A functional reactive programming engine with a Python front-end.

This provides a DSL and runtime to support the computation of results over time, featuring a graph based directed acyclic dependency graph and the concept of time-series properties. The language is function-based, and promotes composition to extend behaviour.

Here is a simple example:

from hgraph import graph, evaluate_graph, GraphConfiguration, const, debug_print

@graph
def main():
    a = const(1)
    c = a + 2
    debug_print("a + 2", c)

evaluate_graph(main, GraphConfiguration())

Results in:

[1970-01-01 00:00:00.000385][1970-01-01 00:00:00.000001] a + 2: 3

See this for more information.

The project is currently configured to make use of uv for dependency management. Take a look at the website to see how best to install the tool.

Here are some useful commands:

First, create a virtual environment in the project directory:

uv venv

Then use the following command to install the project and its dependencies:

# Install the project with all dependencies
uv pip install -e .

# Install with optional dependencies
uv pip install -e ".[docs,web,notebook]"

# Install with all optional dependencies
uv pip install -e ".[docs,web,notebook,test]"

PyCharm can make use of the virtual environment created by uv to setup the project.

# No Coverage
python -m pytest

# Generate Coverage Report
python -m pytest --cov=hgraph --cov-report=xml

This repository includes a baseline configuration for Context7 MCP to improve code search and retrieval quality.

• See docs/context7_indexing.md for guidance.
• The root-level context7.yaml config sets sensible include/exclude rules, priorities, and summarization hints.