LIT-TUI: A Terminal Research Platform for AI Development
Introducing a fast, extensible terminal chat interface built for research and advancing human-AI collaboration.
TL;DR
LIT-TUI is a new terminal-based chat interface for local AI models, designed as a research platform for testing AI capabilities and collaboration patterns. Available now on PyPI with MCP integration, keyboard-first design, and millisecond startup times.
Why Another AI Chat Interface?
While AI chat interfaces are proliferating rapidly, most focus on consumer convenience or basic productivity. LIT-TUI was built with a different goal: advancing the research frontier of human-AI collaboration.
We needed a platform that could:
- Test new AI capabilities without vendor limitations
- Experiment with interaction patterns beyond simple request-response
- Evaluate local model performance as alternatives to cloud providers
- Prototype research ideas quickly and iterate rapidly
The result is LIT-TUI—a terminal-native interface that puts research and experimentation first.
Design Philosophy: Terminal-First Research
Speed and Simplicity
LIT-TUI starts in milliseconds, not seconds. No Electron overhead, no complex UI frameworks—just pure Python performance optimized for developer workflows.
That's it. You're in a conversation with your local AI model faster than most web applications can load their JavaScript.
Native Terminal Integration
Rather than fighting your terminal's appearance, LIT-TUI embraces it. The interface uses your terminal's default theme and colorscheme, creating a native experience that feels like part of your development environment.
This isn't just aesthetic—it's strategic. Developers live in terminals, and AI tools should integrate seamlessly rather than forcing context switches to separate applications.
Research Platform Capabilities
MCP Integration for Dynamic Tools
LIT-TUI includes full support for the Model Context Protocol (MCP), enabling dynamic tool discovery and execution. This allows researchers to:
- Test how AIs use different tool combinations
- Experiment with new tool designs
- Evaluate tool effectiveness across different models
- Prototype AI capability extensions
{
"mcp": {
"enabled": true,
"servers": [
{
"name": "filesystem",
"command": "mcp-server-filesystem",
"args": ["--root", "/home/user/projects"]
},
{
"name": "git",
"command": "mcp-server-git"
}
]
}
}
Local Model Testing
One of our key research interests is AI independence—reducing reliance on centralized providers who could restrict or limit access. LIT-TUI makes it trivial to switch between local models and evaluate their capabilities:
This enables systematic comparison of local model performance against cloud providers, helping identify capability gaps and research priorities.
Real-World Research Applications
Memory System Experiments
We recently used LIT-TUI as a testbed for AI-curated memory systems—approaches where AIs manage their own persistent memory rather than relying on human-directed memory curation.
The sparse terminal interface proved ideal for this research because it eliminated visual distractions and forced focus on the core question: "Can the AI maintain useful context across sessions?"
Collaboration Pattern Testing
LIT-TUI's keyboard-first design makes it perfect for testing different human-AI collaboration patterns:
- Strategic vs Tactical: High-level planning vs detailed implementation
- Iterative Refinement: Quick feedback loops for complex problems
- Tool-Mediated Collaboration: How tools change interaction dynamics
Architecture for Extensibility
Clean Async Foundation
LIT-TUI is built on a clean async architecture using Python's asyncio and the Textual framework. This provides:
- Responsive interactions without blocking
- Concurrent tool execution for complex workflows
- Extensible plugin system for research experiments
- Performance optimization for local model inference
Modular Design
The codebase separates concerns cleanly:
lit-tui/
├── screens/ # UI screens and navigation
├── services/ # Core services (Ollama, MCP, storage)
├── widgets/ # Reusable UI components
└── config/ # Configuration management
This makes it straightforward to prototype new features, test experimental capabilities, or integrate with research infrastructure.
Beyond Basic Chat: Research Directions
Project Context Integration
We're exploring standardized project context through PROJECT.md files—a universal approach that any AI interface could adopt, rather than vendor-specific project systems.
Human-AI Gaming Platforms
The terminal interface is perfectly suited for text-based games designed specifically for human-AI collaboration. Imagine strategy games where AI computational thinking becomes a game mechanic, or collaborative storytelling that leverages both human creativity and AI capability.
Local Model Enhancement
LIT-TUI serves as a testbed for techniques that could bring local models closer to parity with cloud providers:
- Enhanced prompting systems using our system-prompt-composer library
- Memory augmentation for limited context windows
- Tool orchestration to extend model capabilities
- Collaboration patterns optimized for "good enough" local models
The Broader Mission
LIT-TUI is part of a larger research initiative to advance human-AI collaboration while maintaining independence from centralized providers. We're treating this as research work rather than rushing to monetization, because the questions we're exploring matter for the long-term future of AI development.
Key research areas include:
- AI-curated memory systems that preserve context across sessions
- Dynamic tool creation where AIs build tools for themselves
- Homeostatic vs conversation-driven AI paradigms
- Strategic collaboration patterns for complex projects
Getting Started
LIT-TUI is available now on PyPI and requires a running Ollama instance:
# Installation
pip install lit-tui
# Prerequisites - IMPORTANT
# - Python 3.8+
# - Ollama running locally (required!)
# - Unicode-capable terminal
# Start Ollama first
ollama serve
# Then use LIT-TUI
lit-tui # Default model
lit-tui --model llama3.1 # Specific model
lit-tui --debug # Debug logging
Enhanced Experience with System Prompt Composer
For the best experience, install our system-prompt-composer library alongside LIT-TUI:
This enables sophisticated prompt engineering capabilities that can significantly improve AI performance, especially with local models where every bit of optimization matters.
Contributing and Extending
The project is open source and actively seeking contributors. Whether you're interested in:
- Adding new MCP server integrations
- Improving terminal UI components
- Experimenting with collaboration patterns
- Optimizing local model performance
- Building research tools and analytics
We welcome pull requests and encourage forking for your own research needs. The modular architecture makes it straightforward to add new capabilities without breaking existing functionality.
Future Directions: Zero-Friction AI Development
Super Easy MCP Installation
One exciting development on our roadmap is in-app MCP server installation. Imagine being able to type:
And having LIT-TUI automatically:
- Download and install the MCP server
- Configure your MCP settings
- Live reload the interface with new capabilities
- Provide immediate access to new tools
Project-Aware AI Collaboration
We're also exploring intelligent project integration where LIT-TUI automatically understands your project context:
cd /my-awesome-project
lit-tui # Automatically reads PROJECT.md, plans/, README.md
/cd /other-project # Switch project context instantly
/project status # See current project awareness
This would create a universal project standard that any AI interface could adopt—no more vendor lock-in to proprietary project systems. Just standard markdown files that enhance AI collaboration across any tool.
Multi-Provider Model Support
While LIT-TUI currently requires Ollama, we're planning universal model provider support:
lit-tui --provider ollama --model llama3.1
lit-tui --provider nano-vllm --model deepseek-coder
lit-tui --provider openai --model gpt-4 # For comparison
This would enable direct performance comparisons across local and cloud providers, supporting our AI independence research while giving users maximum flexibility in model choice.
The Vision: Zero-Friction Experimentation
Together, these features represent our vision for AI development tools: zero-friction experimentation that lets researchers focus on the interesting questions rather than infrastructure setup. No manual configuration, no restart cycles, no vendor lock-in—just instant capability expansion and intelligent project awareness.
Conclusion: Research as a Public Good
LIT-TUI represents our belief that advancing human-AI collaboration requires open research platforms that anyone can use, modify, and improve. Rather than building proprietary tools that lock in users, we're creating open infrastructure that enables better collaboration patterns for everyone.
The terminal might seem like an unusual choice for cutting-edge AI research, but it offers something valuable: clarity. Without visual complexity, we can focus on the fundamental questions of how humans and AIs can work together most effectively.
Try LIT-TUI today and join us in exploring the future of human-AI collaboration. The research is just beginning.
Resources:
LIT-TUI is developed by LIT as part of our research into advancing human-AI collaboration through open-source innovation.