100/ 100 · A

A top-tier open source project. Docs, tests, and CI are all in excellent shape.

Build Real-Time Knowledge Graphs for AI Agents

Python27,414 starsApache-2.0updated 4d ago

Perfect 100/100. An exceptionally rare score. This repo is a model of open source craftsmanship. Congratulations!

DocumentationREADME, setup, examples, license
100
EngineeringTests, CI, linting, lockfiles
100
Project healthDescription, activity, stars, deps
100

What to fix first

The highest-impact improvements for this repo.

  1. 1
    CI/CD
    EngineeringInfo

    Upload coverage to Codecov, Coveralls, or report it with `--coverage` flags.

  2. 2
    Reproducibility
    EngineeringInfo

    Use a multi-stage Dockerfile.

  3. 3
    Reproducibility
    EngineeringInfo

    Add a `package-ecosystem: github-actions` entry to keep workflow action versions up to date.

Detailed breakdown

Documentation

100
  • README100
    • README is present.
    • README is well structured with multiple sections.
    • README includes screenshots or visuals. Great for first impressions.
    • README has code examples.
    • README links to a live demo or deployed app.
    • README includes status badges.
  • Install and run instructions100
    • README documents how to install the project.
    • README documents how to run the project.
    • .env.example is present. Contributors can see exactly which env vars to set.
  • License100
    • Licensed under Apache-2.0.
  • Contributing guide100
    • Contributing guide is detailed and thorough.
    • Contributing guide includes setup/install instructions.
    • Contributing guide describes code style expectations.
    • Contributing guide explains how to run tests.
    • Contributing guide describes the PR/review workflow.
    • Contributing guide includes code examples.
    • Code of conduct present.

Engineering

100
  • Tests100
    • Test files detected (conftest.py).
    • Pytest configured via [tool.pytest.ini_options] in pyproject.toml with test files present.
  • CI/CD100

    Not applicable?

    • CI is configured (.github/workflows/lint.yml).
    • CI workflow runs tests.
    • CI runs on pull requests, not just on pushes to main.
    • CI workflow runs a lint or format check.
    • CI runs type checking (tsc, mypy, cargo check, etc.).
    • Optional: report test coverage in CI.Upload coverage to Codecov, Coveralls, or report it with `--coverage` flags.
    • CI caches dependencies for faster runs.
  • Linting and formatting100
    • pyproject.toml configures both a formatter/linter (ruff/black) and type checking (mypy).
  • Reproducibility100
    • Lockfile present (uv.lock). Installs are reproducible.
    • Environment pinned via Dockerfile.
    • Consider using a multi-stage Dockerfile (builder + runtime stage) for smaller, more secure images.Use a multi-stage Dockerfile.
    • Dockerfile runs as a non-root user.
    • Dependabot covers 3 ecosystems (pip, pip, pip). Dependencies stay current.
    • Dependabot is not configured for GitHub Actions.Add a `package-ecosystem: github-actions` entry to keep workflow action versions up to date.
  • Issue and PR templates100
    • Issue or PR templates present.
    • Security policy present.

Project health

100
  • Dependency manifest100
    • Dependency manifest found (pyproject.toml).
    • pyproject.toml has a [project] table with package metadata.
    • pyproject.toml includes a description.
    • pyproject.toml specifies requires-python, preventing installs on incompatible versions.
    • pyproject.toml has a [build-system] table. The package can be built and published.
  • Repository metadata100
    • Repository has a description.
    • Primary language detected: Python.
    • pyproject.toml [project] metadata is complete (description, authors, urls).
  • Activity100
    • Actively maintained (pushed within the last month).
    • 27,414 stars.
  • Housekeeping100
    • .gitignore present.
Repository files31 root entries
  • .github
    Good: CI is configured (.github/workflows/lint.yml).
    Good: Dependabot covers 3 ecosystems (pip, pip, pip). Dependencies stay current.
    Good: Issue or PR templates present.
  • examples
  • graphiti_core
  • images
  • mcp_server
  • server
  • signatures
  • spec
  • tests
  • .env.example
  • .gitignore
    Good: .gitignore present.
  • AGENTS.md
  • CLAUDE.md
  • CODE_OF_CONDUCT.md
    Good: Code of conduct present.
  • conftest.py
    Good: Test files detected (conftest.py).
  • CONTRIBUTING.md
    Good: Contributing guide is detailed and thorough.
    Good: Contributing guide includes setup/install instructions.
    Good: Contributing guide describes code style expectations.
    Good: Contributing guide explains how to run tests.
    Good: Contributing guide describes the PR/review workflow.
    Good: Contributing guide includes code examples.
  • depot.json
  • docker-compose.test.yml
  • docker-compose.yml
  • Dockerfile
    Good: Environment pinned via Dockerfile.
  • ellipsis.yaml
  • LICENSE
    Good: Licensed under Apache-2.0.
  • Makefile
  • OTEL_TRACING.md
  • py.typed
  • pyproject.toml
    Good: Dependency manifest found (pyproject.toml).
  • pytest.ini
  • README.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Good: README includes screenshots or visuals. Great for first impressions.
    Good: README has code examples.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Good: README documents how to install the project.
    Good: README documents how to run the project.
  • SECURITY.md
    Good: Security policy present.
  • uv.lock
    Good: Lockfile present (uv.lock). Installs are reproducible.
  • Zep-CLA.md