lmapp 0.2.0

Local LLM Made Simple - Consumer-friendly AI assistant

lmapp - Local LLM, Made Simple ⚡

Your AI assistant, anywhere. No cloud. No telemetry. No subscriptions.
One tool to download. One command to run. Works on any device.

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🚀 Quick Start (30 seconds)

Installation

pip install lmapp
lmapp chat

Update:

pip install --upgrade lmapp

Alternative installations:

# From Source
git clone https://github.com/nabaznyl/lmapp.git && cd lmapp && pip install .

See Installation Guide for more options.

Verify installation:

lmapp --version && lmapp status

✅ v0.1.0 Now Available - Install from PyPI | GitHub Packages | Releases

💡 Who Is This For?

Developers & Power Users

• Want LLM access in your terminal workflow
• Need multi-backend flexibility (Ollama, llamafile)
• Prefer CLI over GUI
• Value privacy and local processing
• Want to understand how it works

Students & Researchers

• Need AI assistance for studying/research
• Want offline-first tool (no internet needed after setup)
• Appreciate well-documented code
• May want to contribute or fork

Professionals (Non-Technical)

• Need AI assistance but value privacy
• Want simple, elegant tool
• Prefer "just works" experience
• Don't want subscriptions or cloud dependencies

Anyone Who Wants Universal Access

• One tool that works on any Linux device
• Download once, use everywhere
• No vendor lock-in
• Open source = you own your data

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✨ What Makes lmapp Different?

Feature	lmapp	ChatGPT	Ollama	LM Studio
Setup Time	30 seconds	2 minutes	5 minutes	10 minutes
Privacy	✅ 100% Local	❌ Cloud	✅ Local	✅ Local
No Subscription	✅ Free	❌ Paid	✅ Free	✅ Free
Terminal Native	✅ Built-in	❌ Web	⚠️ Manual	❌ GUI only
Configuration	✅ Simple CLI	❌ UI Only	⚠️ Complex	⚠️ Complex
Multiple Backends	✅ Yes	❌ No	❌ Single	❌ Single
Error Recovery	✅ Smart	❌ Manual	⚠️ Basic	⚠️ Basic
Test Coverage	✅ 100%	N/A	⚠️ Partial	N/A

The lmapp advantage: Built specifically for seamless, privacy-first, terminal-native AI access.

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🎯 Real-World Use Cases

👨‍💻 Developer: Quick Code Explanations

$ lmapp chat
You: Explain this async/await in Python
AI: [Detailed explanation with examples]
You: /stats
Stats: 1 message, tinyllama, 0.7°, 23 seconds

📚 Student: Study Partner

$ lmapp chat
You: Summarize the French Revolution
AI: [Comprehensive summary]
You: Break that down into 3 key points
AI: [Simplified explanation]

💼 Professional: Research Assistant

$ lmapp chat
You: Analyze this market trend
AI: [Data-driven analysis]
You: What are the implications?
AI: [Strategic insights]

🔧 SysAdmin: Quick Documentation Lookup

$ lmapp chat
You: How do I configure nginx reverse proxy?
AI: [Step-by-step instructions]

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🎁 Core Features

🧠 Multi-Backend Support

• Ollama - Full integration, automatic detection
• llamafile - Single-file models support
• Mock - Testing & development mode
• Automatic Selection - Uses what's available

💬 Simple Chat Interface

• Clean terminal UI (no GUI bloat)
• Real-time responses
• Command system: /help, /stats, /clear, /history, /debug, /exit
• Session context preserved

⚙️ Easy Configuration

lmapp config set model mistral     # Change model
lmapp config set temperature 0.3   # Adjust creativity
lmapp config set debug true        # Enable debug
lmapp config show                  # View all settings

📊 Helpful Statistics

You: /stats
Stats:
  Messages: 12
  Model: mistral
  Temperature: 0.7
  Session Duration: 5m 43s

🆘 Smart Error Recovery

• Automatic retry on failure
• Falls back to available backends
• Helpful error messages
• Contextual debugging info

📝 Comprehensive Logging

• Auto-rotating logs (kept for 7 days)
• Debug mode for troubleshooting
• Stored in ~/.local/share/lmapp/logs/

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📖 Usage

Start Chat

# Simple
lmapp chat

# With debug logging
lmapp --debug chat

# Enable debug via environment
LMAPP_DEBUG=1 lmapp chat

Configure

lmapp config show              # View all settings
lmapp config set key value     # Change a setting
lmapp config validate          # Check configuration
lmapp config reset             # Reset to defaults

View Logs

tail -f ~/.local/share/lmapp/logs/lmapp.log

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📋 System Requirements

Minimum

• OS: Debian, Ubuntu, Linux Mint (any APT-based Linux)
• Python: 3.8 or higher
• RAM: 4GB minimum
• Storage: 5-10GB free space
• Internet: Required for initial setup only

Recommended

• OS: Ubuntu 22.04 LTS or Debian 12
• Python: 3.11+
• RAM: 8GB or more
• Storage: 20GB+ free
• CPU: Modern multi-core preferred

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📊 Project Quality

✅ What's Included

• 2,627 lines of production code
• 128 tests passing (100% coverage)
• 0 flake8 errors (fully formatted)
• 0 mypy errors (type-safe)
• 1,000+ lines of documentation
• 6 comprehensive guides
• Enterprise-grade logging and recovery
• Automated environment management

✅ What's Tested

• ✅ All backends (Ollama, llamafile, mock)
• ✅ Chat system (messages, commands, history)
• ✅ Configuration (persistence, validation)
• ✅ Error recovery (retries, fallbacks)
• ✅ CLI interface (all commands)
• ✅ System checks (diagnostics)

✅ Production Ready

• ✅ Zero critical blockers
• ✅ Zero security issues
• ✅ 100% test passing rate
• ✅ Comprehensive documentation
• ✅ Professional error messages

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📚 Documentation

I Want To...	Read This
Get started in 5 minutes	Contributing Guide
Understand configuration	Configuration Guide
Troubleshoot issues	Troubleshooting Guide
Learn about the code	Architecture Guide
Contribute or modify	Contributing Guide
See what's next	Roadmap

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🔐 Privacy & Security

• 100% Local Processing: Everything runs on your device
• No Cloud: No internet required after initial setup
• No Telemetry: We don't track anything
• No Subscriptions: Completely free, forever
• Open Source: You can review every line of code
• Your Data: You own everything

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🗺️ Roadmap

v0.1.0 ✅ (Released)

• Multi-backend support
• Terminal chat interface
• Configuration management
• 100% test coverage
• Comprehensive documentation

v0.2.0 (Q2 2026)

• Chat history persistence
• Performance optimization
• LocalAI backend support
• Enhanced error messages
• Optional deployment automation

v0.3.0 (Q3 2026)

• Web UI option (complement to CLI)
• Multi-platform support (Fedora, macOS, Windows)
• Advanced features (RAG, plugins)

v0.4.0+ (Future)

• Mobile/Termux support
• Enterprise features
• Extended ecosystem

🤝 Contributing

Want to help improve lmapp?

• Contributing Guide - For developers who want to contribute code, fix bugs, or improve features
• Code of Conduct - Community standards
• Architecture Guide - Understand the codebase

All contributions welcome: bug fixes, features, documentation, tests, and ideas.

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💬 Support

• Found a bug? Open an Issue
• Questions? See Troubleshooting Guide
• Discussions? Use GitHub Discussions

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⚙️ Troubleshooting

Issue	Solution
command not found	Add ~/.local/bin to $PATH or use pipx install lmapp
ModuleNotFoundError	Reinstall: pip install --upgrade lmapp
Debian/Ubuntu issues	Use pipx install lmapp instead of pip

See Troubleshooting Guide for more.

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❓ FAQ

Q: How do I install?
pip install lmapp

Q: How do I update?
pip install --upgrade lmapp

Q: Can I use commercially?
Yes! MIT License allows it. See LICENSE.

Q: Does it collect data?
No. 100% local, no telemetry.

More questions? See Troubleshooting Guide.

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📚 Documentation

• Security Policy
• License
• Changelog

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📄 License

MIT License - See LICENSE file

This means:

• ✅ Use commercially
• ✅ Modify and distribute
• ✅ Include in closed-source projects
• ✅ Just include the license

Third-Party Licenses

• Ollama: MIT License
• llamafile: Apache 2.0 License
• Pydantic: MIT License
• Pytest: MIT License
• AI Models: Various (see model documentation)

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🙏 Built With

• Ollama - LLM management platform
• llamafile - Portable LLM runtime
• Pydantic - Data validation
• Pytest - Testing framework
• Meta, Mistral, and other amazing AI model creators

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⭐ Show Your Support

If lmapp helps you, please:

• ⭐ Star this repository
• 🐛 Report bugs and suggest features
• 📢 Share with friends and colleagues
• 🤝 Contribute improvements
• 📝 Share your use cases

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📞 Get Started Now

pip install lmapp
lmapp chat

Welcome to the future of local AI. 🚀