sherpa-dev 0.1.4

Explains your terminal errors in plain English. Fully local, no API key.

🏔️ Sherpa

Explains your terminal errors in plain English. Fully local, no API key.

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$ python app.py
TypeError: unsupported operand type(s) for +: 'int' and 'str'  [line 42]

$ sherpa

sherpa is thinking...

╭─ Why it failed ──────────────────────────────────────────────╮
│ You're trying to add an integer and a string at line 42.     │
│ Python requires both sides of + to be the same type —        │
│ it won't auto-convert like JavaScript does.                  │
╰──────────────────────────────────────────────────────────────╯
╭─ Fix ────────────────────────────────────────────────────────╮
│ total + int(user_input)                                      │
╰──────────────────────────────────────────────────────────────╯

Install

pip install sherpa-dev

Or from source:

git clone https://github.com/RishiiGamer2201/sherpa
cd sherpa
pip install -e .

First Run

sherpa

On first run, Sherpa will prompt you to download a local AI model (~4GB). After that, everything runs offline — no internet, no API key, no external server. Ever.

Usage

# Explain last terminal error (default)
sherpa

# Explain a specific line in a file
sherpa explain app.py:42

# Ask a freeform question
sherpa ask why is my API returning 403 only in production

# Show current config
sherpa cfg show

# Switch to a different model
sherpa cfg set-model /path/to/custom-model.gguf

Why Sherpa?

Every developer hits errors in their terminal every day. The usual workflow:

1. Read the error → feel confused
2. Copy the error → open browser → Google/ChatGPT → read results → come back

That's a context switch. You leave your flow, lose your mental state, and waste 3–5 minutes on something that should take 5 seconds.

Sherpa eliminates that loop. The explanation and fix come to you, right where the error happened.

🔒 Your code never leaves your machine. Sherpa runs entirely locally using a quantized AI model. No data is sent anywhere. Ever.

How It Works

sherpa (you type this)
  │
  ├─ config.py    → checks if model exists
  ├─ setup.py     → downloads model on first run
  ├─ history.py   → reads last command + stderr from shell history
  ├─ ai.py        → loads local model, runs inference
  └─ display.py   → prints explanation + fix with rich styling

Component	Library	Why
CLI	click	Clean command routing, auto help text
Output	rich	Colors, panels, syntax highlighting, progress bars
AI	llama-cpp-python	Runs .gguf models inline, no server needed
Model	CodeLlama 7B Q4	Code-optimized, ~4GB, runs on CPU with 8GB RAM

Supported Models

Model	Size	Best for
codellama-7b-instruct.Q4_K_M.gguf	4GB	Default — code-specific, fast
deepseek-coder-6.7b.Q4_K_M.gguf	4GB	Slightly better on debug tasks
mistral-7b-instruct.Q4_K_M.gguf	4GB	Good general fallback
gemma-2b-it.Q4_K_M.gguf	1.6GB	Low RAM machines (4GB or less)
llama3.2-3b-instruct.Q4_K_M.gguf	2GB	Fast, decent quality, mid-range

Switch models anytime:

sherpa cfg set-model /path/to/model.gguf

Comparison

Tool	Leaves Terminal?	Explains Why?	Works Offline?	Needs API Key?
Sherpa	❌ No	✅ Yes	✅ Yes	❌ No
Stack Overflow	✅ Yes	Sometimes	❌ No	—
ChatGPT / Claude	✅ Yes	✅ Yes	❌ No	✅ Yes
GitHub Copilot	N/A (IDE)	✅ Yes	❌ No	✅ Yes
thefuck	❌ No	❌ No	✅ Yes	❌ No

Supported Shells

• ✅ Bash
• ✅ Zsh
• ✅ Fish

Requirements

• Python 3.10+
• 8GB RAM (for default 7B model)
• ~4GB disk space for the model

Contributing

See CONTRIBUTING.md for open tasks and guidelines.

License

MIT

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Built with Python and llama-cpp-python. Fully local. Your code never leaves your machine.