litmus-llm 0.2.1

Litmus — LLM scenario runner TUI

Litmus 🧪

Terminal UI for running LLM agent scenarios and comparing their performance.

Litmus executes coding tasks across multiple AI agents and models, runs tests against the results, and produces detailed evaluation reports — all from a single TUI.

What it does

1. Detects agents installed on your system (Claude Code, Codex, Aider, Cursor Agent, KiloCode, OpenCode)
2. Runs scenarios — each scenario is a coding task with tests and scoring criteria
3. Evaluates results — an LLM judge scores agent and model performance across 20 criteria each
4. Generates reports — HTML reports with per-scenario breakdowns, logs, and scores

Supported agents

Agent	Binary	Model listing
Claude Code	claude	Built-in list
Codex	codex	Built-in list
OpenCode	opencode	opencode models
KiloCode	kilocode	kilocode models
Aider	aider	aider --list-models
Cursor Agent	agent	agent models

Litmus auto-detects which agents are available and queries their model lists.

Quick start

Requires Python 3.12+ and uv.

# Run without installing — uv fetches everything automatically
uvx --from git+https://github.com/ivkond/litmus.git litmus

On first launch Litmus will detect installed agents, generate a config, and open the TUI.

Alternative ways to install

# Install as a global tool
uv tool install git+https://github.com/ivkond/litmus.git
litmus

# Or clone for development
git clone https://github.com/ivkond/litmus.git
cd litmus
uv sync
uv run litmus

Once published to PyPI, install will simplify to uvx litmus.

TUI workflow

1. 📋 Models — select agents and models to test
2. 🧩 Scenarios — pick which coding tasks to run
3. ▶️ Run — watch execution progress in real time
4. 📊 Analysis — review LLM-judged scores
5. 📄 Reports — browse generated HTML reports

How it works

Each scenario lives in template/<id>/ and contains:

template/1-data-structure/
  prompt.txt        # Task description sent to the agent
  task.txt          # Detailed requirements
  scoring.csv       # Evaluation criteria
  project/          # Starter code with tests

Execution pipeline per scenario:

uv sync  ->  agent call  ->  pytest  ->  collect logs

After all runs complete, an LLM judge evaluates the results using 20 agent criteria (tool efficiency, error recovery, reasoning depth...) and 20 model criteria (code correctness, instruction following, hallucination resistance...).

Configuration

On first launch, Litmus generates a config file with detected agents and their settings. Configure the analysis model (any OpenAI-compatible API) through the TUI settings screen.

Scenario packs

Litmus supports exporting and importing scenario archives (.litmus-pack ZIP files) for sharing test suites between machines or teams.

Project structure

src/litmus/
  __init__.py     # Entry point, PROJECT_ROOT
  app.py          # Textual TUI (screens, widgets)
  agents.py       # Agent registry, detection, model listing
  run.py          # Scenario execution engine
  analysis.py     # LLM-powered evaluation (20+20 criteria)
  report.py       # HTML report generation
  pack/           # Scenario export/import

Tech stack

• Textual — TUI framework
• Rich — terminal formatting
• Pydantic — structured evaluation models
• OpenAI SDK — LLM judge (any compatible API)

License

MIT