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

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Merged
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
comment
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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54 changes: 43 additions & 11 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 @@ -231,7 +231,10 @@ export class OpenAIAgentsTraceProcessor implements TracingProcessor {
if (typeof resp.output === 'string') {
otelSpan.setAttribute(OpenTelemetryConstants.GEN_AI_OUTPUT_MESSAGES_KEY, resp.output);
} else {
otelSpan.setAttribute(OpenTelemetryConstants.GEN_AI_OUTPUT_MESSAGES_KEY, JSON.stringify(resp.output));
otelSpan.setAttribute(
OpenTelemetryConstants.GEN_AI_OUTPUT_MESSAGES_KEY,
this.buildOutputMessages(resp.output as Array<{ role: string; content: Array<{ type: string; text: string }> }>)
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Copilot AI Jan 13, 2026

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Extra whitespace before 'as' keyword. There should be only one space between 'output' and 'as'.

Suggested change
this.buildOutputMessages(resp.output as Array<{ role: string; content: Array<{ type: string; text: string }> }>)
this.buildOutputMessages(resp.output as Array<{ role: string; content: Array<{ type: string; text: string }> }>)

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);
}
}

Expand All @@ -250,24 +253,53 @@ export class OpenAIAgentsTraceProcessor implements TracingProcessor {

if (inputObj && !this.suppressInvokeAgentInput) {
if (typeof inputObj === 'string') {
try {
const parsed = JSON.parse(inputObj as string);
if (Array.isArray(parsed)) {
otelSpan.setAttribute(
OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY,
this.buildInputMessages(parsed)
);
return;
}
} catch {
// If parsing fails, fall back to raw string behavior
}
otelSpan.setAttribute(OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY, inputObj);
} else if (Array.isArray(inputObj)) {
// Store the complete _input structure as JSON
// build the input messages from array
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Comment should start with an uppercase letter for consistency with the codebase style. Change 'build' to 'Build'.

Suggested change
// build the input messages from array
// Build the input messages from array

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otelSpan.setAttribute(
OpenTelemetryConstants.GEN_AI_INPUT_MESSAGES_KEY,
JSON.stringify(inputObj)
this.buildInputMessages(inputObj)
);
}
}
}

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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; content: string }>): string {
const userTexts = arr
.filter((m) => m && m.role === 'user' && typeof m.content === 'string')
.map((m) => m.content);

return JSON.stringify(userTexts.length ? userTexts : arr);
}

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The new private method 'buildOutputMessages' 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 serialized representation of output messages from the agent response.
*
* Extracts text from message content parts where the part type is `output_text`
* and returns a JSON string of those texts. If no such texts are found, the
* original message array is serialized instead.
*
* @param arr - Array of response messages, each containing a role and a content
* array of typed text parts.
* @returns JSON string containing the extracted output texts or the original
* message array as a fallback.
*/

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private buildOutputMessages(arr: Array<{ role: string; content: Array<{ type: string; text: string }> }>): 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 { content } of arr) {
if (!Array.isArray(content)) {
continue;
Comment on lines +291 to +292
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Missing test coverage for the edge case where elements in the output array have non-array content properties. The code handles this case with a check on line 291-292, but there's no test verifying this behavior works correctly.

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}

for (const { type, text } of content) {
Comment on lines +290 to +295
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The destructuring pattern 'for (const { content } of arr)' on line 290 will throw a runtime error if any element in the array is null, undefined, or doesn't have a 'content' property. Add a null check before destructuring to prevent potential runtime errors.

Suggested change
for (const { content } of arr) {
if (!Array.isArray(content)) {
continue;
}
for (const { type, text } of content) {
for (const message of arr) {
if (!message || !Array.isArray(message.content)) {
continue;
}
for (const part of message.content) {
if (!part) {
continue;
}
const { type, text } = part;

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The destructuring pattern 'for (const { type, text } of content)' on line 295 will throw a runtime error if any element in the content array is null, undefined, or doesn't have 'type' or 'text' properties. Add a null check before destructuring to prevent potential runtime errors.

Suggested change
for (const { type, text } of content) {
for (const item of content) {
if (!item) {
continue;
}
const { type, text } = item;

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if (type === 'output_text' && typeof text === 'string') {
userTexts.push(text);
}
}
}

return JSON.stringify(userTexts.length ? userTexts : arr);
}

/**
Expand Down
205 changes: 205 additions & 0 deletions tests/observability/extension/openai/OpenAIAgentsTraceProcessor.test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -550,5 +550,210 @@ 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',
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);
});
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', content: 'Hello user 1' },
{ role: 'user', 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']);
});

it('parses stringified array _input and records only user text content', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-array-input-string', name: 'Agent' } as any;
await processor.onTraceStart(traceData);

const inputArray = [
{ role: 'user', content: 'Hello user 1' },
{ role: 'user', content: 'Hello user 2' },
{ role: 'assistant', content: 'Assistant reply' },
];

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

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

const respMock = spansByName['ResponseArrayString'];
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']);
});

it('records [gen_ai.input.messages] attribute for array input with non standard schema on response spans', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-array-input', name: 'Agent' } as any;
await processor.onTraceStart(traceData);
const inputArray = [
{ type: 'text', content: 'message 1' },
{ type: 'text', content: 'message 2' },
];
const respSpan = {
spanId: 'resp-array-span',
traceId: 'trace-array-input',
startedAt: new Date().toISOString(),
spanData: {
type: 'response' as const,
name: 'ResponseArray',
_input: inputArray,
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Extra whitespace before 'inputArray'. There should be only one space after the colon.

Suggested change
_input: inputArray,
_input: inputArray,

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_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(inputArray);
});

it('records GEN_AI_OUTPUT_MESSAGES as plain string when output is a string', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-output-string', name: 'Agent' } as any;
await processor.onTraceStart(traceData);

const respSpan = {
spanId: 'resp-output-string',
traceId: 'trace-output-string',
startedAt: new Date().toISOString(),
spanData: {
type: 'response' as const,
name: 'ResponseOutputString',
_input: 'ignored',
_response: { model: 'gpt-4', output: 'final answer' },
},
} as any;

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

const respMock = spansByName['ResponseOutputString'];
const attrs = respMock._attrs as Array<[string, unknown]>;
const entry = attrs.find(([k]) => k === OpenTelemetryConstants.GEN_AI_OUTPUT_MESSAGES_KEY);
expect(entry).toBeDefined();
expect(entry![1]).toBe('final answer');
});

it('records GEN_AI_OUTPUT_MESSAGES as aggregated texts when output is structured', async () => {
const processor = new OpenAIAgentsTraceProcessor(tracer);
const traceData = { traceId: 'trace-output-structured', name: 'Agent' } as any;
await processor.onTraceStart(traceData);

const outputArray = [
{
role: 'assistant',
content: [
{ type: 'output_text', text: 'Hello user 1' },
{ type: 'output_text', text: 'Hello user 2' },
],
},
];

const respSpan = {
spanId: 'resp-output-structured',
traceId: 'trace-output-structured',
startedAt: new Date().toISOString(),
spanData: {
type: 'response' as const,
name: 'ResponseOutputStructured',
_input: 'ignored',
_response: { model: 'gpt-4', output: outputArray },
},
} as any;

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

const respMock = spansByName['ResponseOutputStructured'];
const attrs = respMock._attrs as Array<[string, unknown]>;
const entry = attrs.find(([k]) => k === OpenTelemetryConstants.GEN_AI_OUTPUT_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']);
});
});
});
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