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FlowPilot
Open-source control loop for long AI agent work
FlowPilot is an open-source runtime that lets an AI agent run long software projects as a role-based team: PM, worker, reviewer, and FlowGuard operator. It plans routes, dispatches background agents, reviews evidence, replans when checks fail, and closes only with a completion ledger.
Long AI-agent work is starting to be described as a loop, but a prompt that says "keep going" is not enough for real software projects.
FlowPilot makes the loop explicit and turns a single AI run into a role-based project team. It can plan a route, dispatch background agents into bounded roles like PM, worker, reviewer, and FlowGuard operator, then bring their results back through sealed packets, role-separated review, repair gates, and completion evidence.
The important part is that the loop is model-first. FlowGuard-style process simulation checks the route before the run treats it as safe. During the project, FlowPilot can replan when requirements change, evidence becomes stale, or a check fails. The agent is not just repeating "continue"; it is working through a route it can inspect, challenge, and update.
The practical startup flow is intentionally simple:
1. Open an AI-agent interface that can use local tools.
2. Turn on goal, target, or objective mode if the host has one.
3. Ask the agent to formally start FlowPilot and keep using FlowPilot until terminal return, user input, or explicit stop permission.
4. Wait for the startup window.
5. Enter the project goal, constraints, and expected outcome.
6. Let FlowPilot control the run.
FlowPilot is open-source and local. The long-term idea is not a black-box hosted agent, but an inspectable project-control runtime that people can change, extend, or replace for their own workflows.
I would love feedback on three things:
1. Does "open-source control loop for AI agents" make the product clear?
2. Is the role-based background-agent workflow understandable enough?
3. Which part of long AI-agent work do you most want a runtime to control?
FlowPilot was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #151 on the daily leaderboard. FlowPilot is an open-source runtime that lets an AI agent run long software projects as a role-based team: PM, worker, reviewer, and FlowGuard operator. It plans routes, dispatches background agents, reviews evidence, replans when checks fail, and closes only with a completion ledger.
On the analytics side, FlowPilot competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how FlowPilot performed against the three products that launched closest to it on the same day.
Who hunted FlowPilot?
FlowPilot 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 FlowPilot including community comment highlights and product details, visit the product overview.
Yingxu Liu
Long AI-agent work is starting to be described as a loop, but a prompt that says "keep going" is not enough for real software projects.
FlowPilot makes the loop explicit and turns a single AI run into a role-based project team. It can plan a route, dispatch background agents into bounded roles like PM, worker, reviewer, and FlowGuard operator, then bring their results back through sealed packets, role-separated review, repair gates, and completion evidence.
The important part is that the loop is model-first. FlowGuard-style process simulation checks the route before the run treats it as safe. During the project, FlowPilot can replan when requirements change, evidence becomes stale, or a check fails. The agent is not just repeating "continue"; it is working through a route it can inspect, challenge, and update.
The practical startup flow is intentionally simple:
1. Open an AI-agent interface that can use local tools.
2. Turn on goal, target, or objective mode if the host has one.
3. Ask the agent to formally start FlowPilot and keep using FlowPilot until terminal return, user input, or explicit stop permission.
4. Wait for the startup window.
5. Enter the project goal, constraints, and expected outcome.
6. Let FlowPilot control the run.
FlowPilot is open-source and local. The long-term idea is not a black-box hosted agent, but an inspectable project-control runtime that people can change, extend, or replace for their own workflows.
I would love feedback on three things:
1. Does "open-source control loop for AI agents" make the product clear?
2. Is the role-based background-agent workflow understandable enough?
3. Which part of long AI-agent work do you most want a runtime to control?
Repository:
https://github.com/liuyingxuvka/FlowPilot