LlmPromptTesting 2.0.0

LlmPromptTesting

Record and replay LLM responses in xUnit v3 tests. Captures live IChatClient responses as snapshots and replays them locally — no API key needed after the first run.

Why?

LLM-powered features are hard to test. Real API calls are slow, expensive, and non-deterministic. Mocking them throws away the very thing you want to verify: that your prompts actually produce useful output.

LlmPromptTesting solves this with snapshot testing for LLM responses:

1. First run — calls the real API, saves the response to a .llm-cache/ directory as a JSON snapshot.
2. Subsequent runs — replays the cached response instantly, with no API key required.
3. CI — replays from the committed cache by default, so PRs cost zero credits. Set LLM_PROMPT_TESTING_FORCE_API=true (with an API key) to refresh snapshots against the real API.

This gives you deterministic, fast, offline-capable tests that still validate real LLM output. It also ships an LLM-as-a-judge assertion (LlmAssert.JudgeAsync) so you can assert that responses meet human-readable criteria without brittle string matching.

Installation

# Core package (works with any IChatClient)
dotnet add package LlmPromptTesting

# Anthropic convenience fixture
dotnet add package LlmPromptTesting.Anthropic

Quick start

1. Create a test fixture

The fixture provides an IChatClient that automatically records and replays responses.

Using Anthropic (Claude):

// The built-in AnthropicChatClientFixture reads ANTHROPIC_API_KEY
// from the environment and wires everything up for you.
[CollectionDefinition(nameof(LlmCollection))]
public class LlmCollection : ICollectionFixture<AnthropicChatClientFixture>;

Using any other provider:

Subclass BaseChatClientFixture and supply your own client factory:

public class OpenAiChatClientFixture : BaseChatClientFixture
{
    public OpenAiChatClientFixture() : base(
        apiKeyFactory: () => Environment.GetEnvironmentVariable("OPENAI_API_KEY"),
        chatClientFactory: apiKey => new OpenAIClient(apiKey)
            .GetChatClient("gpt-4o")
            .AsIChatClient())
    {
    }
}

2. Write a test

[Collection(nameof(LlmCollection))]
public class when_asking_for_a_haiku(AnthropicChatClientFixture fixture)
{
    [Fact]
    public async Task it_returns_a_haiku()
    {
        // Arrange
        var messages = new ChatMessage[]
        {
            new(ChatRole.User, "Write a haiku about testing software.")
        };

        var options = new ChatOptions
        {
            ModelId = "claude-haiku-4-5-20251001"
        };

        // Act
        var response = await fixture.ChatClient.GetResponseAsync(
            messages,
            options,
            TestContext.Current.CancellationToken);

        // Assert — use an LLM judge instead of brittle string matching
        await LlmAssert.JudgeAsync(
            fixture.ChatClient,
            response,
            "Is this a valid haiku (three lines, 5-7-5 syllable pattern)?",
            "claude-haiku-4-5-20251001");
    }
}

The first time this test runs, it calls Claude, saves the response to .llm-cache/, and evaluates it. Every subsequent run replays the cached response — no network, no cost, same result.

3. Commit the cache

git add .llm-cache/
git commit -m "Add LLM response snapshots"

Now every developer on the team can run the tests without an API key.

LLM-as-a-judge assertions

LlmAssert.JudgeAsync lets you assert that text satisfies a criterion, judged by an LLM. This replaces fragile regex or substring checks with natural-language criteria:

// Assert against a ChatResponse
await LlmAssert.JudgeAsync(
    judge: fixture.ChatClient,
    response: chatResponse,
    criterion: "Does the response include a numbered list of at least 3 items?",
    modelId: "claude-haiku-4-5-20251001");

// Assert against raw text
await LlmAssert.JudgeAsync(
    judge: fixture.ChatClient,
    text: "The quick brown fox jumps over the lazy dog.",
    criterion: "Does this sentence contain every letter of the English alphabet?",
    modelId: "claude-haiku-4-5-20251001");

Fluent syntax

Extension methods provide a more readable alternative:

await response.ShouldSatisfyAsync(
    fixture.ChatClient,
    "Does the response read like a professional email?",
    "claude-haiku-4-5-20251001");

await "Hello, world!".ShouldSatisfyAsync(
    fixture.ChatClient,
    "Is this a greeting?",
    "claude-haiku-4-5-20251001");

Default model

Set a default model to avoid repeating the model ID in every assertion:

LlmAssert.DefaultModelId = "claude-haiku-4-5-20251001";

// Now you can omit the modelId parameter
await LlmAssert.JudgeAsync(
    fixture.ChatClient,
    response,
    "Does this answer the user's question?");

How caching works

The same caching layer is used everywhere — including CI — so tests run against the committed .llm-cache/ snapshots by default.

Cache keys are SHA-256 hashes of the system instructions, messages, and model ID. Changing any of these invalidates the cache and triggers a fresh API call.

Snapshots are stored at .llm-cache/{TestClass}/{TestMethod}_{hash}.json.

Forcing real API calls

Set LLM_PROMPT_TESTING_FORCE_API=true (or 1) to bypass the cache entirely and hit the live IChatClient. Use this when you intentionally want to re-record snapshots against the real API — for example, on a scheduled CI run or after a prompt change.

LLM_PROMPT_TESTING_FORCE_API=true ANTHROPIC_API_KEY=sk-... dotnet test

When the flag is not set, CI behaves exactly like local development: replays from cache, costs nothing in API credits, and only consumes credits if a key is present and a cache entry is missing.