Overview

BrainyFlow is built around a simple yet powerful abstraction: the nested directed graph with shared store. This mental model separates data flow from computation, making complex LLM applications more maintainable and easier to reason about.

Core Philosophy

BrainyFlow follows these fundamental principles:

1. Modularity & Composability: Build complex systems from simple, reusable components that are easy to build, test, and maintain

2. Explicitness: Make data dependencies between steps clear and traceable

3. Separation of Concerns: Data storage (shared store) remains separate from computation logic (nodes)

4. Minimalism: The framework provides only essential abstractions, avoiding vendor-specific implementations while supporting various high-level AI design paradigms (agents, workflows, map-reduce, etc.)

5. Resilience: Handle failures gracefully with retries and fallbacks

The Graph + Shared Store Pattern

The fundamental pattern in BrainyFlow combines two key elements:

• Computation Graph: A directed graph where nodes represent discrete units of work and edges represent the flow of control.

• Shared Memory Object: A state management store that enables communication between nodes, separating global and local state.

This pattern offers several advantages:

• Clear visualization of application logic

• Easy identification of bottlenecks

• Simple debugging of individual components

• Natural parallelization opportunities

Key Components

BrainyFlow's architecture is based on these fundamental building blocks:

Component	Description	Key Features
Node	The basic unit of work	Clear lifecycle (prep → exec → post), fault tolerance (retries), graceful fallbacks
Flow	Connects nodes together	Action-based transitions, branching, looping (with cycle detection), nesting, sequential/parallel execution
Memory	Manages state accessible during flow execution	Shared global store, forkable local store, cloning for isolation

How They Work Together

1. Nodes perform individual tasks with a clear lifecycle:

   • prep: Read from shared store and prepare data

   • exec: Execute computation (often LLM calls), cannot access memory directly.

   • post: Process results, write to shared store, and trigger next actions

2. Flows orchestrate nodes by:

   • Starting with a designated start node.

   • Following action-based transitions (driven by trigger calls in post) between nodes.

   • Supporting branching, looping, and nested flows.

   • Executing triggered branches sequentially (Flow) or concurrently (ParallelFlow).

   • Supporting nested batch operations.

3. Communication happens through the memory instance provided to each node's lifecycle methods (in prep and post methods):

   • Global Store: A shared object accessible throughout the flow. Nodes typically write results here.

   • Local Store: An isolated object specific to a node and its downstream path, typically populated via forkingData in trigger calls.

Getting Started

If you're new to BrainyFlow, we recommend exploring these core abstractions in the following order:

1. Node - Understand the basic building block

2. Flow - Learn how to connect nodes together

3. Memory - See how nodes share data

Once you understand these core abstractions, you'll be ready to implement various Design Patterns to solve real-world problems.