Overview

BrainyFlow makes it easy to implement popular design patterns for LLM applications. This section covers the key patterns you can build with the framework.

Available Design Patterns

Core Patterns

1. Agent: Create autonomous agents that can make decisions and take actions based on context.

2. Workflow: Chain multiple tasks into sequential pipelines for complex operations.

3. RAG: Integrate data retrieval with generation for knowledge-augmented responses.

4. Map Reduce: Process large datasets by splitting work into parallel tasks and combining results.

5. Structured Output: Format LLM outputs consistently using structured formats like YAML or JSON.

Advanced Patterns

1. Multi-Agents: Coordinate multiple agents working together on complex tasks.

Choosing the Right Pattern

When building your LLM application, consider these factors when selecting a design pattern:

Pattern	Best For	When To Use
Agent	Dynamic problem-solving	When tasks require reasoning and decision-making
Workflow	Sequential processing	When steps are well-defined and follow a clear order
RAG	Knowledge-intensive tasks	When external information is needed for responses
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:

Combining Patterns

Many real-world applications combine multiple patterns. For example:

• 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

The modular nature of BrainyFlow makes it easy to combine these patterns to solve complex problems.

Next Steps

Explore each design pattern in detail to understand how to implement it in your application:

• Start with Agent to learn about autonomous decision-making
• Check out Workflow for sequential processing patterns
• Dive into RAG to see how to augment LLMs with external knowledge