evaluate-spring-ai-alibaba

name: evaluate-spring-ai-alibaba description: Sets up evaluation of Spring AI Alibaba graph agents using Dokimos. Use this skill when the user wants to evaluate, test, or benchmark a Spring AI Alibaba ReactAgent or graph run, its tool calls, or its execution trace. Also use when the user mentions Spring AI Alibaba evaluation or integrating Dokimos with a spring-ai-alibaba project.

Evaluate Spring AI Alibaba

Set up Dokimos evaluation for a Spring AI Alibaba graph agent. The user will describe their agent and evaluation goals via $ARGUMENTS.

Where things live

• Support class: dokimos-spring-ai-alibaba/src/main/java/dev/dokimos/springai/alibaba/SpringAiAlibabaSupport.java
• Maven dependency: dev.dokimos:dokimos-spring-ai-alibaba (pulls in dokimos-spring-ai)

Before writing code, read SpringAiAlibabaSupport.java. It reuses SpringAiSupport for the actual message-to-ToolCall extraction, so it stays thin.

How it works

A Spring AI Alibaba graph run carries its whole conversation as standard Spring AI message types (AssistantMessage, ToolResponseMessage) under the OverAllState key "messages". The support class folds that list into one AgentTrace, matching each tool call to its result with per-turn windowing (so a reused tool-call id across turns does not bind to the wrong result).

• SpringAiAlibabaSupport.toAgentTrace(OverAllState) — fold a finished run's state into an AgentTrace.
• toAgentTrace(Optional<OverAllState>) — same, for CompiledGraph.invoke(...) which returns an Optional.
• toAgentTrace(ReactAgent, Map<String,Object> inputs, RunnableConfig) — the one-liner: run the agent's compiled graph with full fidelity and fold.
• toToolDefinitions(List<ToolCallback>) — map the tool callbacks the agent was built with to ToolDefinitions.

Use getAndCompileGraph().invoke(...), not call(...): the latter is lossy and drops intermediate tool calls.

There is no asJudge or asyncTask here. Spring AI Alibaba runs on a standard Spring AI ChatModel, so use SpringAiSupport.asJudge(...) and SpringAiSupport.asyncTask(...) for those.

Evaluation pattern

OverAllState state = agent.getAndCompileGraph().invoke(inputs, config).orElseThrow();
AgentTrace trace = SpringAiAlibabaSupport.toAgentTrace(state);
// or the one-liner:
AgentTrace trace = SpringAiAlibabaSupport.toAgentTrace(agent, inputs, config);

List<ToolDefinition> tools = SpringAiAlibabaSupport.toToolDefinitions(toolCallbacks);
EvalTestCase testCase = trace.toTestCase(userInput, tools);

var validity = ToolCallValidityEvaluator.builder().build().evaluate(testCase);
var correctness = ToolCorrectnessEvaluator.builder().build().evaluate(testCase);

Always construct evaluators with XEvaluator.builder()...build(); they have private constructors.

Reading tool results and arguments back typed

A captured ToolCall keeps its arguments as a Map and its result as the string the tool returned. Read them typed with call.argumentsAs(MyArgs.class) and, when the result is JSON, call.resultAs(MyResult.class) (or OutputType for generics).

Dependencies

<dependency>
    <groupId>dev.dokimos</groupId>
    <artifactId>dokimos-spring-ai-alibaba</artifactId>
    <version>${dokimos.version}</version>
</dependency>

Spring AI Alibaba itself is a provided-scope dependency: the user brings their own version (com.alibaba.cloud.ai:spring-ai-alibaba-agent-framework, the 1.1.x line).

Steps

1. Understand from $ARGUMENTS what the graph agent does and which tools it calls
2. Run the agent through getAndCompileGraph().invoke(...) and fold the state into an AgentTrace
3. Convert to an EvalTestCase with trace.toTestCase(input, tools)
4. Score with the agent evaluators (prefer deterministic ones for CI)
5. For the full agent evaluator set, use the evaluate-agent skill