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[nodejs] Clean up inputs/outputs for InvokeAgent spans based on new OTel format #137

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Merged
PengF (fpfp100) merged 7 commits into from
Jan 13, 2026
Merged

[nodejs] Clean up inputs/outputs for InvokeAgent spans based on new OTel format #137

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b4a7717
update input message
jsl517 Jan 7, 2026
aa10faf
comments
jsl517 Jan 7, 2026
aa0f134
comment
jsl517 Jan 7, 2026
9ecfd83
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jsl517 Jan 7, 2026
a20c19a
add test to cover handling non standard input.
jsl517 Jan 7, 2026
7e9dc62
fix the message process to match open ai agent
jsl517 Jan 12, 2026
e99a70d
Merge branch 'main' into users/pefan/input
fpfp100 Jan 13, 2026
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22 changes: 14 additions & 8 deletions packages/agents-a365-observability-extensions-openai/src/OpenAIAgentsTraceProcessor.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -255,21 +255,27 @@ export class OpenAIAgentsTraceProcessor implements TracingProcessor {
// Store the complete _input structure as JSON
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otelSpan.setAttribute(
OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY,
JSON.stringify(inputObj)
this.buildInputMessages(inputObj)
);
}
}
}

Copilot AI Jan 13, 2026

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The new private method 'buildInputMessages' is missing JSDoc documentation. Other private methods in this file have JSDoc comments explaining their purpose. Add a JSDoc comment describing what this method does, its parameters, and return value.

Suggested change
/**
* Builds a JSON-encoded representation of input messages for tracing.
* Extracts the text content of user-role messages when available, otherwise
* falls back to serializing the full input array.
*
* @param arr - Array of message objects containing a role and string content.
* @returns A JSON string of user message contents, or the original array if no valid user content is found.
*/

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// Get attributes but filter out unwanted ones
const attrs = Utils.getAttributesFromInput(inputObj);
Object.entries(attrs).forEach(([key, value]) => {
if (value !== null && value !== undefined &&
key !== Constants.GEN_AI_REQUEST_CONTENT_KEY) {
otelSpan.setAttribute(key, value as string | number | boolean);
private buildInputMessages(arr: Array<{ role?: string; parts?: Array<{ type: string; content: unknown }> }>): string {
const userTexts: string[] = [];

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The variable name 'userTexts' is misleading in the context of 'buildOutputMessages'. This variable collects output texts (type 'output_text'), not user texts. Consider renaming to 'outputTexts' for clarity.

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for (const message of arr) {
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if (message && message.role === 'user' && Array.isArray(message.parts)) {
for (const p of message.parts) {
if (p && p.type === 'text' && typeof p.content === 'string') {
userTexts.push(p.content);
}
});
}
}
}
return userTexts.length ? JSON.stringify(userTexts) : JSON.stringify(arr);
}


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/**
* Process generation span data
*/
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76 changes: 76 additions & 0 deletions tests/observability/extension/openai/OpenAIAgentsTraceProcessor.test.ts
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Expand Up @@ -550,5 +550,81 @@ describe('OpenAIAgentsTraceProcessor', () => {
const keys = (respMock._attrs as Array<[string, unknown]>).map(([k]) => k);
expect(keys).not.toContain(OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY);
});

it('records full array JSON when only assistant messages are present', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-assistant-only', name: 'Agent' } as any;
await processor.onTraceStart(traceData);

const inputArray = [
{
role: 'assistant',
parts: [
{ type: 'text', content: 'Assistant reply' },
],
},
];

const respSpan = {
spanId: 'resp-assistant-span',
traceId: 'trace-assistant-only',
startedAt: new Date().toISOString(),
spanData: {
type: 'response' as const,
name: 'ResponseAssistantOnly',
_input: inputArray,
_response: { model: 'gpt-4', output: 'ok' },
},
} as any;

await processor.onSpanStart(respSpan);
await processor.onSpanEnd(respSpan);

const respMock = spansByName['ResponseAssistantOnly'];
const attrs = respMock._attrs as Array<[string, unknown]>;
const entry = attrs.find(([k]) => k === OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY);
expect(entry).toBeDefined();

const value = entry![1] as string;
const parsed = JSON.parse(value);
expect(parsed).toEqual(inputArray);
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});
it('records user text content for array _input on response spans', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-array-input', name: 'Agent' } as any;
await processor.onTraceStart(traceData);

const respSpan = {
spanId: 'resp-array-span',
traceId: 'trace-array-input',
startedAt: new Date().toISOString(),
spanData: {
type: 'response' as const,
name: 'ResponseArray',
_input: [
{
role: 'user',
parts: [
{ type: 'text', content: 'Hello user 1' },
{ type: 'text', content: 'Hello user 2' },
],
}
],
_response: { model: 'gpt-4', output: 'ok' },
},
} as any;

await processor.onSpanStart(respSpan);
await processor.onSpanEnd(respSpan);

const respMock = spansByName['ResponseArray'];
const attrs = respMock._attrs as Array<[string, unknown]>;
const entry = attrs.find(([k]) => k === OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY);
expect(entry).toBeDefined();

const value = entry![1] as string;
const parsed = JSON.parse(value);
expect(parsed).toEqual(['Hello user 1', 'Hello user 2']);
});
});
});