This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Questlock
Reuse AI work across agents, with shared token budgets
Why pay for the same AI work twice? Questlock helps your agents share completed work and skip another model call when a matching result is still valid. Run agents on one machine or across your own machines, with a shared token budget. Use local or hosted models, with Questlock as an independent companion to OpenRouter. Less repeated work, more room in your budget for something new. AI tool plugins available!
Hey Product Hunt! I'm Shivam, the maker of Questlock.
The problem I wanted to solve: our agents keep doing work we already paid for. Sometimes the next request only needs a checked edit. Sometimes several workers ask for the same result.
Questlock is an experimental, open-source Go library and self-hosted broker. Cooperating agents can claim work, reuse an eligible completed result and share a daily token allowance across machines. Each process bounds admission using its own CPU and memory headroom.
The mission: As teams add more agents, they can end up running the same work twice and spending their model budget on answers they already have. Questlock helps cooperating agents share completed work, reuse an eligible result and work within a shared token allowance, on one machine or across their own machines. The goal is to make the models and hardware developers already have go further.
I'd love feedback from developers running agents on their own machines: where is repeated work costing you the most?
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About Questlock on Product Hunt
“Reuse AI work across agents, with shared token budgets”
Questlock was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #142 on the daily leaderboard. Why pay for the same AI work twice? Questlock helps your agents share completed work and skip another model call when a matching result is still valid. Run agents on one machine or across your own machines, with a shared token budget. Use local or hosted models, with Questlock as an independent companion to OpenRouter. Less repeated work, more room in your budget for something new. AI tool plugins available!
Questlock was featured in Open Source (69k followers), Internet of Things (227.2k followers) and Artificial Intelligence (482k followers) on Product Hunt. Together, these topics include over 158.9k products, making this a competitive space to launch in.
Who hunted Questlock?
Questlock was hunted by Shiv Pathak. 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.
Want to see how Questlock stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shiv Pathak
Hey Product Hunt! I'm Shivam, the maker of Questlock.
The problem I wanted to solve: our agents keep doing work we already paid for. Sometimes the next request only needs a checked edit. Sometimes several workers ask for the same result.
Questlock is an experimental, open-source Go library and self-hosted broker. Cooperating agents can claim work, reuse an eligible completed result and share a daily token allowance across machines. Each process bounds admission using its own CPU and memory headroom.
The mission: As teams add more agents, they can end up running the same work twice and spending their model budget on answers they already have. Questlock helps cooperating agents share completed work, reuse an eligible result and work within a shared token allowance, on one machine or across their own machines. The goal is to make the models and hardware developers already have go further.
I'd love feedback from developers running agents on their own machines: where is repeated work costing you the most?
Release and setup: https://github.com/spfuzzylink/q...
AI tool plugins available!