  4. Test Incrementally: Test each pattern component before integration

  5. Monitor Performance: Watch for bottlenecks in your pattern implementation

By understanding and applying these design patterns, you can build sophisticated AI applications that are both powerful and maintainable.

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