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FlowGuard
Simulate risky AI-agent workflows before coding
FlowGuard is an open-source finite-state simulator for AI coding agents. Before an agent changes a retry, cache, deduplication, idempotency, module-boundary, argument, or decision workflow, it models the risky boundary as Input x State -> Set(Output x State), enumerates paths, and reports invariant failures and counterexample traces. Source-install only; not the PyPI package named flowguard.
Hey Product Hunt,
I built FlowGuard because I kept seeing AI coding agents fix a local bug while damaging the larger workflow.
The failure pattern is usually not "the function doesn't run." It is something more stateful: the same object gets processed twice, a retry repeats a side effect, cache drifts from the source of truth, a module writes state it should not own, or a workflow has an exit path but no real progress guarantee.
FlowGuard is an open-source finite-state workflow simulator for those moments. Before an AI agent changes risky workflow behavior, it can model the boundary as:
```text
Input x State -> Set(Output x State)
```
Then FlowGuard enumerates the reachable paths and reports invariant failures, loop/progress issues, contract problems, and counterexample traces.
It is not an LLM wrapper, not a prompt tool, and not a replacement for tests. It is meant to sit next to planning/spec/code-review/bug-fix skills as a behavior cross-check before implementation.
The easiest way to try it is to give the GitHub repo to a capable coding agent and ask it to install the source checkout, read the included `model-first-function-flow` skill, and use it on a real stateful workflow.
Repo: https://github.com/liuyingxuvka/...
One important note: this project is source-install only right now. The PyPI package named `flowguard` is a different project, so please use the GitHub repo directly.
The feedback I care about most:
After trying FlowGuard on a real AI-agent workflow, does it catch a pain point you actually have, and does it work as a useful cross-check alongside your existing bug-fix, code-review, or planning skills rather than duplicating them?
About FlowGuard on Product Hunt
“Simulate risky AI-agent workflows before coding”
FlowGuard was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #156 on the daily leaderboard. FlowGuard is an open-source finite-state simulator for AI coding agents. Before an agent changes a retry, cache, deduplication, idempotency, module-boundary, argument, or decision workflow, it models the risky boundary as Input x State -> Set(Output x State), enumerates paths, and reports invariant failures and counterexample traces. Source-install only; not the PyPI package named flowguard.
On the analytics side, FlowGuard competes within Open Source, Developer Tools and GitHub — topics that collectively have 622k followers on Product Hunt. The dashboard above tracks how FlowGuard performed against the three products that launched closest to it on the same day.
Who hunted FlowGuard?
FlowGuard was hunted by Yingxu Liu. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of FlowGuard including community comment highlights and product details, visit the product overview.
Yingxu Liu