robotframework-chat 1.0.0

Robot Framework-based test harness for systematically testing LLMs

robotframework-chat

A Robot Framework-based test harness for systematically testing Large Language Models (LLMs) using LLMs as both the system under test and as automated graders. Test results are archived to SQL and visualized in Apache Superset dashboards.

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Quick Start

Prerequisites

• Python 3.11+ and astral-uv for dependency management
• Docker for containerized code execution, LLM testing, and the Superset stack
• Ollama (optional) for local LLM testing

Installation (Linux / macOS)

make install                # Install all dependencies
pre-commit install          # Install pre-commit hooks
ollama pull llama3          # Pull default LLM model (optional)

Installation (Windows)

The tasks.py script provides a cross-platform alternative to the Makefile. It requires only Python and uv — no make, bash, or Unix tools needed.

uv run python tasks.py install      # Install all dependencies
uv run pre-commit install           # Install pre-commit hooks
ollama pull llama3                  # Pull default LLM model (optional)
uv run python tasks.py help         # List all available targets

Note: Docker-based tests require Docker Desktop for Windows with the WSL 2 backend enabled.

Running Tests

# Linux / macOS
make robot                  # Run all Robot Framework test suites
make robot-math             # Run math tests
make robot-docker           # Run Docker tests
make robot-safety           # Run safety tests

# All platforms (including Windows)
uv run python tasks.py robot        # Run all suites
uv run python tasks.py robot-math   # Run math tests
uv run python tasks.py robot-dryrun # Validate tests (dry run)
uv run python tasks.py check        # Lint + typecheck + coverage

Superset Dashboard

# Linux / macOS
cp .env.example .env        # Configure environment
make docker-up              # Start PostgreSQL + Redis + Superset
make bootstrap              # First-time Superset initialization

# Windows — tasks.py copies .env automatically if missing
uv run python tasks.py docker-up

Open http://localhost:8088 to view the dashboard.

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Example Test

*** Test Cases ***
LLM Can Do Basic Math
    ${answer}=    Ask LLM    What is 2 + 2?
    ${score}    ${reason}=    Grade Answer    What is 2 + 2?    4    ${answer}
    Should Be Equal As Integers    ${score}    1

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Core Philosophy

• LLMs are software — test them like software
• Determinism before intelligence — structured, machine-verifiable evaluation first
• Constrained grading — scores, categories, pass/fail; no prose from the evaluation layer
• Modular by design — composable pieces; new providers and graders plug in without rewriting core
• Robot Framework as the orchestration layer — readable, keyword-driven tests
• Every test run is archived — listeners always active, results flow to SQL
• CI-native, regression-focused — if it can't run unattended, it's not done

See ai/AGENTS.md for the full philosophy.

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Documentation

Document	Description
docs/TEST_DATABASE.md	Database schema and usage
docs/GITLAB_CI_SETUP.md	CI/CD setup guide
docs/GRAFANA_SUPERSET_SETUP.md	Superset visualization stack setup (Grafana deferred to v2+)
docs/SUPERSET_EXPORT_GUIDE.md	Superset dashboard export, import, and backup

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Contributing

1. Read ai/DEV.md for the development workflow and TDD discipline
2. Follow the code style guidelines in ai/AGENTS.md
3. Add tests for new features (see ai/CLAUDE.md for grading tiers)
4. Run pre-commit run --all-files before committing