Installation

Requirements

• Python 3.11+
• An OpenAI-compatible LLM endpoint (OpenAI, Ollama, vLLM, GitHub Models, llama.cpp, Azure through a compatible gateway, etc.)

From PyPI

pip install executionkit

This pulls zero runtime dependencies — the default backend is stdlib urllib.

With connection pooling (httpx)

For high-throughput workloads (e.g. consensus with many samples, or long react_loop chains), install the optional httpx extra:

pip install "executionkit[httpx]"

The Provider automatically detects httpx at import time and uses an httpx.AsyncClient with connection pooling. With the stdlib backend, every LLM call opens a fresh TCP+TLS connection.

Verify the install

python -c "from executionkit import Provider, consensus, refine_loop, react_loop; print('OK')"

From source (development)

git clone https://github.com/tafreeman/executionkit.git
cd executionkit
python -m venv .venv
source .venv/bin/activate     # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

The [dev] extra adds pytest, pytest-asyncio, pytest-cov, ruff, mypy, bandit, build, and the optional httpx backend. See Contributing for the full dev workflow.

Build the docs locally

pip install -e ".[docs]"
mkdocs build --strict

The docs extra includes MkDocs Material, mkdocstrings, and Mermaid support used by the public site.

Run the test suite

pytest                                                   # deterministic tests, no API keys
pytest --cov=executionkit --cov-fail-under=80            # full suite with coverage

Next

• Quick Start — first call in 5 lines.
• Provider Setup — configure OpenAI, Ollama, GitHub Models, Together, Groq, and Azure through a compatible gateway.