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Installation

This guide covers how to install Hayhooks and its dependencies.

System Requirements

  • Python 3.10+
  • Operating System: Linux, macOS, or Windows
  • Memory: Minimum 512MB RAM, 2GB+ recommended
  • Storage: Minimum 100MB free space

Install from PyPI

=== "Standard Installation"

```bash
pip install hayhooks
```

This includes all core features for deploying and running pipelines.

=== "With MCP Support"

```bash
pip install hayhooks[mcp]
```

Includes all standard features plus [MCP Server](../features/mcp-support.md) support for integration with AI development tools like Cursor and Claude Desktop.

!!! warning "Python 3.10+ Required"
    You'll need to run at least Python 3.10+ to use the MCP Server.

=== "With Tracing Support"

```bash
pip install "hayhooks[tracing]"
```

Includes OpenTelemetry SDK, OTLP exporter support, and FastAPI/Starlette instrumentors so Hayhooks can emit
request-level traces alongside Haystack span instrumentation. Hayhooks also auto-bootstraps OTLP tracing
when an OTLP endpoint is configured via `OTEL_EXPORTER_OTLP_ENDPOINT` or `OTEL_EXPORTER_OTLP_TRACES_ENDPOINT`.
It also ships the dashboard frontend source so `hayhooks run --with-tracing-dashboard` can build and mount
`/dashboard` locally at runtime.
For dashboard frontend local development commands, see the
[dashboard frontend README](https://github.com/deepset-ai/hayhooks/blob/main/dashboard/README.md).

=== "From Source"

```bash
git clone https://github.com/deepset-ai/hayhooks.git
cd hayhooks
pip install -e .
```

Useful for development or testing the latest unreleased features.

Verify Installation

After installation, verify that Hayhooks is installed correctly:

# Check version
hayhooks --version

# Show help
hayhooks --help

Development Installation

If you want to contribute to Hayhooks, we recommend using Hatch, the project's build and environment management tool:

# Clone the repository
git clone https://github.com/deepset-ai/hayhooks.git
cd hayhooks

# Install Hatch (if not already installed)
pip install hatch

# Run unit tests
hatch run test:unit

# Run integration tests
hatch run test:integration

# Run tests
hatch run test:all

# Format code
hatch run fmt

# Serve documentation locally
hatch run docs:serve

Hatch automatically manages virtual environments and dependencies for you. See available commands in pyproject.toml.

Alternative: Manual Installation

If you prefer manual setup:

# Clone the repository
git clone https://github.com/deepset-ai/hayhooks.git
cd hayhooks

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

Docker Installation

Using Docker Hub

# Pull the image corresponding to Hayhooks main branch
docker pull deepset/hayhooks:main

# Run Hayhooks
docker run -p 1416:1416 deepset/hayhooks:main

You can inspect all available images on Docker Hub.

Building from Source

# Clone the repository
git clone https://github.com/deepset-ai/hayhooks.git
cd hayhooks

# Build with Docker Buildx Bake (current platform) and load into Docker
cd docker
IMAGE_NAME=hayhooks IMAGE_TAG_SUFFIX=local docker buildx bake --load

# Run the image
docker run -p 1416:1416 hayhooks:local

Next Steps

After successful installation: