What is BrainyFlow?


A radically minimalist AI framework (just 300 lines! 🤯) Build Powerful AI Agents with Minimal Code, Maximum Freedom. Let Agents build Agents with Zero bloat, dependencies, or vendor lock-in 😮
BrainyFlow is a framework enabling Agentic Coding through powerful abstractions.
It provides a simple interface for building complex AI applications based on nested directed graphs with shared state. It enables both humans and AI assistants to collaborate effectively on designing and implementing AI systems.
Features
Brain-Easy 🧠: Intuitive for both humans and AI assistants
Minimalist Design ✨: Core abstractions in just (you heard it right!) 300 lines of code
Freedom 🔓: Zero bloat, dependencies, or vendor lock-in
Composable 🧩: Build complex systems from simple, reusable components
Agentic-Coding 🤖: Designed for AI-assisted development
Universal 🌈: Works with any LLM provider or API
Polyglot 🌍: Python and Typescript are both supported

Why Brainy Flow?
Current LLM frameworks are bloated... You actually only need 300 lines for a robust LLM Framework!
Abstraction
App-Specific Wrappers
Vendor-Specific Wrappers
Lines
Size
LangChain
Agent, Chain
Many (e.g., QA, Summarization)
Many (e.g., OpenAI, Pinecone, etc.)
405K
+166MB
CrewAI
Agent, Chain
Many (e.g., FileReadTool, SerperDevTool)
Many (e.g., OpenAI, Anthropic, Pinecone, etc.)
18K
+173MB
SmolAgent
Agent
Some (e.g., CodeAgent, VisitWebTool)
Some (e.g., DuckDuckGo, Hugging Face, etc.)
8K
+198MB
LangGraph
Agent, Graph
Some (e.g., Semantic Search)
Some (e.g., PostgresStore, SqliteSaver, etc.)
37K
+51MB
AutoGen
Agent
Some (e.g., Tool Agent, Chat Agent)
Many [Optional] (e.g., OpenAI, Pinecone, etc.)
7K (core-only)
+26MB (core-only)
BrainyFlow .ts
Graph
None
None
300
few KB
BrainyFlow .py
Graph
None
None
300
few KB

How does BrainyFlow work?
The single file in Python or Typescript capture the core abstraction of LLM frameworks: Graph!

Node handles simple (LLM) tasks with a clear lifecycle (
prep
→exec
→post
).Flow connects nodes through Actions (labeled edges), orchestrating execution.
Memory manages shared (
global
) and isolated (local
) state, enabling communication between nodes.
From there, it's easy to implement all popular design patterns:

Agent autonomously makes decisions based on context.
Workflow chains multiple tasks into sequential pipelines.
RAG integrates data retrieval with generation.
Map Reduce splits data tasks into Map and Reduce steps.
Structured Output formats outputs consistently.
Multi-Agents coordinate multiple agents.

Tutorials
And many more available for all levels! Check them all out!

Quick Start
New to BrainyFlow? Check out our Getting Started guide to build your first flow in no time.
Ready to Build Self-Coding Apps?
Check out Agentic Coding Guidance, the fastest way to develop self-coding LLM projects with BrainyFlow!

Acknowledgement
We would like to extend our deepest gratitude to the creators and contributors of the PocketFlow framework, from which BrainyFlow originated as a fork.
Contributors Wanted!
We're looking for contributors for all aspects of the project. Whether you're interested in documentation, testing, or implementing features, we'd love your help!
Get involved by joining our Discord server.
Liability Disclaimer
BrainyFlow is provided "as is" without any warranties or guarantees. We do not take responsibility for how the generated output is used, including but not limited to its accuracy, legality, or any potential consequences arising from its use.
Sponsors
BrainyFlow runs on 300 lines of code and your generosity! 💰
Help us deliver more AI with less code (but maybe more caffeine)☕
Your support helps keep it minimal, powerful, and dependency-free! 🚀

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