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MLLPong
A lightweight mock server for HL7 v2 messages over MLLP
A lightweight mock server for HL7 v2 messages over MLLP — always-ACK, always-NACK, and rule-based smart handler
I was building an HL7 integration and needed something to test against, a server that would just receive my messages and ACK them back. I searched for an existing MLLP mock server and found nothing that was simple to spin up, Docker-ready, and actually useful for real testing scenarios. Every option was either buried inside a massive enterprise tool, unmaintained, or required writing your own test harness from scratch. I built MLLPong in a weekend out of frustration, and it's been my go-to ever since.
Healthcare developers working with HL7 v2 over MLLP have no lightweight, standalone mock server to test against. HL7 integration work typically requires a real EHR or middleware system on the other end, which means you need VPN access, test environments, and coordination with other teams just to verify your message format is correct. MLLPong eliminates that dependency. Spin it up locally or in CI with one Docker command, and your HL7 sender has something real to talk to immediately.
Who is it for? Backend engineers, integration developers, and QA engineers who build or test HL7 v2 pipelines, especially those working with EHR systems, clinical data platforms, hospital integrations, or health information exchanges.
MLLPong gives you three ports out of the box: one that always ACKs, one that always rejects, and a smart one that responds differently per message type based on a JSON rules file you can swap without rebuilding. No configuration UI, no license, no 200MB download, just a 10MB Docker image.
About MLLPong on Product Hunt
“A lightweight mock server for HL7 v2 messages over MLLP”
MLLPong was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #100 on the daily leaderboard. A lightweight mock server for HL7 v2 messages over MLLP — always-ACK, always-NACK, and rule-based smart handler
On the analytics side, MLLPong competes within Software Engineering, Developer Tools and GitHub — topics that collectively have 595.3k followers on Product Hunt. The dashboard above tracks how MLLPong performed against the three products that launched closest to it on the same day.
Who hunted MLLPong?
MLLPong was hunted by Noval Agung Prayogo. 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 MLLPong including community comment highlights and product details, visit the product overview.
Noval Agung Prayogo