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LLM Test Server
Your LLM tests, without the provider
LLM Test Server is a local, deterministic stand-in for 13 LLM provider APIs. Your app keeps its official SDK and points it at namespaced routes like /openai/v1; answers, tool calls, streams and failures come from fixtures your test sets up. The dashboard shows where every token goes: system prompt, tool schemas, conversation turns, prompt-cache reads and writes. Built forNET and Aspire, and shipped as a Docker image so any stack can use it. Free and open source.
Hi Product Hunt, I'm Konstantin, and I build LLM Test Server so you don't have to.
I found that a lot of the bugs in AI API are about incorrect api usage. The application sent the wrong request, dropped a tool result, stopped reading a stream too early, or retried an error badly. Those are ordinary integration bugs, and I wanted to test them the ordinary way, so ai agetns can fix it, before deploy. So same input, same result, for every run, with no live model in the loop.
LLM Test Server is a local server that speaks the HTTP APIs of 13 LLM providers, each under its own namespace such as /openai/v1 or /anthropic/v1.
Your app keeps its official SDK , just provide our base URL from the test server.
Of course you can setup script and simulate any api behaivor you may need for tests.
The part I like most is chaching oversvation. Becase are you sure your conversation collected correctly and did not break the cache?
For every request the we collect data and show it in the dashboard.
So what the context is made of (system prompt, tool schemas, user and assistant turns, tool results) and how much of it the prompt hit the cache.
It is for every stack. On .NET you get NuGet packages, an Aspire integration and a typed client. Everywhere else you run one Docker image, configure it over HTTP and point your Python, TypeScript or Go SDK at it.
I would love to hear which LLM integration failure is hardest for you to reproduce today.
About LLM Test Server on Product Hunt
“Your LLM tests, without the provider”
LLM Test Server was submitted on Product Hunt and earned 21 upvotes and 8 comments, placing #31 on the daily leaderboard. LLM Test Server is a local, deterministic stand-in for 13 LLM provider APIs. Your app keeps its official SDK and points it at namespaced routes like /openai/v1; answers, tool calls, streams and failures come from fixtures your test sets up. The dashboard shows where every token goes: system prompt, tool schemas, conversation turns, prompt-cache reads and writes. Built forNET and Aspire, and shipped as a Docker image so any stack can use it. Free and open source.
On the analytics side, LLM Test Server competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how LLM Test Server performed against the three products that launched closest to it on the same day.
Who hunted LLM Test Server?
LLM Test Server was hunted by KSemenenko. 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 LLM Test Server including community comment highlights and product details, visit the product overview.
KSemenenko
Hi Product Hunt, I'm Konstantin, and I build LLM Test Server so you don't have to.
I found that a lot of the bugs in AI API are about incorrect api usage. The application sent the wrong request, dropped a tool result, stopped reading a stream too early, or retried an error badly. Those are ordinary integration bugs, and I wanted to test them the ordinary way, so ai agetns can fix it, before deploy. So same input, same result, for every run, with no live model in the loop.
LLM Test Server is a local server that speaks the HTTP APIs of 13 LLM providers, each under its own namespace such as /openai/v1 or /anthropic/v1.
Your app keeps its official SDK , just provide our base URL from the test server.
Of course you can setup script and simulate any api behaivor you may need for tests.
The part I like most is chaching oversvation. Becase are you sure your conversation collected correctly and did not break the cache?
For every request the we collect data and show it in the dashboard.
So what the context is made of (system prompt, tool schemas, user and assistant turns, tool results) and how much of it the prompt hit the cache.
It is for every stack. On .NET you get NuGet packages, an Aspire integration and a typed client. Everywhere else you run one Docker image, configure it over HTTP and point your Python, TypeScript or Go SDK at it.
The site is https://llmtest.managed-code.com
I would love to hear which LLM integration failure is hardest for you to reproduce today.