langgraph vs loopy
langgraph is much bigger: 41.1k stars against 3.1k. Over the days we have tracked them langgraph moved +19.2% and loopy +4335.7%, so loopy is growing faster right now.
loopy leads on context depth and noise control and customization and model freedom. langgraph does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides low-level orchestration for long-running, stateful agents with durable execution and human-in-the-loop capabilities that typical stateless workflow engines do not emphasize.
- Stars
- 41.1k
- Tracked growth
- +19.2%
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- Python
- License
- MIT
- Cost to run
- Free, open-source
Combines a public, curated catalog of reusable agent loops with an installable in-agent skill that discovers, crafts, audits, runs, and prepares loops for publication.
- Stars
- 3.1k
- Tracked growth
- +4335.7%
- Maturity
- ●●●●●
- Last commit
- 41d ago
- Language
- JavaScript
- License
- MIT
- Cost to run
- Requires cloud agent provider (may incur provider cost)
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | langgraph | loopy |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff only | ●●●●● Whole-repo analysis |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● None mentioned | ●●●●● Acceptance gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● No config | ●●●●● Adapt & save loops |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Third-party cloud | ●●●●● Depends on cloud agents |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Fixed provider | ●●●●● Multiple agents supported |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-command install | ●●●●● One-command install |
Which one to pick
Pick langgraph if…
Durable execution — choose LangGraph when you need persistent, stateful agents with human-in-the-loop oversight and production deployment support.
Pick loopy if…
Easy setup — a one-command npx installer and in-agent slash-commands let you discover, adapt, run, and publish repeatable agent workflows with built-in acceptance checks and explicit approvals.
- Context depth: Whole-repo analysis (5/5 against 1/5)
- Noise control: Acceptance gating (5/5 against 1/5)
- Customization: Adapt & save loops (3/5 against 1/5)
- Model freedom: Multiple agents supported (3/5 against 1/5)
What people want from each one
langchain-ai/langgraph
Hacker News: We chose LangGraph to build our coding agent drew 83 points and 20 comments.
Forward-Future/loopy
Questions people ask
Is langgraph better than loopy?
loopy leads on context depth and noise control and customization and model freedom. langgraph does not take any axis by a clear margin. langgraph is worth picking when durable execution — choose LangGraph when you need persistent, stateful agents with human-in-the-loop oversight and production deployment support.
Which of langgraph and loopy keeps my code private?
langgraph: Third-party cloud (1/5). loopy: Depends on cloud agents (2/5).
What does each one cost to run?
langgraph: Free, open-source. loopy: Requires cloud agent provider (may incur provider cost).
Full profiles: langchain-ai/langgraph and Forward-Future/loopy. Everything else in Workflow & graph engines.