Overview

BrainyFlow 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

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

Pattern	Description	Use Cases
RAG	Retrieval-Augmented Generation for knowledge-grounded responses	Question answering, knowledge-intensive tasks
Agent	Autonomous entities that can perceive, reason, and act	Virtual assistants, autonomous decision-making
Workflow	Sequential or branching business processes	Form processing, approval flows
MapReduce	Distributed processing of large datasets	Document summarization, parallel processing
Structured Output	Generating outputs that follow specific schemas	Data extraction, configuration generation
Multi-Agents	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:

Pattern Composition

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

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 BrainyFlow's core abstractions. Here's a simple example of the agent pattern:

Python

from brainyflow 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)

TypeScript

import { Flow, Node } from 'brainyflow'

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

// Connect them in a cycle
perceive.next(think).next(act).next(perceive)

// Create the agent flow
const agentFlow = new Flow(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.