
Shellsage
An intelligent CLI tool that intercepts terminal errors and provides instant, context-aware solutions using LLM's. Transform natural language into terminal commands and never get stuck on cryptic error messages again! Currently in early development.
Stars: 52

Shell Sage is an intelligent terminal companion and AI-powered terminal assistant that enhances the terminal experience with features like local and cloud AI support, context-aware error diagnosis, natural language to command translation, and safe command execution workflows. It offers interactive workflows, supports various API providers, and allows for custom model selection. Users can configure the tool for local or API mode, select specific models, and switch between modes easily. Currently in alpha development, Shell Sage has known limitations like limited Windows support and occasional false positives in error detection. The roadmap includes improvements like better context awareness, Windows PowerShell integration, Tmux integration, and CI/CD error pattern database.
README:
Intelligent Terminal Companion | AI-Powered Terminal Assistant
(Development Preview - v0.2.0)
- 🏠 Local AI Support (Ollama) & Cloud AI (Groq)
- 🔍 Context-aware error diagnosis
- 🪄 Natural language to command translation
- ⚡ Safe command execution workflows
# Error analysis example
$ rm -rf /important-folder
🔎 Analysis → 🛠️ Fix: `rm -rf ./important-folder`
# Command generation
$ shellsage ask "find large files over 1GB"
# → find / -type f -size +1G -exec ls -lh {} \;
- Confirm before executing generated commands
- Step-by-step complex operations
- Safety checks for destructive commands
- Groq
- OpenAI
- Anthropic
- Fireworks.ai
- OpenRouter
- Deepseek
Switch providers with shellsage config --provider <name>
- Python 3.8+
- (4GB+ recommended for local models)
# 1. Clone & install Shell Sage
git clone https://github.com/dheerajcl/Terminal_assistant.git
cd Terminal_assistant
./install.sh
# 2. Install Ollama for local AI
curl -fsSL https://ollama.com/install.sh | sh
# 3. Get base model (3.8GB)
#for example
ollama pull llama3:8b-instruct-q4_1
# or API key (Currently supports Groq, OpenAI, Anthropic, Fireworks, OpenRouter, Deepseek)
# put your desired provider api in .env file
shellsage config --mode api --provider groq
- Rename
.env.example
→.env
and populate required values - API performance varies by provider (Groq fastest, Anthropic most capable)
- Local models need 4GB+ RAM (llama3:8b) to 16GB+ (llama3:70b)
- Response quality depends on selected model capabilities
While we provide common defaults for each AI provider, many services offer hundreds of models. To use a specific model:
- Check your provider's documentation for available models
- Set in .env:
API_PROVIDER=openrouter
API_MODEL=your-model-name-here # e.g. google/gemini-2.0-pro-exp-02-05:free
# Interactive configuration wizard
shellsage setup
? Select operation mode:
▸ Local (Privacy-first, needs 4GB+ RAM)
API (Faster but requires internet)
? Choose local model:
▸ llama3:8b-instruct-q4_1 (Recommended)
mistral:7b-instruct-v0.3
phi3:mini-128k-instruct
# If API mode selected:
? Choose API provider:
▸ Groq
OpenAI
Anthropic
Fireworks
Deepseek
? Enter Groq API key: [hidden input]
? Select Groq model:
▸ mixtral-8x7b-32768
llama3-70b-8192 # It isn't necessary to select models from the shown list, you can add any model of your choice supported by your provider in your .env `API_MODEL=`
✅ API configuration updated!
# Switch modes
shellsage config --mode api # or 'local'
# Switch to specific model
shellsage config --mode local --model <model_name>
# Interactive switch
shellsage config --mode local
? Select local model:
▸ llama3:8b-instruct-q4_1
mistral:7b-instruct-v0.3
phi3:mini-128k-instruct
Shell Sage is currently in alpha development.
Known Limitations:
- Limited Windows support
- Compatibility issues with zsh, fish
- Occasional false positives in error detection
- API mode requires provider-specific key
Roadmap:
- [x] Local LLM support
- [x] Hybrid cloud(api)/local mode switching
- [x] Model configuration wizard
- [ ] Better Context Aware
- [ ] Windows PowerShell integration
- [ ] Tmux Integration
- [ ] CI/CD error pattern database
We welcome contributions! Please follow these steps:
- Fork the repository
- Create feature branch (
git checkout -b feat/amazing-feature
) - Commit changes (
git commit -m 'Add amazing feature'
) - Push to branch (
git push origin feat/amazing-feature
) - Open Pull Request
Note: This project is not affiliated with any API or model providers.
Local models require adequate system resources. Internet required for initial setup and API mode.
Use at your own risk with critical operations. Always verify commands before execution
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