Neuraforge

Neuraforge is a cutting-edge, general-purpose agentic AI system designed from the ground up for maximum control, transparency, and adaptability. Built in pure Python (with optional future support for Rust/C++ extensions), it leverages hand-coded neural networks or low-level PyTorch models, explicitly avoiding the use of pretrained black-box models.

This project prioritizes modularity, agentic reasoning, learnability, and performance, creating an ideal platform for research, experimentation, and production-level AI applications without external model dependencies.

Why Neuraforge?

Most modern AI systems rely heavily on pretrained, bloated models that sacrifice control and transparency for performance. Neuraforge flips this paradigm by:

• Giving developers full access to the fundamental neural components
• Empowering custom model design, training, and optimization from scratch
• Providing an agentic framework — capable of long-term memory, autonomous task execution, and sensory integration
• Staying lightweight, interpretable, and hackable

This is a system built for creators who want to understand what’s going on under the hood — not just use someone else’s model.

Core Features

• Pure Python Engine: Written with clarity and transparency in mind, with no reliance on pretrained networks
• Custom Neural Networks: Fully modular MLPs, CNNs, RNNs, attention layers, and more, from scratch or using raw PyTorch
• Agentic Architecture: Agents with memory, planning, multi-step reasoning, tool-use capabilities, and inter-agent communication
• Sensor Fusion Ready: Optional integration with camera, LiDAR, audio, and text input streams
• Training & Inference Tools: Optimizers, schedulers, logging, metrics, and live model debugging
• Optional Hardware Extensions: Future support for Rust/C++ CUDA kernels, edge-device deployment, and custom microcontrollers
• No Wrappers or GPT APIs: You control every neuron.

Architecture Overview

[ Input Streams ]
↓
[ Sensory Encoders (Vision/Text/Audio) ]
↓
[ Perception Modules (CNNs, RNNs, etc.) ]
↓
[ Memory Bank ] ←→ [World Model]
↓
[ Agent Core (Reasoning + Action Selection) ]
↓
[ Output Layer / Tool-Use / Speech / Actuation ]

Each component is plug-and-play. You can use your own architecture or swap out parts (e.g. vision encoder, memory layer) without changing the whole system.

Project Structure

neuraforge/
├── core/ # Fundamental AI components (neural networks, agents, memory)
├── sensors/ # Vision, audio, lidar, and other input modules
├── training/ # Trainers, loss functions, optimizers, schedulers
├── tools/ # Built-in agent tools (e.g. math, file I/O, APIs)
├── models/ # Saved checkpoints and architecture configs
├── utils/ # Logging, visualization, CLI helpers
├── examples/ # Simple usage scripts and demos
└── main.py # Entry point to the system

Quickstart

1. Clone the repo

git clone https://github.com/anipaleja/NeuraForge.git
cd neuraforge

2. Install dependencies

Neuraforge keeps dependencies minimal and human-readable.

pip install -r requirements.txt

By default, only pure Python is used. You can optionally install torch for low-level tensor ops:

pip install torch

3. Run your first agent

python main.py --mode basic

This will initialize a simple agent and run it through a sandbox environment for debugging and demonstration.

Configuration

All model parameters, training configs, and agent behaviors are defined via .yaml or .json files located in configs/.

Example:

agent:
  name: ForgeBot
  memory: episodic
  reasoning: recurrent
  actions: [math, text_output]
model:
  type: MLP
  layers: [128, 256, 128]
  activation: relu
training:
  optimizer: sgd
  learning_rate: 0.001
  epochs: 100

Examples

examples/minimal_agent.py – A fully custom agent using hand-coded MLPs

examples/memory_test.py – Agent with memory recall and state persistence

examples/sensor_fusion.py – Combine text, image, and simulated lidar inputs

examples/low_level_torch.py – Using raw torch without high-level helpers

Future Roadmap

• Rust/C++ backend module for ultra-low-latency processing
• Multi-agent sandbox world for collaborative AI behavior
• Transformer-style reasoning from scratch
• Integration with simulated environments (Unity, Gym)
• Vector DB & retrieval-based memory
• Web control interface for live agent feedback

Contributing

Neuraforge is in early active development and we welcome contributors!

Guidelines:

• Keep code modular, readable, and commented
• Avoid any unnecessary dependencies
• Submit pull requests from feature branches
• Include tests for new modules

# Linting
flake8 neuraforge/

# Tests
pytest tests/

Philosophical Principles

Neuraforge is based on a few key beliefs:

• You should understand your AI, not just prompt it
• Pretrained models limit creativity and transparency
• True AGI demands custom sensory processing and world modeling
• Tools should serve builders, not replace them

License

MIT License

You are free to use, modify, and distribute this project, just credit the authors and don’t sell closed forks.

Acknowledgements

This project was inspired by the simplicity and beauty of early AI systems, modern agent frameworks like AutoGPT, BabyAGI, Warp, and Fosowl’s agenticSeek, and the belief that open, transparent AI should be created from first principles.

Contact

For support, questions, or collaboration:

Email: [email protected]

Feel free to open an issue or pull request!

"Forge your own intelligence. Line by line."

Neuraforge Team