# What Are the Dependencies for Running the Hiring Agent? > Discover the essential dependencies for running the Hiring Agent. Learn about required Python versions, key packages like PyMuPDF and Ollama, and environment variable setup for resume scoring. - Repository: [HackerRank/hiring-agent](https://github.com/interviewstreet/hiring-agent) - Tags: how-to-guide - Published: 2026-07-24 --- **The Hiring Agent requires Python 3.11 or newer, nine pinned Python packages (including PyMuPDF, Ollama, and Pydantic), and properly configured environment variables to execute the resume scoring pipeline.** The Hiring Agent from the `interviewstreet/hiring-agent` repository is a pure Python application that orchestrates PDF resume parsing, GitHub profile enrichment, and LLM-based evaluation. Understanding the dependencies for running the Hiring Agent is essential before executing the end-to-end scoring workflow on local infrastructure. ## Core Runtime Requirements The foundation of the Hiring Agent is a modern Python interpreter. According to the `.python-version` file in the repository root, the project targets **Python 3.11 or newer**. This version requirement ensures compatibility with the type hinting and async features used throughout the codebase, particularly in [`models.py`](https://github.com/interviewstreet/hiring-agent/blob/main/models.py) and the LLM provider abstractions. All dependencies are strictly pinned in [`requirements.txt`](https://github.com/interviewstreet/hiring-agent/blob/main/requirements.txt) to ensure reproducible behavior across different environments. ## Python Package Dependencies The Hiring Agent relies on nine production dependencies and one development tool, each serving a specific function in the pipeline: ### PDF Processing and Extraction - **PyMuPDF (1.26.3)** — Handles PDF file I/O and extracts raw text/structure from resume documents. - **pymupdf4llm (0.0.27)** — Converts extracted PDF content into Markdown-formatted prompts suitable for LLM consumption, as implemented in [`pymupdf_rag.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pymupdf_rag.py). ### LLM Integration and AI Services - **ollama (0.5.1)** — Client library for communicating with a local Ollama LLM server, enabling on-premise inference without external API calls. - **google-generativeai (0.4.0)** — Official Google client for Gemini integration, providing an optional cloud-based LLM backend alternative to Ollama. ### Data Validation and HTTP Communication - **pydantic (2.11.7)** — Powers the JSON-Resume data models and schema validation defined in [`models.py`](https://github.com/interviewstreet/hiring-agent/blob/main/models.py), ensuring type safety across the extraction and enrichment stages. - **requests (2.32.4)** — Standard HTTP client used by [`github.py`](https://github.com/interviewstreet/hiring-agent/blob/main/github.py) to fetch candidate repository data and by LLM wrapper classes for API communication. ### Template Rendering and Configuration - **Jinja2 (3.1.6)** — Renders dynamic prompt templates stored in `prompts/templates/`, allowing parameterized system prompts for different evaluation criteria. - **python-dotenv (1.0.1)** — Loads runtime configuration from `.env` files, managing secrets like `GEMINI_API_KEY` and `GITHUB_TOKEN` outside the source code. ### Development Tools - **black (25.9.0)** — Code formatter included for development convenience to maintain consistent style across the codebase. ## Environment Configuration Dependencies Beyond Python packages, the Hiring Agent requires specific environment variables defined in a `.env` file. The repository provides `.env.example` as a template containing: - `LLM_PROVIDER` — Set to `ollama` or `gemini` to select the backend. - `DEFAULT_MODEL` — Specifies the model name (e.g., `gemma3:4b` for Ollama). - `GEMINI_API_KEY` — Required when using the Google Gemini backend. - `GITHUB_TOKEN` — Personal access token for GitHub API rate limits and private repository access. ## Installation and Verification To satisfy all dependencies for running the Hiring Agent, execute the following steps: ```bash # Clone the repository and create an isolated environment git clone https://github.com/interviewstreet/hiring-agent cd hiring-agent python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate ``` ```bash # Install the pinned dependency tree pip install -r requirements.txt ``` ```bash # Configure environment variables cp .env.example .env # Edit .env to set LLM_PROVIDER, DEFAULT_MODEL, and API keys ``` ```bash # Verify installation by running the scoring pipeline python score.py /path/to/resume.pdf ``` ## Key Source Files and Dependency Mapping Understanding which source files rely on specific dependencies helps debug installation issues: - **[`requirements.txt`](https://github.com/interviewstreet/hiring-agent/blob/main/requirements.txt)** — The single source of truth for all pinned package versions. - **[`pymupdf_rag.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pymupdf_rag.py)** — Imports `fitz` (PyMuPDF) and `pymupdf4llm` for PDF-to-Markdown conversion. - **[`github.py`](https://github.com/interviewstreet/hiring-agent/blob/main/github.py)** — Depends on `requests` for HTTP operations against the GitHub API. - **[`models.py`](https://github.com/interviewstreet/hiring-agent/blob/main/models.py)** — Uses `pydantic` for schema definitions and `google.generativeai` for Gemini model interactions. - **`prompts/templates/`** — Relies on `jinja2` for template inheritance and variable substitution. - **[`score.py`](https://github.com/interviewstreet/hiring-agent/blob/main/score.py)** — The CLI entry point that orchestrates all dependencies into the evaluation workflow. ## Summary - **Python 3.11+** is mandatory for running the Hiring Agent pipeline. - **Nine production packages** are pinned in [`requirements.txt`](https://github.com/interviewstreet/hiring-agent/blob/main/requirements.txt), including PyMuPDF for PDF processing, Ollama for local LLM inference, and Pydantic for data validation. - **Environment variables** defined in `.env` control LLM provider selection and API authentication. - **Jinja2 templates** in `prompts/templates/` drive the prompt engineering layer. - The **Black formatter** is included as a development convenience but is not required for runtime execution. ## Frequently Asked Questions ### Is the Hiring Agent compatible with Python 3.10 or older versions? No. The repository explicitly requires Python 3.11 or newer as specified in the `.python-version` file. The codebase utilizes modern Python features for type safety and async operations that are not available in earlier versions. ### Can I run the Hiring Agent without installing Ollama? Yes, but you must configure the Google Gemini backend instead. Set `LLM_PROVIDER=gemini` and provide a valid `GEMINI_API_KEY` in your `.env` file. However, you still need to install the `ollama` Python package (0.5.1) as it remains a declared dependency in [`requirements.txt`](https://github.com/interviewstreet/hiring-agent/blob/main/requirements.txt) even if unused at runtime. ### Why does the project require both PyMuPDF and pymupdf4llm? **PyMuPDF** provides low-level PDF parsing and text extraction capabilities, while **pymupdf4llm** (version 0.0.27) offers high-level utilities specifically designed to convert PDF content into LLM-optimized Markdown prompts. The [`pymupdf_rag.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pymupdf_rag.py) module uses both libraries to bridge raw document extraction and prompt engineering. ### Are the dependencies for GitHub integration mandatory? Yes, if you want to enrich candidate profiles with repository data. The [`github.py`](https://github.com/interviewstreet/hiring-agent/blob/main/github.py) module depends on `requests` (2.32.4) to fetch public and private repository information. Without a valid `GITHUB_TOKEN` set in your environment, the GitHub enrichment stage will fail or operate under strict rate limits.