langgraph vs unlazy
langgraph is much bigger: 41.2k stars against 3.1k. Over the days we have tracked them langgraph moved +19.4% and unlazy +164.7%, so unlazy is growing faster right now.
unlazy leads on context depth and noise control and customization and privacy 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
- 41.2k
- Tracked growth
- +19.4%
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- Python
- License
- MIT
- Cost to run
- Free, open-source
Implements the Depth Tree decomposition with a strict, evidence-backed runnable gate ledger that enforces approvals and re-verification for substantial agent tasks.
- Stars
- 3.1k
- Tracked growth
- +164.7%
- Maturity
- ●●●●●
- Last commit
- 4d ago
- Language
- JavaScript
- License
- MIT
- Cost to run
- Free to run locally; external agent API costs may apply
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 | unlazy |
|---|---|---|
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 | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● None mentioned | ●●●●● Approval & strict checks |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● No config | ●●●●● Config files & templates |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Third-party cloud | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Fixed provider | ●●●●● Specific agents |
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 unlazy if…
Reproducible verification — use when you need strict, executable acceptance gates, recorded approvals, and re-verification for long-horizon agent work.
- Context depth: Diff + related files (3/5 against 1/5)
- Noise control: Approval & strict checks (4/5 against 1/5)
- Customization: Config files & templates (3/5 against 1/5)
- Privacy: Self-hostable (4/5 against 1/5)
- Model freedom: Specific agents (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.
Questions people ask
Is langgraph better than unlazy?
unlazy leads on context depth and noise control and customization and privacy 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 unlazy keeps my code private?
langgraph: Third-party cloud (1/5). unlazy: Self-hostable (4/5).
What does each one cost to run?
langgraph: Free, open-source. unlazy: Free to run locally; external agent API costs may apply.
Full profiles: langchain-ai/langgraph and Leonxlnx/unlazy. Everything else in Workflow & graph engines.