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Updated today

by google

lightflow

Lightweight, local-first Python DAG workflow compiler and execution engine with human-in-the-loop checkpoints for CLI and AI-agent workflows.

  • New — Created in the last 30 days
  • Active — Updated in the last 14 days
Works with
  • Claude
  • Gemini
View on GitHub (opens in a new tab)

14 GitHub stars (14)

+14 in 24 days

Stars gained per day · daily snapshots since Oct 3
Stars gained per day, Oct 3 to Oct 17: +2 in total; the busiest day was Oct 8 with +1 stars.Oct 4: 0 starsOct 5: 0 starsOct 6: 0 starsOct 7: 0 starsOct 8: +1 starsOct 9: +1 starsOct 10: 0 starsOct 11: 0 starsOct 12: 0 starsOct 13: 0 starsOct 14: 0 starsOct 15: 0 starsOct 16: 0 starsOct 17: 0 stars
Stars gained between daily snapshots
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  • 3 skills (3 skills found in its SKILL.md files)
  • 1 forks (1 forks)
  • Today last update (Last updated today)

Install

Skills CLI

npx skills add google/lightflow

Asks which skills and agents to install. Just one: npx skills add google/lightflow --skill benchmark_lightflow — add -g for every project.

Claude Code

git clone https://github.com/google/lightflowcp -r "lightflow/benchmarks" ~/.claude/skills/

Copies the benchmark_lightflow skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.

Plugin

/plugin marketplace add google/lightflow

Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.

Skills can run code on your machine — read the SKILL.md first.

What it does

In the author's own words, from each skill's SKILL.md.

  • benchmark_lightflow

    Runs the Lightflow benchmark suite (`python3 benchmarks/run_benchmark.py`) and live A/B subagent trials to verify that Lightflow's deterministic structural backbone (human gates, JSON Schema validation, automatic rollback, surgical resume) operates at lower token and latency cost than unstructured skills.

    benchmarks/SKILL.md on GitHub (opens in a new tab)

  • create_lightflows

    Guides the design, authoring, compilation, and verification of new Python DAG workflows using Lightflow (YAML, JSON, or Textproto). Use when building a new multi-stage pipeline, authoring `actions.py` and `lightflow.yaml`, or scaffolding a workflow with the `create_lightflow` meta-workflow. For merely running or resuming an existing workflow, use `run_lightflows` instead.

    skills/create_lightflows/SKILL.md on GitHub (opens in a new tab)

  • run_lightflows

    Executes, inspects, and resumes existing Lightflow DAG workflows via CLI (`lightflow`, or the equivalent `python3 -m lightflow`) or MCP (`lightflow-mcp`). Use when asked to run, dry-run, check status, visualize, resolve an `operator_action` human-in-the-loop checkpoint (`EXIT_CODE=2`), or recover a failed Lightflow run without authoring a new workflow. Run `start` directly without pre-reading the…

    skills/run_lightflows/SKILL.md on GitHub (opens in a new tab)

Review before you install

A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.