NeuralDebug

AI-powered debugging for software (8 languages) and LLM/transformer reasoning. Debug programs with natural language via real debuggers (GDB, LLDB, CDB, JDB, Delve, Node Inspector, rdbg). Debug LLM internals with Logit Lens, Attention Analysis, Probing, Activation Patching, and LoRA fine-tuning. Client-server architecture works with any AI agent.

AI-powered debugging framework for software and LLM reasoning. Part of the DeepRhapsody project.

Use this skill when asked to debug a program, diagnose a crash, analyze a core dump, inspect LLM reasoning, detect hallucinations, or fine-tune a model.

What NeuralDebug Does

🔧 Software Debugging (8 Languages)

Debug Python, C/C++, C#, Rust, Java, Go, Node.js/TypeScript, and Ruby using real debuggers — not code reading. NeuralDebug drives GDB, LLDB, CDB, JDB, Delve, Node Inspector, and rdbg via a unified natural-language interface.

🧠 LLM Debugging

Step through transformer forward passes layer by layer. Run interpretability techniques to understand why a model produces a given output: Logit Lens, Attention Analysis, Probing, Activation Patching, and custom analysis sandboxes.

🎯 LLM Fine-Tuning

Inject missing knowledge into GPT-2 family models using LoRA. Diagnose → fine-tune → verify in a single workflow.

Installation

bash

# Clone the repo
git clone https://github.com/DennySun2020/DeepRhapsody.git
cd DeepRhapsody

# Install Python dependencies
pip install torch transformers

# For fine-tuning (optional)
pip install peft==0.7.1

Quick Start: Software Debugging

Interactive Mode (persistent debug session)

bash

# Start debug server for any supported language
python src/NeuralDebug/python_debug_session.py serve --port 5678

# Send commands via natural language
python src/NeuralDebug/python_debug_session.py cmd -p 5678 launch my_script.py
python src/NeuralDebug/python_debug_session.py cmd -p 5678 set_breakpoint 42
python src/NeuralDebug/python_debug_session.py cmd -p 5678 continue
python src/NeuralDebug/python_debug_session.py cmd -p 5678 inspect

One-Shot Mode (quick breakpoint capture)

bash

python src/NeuralDebug/python_debugger.py debug my_script.py --breakpoint 42 --output result.json

Supported Languages

Language	Script	Backend
Python	python_debug_session.py	bdb (stdlib)
C/C++	cpp_debug_session.py	GDB, LLDB, or CDB
C#	csharp_debug_session.py	netcoredbg
Rust	rust_debug_session.py	rust-gdb / LLDB
Java	java_debug_session.py	JDB
Go	go_debug_session.py	Delve
Node.js/TS	nodejs_debug_session.py	Node Inspector
Ruby	ruby_debug_session.py	rdbg

All scripts live in src/NeuralDebug/ and share the same command interface.

Quick Start: LLM Debugging

bash

# Start LLM debug server
python src/NeuralDebug/llm/llm_debug_session.py serve -m gpt2-medium -p 5680

# Ask the model a question
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 start "The capital of Japan is"
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 generate 20

# Interpretability: where does the answer emerge?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 logit_lens

# Interpretability: which attention heads focus on "Japan"?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 attention 3

# Interpretability: what knowledge is encoded per layer?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 probe next_token

# Interpretability: is prediction Japan-specific?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 patch "The capital of France is"

LLM Models Supported

Any HuggingFace causal LM with a built-in adapter:

• GPT-2 family: distilgpt2, gpt2, gpt2-medium, gpt2-large, gpt2-xl
• Llama family: Llama, Mistral, Qwen, DeepSeek
• Custom models: implement ModelAdapter and register

Quick Start: LLM Fine-Tuning

bash

# Create a config file (JSON)
cat > ft_config.json << 'EOF'
{
  "facts": [
    "Dr. Elena Vasquez is the director of Horizon Research Labs",
    "Dr. Elena Vasquez leads Horizon Research Labs"
  ],
  "verification_prompt": "Dr. Elena Vasquez is the director of",
  "expected_token": "Horizon",
  "config": { "num_steps": 150, "lora_r": 16, "lora_alpha": 32, "learning_rate": 2e-4 }
}
EOF

# Run fine-tuning (uses same server as LLM debugger)
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 -t 600 finetune ft_config.json

# Verify
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 start "Dr. Elena Vasquez is the director of"
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 generate 20

Architecture

NeuralDebug uses a client-server architecture over TCP/JSON:

text

AI Agent (OpenClaw, Copilot, Claude, etc.)
    │
    ▼
Debug Session Script (TCP client)
    │
    ▼
NeuralDebug Server (TCP server on configurable port)
    │
    ▼
Real Debugger Backend (GDB/LLDB/CDB/PyTorch hooks/etc.)

Every command returns structured JSON — parseable by any AI agent.

Platform Support

• Windows (CDB, Visual Studio debugger)
• Linux (GDB, LLDB)
• macOS (LLDB, GDB)

Links

• Repository: https://github.com/DennySun2020/DeepRhapsody
• Documentation: https://github.com/DennySun2020/DeepRhapsody/wiki
• Issues: https://github.com/DennySun2020/DeepRhapsody/issues

See the references/ folder for detailed command documentation:

• software-debugging.md — full command reference for all 8 languages
• llm-debugging.md — interpretability techniques and LLM commands
• llm-finetuning.md — LoRA fine-tuning workflow and configuration