> ## Documentation Index > Fetch the complete documentation index at: https://emartai.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Examples > Starter projects you can copy to bootstrap common evalflow setups. The `examples/` directory in the evalflow repository contains three ready-to-run projects. Each one demonstrates a different setup. Pick the example closest to your stack, copy its `evalflow.yaml` and dataset, then adapt them to your own prompts. ## Starting from an example ```bash theme={null} # Clone the repository git clone https://github.com/emartai/evalflow cd evalflow/examples/ # Install evalflow pip install evalflow # Copy the environment template cp .env.example .env # Check your setup evalflow doctor # Run the quality gate evalflow eval ``` *** ## `examples/openai-basic` A minimal local setup using OpenAI. This is the best starting point if you are new to evalflow or want the simplest possible configuration. **What it demonstrates** * A single-provider `evalflow.yaml` configured for OpenAI * Three test cases covering summarization, classification, and QA * Both `exact_match` and `embedding_similarity` eval methods * A prompt registry entry in `prompts/` **Files** ```text theme={null} examples/openai-basic/ ├── evalflow.yaml ├── evals/ │ └── dataset.json ├── prompts/ │ └── assistant.yaml ├── .env.example └── README.md ``` **`evalflow.yaml`** ```yaml theme={null} version: "1.0" project: openai-basic providers: openai: api_key_env: OPENAI_API_KEY default_model: gpt-4o-mini eval: dataset: evals/dataset.json baseline_file: .evalflow/baseline.json default_provider: openai consistency_runs: 3 thresholds: task_success: 0.80 relevance: 0.75 hallucination_max: 0.10 consistency_min: 0.85 judge: provider: groq model: llama-3.1-8b-instant prompts: directory: prompts/ ``` **Running it** ```bash theme={null} cd examples/openai-basic cp .env.example .env # Edit .env and set OPENAI_API_KEY evalflow eval ``` ```text theme={null} Running 3 test cases against gpt-4o-mini... ✓ summarize-release-notes 0.92 ✓ classify-support-priority 1.00 ✓ answer-billing-question 0.88 Quality Gate: PASS Failures: 0 Run ID: 20240315-a3f9c2d81b4e Duration: 3.8s ``` *** ## `examples/groq-ci` A Groq-based setup with a GitHub Actions workflow included. Groq's free tier makes this example well suited for CI pipelines where you want zero-cost quality gates on every pull request. **What it demonstrates** * A single-provider `evalflow.yaml` configured for Groq * A working GitHub Actions workflow (`.github/workflows/evalflow.yml`) * How to pass the API key as a GitHub Actions secret * Test cases for summarization, classification, and QA **Files** ```text theme={null} examples/groq-ci/ ├── evalflow.yaml ├── .github/ │ └── workflows/ │ └── evalflow.yml ├── evals/ │ └── dataset.json ├── .env.example └── README.md ``` **`evalflow.yaml`** ```yaml theme={null} version: "1.0" project: groq-ci providers: groq: api_key_env: GROQ_API_KEY default_model: llama-3.1-8b-instant eval: dataset: evals/dataset.json baseline_file: .evalflow/baseline.json default_provider: groq consistency_runs: 3 thresholds: task_success: 0.80 relevance: 0.75 hallucination_max: 0.10 consistency_min: 0.85 judge: provider: groq model: llama-3.1-8b-instant prompts: directory: prompts/ ``` **GitHub Actions workflow** ```yaml theme={null} # .github/workflows/evalflow.yml name: LLM Quality Gate on: pull_request: paths: - "prompts/**" - "evals/**" - "**.py" jobs: eval: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: "3.11" - run: pip install evalflow - run: evalflow eval --provider groq env: GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }} ``` **Running it locally** ```bash theme={null} cd examples/groq-ci cp .env.example .env # Edit .env and set GROQ_API_KEY evalflow eval --provider groq ``` ```text theme={null} Running 3 test cases against llama-3.1-8b-instant... ✓ summarize-pr-description 0.91 ✓ classify-ci-failure 1.00 ✓ answer-readme-question 0.86 Quality Gate: PASS Failures: 0 Run ID: 20240315-c7e8b1d23f9a Duration: 3.1s ``` Add `GROQ_API_KEY` to your repository's **Settings → Secrets and variables → Actions** to enable the GitHub Actions workflow. *** ## `examples/langchain-app` An example that runs evalflow alongside a LangChain application. It shows how to add a quality gate to a project that already uses LangChain for its LLM calls, without changing any application code. **What it demonstrates** * Running `evalflow eval` independently of your application code * How evalflow's prompt registry integrates with a LangChain app (`app.py` loads the production prompt from the registry) * Test cases that mirror the prompts used in the application **Files** ```text theme={null} examples/langchain-app/ ├── evalflow.yaml ├── app.py ├── evals/ │ └── dataset.json ├── prompts/ │ └── assistant.yaml ├── .env.example └── README.md ``` **`evalflow.yaml`** ```yaml theme={null} version: "1.0" project: langchain-app providers: openai: api_key_env: OPENAI_API_KEY default_model: gpt-4o-mini eval: dataset: evals/dataset.json baseline_file: .evalflow/baseline.json default_provider: openai consistency_runs: 3 thresholds: task_success: 0.80 relevance: 0.75 hallucination_max: 0.10 consistency_min: 0.85 judge: provider: groq model: llama-3.1-8b-instant prompts: directory: prompts/ ``` **Running it** ```bash theme={null} cd examples/langchain-app pip install evalflow langchain-openai langchain-core cp .env.example .env # Edit .env and set OPENAI_API_KEY # Run the LangChain app python app.py # Run the quality gate evalflow eval ``` ```text theme={null} > python app.py Reply: The assistant answers using the production prompt body. > evalflow eval Running 3 test cases against gpt-4o-mini... ✓ summarize-meeting-notes 0.90 ✓ classify-user-intent 1.00 ✓ answer-faq-context 0.87 Quality Gate: PASS Failures: 0 Run ID: 20240315-b2d9f4e67c1b Duration: 4.0s ```