chore: sync with main

<!--A few plain bullets saying what changed and why, for a reviewer
skimming it - not a wall of text, not a restatement of the diff, not
generated boilerplate.-->
#### Description
- Anthropic Messages: formats a request `{system, messages}` or a
response `{content, stop_reason}`, with every image and document source
type, `tool_use`, `tool_result`, thinking and the server tool blocks.
- Gemini generateContent: formats a request `{contents,
systemInstruction}` or a response `{candidates}`, in REST camelCase or
SDK snake_case, with inline and file data, thought parts, `functionCall`
and `functionResponse`.
- Tool results that both APIs carry inside the next user turn become a
tool message before it. The Anthropic stop reasons and Gemini finish
reasons map onto the schema's enum.

<!--Reference issues using `Closes #issue-number` to enable automatic
closure on merge. -->
#### Issues closed by this PR
Part of https://github.com/SigNoz/nerve-pod/issues/266


<!--Anything reviewers should keep in mind while reviewing -->
#### Additional Information
Eighth and last of the stack, on top of #13115. No capture records
either body, OpenInference's flattened messages cover its users; every
case cites the SDK type or API doc behind it. These serve gateways
recording raw provider bodies and the fallback path.

.github/workflows/e2eci.yaml

@@ -3,16 +3,44 @@ name: e2eci
 on:
   pull_request:
     types:
+      - opened
+      - synchronize
+      - reopened
       - labeled
   pull_request_target:
     types:
       - labeled
+  merge_group:
+    types:
+      - checks_requested
+
+concurrency:
+  group: e2eci-${{ github.event_name }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
 
 jobs:
+  changes:
+    if: github.event_name != 'merge_group'
+    runs-on: ubuntu-latest
+    outputs:
+      e2e: ${{ steps.changes.outputs.e2e }}
+    steps:
+      - name: changes
+        id: changes
+        env:
+          GH_TOKEN: ${{ github.token }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          LABELED: ${{ contains(github.event.pull_request.labels.*.name, 'safe-to-e2e') }}
+        run: |
+          files=$(gh api "repos/$GITHUB_REPOSITORY/pulls/$PR_NUMBER/files" --paginate --jq '.[].filename')
+          if [ "$LABELED" = true ] || grep -qE '^(frontend|tests/e2e)/' <<< "$files"; then
+            echo "e2e=true" >> "$GITHUB_OUTPUT"
+          fi
   fmtlint:
+    needs: changes
     if: |
       ((github.event_name == 'pull_request' && ! github.event.pull_request.head.repo.fork && github.event.pull_request.user.login != 'dependabot[bot]' && ! contains(github.event.pull_request.labels.*.name, 'safe-to-test')) ||
-      (github.event_name == 'pull_request_target' && contains(github.event.pull_request.labels.*.name, 'safe-to-test'))) && contains(github.event.pull_request.labels.*.name, 'safe-to-e2e')
+      (github.event_name == 'pull_request_target' && contains(github.event.pull_request.labels.*.name, 'safe-to-test'))) && needs.changes.outputs.e2e == 'true'
     runs-on: ubuntu-latest
     steps:
       - name: checkout
@@ -41,9 +69,10 @@ jobs:
         project:
           - chromium
         shard: [1, 2, 3, 4]
+    needs: changes
     if: |
       ((github.event_name == 'pull_request' && ! github.event.pull_request.head.repo.fork && github.event.pull_request.user.login != 'dependabot[bot]' && ! contains(github.event.pull_request.labels.*.name, 'safe-to-test')) ||
-      (github.event_name == 'pull_request_target' && contains(github.event.pull_request.labels.*.name, 'safe-to-test'))) && contains(github.event.pull_request.labels.*.name, 'safe-to-e2e')
+      (github.event_name == 'pull_request_target' && contains(github.event.pull_request.labels.*.name, 'safe-to-test'))) && needs.changes.outputs.e2e == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 30
     steps:
@@ -152,3 +181,17 @@ jobs:
           name: playwright-artifacts-${{ matrix.project }}-${{ matrix.shard }}
           path: tests/e2e/artifacts/
           retention-days: 5
+  # Required status check. Fails when e2e was needed but did not pass,
+  # including fork and dependabot PRs still waiting on safe-to-test.
+  e2e:
+    if: always()
+    needs: [changes, fmtlint, test]
+    runs-on: ubuntu-latest
+    steps:
+      - name: verdict
+        if: |
+          github.event_name != 'merge_group' && (needs.changes.result != 'success' ||
+          (needs.changes.outputs.e2e == 'true' && (needs.fmtlint.result != 'success' || needs.test.result != 'success')))
+        run: |
+          echo "::error::e2e did not pass. Fork and dependabot PRs need the safe-to-test label."
+          exit 1

docs/api/openapi.yml

@@ -16064,10 +16064,8 @@ paths:
                 $ref: '#/components/schemas/RenderErrorResponse'
           description: Internal Server Error
       security:
-      - api_key:
-        - VIEWER
-      - tokenizer:
-        - VIEWER
+      - api_key: []
+      - tokenizer: []
       summary: Get stats
       tags:
       - stats

frontend/src/api/__tests__/ErrorResponseHandler.test.ts

@@ -9,12 +9,14 @@ describe('ErrorResponseHandler', () => {
 			.spyOn(console, 'error')
 			.mockImplementation(() => undefined);
 
-		expect(ErrorResponseHandler(new CanceledError() as AxiosError)).toEqual({
-			statusCode: 500,
-			payload: null,
-			error: 'Something went wrong',
-			message: null,
-		});
+		expect(ErrorResponseHandler(new CanceledError() as AxiosError)).toStrictEqual(
+			{
+				statusCode: 500,
+				payload: null,
+				error: 'Something went wrong',
+				message: null,
+			},
+		);
 		expect(consoleError).not.toHaveBeenCalled();
 
 		consoleError.mockRestore();

frontend/src/pages/TraceDetailsV3/EntityMetadata/EntityMetadataItem.tsx

@@ -1,11 +1,11 @@
 import { ReactNode } from 'react';
-import { Tooltip } from '@signozhq/ui/tooltip';
+import { Tooltip, TooltipProps } from '@signozhq/ui/tooltip';
 import { Typography } from '@signozhq/ui/typography';
 
 import styles from './EntityMetadataItem.module.scss';
 
 interface EntityMetadataItemProps {
-	tooltip: string;
+	tooltip: TooltipProps['title'];
 	icon?: ReactNode;
 	children: ReactNode;
 }

frontend/src/pages/TraceDetailsV3/EntityMetadata/EntityMetadataRow.module.scss

@@ -8,3 +8,12 @@
 	// Keep Typography.Text at the row's size (it reads this var, not inherited font-size).
 	--typography-font-size: var(--periscope-font-size-base);
 }
+
+.tokenTooltip {
+	composes: tooltipBody from '../shared.module.scss';
+	min-width: 180px;
+}
+
+.tokenTooltipTitle {
+	padding-bottom: var(--spacing-4);
+}

frontend/src/pages/TraceDetailsV3/EntityMetadata/EntityMetadataRow.tsx

@@ -1,10 +1,18 @@
-import { CalendarClock, Server, Timer } from '@signozhq/icons';
+import { CalendarClock, Coins, Landmark, Server, Timer } from '@signozhq/icons';
 import { Badge } from '@signozhq/ui/badge';
 import cx from 'classnames';
 import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
 import HttpStatusBadge from 'components/HttpStatusBadge/HttpStatusBadge';
 
+import {
+	AiTokenCounts,
+	formatCost,
+	formatTokens,
+	getTotalInputTokens,
+	hasValue,
+} from '../utils/aiTraceUtils';
 import EntityMetadataItem from './EntityMetadataItem';
+import TokenUsageTooltip from './TokenUsageTooltip';
 
 import styles from './EntityMetadataRow.module.scss';
 
@@ -16,6 +24,8 @@ interface EntityMetadataRowProps {
 	execTimePercent?: number;
 	timestamp?: string;
 	statusCode?: string | number;
+	tokens?: AiTokenCounts;
+	cost?: number | null;
 }
 
 const ICON_SIZE = 14;
@@ -32,8 +42,11 @@ function EntityMetadataRow({
 	execTimePercent,
 	timestamp,
 	statusCode,
+	tokens,
+	cost,
 }: EntityMetadataRowProps): JSX.Element {
 	const entityLabel = entity === 'trace' ? 'Trace' : 'Span';
+	const totalInput = tokens && getTotalInputTokens(tokens);
 	const durationTooltip =
 		entity === 'trace' ? 'Trace Duration' : 'Span Duration';
 	// Single source of duration formatting so both rows label units identically.
@@ -91,6 +104,24 @@ function EntityMetadataRow({
 					<HttpStatusBadge statusCode={statusCode} />
 				</EntityMetadataItem>
 			)}
+
+			{tokens && (
+				<EntityMetadataItem
+					tooltip={<TokenUsageTooltip tokens={tokens} />}
+					icon={<Coins size={ICON_SIZE} />}
+				>
+					Tokens: {formatTokens(totalInput)} → {formatTokens(tokens.output)}
+				</EntityMetadataItem>
+			)}
+
+			{hasValue(cost) && (
+				<EntityMetadataItem
+					tooltip="Total cost"
+					icon={<Landmark size={ICON_SIZE} />}
+				>
+					Cost — {formatCost(cost)}
+				</EntityMetadataItem>
+			)}
 		</div>
 	);
 }
@@ -102,6 +133,8 @@ EntityMetadataRow.defaultProps = {
 	execTimePercent: undefined,
 	timestamp: undefined,
 	statusCode: undefined,
+	tokens: undefined,
+	cost: undefined,
 };
 
 export default EntityMetadataRow;

frontend/src/pages/TraceDetailsV3/EntityMetadata/TokenUsageTooltip.tsx

@@ -0,0 +1,35 @@
+import { Typography } from '@signozhq/ui/typography';
+
+import TokenBreakdown from '../TokenBreakdown/TokenBreakdown';
+import { AiTokenCounts } from '../utils/aiTraceUtils';
+
+import styles from './EntityMetadataRow.module.scss';
+
+interface TokenUsageTooltipProps {
+	tokens: AiTokenCounts;
+}
+
+function TokenUsageTooltip({ tokens }: TokenUsageTooltipProps): JSX.Element {
+	const { input, output, cacheRead, cacheWrite, reasoning } = tokens;
+
+	return (
+		<div className={styles.tokenTooltip}>
+			<Typography.Text
+				size="small"
+				weight="normal"
+				className={styles.tokenTooltipTitle}
+			>
+				Usage Breakdown
+			</Typography.Text>
+			<TokenBreakdown
+				input={input}
+				output={output}
+				cacheRead={cacheRead}
+				cacheWrite={cacheWrite}
+				reasoning={reasoning}
+			/>
+		</div>
+	);
+}
+
+export default TokenUsageTooltip;

frontend/src/pages/TraceDetailsV3/SpanDetailsPanel/SpanSummary.tsx

@@ -1,9 +1,11 @@
+import { useMemo } from 'react';
 import { Link2 } from '@signozhq/icons';
 import dayjs from 'dayjs';
 import KeyValueLabel from 'periscope/components/KeyValueLabel';
 import { SpanV3 } from 'types/api/trace/getTraceV3';
 
 import EntityMetadataRow from '../EntityMetadata/EntityMetadataRow';
+import { getAIUsageDetails, convertToAiTokens } from '../utils/aiTraceUtils';
 import { HIGHLIGHTED_OPTIONS } from './config';
 import {
 	LinkedSpansPanel,
@@ -31,6 +33,10 @@ function SpanSummary({
 }: SpanSummaryProps): JSX.Element {
 	const percentile = useSpanPercentile(selectedSpan);
 	const linkedSpans = useLinkedSpans((selectedSpan as any).references);
+	const aiUsage = useMemo(
+		() => getAIUsageDetails(selectedSpan)?.usage,
+		[selectedSpan],
+	);
 
 	return (
 		<>
@@ -63,6 +69,8 @@ function SpanSummary({
 							: undefined
 					}
 					timestamp={dayjs(selectedSpan.timestamp).format('HH:mm:ss — MMM D, YYYY')}
+					tokens={aiUsage && convertToAiTokens(aiUsage)}
+					cost={aiUsage?.cost}
 				/>
 
 				<div className={styles.spanInfoItem}>

frontend/src/pages/TraceDetailsV3/SpanDetailsPanel/__tests__/SpanSummary.test.tsx

@@ -95,4 +95,26 @@ describe('SpanSummary', () => {
 
 		expect(screen.queryByText(/of total exec time/)).not.toBeInTheDocument();
 	});
+
+	it('renders tokens and cost from the gen_ai usage attributes', () => {
+		const span = {
+			...createMockSpan(),
+			attributes: {
+				'gen_ai.usage.input_tokens': 1000,
+				'gen_ai.usage.output_tokens': 9,
+				'signoz.gen_ai.usage.tokens.cost': 0.000009,
+			},
+		};
+		render(<SpanSummary selectedSpan={span} />);
+
+		expect(screen.getByText(/Tokens: 1,000 → 9/)).toBeInTheDocument();
+		expect(screen.getByText(/Cost — \$ 0.000009/)).toBeInTheDocument();
+	});
+
+	it('omits tokens and cost for non-AI spans', () => {
+		render(<SpanSummary selectedSpan={createMockSpan()} />);
+
+		expect(screen.queryByText(/Tokens:/)).not.toBeInTheDocument();
+		expect(screen.queryByText(/Cost —/)).not.toBeInTheDocument();
+	});
 });

frontend/src/pages/TraceDetailsV3/SpanHintBadge/SpanHintBadge.module.scss

@@ -0,0 +1,6 @@
+.aiBadge {
+	display: inline-flex;
+	--badge-primary-outlined-label: var(--ai-badge-color);
+	--badge-primary-outlined-background: var(--ai-badge-color);
+	--badge-primary-outlined-border: var(--ai-badge-color);
+}

frontend/src/pages/TraceDetailsV3/SpanHintBadge/SpanHintBadge.tsx

@@ -0,0 +1,44 @@
+import { CSSProperties } from 'react';
+import { Badge } from '@signozhq/ui/badge';
+import HttpStatusBadge from 'components/HttpStatusBadge/HttpStatusBadge';
+import { SpanV3 } from 'types/api/trace/getTraceV3';
+
+import { getAiSpanKind } from '../utils/aiTraceUtils';
+import { AI_SPAN_BADGES } from './constants';
+
+import styles from './SpanHintBadge.module.scss';
+
+interface SpanHintBadgeProps {
+	span: SpanV3;
+}
+
+function SpanHintBadge({ span }: SpanHintBadgeProps): JSX.Element | null {
+	const aiSpanKind = getAiSpanKind(span);
+
+	if (aiSpanKind) {
+		const { label, color } = AI_SPAN_BADGES[aiSpanKind];
+		return (
+			<span
+				className={styles.aiBadge}
+				// The outline variant derives text, fill and border from this color.
+				style={{ '--ai-badge-color': color } as CSSProperties}
+			>
+				<Badge
+					variant="outlined"
+					color="primary"
+					testId={`span-hint-badge-${aiSpanKind}`}
+				>
+					{label}
+				</Badge>
+			</span>
+		);
+	}
+
+	if (!span.response_status_code) {
+		return null;
+	}
+
+	return <HttpStatusBadge statusCode={span.response_status_code} />;
+}
+
+export default SpanHintBadge;

frontend/src/pages/TraceDetailsV3/SpanHintBadge/constants.ts

@@ -0,0 +1,10 @@
+import { AiSpanKind } from '../utils/aiTraceUtils';
+
+export const AI_SPAN_BADGES: Record<
+	AiSpanKind,
+	{ label: string; color: string }
+> = {
+	[AiSpanKind.Llm]: { label: 'LLM', color: 'rgba(155, 255, 0, 1)' },
+	[AiSpanKind.Tool]: { label: 'TOOL', color: 'rgba(255, 209, 0, 1)' },
+	[AiSpanKind.Agent]: { label: 'AGENT', color: 'rgba(255, 10, 138, 1)' },
+};

frontend/src/pages/TraceDetailsV3/SpanHoverCard/EventTooltipContent.module.scss

@@ -8,16 +8,12 @@
 	display: inline-flex;
 	align-items: center;
 	gap: 4px;
-	background: var(--l2-background);
-	border: 1px solid var(--l2-border);
 	border-radius: 3px;
-	padding: 2px 6px;
 	font-size: 10px;
 	font-weight: 600;
 	text-transform: uppercase;
 	letter-spacing: 0.5px;
 	color: var(--l2-foreground);
-	margin-bottom: 6px;
 }
 
 .name {

frontend/src/pages/TraceDetailsV3/SpanHoverCard/SpanHoverCard.module.scss

@@ -9,28 +9,17 @@
 }
 
 .content {
-	font-size: 12px;
-	color: var(--l1-foreground);
+	composes: tooltipBody from '../shared.module.scss';
+	min-width: 223px;
+	max-width: 360px;
 }
 
-.name {
-	font-weight: 600;
-	margin-bottom: 4px;
+.title {
+	composes: tooltipTitle from '../shared.module.scss';
 }
 
-.row {
-	line-height: 1.5;
-	color: var(--l2-foreground);
-}
-
-.preview {
-	// container for additional preview rows
-}
-
-.previewKey {
-	color: var(--l2-foreground);
-}
-
-.previewValue {
-	color: var(--l1-foreground);
+.header {
+	display: flex;
+	flex-direction: column;
+	padding-bottom: var(--spacing-2);
 }

frontend/src/pages/TraceDetailsV3/SpanHoverCard/SpanHoverCard.tsx

@@ -1,4 +1,5 @@
 import { Tooltip } from '@signozhq/ui/tooltip';
+import { Typography } from '@signozhq/ui/typography';
 import { convertTimeToRelevantUnit } from 'utils/traceUtils';
 import { useIsDarkMode } from 'hooks/useDarkMode';
 import { useTraceStore } from 'pages/TraceDetailsV3/stores/traceStore';
@@ -7,6 +8,11 @@ import { useMemo } from 'react';
 import { SpanV3 } from 'types/api/trace/getTraceV3';
 import { toFixed } from 'utils/toFixed';
 
+import { getAIUsageDetails, SpanAiDetails } from '../utils/aiTraceUtils';
+import SpanUsageBreakdown from './SpanUsageBreakdown';
+import TraceTooltipRow from '../TraceTooltip/TraceTooltipRow';
+import TraceTooltipSection from '../TraceTooltip/TraceTooltipSection';
+
 import styles from './SpanHoverCard.module.scss';
 
 /**
@@ -33,6 +39,7 @@ export interface SpanTooltipContentProps {
 	relativeStartMs: number;
 	durationMs: number;
 	previewRows?: SpanPreviewRow[];
+	ai?: SpanAiDetails;
 }
 
 export function SpanTooltipContent({
@@ -42,33 +49,54 @@ export function SpanTooltipContent({
 	relativeStartMs,
 	durationMs,
 	previewRows,
+	ai,
 }: SpanTooltipContentProps): JSX.Element {
 	const { time: formattedDuration, timeUnitName } =
 		convertTimeToRelevantUnit(durationMs);
 
 	return (
 		<div className={styles.content}>
-			<div className={styles.name} style={{ color }}>
-				{spanName}
-			</div>
-			<div className={styles.row}>status: {hasError ? 'error' : 'ok'}</div>
-			<div className={styles.row}>start: {toFixed(relativeStartMs, 2)} ms</div>
-			<div className={styles.row}>
-				duration: {toFixed(formattedDuration, 2)} {timeUnitName}
+			<div className={styles.header}>
+				<Typography.Text
+					className={styles.title}
+					size="small"
+					weight="normal"
+					style={{ color }}
+				>
+					{spanName}
+				</Typography.Text>
+				{ai?.usage && (
+					<Typography.Text size="small" weight="normal">
+						Usage Breakdown
+					</Typography.Text>
+				)}
 			</div>
+			{ai?.usage && <SpanUsageBreakdown usage={ai.usage} />}
+			<TraceTooltipSection>
+				{ai?.model && <TraceTooltipRow label="model" value={ai.model} />}
+				{ai?.toolName && <TraceTooltipRow label="tool" value={ai.toolName} />}
+				{ai?.agentName && <TraceTooltipRow label="agent" value={ai.agentName} />}
+				<TraceTooltipRow label="status" value={hasError ? 'error' : 'ok'} />
+				<TraceTooltipRow
+					label="start"
+					value={`${toFixed(relativeStartMs, 2)} ms`}
+				/>
+				<TraceTooltipRow
+					label="duration"
+					value={`${toFixed(formattedDuration, 2)} ${timeUnitName}`}
+				/>
+			</TraceTooltipSection>
 			{previewRows && previewRows.length > 0 && (
-				<div className={styles.preview}>
+				<TraceTooltipSection>
 					{previewRows.map((row) => (
-						<div
+						<TraceTooltipRow
 							key={row.key}
-							className={styles.row}
-							data-testid={`span-hover-card-preview-${row.key}`}
-						>
-							<span className={styles.previewKey}>{row.key}:</span>{' '}
-							<span className={styles.previewValue}>{row.value}</span>
-						</div>
+							label={row.key}
+							value={row.value}
+							testId={`span-hover-card-preview-${row.key}`}
+						/>
 					))}
-				</div>
+				</TraceTooltipSection>
 			)}
 		</div>
 	);
@@ -130,6 +158,7 @@ export function SpanHoverCard({
 				relativeStartMs: span.timestamp - traceStartTime,
 				durationMs: span.duration_nano / 1e6,
 				previewRows,
+				ai: getAIUsageDetails(span),
 			},
 		};
 	}, [

frontend/src/pages/TraceDetailsV3/SpanHoverCard/SpanUsageBreakdown.tsx

@@ -0,0 +1,38 @@
+import { formatCost, hasValue, SpanAiUsage } from '../utils/aiTraceUtils';
+import TokenBreakdown from '../TokenBreakdown/TokenBreakdown';
+import TraceTooltipRow from '../TraceTooltip/TraceTooltipRow';
+import TraceTooltipSection from '../TraceTooltip/TraceTooltipSection';
+
+interface SpanUsageBreakdownProps {
+	usage: SpanAiUsage;
+}
+
+function SpanUsageBreakdown({ usage }: SpanUsageBreakdownProps): JSX.Element {
+	const {
+		inputTokens,
+		outputTokens,
+		cacheReadTokens,
+		cacheCreationTokens,
+		reasoningTokens,
+		cost,
+	} = usage;
+
+	return (
+		<>
+			<TokenBreakdown
+				input={inputTokens}
+				output={outputTokens}
+				cacheRead={cacheReadTokens}
+				cacheWrite={cacheCreationTokens}
+				reasoning={reasoningTokens}
+			/>
+			{hasValue(cost) && (
+				<TraceTooltipSection>
+					<TraceTooltipRow isHeading label="Cost" value={formatCost(cost)} />
+				</TraceTooltipSection>
+			)}
+		</>
+	);
+}
+
+export default SpanUsageBreakdown;

frontend/src/pages/TraceDetailsV3/TokenBreakdown/TokenBreakdown.tsx

@@ -0,0 +1,81 @@
+import {
+	AiTokenCounts,
+	formatTokens,
+	getTokenLabel,
+	getTotalInputTokens,
+} from '../utils/aiTraceUtils';
+import TraceTooltipRow from '../TraceTooltip/TraceTooltipRow';
+import TraceTooltipSection from '../TraceTooltip/TraceTooltipSection';
+
+type TokenBreakdownProps = AiTokenCounts;
+
+function TokenBreakdown({
+	input,
+	output,
+	cacheRead,
+	cacheWrite,
+	reasoning,
+}: TokenBreakdownProps): JSX.Element {
+	const totalInput = getTotalInputTokens({ input, cacheRead, cacheWrite });
+
+	return (
+		<>
+			<TraceTooltipSection>
+				<TraceTooltipRow
+					label={getTokenLabel('Total Input', totalInput)}
+					value={formatTokens(totalInput)}
+					isHeading
+				/>
+				<TraceTooltipRow
+					label={getTokenLabel('Cache Read', cacheRead)}
+					value={formatTokens(cacheRead)}
+					isNested
+				/>
+				<TraceTooltipRow
+					label={getTokenLabel('Cache Write', cacheWrite)}
+					value={formatTokens(cacheWrite)}
+					isNested
+				/>
+				<TraceTooltipRow
+					label={getTokenLabel('Input', input)}
+					value={formatTokens(input)}
+					isNested
+				/>
+			</TraceTooltipSection>
+
+			<TraceTooltipSection>
+				<TraceTooltipRow
+					label={getTokenLabel('Reasoning', reasoning)}
+					value={formatTokens(reasoning)}
+					isHeading
+				/>
+			</TraceTooltipSection>
+
+			<TraceTooltipSection>
+				<TraceTooltipRow
+					label={getTokenLabel('Output', output)}
+					value={formatTokens(output)}
+					isHeading
+				/>
+			</TraceTooltipSection>
+
+			<TraceTooltipSection>
+				<TraceTooltipRow
+					label="Total Token Usage"
+					value={formatTokens(totalInput + (output ?? 0))}
+					isHeading
+				/>
+			</TraceTooltipSection>
+		</>
+	);
+}
+
+TokenBreakdown.defaultProps = {
+	input: undefined,
+	output: undefined,
+	cacheRead: undefined,
+	cacheWrite: undefined,
+	reasoning: undefined,
+};
+
+export default TokenBreakdown;

frontend/src/pages/TraceDetailsV3/TraceDetailsHeader/TraceDetailsHeader.tsx

@@ -186,6 +186,8 @@ function TraceDetailsHeader({
 								DATE_TIME_FORMATS.DD_MMM_YYYY_HH_MM_SS,
 							)}
 							statusCode={traceSummary.rootSpanStatusCode}
+							tokens={traceSummary.ai?.tokens}
+							cost={traceSummary.ai?.totalCost}
 						/>
 					) : (
 						<DetailsLoader />

frontend/src/pages/TraceDetailsV3/TraceDetailsHeader/__tests__/TraceDetailsHeader.test.tsx

@@ -1,6 +1,7 @@
 import { fireEvent, screen } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
 import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
+import type { SpantypesGettableTraceSummaryDTO } from 'api/generated/services/sigNoz.schemas';
 import ROUTES from 'constants/routes';
 import { render } from 'tests/test-utils';
 
@@ -176,9 +177,19 @@ describe('TraceDetailsHeader – trace metadata row', () => {
 		hasMissingSpans: false,
 		totalSpansCount: 42,
 		totalErrorSpansCount: 0,
+		ai: {
+			tokens: {
+				input: 12040,
+				output: 3110,
+				cacheRead: 0,
+				cacheWrite: 0,
+				reasoning: 0,
+			},
+			totalCost: 0.0421,
+		},
 	};
 
-	const mockSummary = (data?: typeof traceMetadata): void => {
+	const mockSummary = (data?: SpantypesGettableTraceSummaryDTO): void => {
 		jest.mocked(useTraceSummary).mockReturnValue({ data, isLoading: false });
 	};
 
@@ -201,6 +212,77 @@ describe('TraceDetailsHeader – trace metadata row', () => {
 		expect(screen.getByText(duration)).toBeInTheDocument();
 	});
 
+	it('renders AI tokens and cost when the summary has them', () => {
+		mockSummary(traceMetadata);
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.getByText('Tokens: 12,040 → 3,110')).toBeInTheDocument();
+		expect(screen.getByText('Cost — $ 0.0421')).toBeInTheDocument();
+	});
+
+	it('omits AI tokens and cost when the summary has no ai field', () => {
+		mockSummary({ ...traceMetadata, ai: undefined });
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.queryByText(/^Tokens:/)).not.toBeInTheDocument();
+		expect(screen.queryByText(/^Cost —/)).not.toBeInTheDocument();
+	});
+
+	it('shows AI tokens when input and output are both zero', () => {
+		mockSummary({
+			...traceMetadata,
+			ai: {
+				...traceMetadata.ai,
+				tokens: { ...traceMetadata.ai.tokens, input: 0, output: 0 },
+			},
+		} as SpantypesGettableTraceSummaryDTO);
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.getByText('Tokens: 0 → 0')).toBeInTheDocument();
+		expect(screen.getByText('Cost — $ 0.0421')).toBeInTheDocument();
+	});
+
+	it('shows zero for a missing input', () => {
+		mockSummary({
+			...traceMetadata,
+			ai: {
+				...traceMetadata.ai,
+				tokens: { ...traceMetadata.ai.tokens, input: undefined },
+			},
+		} as unknown as SpantypesGettableTraceSummaryDTO);
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.getByText('Tokens: 0 → 3,110')).toBeInTheDocument();
+	});
+
+	it('shows zero AI tokens when input and output are both missing', () => {
+		mockSummary({
+			...traceMetadata,
+			ai: {
+				...traceMetadata.ai,
+				tokens: {
+					...traceMetadata.ai.tokens,
+					input: undefined,
+					output: undefined,
+				},
+			},
+		} as unknown as SpantypesGettableTraceSummaryDTO);
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.getByText('Tokens: 0 → 0')).toBeInTheDocument();
+	});
+
+	it('omits the cost when the summary sends null', () => {
+		mockSummary({
+			...traceMetadata,
+			ai: { ...traceMetadata.ai, totalCost: null },
+		} as unknown as SpantypesGettableTraceSummaryDTO);
+		render(<TraceDetailsHeader {...baseProps} showTraceDetailsHeaderOptions />);
+
+		expect(screen.getByText('Tokens: 12,040 → 3,110')).toBeInTheDocument();
+		expect(screen.queryByText(/^Cost —/)).not.toBeInTheDocument();
+	});
+
 	it('is shown by default and can be hidden / shown again via the Trace options menu', async () => {
 		const user = userEvent.setup({ delay: null });
 		mockSummary(traceMetadata);

frontend/src/pages/TraceDetailsV3/TraceFlamegraph/FlamegraphCanvas.module.scss

@@ -30,13 +30,9 @@
 // lives here. Shares visual treatment with the waterfall hover popover
 // but is positioned `fixed` instead of anchored.
 .tooltip {
+	composes: tooltipSurface from '../shared.module.scss';
 	position: fixed;
 	z-index: 1000;
 	pointer-events: none;
-	background-color: var(--l1-background);
-	padding: 8px 12px;
-	border-radius: 4px;
-	box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3);
-	border: 1px solid var(--l2-border);
 	color: var(--l1-foreground);
 }

frontend/src/pages/TraceDetailsV3/TraceFlamegraph/FlamegraphCanvas.tsx

@@ -8,6 +8,7 @@ import { EventTooltipContent } from '../SpanHoverCard/EventTooltipContent';
 import { SpanTooltipContent } from '../SpanHoverCard/SpanHoverCard';
 import { DEFAULT_ROW_HEIGHT } from './constants';
 import { useCanvasSetup } from './hooks/useCanvasSetup';
+import { useCursorTooltipPosition } from './hooks/useCursorTooltipPosition';
 import { useFlamegraphCrosshair } from './hooks/useFlamegraphCrosshair';
 import { useFlamegraphDrag } from './hooks/useFlamegraphDrag';
 import { useFlamegraphDraw } from './hooks/useFlamegraphDraw';
@@ -200,15 +201,11 @@ function FlamegraphCanvas(props: FlamegraphCanvasProps): JSX.Element {
 		handleHoverMouseLeave,
 	]);
 
+	const tooltipRef = useCursorTooltipPosition<HTMLDivElement>(tooltipContent);
+
 	const tooltipElement = tooltipContent
 		? createPortal(
-				<div
-					className={styles.tooltip}
-					style={{
-						left: Math.min(tooltipContent.clientX + 15, window.innerWidth - 220),
-						top: Math.min(tooltipContent.clientY + 15, window.innerHeight - 100),
-					}}
-				>
+				<div ref={tooltipRef} className={styles.tooltip}>
 					{tooltipContent.event ? (
 						<EventTooltipContent
 							eventName={tooltipContent.event.name}

frontend/src/pages/TraceDetailsV3/TraceFlamegraph/hooks/useCursorTooltipPosition.ts

@@ -0,0 +1,36 @@
+import { RefObject, useLayoutEffect, useRef } from 'react';
+
+const CURSOR_OFFSET = 15;
+
+interface CursorAnchor {
+	clientX: number;
+	clientY: number;
+}
+
+function resolveAxis(cursor: number, size: number, viewport: number): number {
+	const after = cursor + CURSOR_OFFSET;
+	if (after + size <= viewport) {
+		return after;
+	}
+	return Math.max(0, cursor - CURSOR_OFFSET - size);
+}
+
+/** Places a fixed element beside the cursor, flipping sides when it would overflow the viewport. */
+export function useCursorTooltipPosition<T extends HTMLElement>(
+	anchor: CursorAnchor | null,
+): RefObject<T> {
+	const ref = useRef<T>(null);
+
+	useLayoutEffect(() => {
+		const el = ref.current;
+		if (!el || !anchor) {
+			return;
+		}
+		const left = resolveAxis(anchor.clientX, el.offsetWidth, window.innerWidth);
+		const top = resolveAxis(anchor.clientY, el.offsetHeight, window.innerHeight);
+		el.style.left = `${left}px`;
+		el.style.top = `${top}px`;
+	}, [anchor]);
+
+	return ref;
+}

frontend/src/pages/TraceDetailsV3/TraceTooltip/TraceTooltip.module.scss

@@ -0,0 +1,47 @@
+.section {
+	display: flex;
+	flex-direction: column;
+	gap: var(--spacing-2);
+	padding: var(--spacing-2) 0;
+	border-top: 1px solid;
+	border-image: linear-gradient(
+			90deg,
+			var(--l3-background-transparent),
+			var(--l3-border),
+			var(--l3-background-transparent)
+		)
+		1;
+}
+
+.section:first-child {
+	padding-top: 0;
+	border-top: none;
+}
+
+.section:last-child {
+	padding-bottom: 0;
+}
+
+.row {
+	display: grid;
+	grid-template-columns: auto auto;
+	justify-content: space-between;
+	column-gap: var(--spacing-2);
+	min-width: 0;
+}
+
+.nestedRow {
+	padding-left: var(--spacing-6);
+}
+
+.rowLabel {
+	min-width: 0;
+	overflow-wrap: anywhere;
+}
+
+.rowValue {
+	min-width: 0;
+	overflow-wrap: anywhere;
+	text-align: right;
+	font-variant-numeric: tabular-nums;
+}

frontend/src/pages/TraceDetailsV3/TraceTooltip/TraceTooltipRow.tsx

@@ -0,0 +1,42 @@
+import { Typography } from '@signozhq/ui/typography';
+import cx from 'classnames';
+
+import styles from './TraceTooltip.module.scss';
+
+interface TraceTooltipRowProps {
+	label: string;
+	value?: string;
+	isNested?: boolean;
+	isHeading?: boolean;
+	testId?: string;
+}
+
+function TraceTooltipRow({
+	label,
+	value,
+	isNested,
+	isHeading,
+	testId,
+}: TraceTooltipRowProps): JSX.Element {
+	return (
+		<div
+			className={cx(styles.row, isNested && styles.nestedRow)}
+			data-testid={testId}
+		>
+			<Typography.Text
+				size="small"
+				color={isHeading ? undefined : 'muted'}
+				className={styles.rowLabel}
+			>
+				{label}
+			</Typography.Text>
+			{value !== undefined && (
+				<Typography.Text size="small" className={styles.rowValue}>
+					{value}
+				</Typography.Text>
+			)}
+		</div>
+	);
+}
+
+export default TraceTooltipRow;

frontend/src/pages/TraceDetailsV3/TraceTooltip/TraceTooltipSection.tsx

@@ -0,0 +1,15 @@
+import { ReactNode } from 'react';
+
+import styles from './TraceTooltip.module.scss';
+
+interface TraceTooltipSectionProps {
+	children: ReactNode;
+}
+
+function TraceTooltipSection({
+	children,
+}: TraceTooltipSectionProps): JSX.Element {
+	return <div className={styles.section}>{children}</div>;
+}
+
+export default TraceTooltipSection;

frontend/src/pages/TraceDetailsV3/TraceWaterfall/TraceWaterfallStates/Success/Success.module.scss

@@ -203,8 +203,9 @@
 .statusCell {
 	display: flex;
 	align-items: center;
-	justify-content: center;
+	justify-content: flex-start;
 	cursor: pointer;
+	padding-left: var(--spacing-2);
 
 	--badge-border-radius: 2px;
 	--badge-padding: 3px 6px;
@@ -394,6 +395,11 @@
 	}
 }
 
+.aiSpanIcon {
+	flex-shrink: 0;
+	margin: 0 var(--spacing-2);
+}
+
 .treeLabel {
 	color: var(--l1-foreground);
 	font-family: 'Inter';

frontend/src/pages/TraceDetailsV3/TraceWaterfall/TraceWaterfallStates/Success/Success.tsx

@@ -20,7 +20,6 @@ import {
 } from '@tanstack/react-table';
 import { useVirtualizer, Virtualizer } from '@tanstack/react-virtual';
 import cx from 'classnames';
-import HttpStatusBadge from 'components/HttpStatusBadge/HttpStatusBadge';
 import TimelineV3 from 'components/TimelineV3/TimelineV3';
 import { convertTimeToRelevantUnit } from 'utils/traceUtils';
 import { useCopySpanLink } from 'hooks/trace/useCopySpanLink';
@@ -28,7 +27,13 @@ import { useIsDarkMode } from 'hooks/useDarkMode';
 import { useSafeNavigate } from 'hooks/useSafeNavigate';
 import useUrlQuery from 'hooks/useUrlQuery';
 import { colorToRgb } from 'lib/uPlotLib/utils/generateColor';
-import { ChevronDown, ChevronRight, Link, ListPlus } from '@signozhq/icons';
+import {
+	ChevronDown,
+	ChevronRight,
+	Link,
+	ListPlus,
+	Sparkle,
+} from '@signozhq/icons';
 import { useTraceStore } from 'pages/TraceDetailsV3/stores/traceStore';
 import { resolveSpanColor } from 'pages/TraceDetailsV3/utils';
 import { useBoundaryPagination } from 'pages/TraceDetailsV3/TraceWaterfall/hooks/useBoundaryPagination';
@@ -39,6 +44,8 @@ import { toFixed } from 'utils/toFixed';
 
 import { EventTooltipContent } from '../../../SpanHoverCard/EventTooltipContent';
 import { SpanHoverCard } from '../../../SpanHoverCard/SpanHoverCard';
+import SpanHintBadge from '../../../SpanHintBadge/SpanHintBadge';
+import { isAiSpan } from '../../../utils/aiTraceUtils';
 import AddSpanToFunnelModal from '../../AddSpanToFunnelModal/AddSpanToFunnelModal';
 import { IInterestedSpan } from '../../types';
 
@@ -318,14 +325,23 @@ const SpanOverview = memo(function SpanOverview({
 			</span>
 
 			{/* Colored service dot */}
-			<span
-				className={cx(styles.treeIcon, { [styles.hasError]: span.has_error })}
-				style={
-					{
-						'--service-dot-color': effectiveColor,
-					} as React.CSSProperties
-				}
-			/>
+			{isAiSpan(span) ? (
+				<Sparkle
+					size={10}
+					color={effectiveColor}
+					fill={effectiveColor}
+					className={styles.aiSpanIcon}
+				/>
+			) : (
+				<span
+					className={cx(styles.treeIcon, { [styles.hasError]: span.has_error })}
+					style={
+						{
+							'--service-dot-color': effectiveColor,
+						} as React.CSSProperties
+					}
+				/>
+			)}
 
 			{/* Span name + service name */}
 			<span className={styles.treeLabel}>
@@ -944,9 +960,7 @@ function Success(props: ISuccessProps): JSX.Element {
 									onMouseLeave={(): void => applyHoverClass(null)}
 									onClick={(): void => handleSpanClick(span)}
 								>
-									{span.response_status_code && (
-										<HttpStatusBadge statusCode={span.response_status_code} />
-									)}
+									<SpanHintBadge span={span} />
 								</div>
 							);
 						})}

frontend/src/pages/TraceDetailsV3/shared.module.scss

@@ -0,0 +1,35 @@
+.tooltipBody {
+	display: flex;
+	flex-direction: column;
+	letter-spacing: var(--paragraph-small-400-letter-spacing);
+	color: var(--l1-foreground-hover);
+}
+
+.tooltipTitle {
+	font-family: 'SF Mono', 'Geist Mono', monospace;
+	word-break: break-all;
+}
+
+.tooltipSurface {
+	background-color: color-mix(in srgb, var(--l3-background) 60%, transparent);
+	border: 1px solid var(--l2-border);
+	border-radius: var(--radius-2);
+	padding: var(--spacing-4);
+	box-shadow: 4px 10px 16px 2px rgba(0, 0, 0, 0.2);
+	backdrop-filter: blur(20px);
+}
+
+.tooltipContent {
+	--tooltip-background: color-mix(
+		in srgb,
+		var(--l3-background) 60%,
+		transparent
+	);
+	--tooltip-border-color: var(--l2-border);
+	--tooltip-border-radius: var(--radius-2);
+	--tooltip-padding: var(--spacing-4);
+	--tooltip-box-shadow: 4px 10px 16px 2px rgba(0, 0, 0, 0.2);
+	/* Above the docked span details FloatingPanel (z-index 999). */
+	--tooltip-z-index: 1000;
+	backdrop-filter: blur(20px);
+}

frontend/src/pages/TraceDetailsV3/stories/TraceDetailsV3.stories.mocks.tsx

@@ -46,6 +46,8 @@ import {
 	traceDetailFieldValues,
 	traceFlamegraphResponse,
 	traceSummaryResponse,
+	TRACE_SCENARIOS,
+	type TraceScenario,
 	traceWaterfallResponse,
 } from './__story_mockdata__/traceDetails';
 
@@ -103,6 +105,13 @@ const spanLogScopeOf = (body: QueryRangeRequestV5): SpanLogScope => {
 
 export const traceDetailsMocks = defineStoryMocks({
 	controls: {
+		scenario: choiceControl<TraceScenario>('Scenario', {
+			group: TRACE,
+			description:
+				'The spans the trace is built from: a checkout, one span per HTTP status badge, or an agent run with LLM, tool and agent spans and the AI totals in the header.',
+			options: TRACE_SCENARIOS,
+			value: 'checkout',
+		}),
 		spans: countControl('Spans', {
 			group: TRACE,
 			description:
@@ -151,6 +160,7 @@ export const traceDetailsMocks = defineStoryMocks({
 	},
 	handlers: (values, response) => {
 		const trace = {
+			scenario: values.scenario,
 			spans: values.spans,
 			errors: values.errors,
 			traceStart: traceStartOf(),
@@ -288,7 +298,7 @@ export const traceDetailsMocks = defineStoryMocks({
 	},
 	config: (values) => ({
 		route: values.spanPanel
-			? `/trace/${STORY_TRACE_ID}?spanId=${spanIdAt(focusSpanIndex(values.spans))}`
+			? `/trace/${STORY_TRACE_ID}?spanId=${spanIdAt(focusSpanIndex(values.scenario, values.spans))}`
 			: `/trace/${STORY_TRACE_ID}`,
 	}),
 	effect: (values) => {

frontend/src/pages/TraceDetailsV3/stories/TraceDetailsV3.stories.tsx

@@ -13,7 +13,12 @@ import {
 import { storyMocks } from '@/storybook/controls/defineStoryMocks';
 import type { PageStoryArgs } from '@/storybook/runtime/resolveStory';
 
-import { STORY_TRACE_ID } from './__story_mockdata__/traceDetails';
+import {
+	focusSpanIndex,
+	scenarioSpanCount,
+	spanIdAt,
+	STORY_TRACE_ID,
+} from './__story_mockdata__/traceDetails';
 import {
 	openRowMenu,
 	startDownload,
@@ -96,6 +101,40 @@ export const BottomStripLoading: Story = {
 	args: { bottomStrip: true, dataState: 'loading' },
 };
 
+/**
+ * One span per HTTP status badge the waterfall draws: informational, success,
+ * redirect, client and server errors, and a span with no status code and so no
+ * badge.
+ */
+export const StatusCodes: Story = {
+	args: {
+		scenario: 'status-codes',
+		spans: scenarioSpanCount('status-codes'),
+		spanPanel: false,
+	},
+};
+
+const AI_SPANS = scenarioSpanCount('ai');
+
+/**
+ * An agent run: LLM, tool and agent badges with sparkle icons in the waterfall,
+ * token and cost totals in the header, the span panel on an LLM call with its
+ * tokens and cost, and that span's hover card open on its usage breakdown.
+ */
+export const AiTrace: Story = {
+	args: { scenario: 'ai', spans: AI_SPANS },
+	play: async ({ canvasElement }): Promise<void> => {
+		const row = await within(canvasElement).findByTestId(
+			`cell-0-${spanIdAt(focusSpanIndex('ai', AI_SPANS))}`,
+			undefined,
+			untilLoaded,
+		);
+
+		await fireEvent.mouseOver(row);
+		await screen.findByText('Usage Breakdown', undefined, untilLoaded);
+	},
+};
+
 /**
  * Turns on Highlight errors, which writes `has_error = true` into the filter and
  * expands the bar to show it.

frontend/src/pages/TraceDetailsV3/stories/__story_mockdata__/traceDetails.ts

@@ -23,6 +23,10 @@ interface SpanTemplate {
 	name: string;
 	kind: string;
 	failing?: boolean;
+	/** The span the panel opens on when nothing fails. */
+	focus?: boolean;
+	/** `response_status_code`; failing spans default to 503, the rest to 200. */
+	statusCode?: string;
 	attributes: Record<string, string | number | boolean>;
 }
 
@@ -123,6 +127,145 @@ const SPAN_TEMPLATES: SpanTemplate[] = [
 	},
 ];
 
+/** One span per badge colour, plus a span with no status code and so no badge. */
+const STATUS_CODE_TEMPLATES: SpanTemplate[] = [
+	['200', 'GET /status/ok'],
+	['101', 'GET /ws/upgrade'],
+	['201', 'POST /status/created'],
+	['204', 'DELETE /status/no-content'],
+	['301', 'GET /status/moved'],
+	['304', 'GET /status/not-modified'],
+	['400', 'POST /status/bad-request'],
+	['401', 'GET /status/unauthorized'],
+	['404', 'GET /status/not-found'],
+	['429', 'GET /status/rate-limited'],
+	['500', 'GET /status/internal-error'],
+	['502', 'GET /status/bad-gateway'],
+	['503', 'GET /status/unavailable'],
+	['504', 'GET /status/timeout'],
+	['', 'internal.compute'],
+].map(
+	([statusCode, name]): SpanTemplate => ({
+		service: 'status-api',
+		name,
+		kind: statusCode ? 'Server' : 'Internal',
+		failing: Number(statusCode) >= 500,
+		statusCode,
+		attributes: statusCode
+			? {
+					'http.method': name.split(' ')[0],
+					'http.status_code': Number(statusCode),
+				}
+			: {},
+	}),
+);
+
+/**
+ * An agent run: agent, LLM and tool spans, each badged by kind. The LLM spans
+ * cover cache counted inside input (OpenAI), cache counted beside it
+ * (Anthropic), reasoning, and an embedding with input only.
+ */
+const AI_TEMPLATES: SpanTemplate[] = [
+	{
+		service: 'support-agent',
+		name: 'invoke_agent support-agent',
+		kind: 'Internal',
+		attributes: { 'gen_ai.agent.name': 'support-agent' },
+	},
+	{
+		service: 'support-agent',
+		name: 'chat gpt-4o',
+		kind: 'Client',
+		attributes: {
+			'gen_ai.request.model': 'gpt-4o-2024-08-06',
+			'gen_ai.usage.input_tokens': 1840,
+			'gen_ai.usage.output_tokens': 212,
+			'gen_ai.usage.cache_read.input_tokens': 1024,
+			'gen_ai.usage.cache_creation.input_tokens': 0,
+			'signoz.gen_ai.usage.tokens.cost': 0.00566,
+		},
+	},
+	{
+		service: 'support-agent',
+		name: 'execute_tool search_docs',
+		kind: 'Internal',
+		attributes: { 'gen_ai.tool.name': 'search_docs' },
+	},
+	{
+		service: 'support-agent',
+		name: 'chat claude-sonnet-4',
+		kind: 'Client',
+		focus: true,
+		attributes: {
+			'gen_ai.request.model': 'claude-sonnet-4-20250514',
+			'gen_ai.usage.input_tokens': 86,
+			'gen_ai.usage.output_tokens': 640,
+			'gen_ai.usage.cache_read.input_tokens': 4200,
+			'gen_ai.usage.cache_creation.input_tokens': 1300,
+			'signoz.gen_ai.usage.tokens.cost': 0.01853,
+		},
+	},
+	{
+		service: 'docs-api',
+		name: 'GET /docs/search',
+		kind: 'Server',
+		attributes: {
+			'http.method': 'GET',
+			'http.route': '/docs/search',
+			'http.status_code': 200,
+		},
+	},
+	{
+		service: 'support-agent',
+		name: 'execute_tool fetch_ticket',
+		kind: 'Internal',
+		failing: true,
+		attributes: { 'gen_ai.tool.name': 'fetch_ticket' },
+	},
+	{
+		service: 'support-agent',
+		name: 'invoke_agent triage-agent',
+		kind: 'Internal',
+		attributes: { 'gen_ai.agent.name': 'triage-agent' },
+	},
+	{
+		service: 'support-agent',
+		name: 'embeddings text-embedding-3-small',
+		kind: 'Client',
+		attributes: {
+			'gen_ai.request.model': 'text-embedding-3-small',
+			'gen_ai.usage.input_tokens': 312,
+			'signoz.gen_ai.usage.tokens.cost': 0.00000624,
+		},
+	},
+	{
+		service: 'support-agent',
+		name: 'chat o3-mini',
+		kind: 'Client',
+		attributes: {
+			'gen_ai.request.model': 'o3-mini-2025-01-31',
+			'gen_ai.usage.input_tokens': 920,
+			'gen_ai.usage.output_tokens': 1480,
+			'gen_ai.usage.reasoning.output_tokens': 1152,
+			'signoz.gen_ai.usage.tokens.cost': 0.007524,
+		},
+	},
+];
+
+export const TRACE_SCENARIOS = ['checkout', 'status-codes', 'ai'] as const;
+
+export type TraceScenario = (typeof TRACE_SCENARIOS)[number];
+
+const SCENARIO_TEMPLATES: Record<TraceScenario, SpanTemplate[]> = {
+	checkout: SPAN_TEMPLATES,
+	'status-codes': STATUS_CODE_TEMPLATES,
+	ai: AI_TEMPLATES,
+};
+
+/** Spans it takes for the scenario to show every template once. */
+export const scenarioSpanCount = (scenario: TraceScenario): number =>
+	SCENARIO_TEMPLATES[scenario].length;
+
 const TRACE_SERVICES = [
 	...new Set(SPAN_TEMPLATES.map(({ service }) => service)),
 ];
@@ -146,6 +289,7 @@ interface BuiltSpan {
 }
 
 interface TraceOptions {
+	scenario: TraceScenario;
 	spans: number;
 	errors: boolean;
 	/** Epoch milliseconds the root span starts at. */
@@ -156,24 +300,33 @@ export const spanIdAt = (index: number): string =>
 	`b17e${index.toString(16).padStart(12, '0')}`;
 
 /**
- * The span the story opens the panel on: the first failing one, so its events
- * and error styling are on screen, and the root when nothing fails.
+ * The span the story opens the panel on: the scenario's focus span, else the
+ * first failing one, so its events and error styling are on screen, and the
+ * root when neither is in the trace.
  */
-export const focusSpanIndex = (spans: number): number => {
-	const failing = SPAN_TEMPLATES.findIndex(({ failing }) => failing);
+export const focusSpanIndex = (
+	scenario: TraceScenario,
+	spans: number,
+): number => {
+	const templates = SCENARIO_TEMPLATES[scenario];
+	const focus = templates.findIndex((template) => template.focus);
+	const failing = templates.findIndex((template) => template.failing);
+	const index = focus >= 0 ? focus : failing;
 
-	return failing >= 0 && failing < spans ? failing : 0;
+	return index >= 0 && index < spans ? index : 0;
 };
 
 const buildSpans = ({
+	scenario,
 	spans,
 	errors,
 	traceStart,
 }: TraceOptions): BuiltSpan[] => {
 	const built: BuiltSpan[] = [];
+	const templates = SCENARIO_TEMPLATES[scenario];
 
 	for (let index = 0; index < spans; index += 1) {
-		const template = SPAN_TEMPLATES[index % SPAN_TEMPLATES.length];
+		const template = templates[index % templates.length];
 		const parent = index === 0 ? undefined : built[parentIndexOf(index)];
 		// Children fit inside their parent's window, second child after the first,
 		// so no span outlives its parent and no two siblings overlap.
@@ -257,7 +410,8 @@ const waterfallSpan = (span: BuiltSpan): SpantypesWaterfallSpanDTO => ({
 	status_code: span.hasError ? 2 : 1,
 	status_code_string: span.hasError ? 'Error' : 'Ok',
 	status_message: span.hasError ? 'upstream unavailable' : '',
-	response_status_code: span.hasError ? '503' : '200',
+	response_status_code:
+		span.template.statusCode ?? (span.hasError ? '503' : '200'),
 	has_children: span.hasChildren,
 	sub_tree_node_count: span.subTreeNodeCount,
 	level: span.level,
@@ -304,6 +458,36 @@ export const traceWaterfallResponse = (
 	};
 };
 
+const numberAttribute = (span: BuiltSpan, key: string): number =>
+	Number(span.template.attributes[key] ?? 0);
+
+/** The trace's AI totals, summed over its spans the way the backend does. */
+const traceAiSummary = (
+	spans: BuiltSpan[],
+): GetTraceSummary200['data']['ai'] => {
+	const aiSpans = spans.filter(({ template }) =>
+		Object.keys(template.attributes).some((key) => key.includes('gen_ai.')),
+	);
+
+	if (aiSpans.length === 0) {
+		return undefined;
+	}
+
+	const sum = (key: string): number =>
+		aiSpans.reduce((total, span) => total + numberAttribute(span, key), 0);
+
+	return {
+		tokens: {
+			input: sum('gen_ai.usage.input_tokens'),
+			output: sum('gen_ai.usage.output_tokens'),
+			cacheRead: sum('gen_ai.usage.cache_read.input_tokens'),
+			cacheWrite: sum('gen_ai.usage.cache_creation.input_tokens'),
+			reasoning: sum('gen_ai.usage.reasoning.output_tokens'),
+		},
+		totalCost: sum('signoz.gen_ai.usage.tokens.cost'),
+	};
+};
+
 export const traceSummaryResponse = (
 	options: TraceOptions & { missingSpans: boolean },
 ): GetTraceSummary200 => {
@@ -321,6 +505,7 @@ export const traceSummaryResponse = (
 			totalSpansCount: spans.length,
 			totalErrorSpansCount: spans.filter(({ hasError }) => hasError).length,
 			hasMissingSpans: options.missingSpans,
+			ai: traceAiSummary(spans),
 		},
 	};
 };
@@ -472,7 +657,7 @@ export const spanLogRows = (
 ): RawRow[] => {
 	const step = ROOT_DURATION_MS / Math.max(count, 1);
 	const spans = buildSpans(options);
-	const focus = spans[focusSpanIndex(options.spans)];
+	const focus = spans[focusSpanIndex(options.scenario, options.spans)];
 
 	return Array.from({ length: count }, (_, index) => {
 		const at = options.traceStart + SCOPE_OFFSET_MS[scope] + index * step;

frontend/src/pages/TraceDetailsV3/utils/__tests__/aiTraceUtils.test.ts

@@ -0,0 +1,51 @@
+import { SpanV3 } from 'types/api/trace/getTraceV3';
+
+import {
+	AiSpanKind,
+	getAiSpanKind,
+	getTotalInputTokens,
+	isAiSpan,
+} from '../aiTraceUtils';
+
+const spanWith = (attributes: Record<string, string | number>): SpanV3 =>
+	({ attributes, resource: {} }) as unknown as SpanV3;
+
+describe('getAiSpanKind', () => {
+	it.each<[Record<string, string | number>, AiSpanKind]>([
+		[{ 'gen_ai.request.model': 'gpt-4o' }, AiSpanKind.Llm],
+		[{ 'gen_ai.usage.input_tokens': 0 }, AiSpanKind.Llm],
+		[{ 'gen_ai.usage.reasoning.output_tokens': 12 }, AiSpanKind.Llm],
+		[{ 'gen_ai.tool.name': 'search' }, AiSpanKind.Tool],
+		[{ 'gen_ai.agent.name': 'planner' }, AiSpanKind.Agent],
+		[
+			{ 'gen_ai.tool.name': 'search', 'gen_ai.request.model': 'gpt-4o' },
+			AiSpanKind.Tool,
+		],
+		[
+			{ 'gen_ai.agent.name': 'planner', 'gen_ai.usage.input_tokens': 10 },
+			AiSpanKind.Agent,
+		],
+	])('classifies %p as %s', (attributes, kind) => {
+		expect(getAiSpanKind(spanWith(attributes))).toBe(kind);
+	});
+
+	it('returns undefined for non-AI spans and empty values', () => {
+		expect(getAiSpanKind(spanWith({ 'http.method': 'GET' }))).toBeUndefined();
+		expect(getAiSpanKind(spanWith({ 'gen_ai.tool.name': '' }))).toBeUndefined();
+		expect(isAiSpan(spanWith({}))).toBe(false);
+	});
+});
+
+describe('getTotalInputTokens', () => {
+	it.each([
+		// Cache reported inside input (OpenAI): input already includes it.
+		[{ input: 1000, cacheRead: 600, cacheWrite: 100 }, 1000],
+		// Cache reported separately (Anthropic): add it.
+		[{ input: 50, cacheRead: 600, cacheWrite: 100 }, 750],
+		[{ input: 71, cacheRead: 0, cacheWrite: 0 }, 71],
+		[{ input: 10 }, 10],
+		[{ cacheRead: 600 }, 600],
+	])('%p → %p', (tokens, expected) => {
+		expect(getTotalInputTokens(tokens)).toBe(expected);
+	});
+});

frontend/src/pages/TraceDetailsV3/utils/aiTraceUtils.ts

@@ -0,0 +1,176 @@
+import { SpanV3 } from 'types/api/trace/getTraceV3';
+
+export const GEN_AI_KEYS = {
+	requestModel: 'gen_ai.request.model',
+	toolName: 'gen_ai.tool.name',
+	agentName: 'gen_ai.agent.name',
+	inputTokens: 'gen_ai.usage.input_tokens',
+	outputTokens: 'gen_ai.usage.output_tokens',
+	cacheReadTokens: 'gen_ai.usage.cache_read.input_tokens',
+	cacheCreationTokens: 'gen_ai.usage.cache_creation.input_tokens',
+	reasoningTokens: 'gen_ai.usage.reasoning.output_tokens',
+	cost: 'signoz.gen_ai.usage.tokens.cost',
+} as const;
+
+export enum AiSpanKind {
+	Llm = 'llm',
+	Tool = 'tool',
+	Agent = 'agent',
+}
+
+const LLM_KEYS = [
+	GEN_AI_KEYS.requestModel,
+	GEN_AI_KEYS.inputTokens,
+	GEN_AI_KEYS.outputTokens,
+	GEN_AI_KEYS.cacheReadTokens,
+	GEN_AI_KEYS.cacheCreationTokens,
+	GEN_AI_KEYS.reasoningTokens,
+];
+
+/** Keeps numeric 0, which `getSpanAttribute`'s `||` chain drops. */
+export function getGenAiValue(
+	span: SpanV3,
+	key: string,
+): string | number | boolean | undefined {
+	const value = span.resource?.[key] ?? span.attributes?.[key];
+	return value === '' ? undefined : value;
+}
+
+function hasAttribute(span: SpanV3, key: string): boolean {
+	return getGenAiValue(span, key) !== undefined;
+}
+
+/** Tool and agent spans may also carry model or usage keys, so they win over LLM. */
+export function getAiSpanKind(span: SpanV3): AiSpanKind | undefined {
+	if (hasAttribute(span, GEN_AI_KEYS.toolName)) {
+		return AiSpanKind.Tool;
+	}
+	if (hasAttribute(span, GEN_AI_KEYS.agentName)) {
+		return AiSpanKind.Agent;
+	}
+	if (LLM_KEYS.some((key) => hasAttribute(span, key))) {
+		return AiSpanKind.Llm;
+	}
+	return undefined;
+}
+
+export function isAiSpan(span: SpanV3): boolean {
+	return getAiSpanKind(span) !== undefined;
+}
+
+/** Token counts; `null` or missing means the backend did not send the value. */
+export interface AiTokenCounts {
+	input?: number;
+	output?: number;
+	cacheRead?: number;
+	cacheWrite?: number;
+	reasoning?: number;
+}
+
+export function hasValue(value: number | null | undefined): value is number {
+	return value !== undefined && value !== null;
+}
+
+/**
+ * Input including cache read/write, counted once. Providers that report cache
+ * inside `input` (OpenAI) have `input >= cache`; those that report it
+ * separately (Anthropic) usually have `input < cache`.
+ */
+export function getTotalInputTokens({
+	input,
+	cacheRead,
+	cacheWrite,
+}: AiTokenCounts): number {
+	const inputValue = input ?? 0;
+	const cache = (cacheRead ?? 0) + (cacheWrite ?? 0);
+	return inputValue < cache ? inputValue + cache : inputValue;
+}
+
+/** `Input Token` for a count of 1, `Input Tokens` otherwise. */
+export function getTokenLabel(
+	prefix: string,
+	count: number | null | undefined,
+): string {
+	return `${prefix} ${count === 1 ? 'Token' : 'Tokens'}`;
+}
+
+/** Missing values render as `0`. */
+export function formatTokens(value: number | null | undefined): string {
+	return hasValue(value) ? value.toLocaleString('en-US') : '0';
+}
+
+export function formatCost(value: number): string {
+	return `$ ${value.toLocaleString('en-US', { maximumFractionDigits: 10 })}`;
+}
+
+export interface SpanAiUsage {
+	inputTokens?: number;
+	outputTokens?: number;
+	cacheReadTokens?: number;
+	cacheCreationTokens?: number;
+	reasoningTokens?: number;
+	cost?: number;
+}
+
+export interface SpanAiDetails {
+	model?: string;
+	toolName?: string;
+	agentName?: string;
+	usage?: SpanAiUsage;
+}
+
+function getGenAiAttributeAsString(
+	span: SpanV3,
+	key: string,
+): string | undefined {
+	const value = getGenAiValue(span, key);
+	return value === undefined ? undefined : String(value);
+}
+
+function getGenAiAttributeAsNumber(
+	span: SpanV3,
+	key: string,
+): number | undefined {
+	const value = getGenAiValue(span, key);
+	if (value === undefined) {
+		return undefined;
+	}
+	const parsed = Number(value);
+	return Number.isFinite(parsed) ? parsed : undefined;
+}
+
+export function getAIUsageDetails(span: SpanV3): SpanAiDetails | undefined {
+	const usage: SpanAiUsage = {
+		inputTokens: getGenAiAttributeAsNumber(span, GEN_AI_KEYS.inputTokens),
+		outputTokens: getGenAiAttributeAsNumber(span, GEN_AI_KEYS.outputTokens),
+		cacheReadTokens: getGenAiAttributeAsNumber(span, GEN_AI_KEYS.cacheReadTokens),
+		cacheCreationTokens: getGenAiAttributeAsNumber(
+			span,
+			GEN_AI_KEYS.cacheCreationTokens,
+		),
+		reasoningTokens: getGenAiAttributeAsNumber(span, GEN_AI_KEYS.reasoningTokens),
+		cost: getGenAiAttributeAsNumber(span, GEN_AI_KEYS.cost),
+	};
+	const hasUsage = Object.values(usage).some((value) => value !== undefined);
+
+	const details: SpanAiDetails = {
+		model: getGenAiAttributeAsString(span, GEN_AI_KEYS.requestModel),
+		toolName: getGenAiAttributeAsString(span, GEN_AI_KEYS.toolName),
+		agentName: getGenAiAttributeAsString(span, GEN_AI_KEYS.agentName),
+		usage: hasUsage ? usage : undefined,
+	};
+
+	return details.model || details.toolName || details.agentName || hasUsage
+		? details
+		: undefined;
+}
+
+export function convertToAiTokens(usage: SpanAiUsage): AiTokenCounts {
+	return {
+		input: usage.inputTokens,
+		output: usage.outputTokens,
+		cacheRead: usage.cacheReadTokens,
+		cacheWrite: usage.cacheCreationTokens,
+		reasoning: usage.reasoningTokens,
+	};
+}

pkg/apiserver/signozapiserver/statsreporter.go

@@ -4,13 +4,12 @@ import (
 	"net/http"
 
 	"github.com/SigNoz/signoz/pkg/http/handler"
-	"github.com/SigNoz/signoz/pkg/types"
 	"github.com/gorilla/mux"
 )
 
 func (provider *provider) addStatsReporterRoutes(router *mux.Router) error {
 	if err := router.Handle("/api/v1/stats", handler.New(
-		provider.authzMiddleware.ViewAccess(provider.statsHandler.Get),
+		provider.authzMiddleware.OpenAccess(provider.statsHandler.Get),
 		handler.OpenAPIDef{
 			ID:                  "GetStats",
 			Tags:                []string{"stats"},
@@ -23,7 +22,7 @@ func (provider *provider) addStatsReporterRoutes(router *mux.Router) error {
 			SuccessStatusCode:   http.StatusOK,
 			ErrorStatusCodes:    []int{},
 			Deprecated:          false,
-			SecuritySchemes:     newSecuritySchemes(types.RoleViewer),
+			SecuritySchemes:     newScopedSecuritySchemes(nil),
 		},
 	)).Methods(http.MethodGet).GetError(); err != nil {
 		return err

pkg/types/aiobservabilitytypes/keys.go

@@ -17,8 +17,10 @@ const (
 	GenAIUsageCacheCreationInputTokens = "gen_ai.usage.cache_creation.input_tokens"
 	GenAIUsageReasoningOutputTokens    = "gen_ai.usage.reasoning.output_tokens"
 
-	GenAIInputMessages  = "gen_ai.input.messages"
-	GenAIOutputMessages = "gen_ai.output.messages"
+	GenAIInputMessages         = "gen_ai.input.messages"
+	GenAIOutputMessages        = "gen_ai.output.messages"
+	GenAISystemInstructions    = "gen_ai.system_instructions"
+	GenAIResponseFinishReasons = "gen_ai.response.finish_reasons"
 
 	GenAIToolCallID        = "gen_ai.tool.call.id"
 	GenAIToolCallArguments = "gen_ai.tool.call.arguments"

pkg/types/aiobservabilitytypes/message.go

@@ -1,5 +1,6 @@
 // The message types mirror the OpenTelemetry GenAI message schemas, one type per schema definition:
-// open-telemetry/semantic-conventions-genai@06ec68e, model/gen-ai/gen-ai-{input,output}-messages.json.
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/gen-ai-input-messages.json
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/gen-ai-output-messages.json
 // Keys the schema does not name are dropped; the raw attribute on the span still has them.
 
 package aiobservabilitytypes

pkg/types/aiobservabilitytypes/message_format.go

@@ -4,25 +4,45 @@ import (
 	"encoding/json"
 	"fmt"
 	"regexp"
+	"sort"
+	"strconv"
 	"strings"
 
 	"github.com/SigNoz/signoz/pkg/valuer"
 )
 
 const (
-	FormatterSemconv    = "semconv"
-	FormatterOpenAIChat = "openai.chat"
-	FormatterText       = "text"
-	FormatterGeneric    = "generic"
+	FormatterSemconv         = "semconv"
+	FormatterOpenInference   = "openinference"
+	FormatterLangChain       = "langchain"
+	FormatterOpenRouter      = "openrouter"
+	FormatterAnthropic       = "anthropic"
+	FormatterGemini          = "gemini"
+	FormatterOpenAIChat      = "openai.chat"
+	FormatterOpenAIResponses = "openai.responses"
+	FormatterText            = "text"
+	FormatterGeneric         = "generic"
 )
 
 var dataURL = regexp.MustCompile(`^data:([^;,]+);base64,(.+)$`)
 
-// Provider spellings of the finish reasons; the enum values themselves pass through.
+// Provider spellings of the finish reasons; the enum values themselves pass through. tool_calls and
+// function_call are OpenAI's, completed, failed, cancelled, incomplete and max_output_tokens the Responses
+// status and incomplete reason, end_turn, stop_sequence, max_tokens, tool_use, refusal and
+// model_context_window_exceeded Anthropic's, the upper-case ones Gemini's, tool-calls and content-filter
+// the Vercel AI SDK's:
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L44782
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69691
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/stop_reason.py#L5
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L510
+// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-finish-reason.ts
 var finishReasonAliases = map[string]FinishReason{
-	"tool_calls": FinishReasonToolCall, "function_call": FinishReasonToolCall,
-	"end_turn": FinishReasonStop, "stop_sequence": FinishReasonStop,
-	"max_tokens": FinishReasonLength,
+	"tool_calls": FinishReasonToolCall, "function_call": FinishReasonToolCall, "tool-calls": FinishReasonToolCall, "tool_use": FinishReasonToolCall,
+	"end_turn": FinishReasonStop, "stop_sequence": FinishReasonStop, "completed": FinishReasonStop,
+	"max_tokens": FinishReasonLength, "max_output_tokens": FinishReasonLength, "model_context_window_exceeded": FinishReasonLength,
+	"content-filter": FinishReasonContentFilter, "refusal": FinishReasonContentFilter, "safety": FinishReasonContentFilter, "recitation": FinishReasonContentFilter,
+	"blocklist": FinishReasonContentFilter, "prohibited_content": FinishReasonContentFilter, "spii": FinishReasonContentFilter, "image_safety": FinishReasonContentFilter,
+	"failed": FinishReasonError, "cancelled": FinishReasonError, "incomplete": FinishReasonError, "malformed_function_call": FinishReasonError,
 }
 
 // FormattedMessages is the OTel GenAI view of one span's messages; Warnings holds what the converter could
@@ -35,19 +55,42 @@ type FormattedMessages struct {
 
 // FormatMessages builds the OTel GenAI view of a span's messages from the formats SDKs write. attributes
 // is the span's JSON attribute document, nested by key segment, so an object value is whole. Each
-// message value is a JSON string or a structured value. A tool call and its result take precedence over
-// messages beside them.
+// message value is a JSON string or a structured value. An embeddings call has no messages, whatever the
+// mapper moved onto the message attributes. OpenInference's flattened messages come first,
+// then a tool call and its result, then the AI SDK's ai.* keys, then the message attributes, then
+// openllmetry's flattened prompts and completions. gen_ai.system_instructions leads the
+// input and gen_ai.response.finish_reasons overrides the finish reasons the converter found, whichever
+// converter ran.
 func FormatMessages(attributes map[string]any) FormattedMessages {
 	f := &formatting{attributes: attributes, out: FormattedMessages{Input: InputMessages{}, Output: OutputMessages{}, Warnings: []string{}}}
+	if isEmbeddings(attributes) {
+		return f.out
+	}
 	switch {
+	case f.openInference():
 	case attribute(f.attributes, GenAIToolCallArguments) != nil || attribute(f.attributes, GenAIToolCallResult) != nil:
 		f.tool()
+	case f.vercel():
 	case attribute(f.attributes, GenAIInputMessages) != nil || attribute(f.attributes, GenAIOutputMessages) != nil:
 		f.messages()
+	case f.traceloop():
 	}
+	f.prependSystemInstructions()
+	f.fillFinishReasons()
 	return f.out
 }
 
+// isEmbeddings matches the operation name as the semconv embeddings, openllmetry's llm.request.type
+// embedding and OpenInference's span kind EMBEDDING, copied by the mapper, or the AI SDK's ai.embed* ids.
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/registry.yaml#L658
+func isEmbeddings(attributes map[string]any) bool {
+	switch strings.ToLower(stringOf(attribute(attributes, GenAIOperationName))) {
+	case "embeddings", "embedding":
+		return true
+	}
+	return strings.HasPrefix(stringOf(attribute(attributes, vercelOperation)), "ai.embed")
+}
+
 type formatting struct {
 	attributes map[string]any
 	out        FormattedMessages
@@ -63,32 +106,104 @@ func (f *formatting) messages() {
 	if input := attribute(f.attributes, GenAIInputMessages); input != nil {
 		var msgs OutputMessages
 		msgs, inputLabel = f.side(decode(input), RoleUser)
-		for _, m := range msgs {
-			f.out.Input = append(f.out.Input, ChatMessage{Role: m.Role, Parts: m.Parts, Name: m.Name})
-		}
+		f.out.Input = inputMessages(msgs)
 	}
 	if output := attribute(f.attributes, GenAIOutputMessages); output != nil {
 		f.out.Output, outputLabel = f.side(decode(output), RoleAssistant)
+		f.completionToolCalls()
 	}
-	if inputLabel != "" && outputLabel != "" && outputLabel != inputLabel {
+	f.warnMixedFormats(inputLabel, outputLabel)
+}
+
+// OpenRouter's answer object or an Anthropic response beside the OpenAI chat request it answers is one
+// format, not a mix: an Anthropic request without system and without Anthropic-only blocks reads as an
+// OpenAI chat request.
+func (f *formatting) warnMixedFormats(inputLabel, outputLabel string) {
+	switch {
+	case inputLabel == "" || outputLabel == "" || outputLabel == inputLabel:
+	case inputLabel == FormatterOpenAIChat && (outputLabel == FormatterOpenRouter || outputLabel == FormatterAnthropic):
+	default:
 		f.warn("output formatted as %s, input as %s", outputLabel, inputLabel)
 	}
 }
 
+// prependSystemInstructions adds gen_ai.system_instructions, a list of parts, as a leading system message,
+// unless the input already starts with a system message of the same text: the OpenInference strands
+// processor keeps the attribute and inserts it as message 0 as well. A span without messages, such as
+// create_agent, stays without them.
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/gen-ai-system-instructions.json
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L152
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L293
+func (f *formatting) prependSystemInstructions() {
+	raw := attribute(f.attributes, GenAISystemInstructions)
+	if raw == nil || (len(f.out.Input) == 0 && len(f.out.Output) == 0) {
+		return
+	}
+	parts := systemParts(decode(raw))
+	if len(parts) == 0 || (len(f.out.Input) > 0 && f.out.Input[0].Role == RoleSystem && textOf(f.out.Input[0].Parts) == textOf(parts)) {
+		return
+	}
+	f.out.Input = append(InputMessages{{Role: RoleSystem, Parts: parts}}, f.out.Input...)
+}
+
+// fillFinishReasons applies gen_ai.response.finish_reasons, one entry per output message, over
+// whatever the converter found in the payload.
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/registry.yaml#L210
+func (f *formatting) fillFinishReasons() {
+	raw := attribute(f.attributes, GenAIResponseFinishReasons)
+	if raw == nil || len(f.out.Output) == 0 {
+		return
+	}
+	reasons := stringList(decode(raw))
+	if len(reasons) != len(f.out.Output) {
+		f.warn("gen_ai.response.finish_reasons has %d entries for %d output messages", len(reasons), len(f.out.Output))
+		return
+	}
+	for i, reason := range reasons {
+		if fr := finishReason(reason); fr != nil {
+			f.out.Output[i].FinishReason = fr
+		}
+	}
+}
+
 // side converts one attribute value by its shape; role is what a bare string on this side is
 // spoken as.
 func (f *formatting) side(value any, role Role) (OutputMessages, string) {
 	switch v := value.(type) {
 	case []any:
 		if first, ok := firstObject(v); ok {
-			if _, ok := first["parts"]; ok {
+			switch {
+			case isGeminiContent(first):
+				return f.geminiContents(v), FormatterGemini
+			case first["parts"] != nil:
 				return f.semconv(v), FormatterSemconv
-			}
-			if _, ok := first["role"]; ok {
+			case isAnthropicMessages(v):
+				return f.anthropicMessages(v), FormatterAnthropic
+			case first["role"] != nil:
 				return f.openAIChatMessages(v), FormatterOpenAIChat
 			}
 		}
 	case map[string]any:
+		switch {
+		case isLangChainRun(v):
+			return f.langChainRun(v), FormatterLangChain
+		case isOpenRouterCompletion(v):
+			return f.openRouterCompletion(v), FormatterOpenRouter
+		case isResponsesResponse(v):
+			return f.responsesResponse(v), FormatterOpenAIResponses
+		case isResponsesRequest(v):
+			return f.responsesRequest(v), FormatterOpenAIResponses
+		case isAnthropicResponse(v):
+			return f.anthropicResponse(v), FormatterAnthropic
+		case isAnthropicRequest(v):
+			return f.anthropicRequest(v), FormatterAnthropic
+		case isGeminiResponse(v):
+			return f.geminiResponse(v), FormatterGemini
+		case isGeminiRequest(v):
+			return f.geminiRequest(v), FormatterGemini
+		case isGeminiContent(v):
+			return OutputMessages{geminiContent(v, role)}, FormatterGemini
+		}
 		if choices, ok := v["choices"].([]any); ok {
 			return f.openAIChatResponse(choices), FormatterOpenAIChat
 		}
@@ -121,6 +236,44 @@ func attribute(doc map[string]any, key string) any {
 	return current
 }
 
+// indexed reads a flattened list, "<key>.<N>.*" keys stored as an object keyed by N, in numeric order.
+// A list value is returned as is.
+func indexed(doc map[string]any, key string) []map[string]any {
+	var items []any
+	switch v := attribute(doc, key).(type) {
+	case []any:
+		items = v
+	case map[string]any:
+		indexes := make([]int, 0, len(v))
+		byIndex := make(map[int]any, len(v))
+		for k, item := range v {
+			n, err := strconv.Atoi(k)
+			if err != nil {
+				continue
+			}
+			indexes = append(indexes, n)
+			byIndex[n] = item
+		}
+		sort.Ints(indexes)
+		for _, n := range indexes {
+			items = append(items, byIndex[n])
+		}
+	}
+	out := make([]map[string]any, 0, len(items))
+	for _, item := range items {
+		if m, ok := item.(map[string]any); ok {
+			out = append(out, m)
+		}
+	}
+	return out
+}
+
+// indexedAt reads one entry of a flattened list by its index.
+func indexedAt(doc map[string]any, key string, i int) map[string]any {
+	m, _ := attribute(doc, key+"."+strconv.Itoa(i)).(map[string]any)
+	return m
+}
+
 func firstObject(list []any) (map[string]any, bool) {
 	if len(list) == 0 {
 		return nil, false
@@ -133,6 +286,31 @@ func genericMessage(value any) OutputMessage {
 	return OutputMessage{Parts: Parts{part(GenericPart{"type": FormatterGeneric, "content": stringOf(value)})}}
 }
 
+func inputMessages(msgs OutputMessages) InputMessages {
+	out := make(InputMessages, 0, len(msgs))
+	for _, m := range msgs {
+		out = append(out, ChatMessage{Role: m.Role, Parts: m.Parts, Name: m.Name})
+	}
+	return out
+}
+
+// foldAssistant merges runs of assistant messages into one and drops messages without parts, for
+// SDKs that write one message per output item.
+func foldAssistant(msgs OutputMessages) OutputMessages {
+	out := make(OutputMessages, 0, len(msgs))
+	for _, msg := range msgs {
+		if len(out) > 0 && msg.Role == RoleAssistant && out[len(out)-1].Role == RoleAssistant {
+			out[len(out)-1].Parts = append(out[len(out)-1].Parts, msg.Parts...)
+			continue
+		}
+		if len(msg.Parts) == 0 {
+			continue
+		}
+		out = append(out, msg)
+	}
+	return out
+}
+
 // decode parses a JSON-encoded attribute value, twice when an SDK encoded the string again.
 // Anything that is not JSON is returned as is.
 func decode(value any) any {
@@ -179,6 +357,23 @@ func finishReason(value string) *FinishReason {
 	return &fr
 }
 
+// stringList reads a list of strings, or a bare string as a list of one.
+func stringList(value any) []string {
+	switch v := value.(type) {
+	case []any:
+		out := make([]string, 0, len(v))
+		for _, item := range v {
+			out = append(out, stringOf(item))
+		}
+		return out
+	case string:
+		if v != "" {
+			return []string{v}
+		}
+	}
+	return nil
+}
+
 func optionalString(value any) *string {
 	s := stringOf(value)
 	if s == "" {
@@ -195,8 +390,61 @@ func textPart(content string) TextPart {
 	return TextPart{Type: PartTypeText, Content: content}
 }
 
-// contentPart converts one OpenAI content block, which semconv parts may also carry; a block of
-// any other type is kept as sent.
+// textOf joins the text parts, for comparing a system message with gen_ai.system_instructions.
+func textOf(parts Parts) string {
+	var texts []string
+	for _, p := range parts {
+		if t, ok := p.Value.(TextPart); ok {
+			texts = append(texts, t.Content)
+		}
+	}
+	return strings.Join(texts, "\n")
+}
+
+// splitToolResults moves tool_call_response parts out of a message into a tool message before it,
+// for APIs that carry tool results inside the next user turn.
+func splitToolResults(msg OutputMessage) OutputMessages {
+	results, rest := Parts{}, Parts{}
+	for _, p := range msg.Parts {
+		if _, ok := p.Value.(ToolCallResponsePart); ok {
+			results = append(results, p)
+		} else {
+			rest = append(rest, p)
+		}
+	}
+	if len(results) == 0 {
+		return OutputMessages{msg}
+	}
+	out := OutputMessages{{Role: RoleTool, Parts: results}}
+	if len(rest) > 0 {
+		msg.Parts = rest
+		out = append(out, msg)
+	}
+	return out
+}
+
+// systemParts reads a system prompt given outside the history, a list of parts or a bare string.
+func systemParts(value any) Parts {
+	switch v := value.(type) {
+	case []any:
+		parts := make(Parts, 0, len(v))
+		for _, item := range v {
+			parts = append(parts, contentPart(item))
+		}
+		return parts
+	case string:
+		if v != "" {
+			return Parts{part(textPart(v))}
+		}
+	}
+	return nil
+}
+
+// contentPart converts one content block in the OpenAI Chat Completions or Responses spelling, which
+// semconv parts may also carry; a text part in the semconv spelling has content where OpenAI has text.
+// A block of any other type is kept as sent.
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L53827
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L57589
 func contentPart(item any) Part {
 	p, ok := item.(map[string]any)
 	if !ok {
@@ -206,11 +454,14 @@ func contentPart(item any) Part {
 		return part(GenericPart{"type": FormatterGeneric, "content": stringOf(item)})
 	}
 	switch stringOf(p["type"]) {
-	case "text":
+	case "text", "input_text", "output_text", "summary_text":
+		if p["text"] == nil {
+			return part(textPart(stringOf(p["content"])))
+		}
 		return part(textPart(stringOf(p["text"])))
 	case "refusal":
 		return part(textPart(stringOf(p["refusal"])))
-	case "image_url":
+	case "image_url", "input_image":
 		url := stringOf(p["image_url"])
 		if ref, ok := p["image_url"].(map[string]any); ok {
 			url = stringOf(ref["url"])
@@ -218,10 +469,20 @@ func contentPart(item any) Part {
 		if url != "" {
 			return part(mediaPart(url, ModalityImage))
 		}
+		if id := stringOf(p["file_id"]); id != "" {
+			return part(FilePart{Type: PartTypeFile, Modality: ModalityImage, FileID: id})
+		}
 	case "file":
 		if f, ok := filePart(p); ok {
 			return f
 		}
+	case "input_file":
+		if f, ok := filePart(map[string]any{"file": p}); ok {
+			return f
+		}
+		if url := stringOf(p["file_url"]); url != "" {
+			return part(mediaPart(url, ModalityDocument))
+		}
 	}
 	return part(GenericPart(p))
 }
@@ -239,6 +500,19 @@ func filePart(block map[string]any) (Part, bool) {
 	return Part{}, false
 }
 
+// modalityOf maps a MIME type to the schema modality, document for anything that is not media.
+func modalityOf(mimeType string) Modality {
+	switch {
+	case strings.HasPrefix(mimeType, "image/"):
+		return ModalityImage
+	case strings.HasPrefix(mimeType, "audio/"):
+		return ModalityAudio
+	case strings.HasPrefix(mimeType, "video/"):
+		return ModalityVideo
+	}
+	return ModalityDocument
+}
+
 // mediaPart places a data URL in a BlobPart, any other URL in a UriPart.
 func mediaPart(url string, modality Modality) any {
 	if m := dataURL.FindStringSubmatch(url); m != nil {

pkg/types/aiobservabilitytypes/message_format_anthropic.go

@@ -0,0 +1,156 @@
+package aiobservabilitytypes
+
+import "strings"
+
+// Format: Anthropic Messages, a request {system, messages} or a response {content, stop_reason}:
+// https://platform.claude.com/docs/en/api/messages
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/message_create_params.py#L181
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/message_param.py#L28
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/message.py#L91
+// Written by: gateways recording raw Anthropic bodies. OpenInference flattens these into
+// llm.*_messages, which take precedence.
+// Handled: string and block content; text, image and document sources of every type, tool_use,
+// tool_result, thinking, redacted_thinking, server_tool_use and the *_tool_result blocks. A tool_result
+// inside a user turn becomes a tool message before it.
+// Not yet: a request without system and without Anthropic-only blocks is indistinguishable from an
+// OpenAI chat request and formats as one.
+
+// Block types only the Messages API has, which tell its message list from an OpenAI chat one:
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/tool_use_block_param.py#L23
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/thinking_block_param.py#L21
+var anthropicBlockTypes = map[string]bool{
+	"tool_use": true, "tool_result": true, "thinking": true, "redacted_thinking": true, "server_tool_use": true, "document": true,
+}
+
+func isAnthropicRequest(m map[string]any) bool {
+	messages, ok := m["messages"].([]any)
+	if !ok {
+		return false
+	}
+	return m["system"] != nil || isAnthropicMessages(messages)
+}
+
+func isAnthropicResponse(m map[string]any) bool {
+	if _, ok := m["content"].([]any); !ok {
+		return false
+	}
+	_, hasStop := m["stop_reason"]
+	return hasStop || stringOf(m["type"]) == "message"
+}
+
+func isAnthropicMessages(list []any) bool {
+	for _, item := range list {
+		m, _ := item.(map[string]any)
+		blocks, _ := m["content"].([]any)
+		for _, block := range blocks {
+			b, _ := block.(map[string]any)
+			typ := stringOf(b["type"])
+			if anthropicBlockTypes[typ] || strings.HasSuffix(typ, "_tool_result") || (typ == "image" && b["source"] != nil) {
+				return true
+			}
+		}
+	}
+	return false
+}
+
+func (f *formatting) anthropicRequest(m map[string]any) OutputMessages {
+	messages, _ := m["messages"].([]any)
+	msgs := f.anthropicMessages(messages)
+	if parts := systemParts(m["system"]); len(parts) > 0 {
+		return append(OutputMessages{{Role: RoleSystem, Parts: parts}}, msgs...)
+	}
+	return msgs
+}
+
+func (f *formatting) anthropicMessages(list []any) OutputMessages {
+	out := make(OutputMessages, 0, len(list))
+	for _, item := range list {
+		m, ok := item.(map[string]any)
+		if !ok {
+			out = append(out, genericMessage(item))
+			continue
+		}
+		msg := OutputMessage{Role: role(stringOf(m["role"])), Parts: anthropicContent(m["content"])}
+		out = append(out, splitToolResults(msg)...)
+	}
+	return out
+}
+
+// anthropicResponse is one assistant message with the stop reason.
+func (f *formatting) anthropicResponse(m map[string]any) OutputMessages {
+	msg := OutputMessage{Role: role(stringOf(m["role"])), Parts: anthropicContent(m["content"]), FinishReason: finishReason(stringOf(m["stop_reason"]))}
+	if msg.Role.IsZero() {
+		msg.Role = RoleAssistant
+	}
+	return OutputMessages{msg}
+}
+
+func anthropicContent(content any) Parts {
+	parts := Parts{}
+	switch v := content.(type) {
+	case string:
+		if v != "" {
+			parts = append(parts, part(textPart(v)))
+		}
+	case []any:
+		for _, item := range v {
+			if b, ok := item.(map[string]any); ok {
+				parts = append(parts, anthropicBlock(b))
+			} else {
+				parts = append(parts, contentPart(item))
+			}
+		}
+	}
+	return parts
+}
+
+// anthropicBlock converts one content block:
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/tool_result_block_param.py#L29
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/redacted_thinking_block_param.py#L15
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/server_tool_use_block_param.py#L33
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/web_search_tool_result_block_param.py#L22
+func anthropicBlock(b map[string]any) Part {
+	typ := stringOf(b["type"])
+	switch {
+	case typ == "text":
+		return part(textPart(stringOf(b["text"])))
+	case typ == "image" || typ == "document":
+		return anthropicSource(b, typ)
+	case typ == "tool_use":
+		return part(ToolCallRequestPart{Type: PartTypeToolCall, ID: optionalString(b["id"]), Name: stringOf(b["name"]), Arguments: b["input"]})
+	case typ == "tool_result":
+		return part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: optionalString(b["tool_use_id"]), Response: toolResponse(b["content"])})
+	case typ == "thinking":
+		return part(ReasoningPart{Type: PartTypeReasoning, Content: stringOf(b["thinking"])})
+	case typ == "redacted_thinking":
+		return part(ReasoningPart{Type: PartTypeReasoning})
+	case typ == "server_tool_use":
+		return part(ServerToolCallPart{Type: PartTypeServerToolCall, ID: optionalString(b["id"]), Name: stringOf(b["name"]), ServerToolCall: GenericServerToolCall(b)})
+	case strings.HasSuffix(typ, "_tool_result"):
+		return part(ServerToolCallResponsePart{Type: PartTypeServerToolCallResponse, ID: optionalString(b["tool_use_id"]), ServerToolCallResponse: GenericServerToolCallResponse(b)})
+	}
+	return part(GenericPart(b))
+}
+
+// anthropicSource reads the {source: {type: base64|url|file|text, ...}} of an image or document block:
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/image_block_param.py#L14
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/document_block_param.py#L16
+func anthropicSource(b map[string]any, typ string) Part {
+	source, _ := b["source"].(map[string]any)
+	mime := optionalString(source["media_type"])
+	modality := ModalityDocument
+	if typ == "image" {
+		modality = ModalityImage
+	}
+	switch stringOf(source["type"]) {
+	case "base64":
+		return part(BlobPart{Type: PartTypeBlob, Modality: modality, MimeType: mime, Content: stringOf(source["data"])})
+	case "url":
+		return part(UriPart{Type: PartTypeURI, Modality: modality, MimeType: mime, URI: stringOf(source["url"])})
+	case "file":
+		return part(FilePart{Type: PartTypeFile, Modality: modality, MimeType: mime, FileID: stringOf(source["file_id"])})
+	case "text":
+		return part(textPart(stringOf(source["data"])))
+	}
+	return part(GenericPart(b))
+}

pkg/types/aiobservabilitytypes/message_format_anthropic_test.go

@@ -0,0 +1,90 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Cases follow the Anthropic Messages API shapes; no capture in SigNoz/scripts records raw Anthropic
+// bodies.
+// https://platform.claude.com/docs/en/api/messages
+// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/message_param.py#L28
+func TestFormat_Anthropic(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// system as a string; end_turn is stop
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/message_create_params.py#L181
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/stop_reason.py#L5
+			name:   "SystemStringAndText_EndTurn",
+			input:  `{"model":"claude-sonnet-4-5","max_tokens":1024,"system":"You are terse.","messages":[{"role":"user","content":"Hi"}]}`,
+			output: `{"id":"msg_1","type":"message","role":"assistant","model":"claude-sonnet-4-5","content":[{"type":"text","text":"Hello."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":10,"output_tokens":3}}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are terse."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Hi"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// tool_result sits inside the next user turn, with text beside it
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/tool_result_block_param.py#L29
+			name:   "ToolRound_ToolResultSplitFromUserTurn",
+			input:  `{"messages":[{"role":"user","content":"Weather in Oslo?"},{"role":"assistant","content":[{"type":"text","text":"Checking."},{"type":"tool_use","id":"toolu_1","name":"get_weather","input":{"city":"Oslo"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_1","content":"{\"temp_c\":4}"},{"type":"text","text":"And Bergen?"}]}]}`,
+			output: `{"type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_2","name":"get_weather","input":{"city":"Bergen"}}],"stop_reason":"tool_use"}`,
+			wantInput: `[
+			  {"role": "user", "parts": [{"type": "text", "content": "Weather in Oslo?"}]},
+			  {"role": "assistant", "parts": [{"type": "text", "content": "Checking."}, {"type": "tool_call", "id": "toolu_1", "name": "get_weather", "arguments": {"city": "Oslo"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "toolu_1", "response": {"temp_c": 4}}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "And Bergen?"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "id": "toolu_2", "name": "get_weather", "arguments": {"city": "Bergen"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// system as text blocks; every image and document source type; thinking blocks in the history and
+			// the answer
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/image_block_param.py#L14
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/document_block_param.py#L16
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/thinking_block_param.py#L21
+			name:   "MediaSourcesAndThinking_MaxTokens",
+			input:  `{"system":[{"type":"text","text":"Describe inputs.","cache_control":{"type":"ephemeral"}}],"messages":[{"role":"user","content":[{"type":"image","source":{"type":"base64","media_type":"image/png","data":"iVBORw0KGgo="}},{"type":"image","source":{"type":"url","url":"https://example.com/a.jpg"}},{"type":"document","source":{"type":"file","file_id":"file_1"}},{"type":"document","source":{"type":"text","media_type":"text/plain","data":"plain notes"},"title":"notes"},{"type":"text","text":"What are these?"}]},{"role":"assistant","content":[{"type":"thinking","thinking":"Two images and two documents.","signature":"sig"},{"type":"redacted_thinking","data":"xx"},{"type":"text","text":"Images and documents."}]}]}`,
+			output: `{"type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Listing them.","signature":"sig"},{"type":"text","text":"A PNG, a JPEG"}],"stop_reason":"max_tokens"}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "Describe inputs."}]},
+			  {"role": "user", "parts": [
+			    {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "iVBORw0KGgo="},
+			    {"type": "uri", "modality": "image", "uri": "https://example.com/a.jpg"},
+			    {"type": "file", "modality": "document", "file_id": "file_1"},
+			    {"type": "text", "content": "plain notes"},
+			    {"type": "text", "content": "What are these?"}
+			  ]},
+			  {"role": "assistant", "parts": [{"type": "reasoning", "content": "Two images and two documents."}, {"type": "reasoning", "content": ""}, {"type": "text", "content": "Images and documents."}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "reasoning", "content": "Listing them."}, {"type": "text", "content": "A PNG, a JPEG"}], "finish_reason": "length"}]`,
+		},
+		{
+			// server_tool_use with its web_search_tool_result in the same message; refusal is a content filter
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/server_tool_use_block_param.py#L33
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/web_search_tool_result_block_param.py#L22
+			// https://github.com/anthropics/anthropic-sdk-python/blob/2a53eaa6966b04560b6a72c28d3ace4389a9ac58/src/anthropic/types/stop_reason.py#L5
+			name:      "ServerToolUseAndResult_Refusal",
+			output:    `{"type":"message","role":"assistant","content":[{"type":"server_tool_use","id":"srvtoolu_1","name":"web_search","input":{"query":"otel genai"}},{"type":"web_search_tool_result","tool_use_id":"srvtoolu_1","content":[{"type":"web_search_result","url":"https://opentelemetry.io","title":"OTel"}]},{"type":"text","text":"Found it."}],"stop_reason":"refusal"}`,
+			wantInput: `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "server_tool_call", "id": "srvtoolu_1", "name": "web_search", "server_tool_call": {"type": "server_tool_use", "id": "srvtoolu_1", "name": "web_search", "input": {"query": "otel genai"}}},
+			  {"type": "server_tool_call_response", "id": "srvtoolu_1", "server_tool_call_response": {"type": "web_search_tool_result", "tool_use_id": "srvtoolu_1", "content": [{"type": "web_search_result", "url": "https://opentelemetry.io", "title": "OTel"}]}},
+			  {"type": "text", "content": "Found it."}
+			], "finish_reason": "content_filter"}]`,
+		},
+		{
+			// a request with neither system nor Anthropic-only blocks is an OpenAI chat request; the
+			// response names the format
+			name:       "PlainRequest_ResponseNamesFormat",
+			input:      `{"model":"claude-sonnet-4-5","messages":[{"role":"user","content":"Hi"}]}`,
+			output:     `{"type":"message","role":"assistant","content":[{"type":"text","text":"Hello."}],"stop_reason":"end_turn"}`,
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "Hi"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello."}], "finish_reason": "stop"}]`,
+		},
+		{
+			name:       "BareMessageList_ToolBlocksNameFormat",
+			input:      `[{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_1","content":[{"type":"text","text":"sunny"}]}]}]`,
+			wantInput:  `[{"role": "tool", "parts": [{"type": "tool_call_response", "id": "toolu_1", "response": "sunny"}]}]`,
+			wantOutput: `[]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_gemini.go

@@ -0,0 +1,165 @@
+package aiobservabilitytypes
+
+// Format: Gemini generateContent, a request {contents, systemInstruction} or a response {candidates} of
+// Content {role, parts} objects, in the REST camelCase or the SDK snake_case spelling:
+// https://ai.google.dev/api/generate-content
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2303
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2626
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L8351
+// Written by: gateways recording raw Gemini bodies. OpenInference flattens these into llm.*_messages,
+// which take precedence.
+// Handled: text, thought text, inlineData, fileData, functionCall, functionResponse; a functionResponse
+// inside a user turn becomes a tool message before it. Any other part is kept as sent.
+// Not yet: a blocked prompt, promptFeedback without candidates.
+
+func isGeminiRequest(m map[string]any) bool {
+	switch m["contents"].(type) {
+	case []any, string:
+		return true
+	}
+	return false
+}
+
+func isGeminiResponse(m map[string]any) bool {
+	_, ok := m["candidates"].([]any)
+	return ok
+}
+
+// isGeminiContent tells a bare Content from a semconv message: both carry parts, only semconv parts
+// have a type.
+func isGeminiContent(m map[string]any) bool {
+	parts, ok := m["parts"].([]any)
+	if !ok {
+		return false
+	}
+	first, ok := firstObject(parts)
+	return ok && first["type"] == nil
+}
+
+// geminiRequest reads contents and systemInstruction:
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L6527
+func (f *formatting) geminiRequest(m map[string]any) OutputMessages {
+	var msgs OutputMessages
+	if system := geminiSystemInstruction(objectAt(m, "systemInstruction", "system_instruction")); system != nil {
+		msgs = append(msgs, *system)
+	}
+	switch contents := m["contents"].(type) {
+	case string:
+		msgs = append(msgs, OutputMessage{Role: RoleUser, Parts: Parts{part(textPart(contents))}})
+	case []any:
+		msgs = append(msgs, f.geminiContents(contents)...)
+	}
+	return msgs
+}
+
+// geminiResponse is one message per candidate with its finish reason.
+func (f *formatting) geminiResponse(m map[string]any) OutputMessages {
+	candidates, _ := m["candidates"].([]any)
+	out := make(OutputMessages, 0, len(candidates))
+	for _, item := range candidates {
+		candidate, ok := item.(map[string]any)
+		if !ok {
+			out = append(out, genericMessage(item))
+			continue
+		}
+		content, _ := candidate["content"].(map[string]any)
+		msg := geminiContent(content, RoleAssistant)
+		msg.FinishReason = finishReason(stringOf(objectAt(candidate, "finishReason", "finish_reason")))
+		out = append(out, msg)
+	}
+	return out
+}
+
+// geminiSystemInstruction accepts a string, a Content or a list of parts.
+func geminiSystemInstruction(value any) *OutputMessage {
+	var parts Parts
+	switch v := value.(type) {
+	case map[string]any:
+		parts = geminiContent(v, RoleSystem).Parts
+	case []any:
+		parts = geminiParts(v)
+	default:
+		parts = systemParts(value)
+	}
+	if len(parts) == 0 {
+		return nil
+	}
+	return &OutputMessage{Role: RoleSystem, Parts: parts}
+}
+
+func (f *formatting) geminiContents(list []any) OutputMessages {
+	out := make(OutputMessages, 0, len(list))
+	for _, item := range list {
+		switch v := item.(type) {
+		case string:
+			out = append(out, OutputMessage{Role: RoleUser, Parts: Parts{part(textPart(v))}})
+		case map[string]any:
+			out = append(out, splitToolResults(geminiContent(v, RoleUser))...)
+		default:
+			out = append(out, genericMessage(item))
+		}
+	}
+	return out
+}
+
+func geminiContent(c map[string]any, fallback Role) OutputMessage {
+	msg := OutputMessage{Role: role(stringOf(c["role"])), Parts: Parts{}}
+	if msg.Role.IsZero() {
+		msg.Role = fallback
+	}
+	parts, _ := c["parts"].([]any)
+	msg.Parts = geminiParts(parts)
+	return msg
+}
+
+func geminiParts(list []any) Parts {
+	parts := Parts{}
+	for _, item := range list {
+		if p, ok := item.(map[string]any); ok {
+			parts = append(parts, geminiPart(p))
+		} else {
+			parts = append(parts, contentPart(item))
+		}
+	}
+	return parts
+}
+
+// geminiPart converts one Part by the one field it carries:
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2324
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2112
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L1731
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L1830
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2032
+func geminiPart(p map[string]any) Part {
+	if text, ok := p["text"].(string); ok {
+		if thought, _ := p["thought"].(bool); thought {
+			return part(ReasoningPart{Type: PartTypeReasoning, Content: text})
+		}
+		return part(textPart(text))
+	}
+	if data, ok := objectAt(p, "inlineData", "inline_data").(map[string]any); ok {
+		mime := stringOf(objectAt(data, "mimeType", "mime_type"))
+		return part(BlobPart{Type: PartTypeBlob, Modality: modalityOf(mime), MimeType: optionalString(mime), Content: stringOf(data["data"])})
+	}
+	if file, ok := objectAt(p, "fileData", "file_data").(map[string]any); ok {
+		mime := stringOf(objectAt(file, "mimeType", "mime_type"))
+		return part(UriPart{Type: PartTypeURI, Modality: modalityOf(mime), MimeType: optionalString(mime), URI: stringOf(objectAt(file, "fileUri", "file_uri"))})
+	}
+	if call, ok := objectAt(p, "functionCall", "function_call").(map[string]any); ok {
+		return part(ToolCallRequestPart{Type: PartTypeToolCall, ID: optionalString(call["id"]), Name: stringOf(call["name"]), Arguments: call["args"]})
+	}
+	if response, ok := objectAt(p, "functionResponse", "function_response").(map[string]any); ok {
+		return part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: optionalString(response["id"]), Response: response["response"]})
+	}
+	return part(GenericPart(p))
+}
+
+// objectAt reads the first of the key spellings that is set.
+func objectAt(m map[string]any, keys ...string) any {
+	for _, key := range keys {
+		if v, ok := m[key]; ok && v != nil {
+			return v
+		}
+	}
+	return nil
+}

pkg/types/aiobservabilitytypes/message_format_gemini_test.go

@@ -0,0 +1,75 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Cases follow the Gemini generateContent shapes, REST camelCase and SDK snake_case; no capture in
+// SigNoz/scripts records raw Gemini bodies.
+// https://ai.google.dev/api/generate-content
+// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2303
+func TestFormat_Gemini(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// systemInstruction as a Content; STOP is stop
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L6527
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L510
+			name:   "SystemInstructionContent_CandidateText_Stop",
+			input:  `{"contents":[{"role":"user","parts":[{"text":"Hi"}]}],"systemInstruction":{"parts":[{"text":"You are terse."}]},"generationConfig":{"temperature":0}}`,
+			output: `{"candidates":[{"content":{"parts":[{"text":"Hello."}],"role":"model"},"finishReason":"STOP","index":0}],"usageMetadata":{"promptTokenCount":5,"candidatesTokenCount":2},"modelVersion":"gemini-2.5-flash"}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are terse."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Hi"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// SDK snake_case spelling; the function response sits in the next user turn
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L1830
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2032
+			name:   "FunctionRoundSnakeCase_ResponseSplitFromUserTurn",
+			input:  `{"model":"gemini-2.5-pro","contents":[{"role":"user","parts":[{"text":"Weather in Oslo?"}]},{"role":"model","parts":[{"function_call":{"name":"get_weather","args":{"city":"Oslo"}}}]},{"role":"user","parts":[{"function_response":{"name":"get_weather","response":{"temp_c":4}}},{"text":"And Bergen?"}]}],"system_instruction":"Use tools."}`,
+			output: `{"candidates":[{"content":{"role":"model","parts":[{"function_call":{"id":"fc_1","name":"get_weather","args":{"city":"Bergen"}}}]},"finish_reason":"STOP"}]}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "Use tools."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Weather in Oslo?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "arguments": {"city": "Oslo"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "response": {"temp_c": 4}}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "And Bergen?"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "id": "fc_1", "name": "get_weather", "arguments": {"city": "Bergen"}}], "finish_reason": "stop"}]`,
+		},
+		{
+			// inline and file data, a thought part, an executable code part kept as sent; two candidates, the
+			// second stopped for SAFETY
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2112
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L1731
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L2344
+			// https://github.com/googleapis/python-genai/blob/78f6d3412caaaed44f99ce57be2d16d0b8c407cb/google/genai/types.py#L510
+			name:   "MediaAndThought_TwoCandidates",
+			input:  `{"contents":[{"parts":[{"inlineData":{"mimeType":"image/png","data":"iVBORw0KGgo="}},{"fileData":{"mimeType":"video/mp4","fileUri":"gs://bucket/a.mp4"}},{"text":"Describe."}]}]}`,
+			output: `{"candidates":[{"content":{"role":"model","parts":[{"text":"Looking.","thought":true},{"executableCode":{"language":"PYTHON","code":"print(1)"}},{"text":"A logo and a clip."}]},"finishReason":"MAX_TOKENS"},{"content":{"role":"model","parts":[]},"finishReason":"SAFETY"}]}`,
+			wantInput: `[{"role": "user", "parts": [
+			  {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "iVBORw0KGgo="},
+			  {"type": "uri", "modality": "video", "mime_type": "video/mp4", "uri": "gs://bucket/a.mp4"},
+			  {"type": "text", "content": "Describe."}
+			]}]`,
+			wantOutput: `[
+			  {"role": "assistant", "parts": [{"type": "reasoning", "content": "Looking."}, {"executableCode": {"language": "PYTHON", "code": "print(1)"}}, {"type": "text", "content": "A logo and a clip."}], "finish_reason": "length"},
+			  {"role": "assistant", "parts": [], "finish_reason": "content_filter"}
+			]`,
+		},
+		{
+			name:       "StringContents_UserText",
+			input:      `{"model":"gemini-2.5-flash","contents":"Hi"}`,
+			output:     `{"role":"model","parts":[{"text":"Hello."}]}`,
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "Hi"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello."}]}]`,
+		},
+		{
+			// a bare Content list is told from semconv messages by its untyped parts
+			name:       "BareContentList_NotSemconv",
+			input:      `[{"role":"user","parts":[{"text":"Hi"}]}]`,
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "Hi"}]}]`,
+			wantOutput: `[]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_langchain.go

@@ -0,0 +1,200 @@
+package aiobservabilitytypes
+
+import "github.com/SigNoz/signoz/pkg/valuer"
+
+// Format: LangChain's serialised run inputs and outputs, {messages: [[Serializable]]} and
+// {generations: [[{text, generation_info, message}]]}, each message {lc: 1, type: "constructor",
+// id: [..., class], kwargs: {content, tool_calls, tool_call_id, name, ...}}:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/load/serializable.py#L297
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/outputs/llm_result.py#L23
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/ai.py#L170
+// Written by: the LangSmith OTel exporter as gen_ai.prompt and gen_ai.completion, which the span mapper
+// moves onto gen_ai.input.messages and gen_ai.output.messages:
+// https://github.com/langchain-ai/langsmith-sdk/blob/bf325f3a0d9f415096a4632099b7faff355cde53/python/langsmith/_internal/otel/_otel_exporter.py#L761
+// https://github.com/langchain-ai/langsmith-sdk/blob/bf325f3a0d9f415096a4632099b7faff355cde53/python/langsmith/_internal/otel/_otel_exporter.py#L843
+// The envelope and the message classes are langchain_core's and shared by every partner package; the
+// partners differ in the key that carries the finish reason and in the content blocks they keep.
+// Handled: string content, langchain_core's standard content blocks and the OpenAI block spellings,
+// tool_calls, tool messages, the finish reason of a generation.
+// Not yet: the provider-native thinking blocks anthropic, bedrock and google keep in the content, kept
+// as sent:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/anthropic/langchain_anthropic/chat_models.py#L866
+// https://github.com/langchain-ai/langchain-aws/blob/910b79b9787eee773cde65bdd9e94266d6cf6f6c/libs/aws/langchain_aws/chat_models/bedrock_converse.py#L401
+// https://github.com/langchain-ai/langchain-google/blob/9f72e9a344235a488396ac2c62d360ceb2b1c81c/libs/genai/langchain_google_genai/chat_models.py#L165
+
+// LangChain message classes to roles; ChatMessage carries its own role in kwargs.
+var langChainRoles = map[string]Role{
+	"SystemMessage": RoleSystem, "SystemMessageChunk": RoleSystem,
+	"HumanMessage": RoleUser, "HumanMessageChunk": RoleUser,
+	"AIMessage": RoleAssistant, "AIMessageChunk": RoleAssistant,
+	"ToolMessage": RoleTool, "ToolMessageChunk": RoleTool, "FunctionMessage": RoleTool, "FunctionMessageChunk": RoleTool,
+}
+
+// isLangChainRun looks for the generations list or a messages batch of serialised messages.
+func isLangChainRun(m map[string]any) bool {
+	if _, ok := m["generations"].([]any); ok {
+		return true
+	}
+	batches, ok := m["messages"].([]any)
+	if !ok || len(batches) == 0 {
+		return false
+	}
+	first, ok := batches[0].([]any)
+	if !ok {
+		return false
+	}
+	msg, ok := firstObject(first)
+	return ok && msg["lc"] != nil
+}
+
+// langChainRun converts the run inputs, every batch of messages in order, or the run outputs, one
+// message per generation.
+func (f *formatting) langChainRun(m map[string]any) OutputMessages {
+	var out OutputMessages
+	if batches, ok := m["messages"].([]any); ok {
+		for _, batch := range batches {
+			if list, ok := batch.([]any); ok {
+				out = append(out, f.langChainMessages(list)...)
+			}
+		}
+		return out
+	}
+	batches, _ := m["generations"].([]any)
+	for _, batch := range batches {
+		list, _ := batch.([]any)
+		for _, item := range list {
+			generation, ok := item.(map[string]any)
+			if !ok {
+				out = append(out, genericMessage(item))
+				continue
+			}
+			msg := OutputMessage{Role: RoleAssistant, Parts: Parts{}}
+			if message, ok := generation["message"].(map[string]any); ok {
+				msg = f.langChainMessage(message)
+			} else if text := stringOf(generation["text"]); text != "" {
+				msg.Parts = Parts{part(textPart(text))}
+			}
+			if info, ok := generation["generation_info"].(map[string]any); ok && msg.FinishReason == nil {
+				msg.FinishReason = finishReason(stringOf(info["finish_reason"]))
+			}
+			out = append(out, msg)
+		}
+	}
+	return out
+}
+
+func (f *formatting) langChainMessages(list []any) OutputMessages {
+	out := make(OutputMessages, 0, len(list))
+	for _, item := range list {
+		m, ok := item.(map[string]any)
+		if !ok {
+			out = append(out, genericMessage(item))
+			continue
+		}
+		out = append(out, f.langChainMessage(m))
+	}
+	return out
+}
+
+// langChainMessage converts one serialised message. A tool_call or tool_use block in the content repeats
+// a call that tool_calls already carries, langchain_core adds the former from the latter and
+// langchain-anthropic keeps the provider's block beside it:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/ai.py#L274
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/anthropic/langchain_anthropic/chat_models.py#L2571
+// A failed call leaves the error body in response_metadata.
+func (f *formatting) langChainMessage(m map[string]any) OutputMessage {
+	kwargs, _ := m["kwargs"].(map[string]any)
+	msg := OutputMessage{Role: langChainRole(m, kwargs), Parts: Parts{}, Name: optionalString(kwargs["name"])}
+	if id := stringOf(kwargs["tool_call_id"]); msg.Role == RoleTool && id != "" {
+		msg.Parts = Parts{part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: &id, Response: toolResponse(kwargs["content"])})}
+		return msg
+	}
+	calls, _ := kwargs["tool_calls"].([]any)
+	switch content := kwargs["content"].(type) {
+	case string:
+		if content != "" {
+			msg.Parts = append(msg.Parts, part(textPart(content)))
+		}
+	case []any:
+		for _, item := range content {
+			if block, ok := item.(map[string]any); ok && len(calls) > 0 && (block["type"] == "tool_call" || block["type"] == "tool_use") {
+				continue
+			}
+			msg.Parts = append(msg.Parts, langChainBlock(item))
+		}
+	}
+	for _, item := range calls {
+		if call, ok := item.(map[string]any); ok {
+			msg.Parts = append(msg.Parts, part(ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(call["name"]), ID: optionalString(call["id"]), Arguments: decode(call["args"])}))
+		}
+	}
+	if metadata, ok := kwargs["response_metadata"].(map[string]any); ok {
+		msg.FinishReason = langChainFinishReason(metadata)
+		if body, ok := metadata["body"].(map[string]any); ok && body["error"] != nil {
+			msg.FinishReason = finishReason("error")
+		}
+	}
+	return msg
+}
+
+// langChainFinishReason reads the key each partner writes into response_metadata: finish_reason for
+// most, stop_reason for anthropic, stopReason for bedrock, done_reason for ollama:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/anthropic/langchain_anthropic/chat_models.py#L2510
+// https://github.com/langchain-ai/langchain-aws/blob/910b79b9787eee773cde65bdd9e94266d6cf6f6c/libs/aws/langchain_aws/chat_models/bedrock_converse.py#L265
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/ollama/langchain_ollama/chat_models.py#L24
+func langChainFinishReason(metadata map[string]any) *FinishReason {
+	for _, key := range []string{"finish_reason", "stop_reason", "stopReason", "done_reason"} {
+		if fr := finishReason(stringOf(metadata[key])); fr != nil {
+			return fr
+		}
+	}
+	return nil
+}
+
+// langChainBlock converts one content block: langchain_core's standard reasoning and media blocks, else
+// the OpenAI spellings contentPart knows:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/content.py#L456
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/content.py#L498
+func langChainBlock(item any) Part {
+	block, ok := item.(map[string]any)
+	if !ok {
+		return contentPart(item)
+	}
+	typ := stringOf(block["type"])
+	switch typ {
+	case "reasoning":
+		return part(ReasoningPart{Type: PartTypeReasoning, Content: stringOf(block["reasoning"])})
+	case "image", "audio", "video", "file":
+		modality := Modality{valuer.NewString(typ)}
+		if typ == "file" {
+			modality = ModalityDocument
+		}
+		mime := optionalString(block["mime_type"])
+		switch {
+		case stringOf(block["url"]) != "":
+			return part(mediaPart(stringOf(block["url"]), modality))
+		case stringOf(block["base64"]) != "":
+			return part(BlobPart{Type: PartTypeBlob, Modality: modality, MimeType: mime, Content: stringOf(block["base64"])})
+		case stringOf(block["file_id"]) != "":
+			return part(FilePart{Type: PartTypeFile, Modality: modality, MimeType: mime, FileID: stringOf(block["file_id"])})
+		}
+	}
+	return contentPart(item)
+}
+
+// langChainRole reads the message class from the serialised id path, falling back to the type
+// alias in kwargs:
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/chat.py#L18
+// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/human.py#L29
+func langChainRole(m, kwargs map[string]any) Role {
+	if id, ok := m["id"].([]any); ok && len(id) > 0 {
+		class := stringOf(id[len(id)-1])
+		if r, ok := langChainRoles[class]; ok {
+			return r
+		}
+		if class == "ChatMessage" || class == "ChatMessageChunk" {
+			return role(stringOf(kwargs["role"]))
+		}
+	}
+	return role(stringOf(kwargs["type"]))
+}

pkg/types/aiobservabilitytypes/message_format_langchain_test.go

@@ -0,0 +1,77 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Captured cases are gen_ai.input.messages and gen_ai.output.messages as the collector stored them for
+// the SigNoz/scripts langsmith capture, the LangSmith OTel exporter's serialised run inputs and outputs;
+// the others follow langchain_core and the partner packages at the linked lines.
+func TestFormat_LangChain(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// langsmith capture, ChatOpenAI span after the tool ran
+			name:   "SerialisedMessages_RolesFromClasses_ToolCallAndToolMessage",
+			input:  `{"messages":[[{"lc":1,"type":"constructor","id":["langchain","schema","messages","SystemMessage"],"kwargs":{"content":"You are a weather assistant. Call get_weather once per city.","type":"system"}},{"lc":1,"type":"constructor","id":["langchain","schema","messages","HumanMessage"],"kwargs":{"content":"What's the weather in Bengaluru?","type":"human"}},{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"","additional_kwargs":{"refusal":null},"response_metadata":{"finish_reason":"tool_calls","logprobs":null},"type":"ai","id":"lc_run--019e844a","tool_calls":[{"name":"get_weather","args":{"city":"Bengaluru"},"id":"call_nbrbkc5fom95q8uMA2bCvH6s","type":"tool_call"}],"invalid_tool_calls":[]}},{"lc":1,"type":"constructor","id":["langchain","schema","messages","ToolMessage"],"kwargs":{"content":"{\"city\": \"Bengaluru\", \"temp_c\": 18, \"summary\": \"Clear\"}","type":"tool","tool_call_id":"call_nbrbkc5fom95q8uMA2bCvH6s","status":"success"}}]]}`,
+			output: `{"generations":[[{"text":"The weather in Bengaluru is clear, 18°C.","generation_info":{"finish_reason":"stop","logprobs":null},"type":"ChatGeneration","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"The weather in Bengaluru is clear, 18°C.","additional_kwargs":{"refusal":null},"response_metadata":{"finish_reason":"stop","model_name":"gpt-4o-mini-2024-07-18"},"type":"ai","tool_calls":[],"invalid_tool_calls":[]}}}]],"llm_output":{"model_name":"gpt-4o-mini-2024-07-18"},"run":null,"type":"LLMResult"}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are a weather assistant. Call get_weather once per city."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "What's the weather in Bengaluru?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_nbrbkc5fom95q8uMA2bCvH6s", "arguments": {"city": "Bengaluru"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_nbrbkc5fom95q8uMA2bCvH6s", "response": {"city": "Bengaluru", "temp_c": 18, "summary": "Clear"}}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "The weather in Bengaluru is clear, 18°C."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// langsmith capture, ChatOpenAI span answering with a tool call: the generation text is empty and
+			// the finish reason sits in generation_info
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/outputs/generation.py#L28
+			name:       "GenerationWithToolCalls_ToolCallPart_FinishFromGenerationInfo",
+			output:     `{"generations":[[{"text":"","generation_info":{"finish_reason":"tool_calls"},"type":"ChatGeneration","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"","response_metadata":{},"type":"ai","tool_calls":[{"name":"get_weather","args":{"city":"Bengaluru"},"id":"call_1","type":"tool_call"}]}}}]]}`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_1", "arguments": {"city": "Bengaluru"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// langsmith capture, ChatOpenAI span of a failed call: the error body sits in response_metadata
+			name:       "GenerationWithErrorBody_EmptyMessage_FinishError",
+			output:     `{"generations":[[{"text":"","generation_info":null,"type":"ChatGeneration","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"","response_metadata":{"body":{"error":{"message":"Incorrect API key provided","type":"invalid_request_error","code":"invalid_api_key"},"status":401},"headers":{}},"type":"ai"}}}]]}`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [], "finish_reason": "error"}]`,
+		},
+		{
+			// a ChatMessage carries its role in kwargs; an AIMessage from langchain-anthropic keeps the tool_use
+			// block in its content beside tool_calls
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/chat.py#L18
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/anthropic/langchain_anthropic/chat_models.py#L2571
+			name:  "ChatMessageRole_ToolUseBlockBesideToolCalls_OneToolCallPart",
+			input: `{"messages":[[{"lc":1,"type":"constructor","id":["langchain","schema","messages","ChatMessage"],"kwargs":{"role":"developer","content":[{"type":"text","text":"Be brief."}]}},{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":[{"type":"text","text":"Calling."},{"type":"tool_use","id":"toolu_1","name":"get_weather","input":{"city":"Paris"}}],"tool_calls":[{"name":"get_weather","args":{"city":"Paris"},"id":"toolu_1"}]}}]]}`,
+			wantInput: `[
+			  {"role": "developer", "parts": [{"type": "text", "content": "Be brief."}]},
+			  {"role": "assistant", "parts": [{"type": "text", "content": "Calling."}, {"type": "tool_call", "name": "get_weather", "id": "toolu_1", "arguments": {"city": "Paris"}}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// the finish reason under the key each partner writes: anthropic stop_reason, ollama done_reason
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/anthropic/langchain_anthropic/chat_models.py#L2510
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/partners/ollama/langchain_ollama/chat_models.py#L24
+			name:       "PartnerFinishReasonKeys_StopReasonAndDoneReason",
+			output:     `{"generations":[[{"text":"Hi.","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"Hi.","response_metadata":{"stop_reason":"end_turn","model":"claude-sonnet-4-5"}}}}],[{"text":"Hel","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":"Hel","response_metadata":{"model":"llama3","done_reason":"length"}}}}]]}`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hi."}], "finish_reason": "stop"}, {"role": "assistant", "parts": [{"type": "text", "content": "Hel"}], "finish_reason": "length"}]`,
+		},
+		{
+			// langchain_core's standard content blocks, as partners write them under output_version v1
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/content.py#L456
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/content.py#L498
+			// https://github.com/langchain-ai/langchain/blob/84ba23c414eca6bf6f6ebd1394a3006378113540/libs/core/langchain_core/messages/content.py#L721
+			name:      "StandardContentBlocks_ReasoningAndMediaParts",
+			output:    `{"generations":[[{"text":"It is a duck.","message":{"lc":1,"type":"constructor","id":["langchain","schema","messages","AIMessage"],"kwargs":{"content":[{"type":"reasoning","reasoning":"Looks like a bird."},{"type":"text","text":"It is a duck."},{"type":"image","base64":"iVBORw0KGgo=","mime_type":"image/png"},{"type":"file","url":"https://example.com/a.pdf","mime_type":"application/pdf"}],"response_metadata":{"finish_reason":"stop","output_version":"v1"}}}}]]}`,
+			wantInput: `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "reasoning", "content": "Looks like a bird."},
+			  {"type": "text", "content": "It is a duck."},
+			  {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "iVBORw0KGgo="},
+			  {"type": "uri", "modality": "document", "uri": "https://example.com/a.pdf"}
+			], "finish_reason": "stop"}]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_openai_chat_test.go

@@ -167,7 +167,8 @@ func TestFormat_OpenAIChat(t *testing.T) {
 			]`,
 		},
 		{
-			// openinference capture, input.value of the ChatCompletion span
+			// ChatCompletionRequestMessageContentPartFile {file: {file_id}}, as OpenInference records the request in input.value:
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L42650
 			name:   "FileByID_FilePart",
 			input:  `{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": [{"type": "text", "text": "Summarise this document in one sentence."}, {"type": "file", "file": {"file_id": "file-6F2ksmvXxt4VdoqmHRw6kL"}}]}], "max_tokens": 60}`,
 			output: `{"id":"chatcmpl-DktFileCapture0001","object":"chat.completion","model":"gpt-4o-mini-2024-07-18","choices":[{"index":0,"finish_reason":"stop","logprobs":null,"message":{"role":"assistant","content":"The document is a short PDF about dragons.","refusal":null,"annotations":[]}}]}`,
@@ -189,7 +190,8 @@ func TestFormat_OpenAIChat(t *testing.T) {
 			]`,
 		},
 		{
-			// openinference capture, input.value of the ChatCompletion span
+			// ChatCompletionRequestMessageContentPartFile {file: {filename, file_data}}, file_data a data URL:
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L42650
 			name:  "FileInline_BlobPart",
 			input: `{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": [{"type": "text", "text": "Summarise this document in one sentence."}, {"type": "file", "file": {"filename": "draconomicon.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQKMSAwIG9iajw8L1R5cGUvQ2F0YWxvZy9QYWdlcyAyIDAgUj4+ZW5kb2JqCjIgMCBvYmo8PC9UeXBlL1BhZ2VzL0tpZHNbXS9Db3VudCAwPj5lbmRvYmoKdHJhaWxlcjw8L1Jvb3QgMSAwIFI+PgolJUVPRgo="}}]}], "max_tokens": 60}`,
 			wantInput: `[

pkg/types/aiobservabilitytypes/message_format_openai_responses.go

@@ -0,0 +1,136 @@
+package aiobservabilitytypes
+
+import "strings"
+
+// Format: OpenAI Responses, a request {input, instructions} or a response {output, status} of items:
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L47597
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69691
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L53834
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L57596
+// Written by: gateways recording raw Responses bodies, and the raw calls OpenInference records for the
+// Responses API and the OpenAI Agents SDK when its flattened messages are absent.
+// Handled: message items with input_text, output_text, refusal, input_image and input_file content;
+// function_call and custom_tool_call with their outputs; reasoning summaries; compaction; every other
+// *_call item as a server tool call and *_output item as its response; the status and incomplete_details
+// of a response.
+// Dropped: reasoning encrypted_content, item ids. Not yet: input_audio content.
+
+// isResponsesRequest tells a Responses request from an embeddings one, which also carries input and
+// model: items are objects, and only Responses takes instructions or tools.
+func isResponsesRequest(m map[string]any) bool {
+	if m["input"] == nil {
+		return false
+	}
+	if m["instructions"] != nil || m["tools"] != nil || m["previous_response_id"] != nil {
+		return true
+	}
+	items, ok := m["input"].([]any)
+	if !ok {
+		return false
+	}
+	_, isItem := firstObject(items)
+	return isItem
+}
+
+func isResponsesResponse(m map[string]any) bool {
+	if _, ok := m["output"].([]any); !ok {
+		return false
+	}
+	return stringOf(m["object"]) == "response" || m["status"] != nil
+}
+
+func (f *formatting) responsesRequest(m map[string]any) OutputMessages {
+	var msgs OutputMessages
+	if parts := systemParts(m["instructions"]); len(parts) > 0 {
+		msgs = append(msgs, OutputMessage{Role: RoleSystem, Parts: parts})
+	}
+	switch input := m["input"].(type) {
+	case string:
+		msgs = append(msgs, OutputMessage{Role: RoleUser, Parts: Parts{part(textPart(input))}})
+	case []any:
+		msgs = append(msgs, foldAssistant(f.responsesItems(input))...)
+	}
+	return msgs
+}
+
+// responsesResponse folds the output items into one message: the API returns a single choice. The
+// finish reason is the incomplete_details reason, else the status:
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69763
+func (f *formatting) responsesResponse(m map[string]any) OutputMessages {
+	output, _ := m["output"].([]any)
+	msgs := foldAssistant(f.responsesItems(output))
+	if len(msgs) == 0 {
+		return msgs
+	}
+	last := &msgs[len(msgs)-1]
+	if details, ok := m["incomplete_details"].(map[string]any); ok {
+		last.FinishReason = finishReason(stringOf(details["reason"]))
+	} else if status := stringOf(m["status"]); status != "" {
+		last.FinishReason = finishReason(status)
+	}
+	return msgs
+}
+
+// responsesItems converts items one message each; the caller folds assistant runs.
+func (f *formatting) responsesItems(items []any) OutputMessages {
+	msgs := make(OutputMessages, 0, len(items))
+	for _, item := range items {
+		m, ok := item.(map[string]any)
+		if !ok {
+			if s, ok := item.(string); ok {
+				msgs = append(msgs, OutputMessage{Role: RoleUser, Parts: Parts{part(textPart(s))}})
+				continue
+			}
+			msgs = append(msgs, genericMessage(item))
+			continue
+		}
+		typ := stringOf(m["type"])
+		switch {
+		case typ == "" || typ == "message":
+			msgs = append(msgs, f.openAIChatMessage(m))
+		case typ == "function_call" || typ == "custom_tool_call":
+			args := m["arguments"]
+			if args == nil {
+				args = m["input"]
+			}
+			call := ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(m["name"]), ID: optionalString(m["call_id"]), Arguments: decode(args)}
+			msgs = append(msgs, OutputMessage{Role: RoleAssistant, Parts: Parts{part(call)}})
+		case typ == "function_call_output" || typ == "custom_tool_call_output":
+			result := ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: optionalString(m["call_id"]), Response: toolResponse(m["output"])}
+			msgs = append(msgs, OutputMessage{Role: RoleTool, Parts: Parts{part(result)}})
+		case typ == "reasoning":
+			if p, ok := reasoningItem(m); ok {
+				msgs = append(msgs, OutputMessage{Role: RoleAssistant, Parts: Parts{p}})
+			}
+		case typ == "compaction":
+			msgs = append(msgs, OutputMessage{Role: RoleAssistant, Parts: Parts{part(CompactionPart{Type: PartTypeCompaction, ID: optionalString(m["id"]), Content: optionalString(m["content"])})}})
+		case strings.HasSuffix(typ, "_call"):
+			call := ServerToolCallPart{Type: PartTypeServerToolCall, Name: strings.TrimSuffix(typ, "_call"), ID: optionalString(m["call_id"]), ServerToolCall: GenericServerToolCall(m)}
+			msgs = append(msgs, OutputMessage{Role: RoleAssistant, Parts: Parts{part(call)}})
+		case strings.HasSuffix(typ, "_output") || typ == "mcp_approval_response":
+			result := ServerToolCallResponsePart{Type: PartTypeServerToolCallResponse, ID: optionalString(m["call_id"]), ServerToolCallResponse: GenericServerToolCallResponse(m)}
+			msgs = append(msgs, OutputMessage{Role: RoleTool, Parts: Parts{part(result)}})
+		default:
+			msgs = append(msgs, OutputMessage{Role: RoleAssistant, Parts: Parts{part(GenericPart(m))}})
+		}
+	}
+	return msgs
+}
+
+// reasoningItem joins the summary texts. An item with neither a summary nor encrypted content
+// carries nothing to show:
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69620
+func reasoningItem(m map[string]any) (Part, bool) {
+	var texts []string
+	if summary, ok := m["summary"].([]any); ok {
+		for _, item := range summary {
+			if s, ok := item.(map[string]any); ok && stringOf(s["text"]) != "" {
+				texts = append(texts, stringOf(s["text"]))
+			}
+		}
+	}
+	if len(texts) == 0 && stringOf(m["encrypted_content"]) == "" {
+		return Part{}, false
+	}
+	return part(ReasoningPart{Type: PartTypeReasoning, Content: strings.Join(texts, "\n")}), true
+}

pkg/types/aiobservabilitytypes/message_format_openai_responses_test.go

@@ -0,0 +1,85 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Cases follow the OpenAI Responses schemas; the first mirrors the raw bodies in the SigNoz/scripts
+// openinference-openai-agents capture, which OpenInference's flattened messages otherwise cover.
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L47597
+// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69691
+func TestFormat_OpenAIResponses(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// openinference-openai-agents capture, Response span: instructions outside the history, a reasoning
+			// item without a summary, then the message
+			name:   "InstructionsAndStringInput_ReasoningWithoutSummaryDropped",
+			input:  `{"include": [], "input": [{"content": "A bat and a ball cost $1.10. How much is the ball?", "role": "user"}], "instructions": "You solve problems step by step and give a final answer.", "model": "o3-mini", "tools": []}`,
+			output: `{"id":"resp_1","object":"response","status":"completed","model":"o3-mini-2025-01-31","output":[{"id":"rs_1","summary":[],"type":"reasoning"},{"id":"msg_1","content":[{"annotations":[],"text":"Final Answer: The ball costs $0.05.","type":"output_text","logprobs":[]}],"role":"assistant","status":"completed","type":"message"}],"usage":{"input_tokens":49,"output_tokens":431}}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You solve problems step by step and give a final answer."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "A bat and a ball cost $1.10. How much is the ball?"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Final Answer: The ball costs $0.05."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// an InputMessage with every InputContent type, then function and custom tool calls with their
+			// outputs, a compaction item and a computer_call_output
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L53827
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L54142
+			name:  "InputItems_EveryContentType_FunctionCallRound",
+			input: `{"model":"gpt-4o-mini","input":[{"role":"user","content":[{"type":"input_text","text":"What is in this image and these files?"},{"type":"input_image","image_url":"https://example.com/duck.jpg","detail":"auto"},{"type":"input_image","file_id":"file-img","detail":"low"},{"type":"input_file","file_id":"file-abc"},{"type":"input_file","filename":"a.pdf","file_data":"data:application/pdf;base64,JVBERi0="},{"type":"input_file","file_url":"https://example.com/a.pdf"}]},{"type":"function_call","call_id":"call_1","name":"get_weather","arguments":"{\"city\":\"Paris\"}"},{"type":"function_call_output","call_id":"call_1","output":"{\"temp_c\":18}"},{"type":"custom_tool_call","call_id":"c1","name":"shell","input":"ls"},{"type":"custom_tool_call_output","call_id":"c1","output":"a.txt"},{"type":"compaction","id":"cmp_1","encrypted_content":"x"},{"type":"computer_call_output","call_id":"cc_1","output":{"type":"computer_screenshot","image_url":"data:image/png;base64,iVBORw0KGgo="}}]}`,
+			wantInput: `[
+			  {"role": "user", "parts": [
+			    {"type": "text", "content": "What is in this image and these files?"},
+			    {"type": "uri", "modality": "image", "uri": "https://example.com/duck.jpg"},
+			    {"type": "file", "modality": "image", "file_id": "file-img"},
+			    {"type": "file", "modality": "document", "file_id": "file-abc", "mime_type": "application/pdf"},
+			    {"type": "blob", "modality": "document", "mime_type": "application/pdf", "content": "JVBERi0="},
+			    {"type": "uri", "modality": "document", "uri": "https://example.com/a.pdf"}
+			  ]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_1", "arguments": {"city": "Paris"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_1", "response": {"temp_c": 18}}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "shell", "id": "c1", "arguments": "ls"}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "c1", "response": "a.txt"}]},
+			  {"role": "assistant", "parts": [{"type": "compaction", "id": "cmp_1"}]},
+			  {"role": "tool", "parts": [{"type": "server_tool_call_response", "id": "cc_1", "server_tool_call_response": {"type": "computer_call_output", "call_id": "cc_1", "output": {"type": "computer_screenshot", "image_url": "data:image/png;base64,iVBORw0KGgo="}}}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// output items folded into the single choice: a ReasoningItem summary, a WebSearchToolCall, the
+			// OutputMessage with output_text and refusal, then a FunctionToolCall
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L57596
+			name:      "OutputItems_FoldedIntoOneMessage_StatusCompleted",
+			output:    `{"id":"resp_2","object":"response","status":"completed","output":[{"id":"rs_1","type":"reasoning","summary":[{"type":"summary_text","text":"Need the weather first."}],"encrypted_content":"gAAA"},{"id":"ws_1","type":"web_search_call","status":"completed","action":{"type":"search","query":"weather Paris"}},{"id":"msg_1","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Checking.","annotations":[]},{"type":"refusal","refusal":"I cannot do that."}]},{"type":"function_call","call_id":"call_1","name":"get_weather","arguments":"{\"city\":\"Paris\"}","id":"fc_1","status":"completed"}]}`,
+			wantInput: `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "reasoning", "content": "Need the weather first."},
+			  {"type": "server_tool_call", "name": "web_search", "server_tool_call": {"id": "ws_1", "type": "web_search_call", "status": "completed", "action": {"type": "search", "query": "weather Paris"}}},
+			  {"type": "text", "content": "Checking."},
+			  {"type": "text", "content": "I cannot do that."},
+			  {"type": "tool_call", "name": "get_weather", "id": "call_1", "arguments": {"city": "Paris"}}
+			], "finish_reason": "stop"}]`,
+		},
+		{
+			// https://github.com/openai/openai-openapi/blob/aeb6a041dc56c011ca9f12127ec898f911100aa0/openapi.yaml#L69763
+			name:       "IncompleteResponse_FinishFromIncompleteDetails",
+			output:     `{"object":"response","status":"incomplete","incomplete_details":{"reason":"max_output_tokens"},"output":[{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Partial"}]}]}`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Partial"}], "finish_reason": "length"}]`,
+		},
+		{
+			name:       "StringInput_UserMessage",
+			input:      `{"model":"gpt-4o-mini","input":"Say hi","instructions":"Be brief."}`,
+			wantInput:  `[{"role": "system", "parts": [{"type": "text", "content": "Be brief."}]}, {"role": "user", "parts": [{"type": "text", "content": "Say hi"}]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// openinference capture, CreateEmbeddings span: an embeddings request also has input and model
+			name:       "EmbeddingsRequest_NotResponses_KeptAsGeneric",
+			input:      `{"input": ["observability is the new monitoring"], "model": "text-embedding-3-small", "encoding_format": "base64"}`,
+			warnings:   []string{"message format not recognised, kept as generic"},
+			wantInput:  `[{"role": "", "parts": [{"type": "generic", "content": "{\"encoding_format\":\"base64\",\"input\":[\"observability is the new monitoring\"],\"model\":\"text-embedding-3-small\"}"}]}]`,
+			wantOutput: `[]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_openinference.go

@@ -0,0 +1,137 @@
+package aiobservabilitytypes
+
+import "github.com/SigNoz/signoz/pkg/valuer"
+
+// Format: the flattened llm.input_messages.N.message.* and llm.output_messages.N.message.* keys, the
+// provider-neutral copy of the conversation the OpenInference instrumentations write on LLM spans. A side
+// without them falls back to the raw provider payload the mapper moved onto gen_ai.*.messages; pipecat
+// streams the reply into output.value alone.
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/semantic_conventions.md#L77
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/tool_calling.md#L37
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/multimodal_attributes.md#L35
+// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/llm_spans.md#L181
+// Handled: message.content, the ordered message.contents items (text, image, audio, video, reasoning),
+// message.tool_calls, the legacy message.function_call_*, tool results, llm.finish_reason. A tool_use
+// content item repeats a call from message.tool_calls and is skipped.
+// Dropped: reasoning id, signature, data and encrypted_content; audio transcripts.
+
+const (
+	openInferenceInputMessages  = "llm.input_messages"
+	openInferenceOutputMessages = "llm.output_messages"
+	openInferenceFinishReason   = "llm.finish_reason"
+)
+
+func (f *formatting) openInference() bool {
+	input := indexed(f.attributes, openInferenceInputMessages)
+	output := indexed(f.attributes, openInferenceOutputMessages)
+	if len(input) == 0 && len(output) == 0 {
+		return false
+	}
+	inputLabel, outputLabel := FormatterOpenInference, FormatterOpenInference
+	if len(input) > 0 {
+		f.out.Input = inputMessages(openInferenceMessages(input))
+	} else if raw := attribute(f.attributes, GenAIInputMessages); raw != nil {
+		var msgs OutputMessages
+		msgs, inputLabel = f.side(decode(raw), RoleUser)
+		f.out.Input = inputMessages(msgs)
+	}
+	if len(output) > 0 {
+		f.out.Output = openInferenceMessages(output)
+		if fr := finishReason(stringOf(attribute(f.attributes, openInferenceFinishReason))); fr != nil {
+			for i := range f.out.Output {
+				f.out.Output[i].FinishReason = fr
+			}
+		}
+	} else if raw := attribute(f.attributes, GenAIOutputMessages); raw != nil {
+		f.out.Output, outputLabel = f.side(decode(raw), RoleAssistant)
+	}
+	f.warnMixedFormats(inputLabel, outputLabel)
+	return true
+}
+
+// openInferenceMessages converts the message buckets and folds runs of assistant messages: the Responses
+// path writes one message per output item, an empty one for a reasoning item without a summary.
+func openInferenceMessages(buckets []map[string]any) OutputMessages {
+	msgs := make(OutputMessages, 0, len(buckets))
+	for _, bucket := range buckets {
+		message, _ := bucket["message"].(map[string]any)
+		msgs = append(msgs, openInferenceMessage(message))
+	}
+	return foldAssistant(msgs)
+}
+
+// openInferenceMessage converts one message.* object. A message without a role is a tool response when
+// it has a tool_call_id, an assistant turn when it has tool calls, else a user turn, as the
+// collector-contrib normalizer decides it:
+// https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/fc79b95297776dec6c47490cd6f6026b942bf6fb/processor/genainormalizerprocessor/internal/openinference/messages.go#L473
+func openInferenceMessage(m map[string]any) OutputMessage {
+	msg := OutputMessage{Role: role(stringOf(m["role"])), Parts: Parts{}, Name: optionalString(m["name"])}
+	id := stringOf(m["tool_call_id"])
+	calls := indexed(m, "tool_calls")
+	if msg.Role.IsZero() {
+		switch {
+		case id != "":
+			msg.Role = RoleTool
+		case len(calls) > 0:
+			msg.Role = RoleAssistant
+		default:
+			msg.Role = RoleUser
+		}
+	}
+	if msg.Role == RoleTool && id != "" {
+		msg.Parts = Parts{part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: &id, Response: decode(m["content"])})}
+		return msg
+	}
+	if content := stringOf(m["content"]); content != "" {
+		msg.Parts = append(msg.Parts, part(textPart(content)))
+	}
+	msg.Parts = append(msg.Parts, openInferenceContents(m)...)
+	for _, call := range calls {
+		if c, ok := call["tool_call"].(map[string]any); ok {
+			msg.Parts = append(msg.Parts, openInferenceToolCall(c))
+		}
+	}
+	if name := stringOf(m["function_call_name"]); name != "" {
+		msg.Parts = append(msg.Parts, part(ToolCallRequestPart{Type: PartTypeToolCall, Name: name, Arguments: decode(m["function_call_arguments_json"])}))
+	}
+	return msg
+}
+
+// openInferenceContents converts the ordered message.contents items. A text item may come without
+// message_content.type, as the collector-contrib normalizer reads it:
+// https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/fc79b95297776dec6c47490cd6f6026b942bf6fb/processor/genainormalizerprocessor/internal/openinference/messages.go#L285
+func openInferenceContents(m map[string]any) Parts {
+	items := indexed(m, "contents")
+	parts := make(Parts, 0, len(items))
+	for _, item := range items {
+		content, _ := item["message_content"].(map[string]any)
+		typ := stringOf(content["type"])
+		if typ == "" && content["text"] != nil {
+			typ = "text"
+		}
+		switch typ {
+		case "text":
+			parts = append(parts, part(textPart(stringOf(content["text"]))))
+		case "reasoning":
+			parts = append(parts, part(ReasoningPart{Type: PartTypeReasoning, Content: stringOf(content["text"])}))
+		case "image", "audio", "video":
+			if url := stringOf(attribute(content, typ+"."+typ+".url")); url != "" {
+				parts = append(parts, part(mediaPart(url, Modality{valuer.NewString(typ)})))
+			}
+		case "tool_use":
+		default:
+			parts = append(parts, part(GenericPart(content)))
+		}
+	}
+	return parts
+}
+
+// openInferenceToolCall reads a tool_call.* object, {id, function: {name, arguments}}.
+func openInferenceToolCall(call map[string]any) Part {
+	fn, _ := call["function"].(map[string]any)
+	p := ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(fn["name"]), ID: optionalString(call["id"])}
+	if args, ok := fn["arguments"]; ok {
+		p.Arguments = decode(args)
+	}
+	return part(p)
+}

pkg/types/aiobservabilitytypes/message_format_openinference_test.go

@@ -0,0 +1,324 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Captured cases are the llm.input_messages.N.* and llm.output_messages.N.* keys as the collector
+// stored them for the SigNoz/scripts static-telemetry-generator captures.
+func TestFormat_OpenInference(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// openinference capture, ChatCompletion span: the flattened keys win over the raw request the
+			// mapper moved onto gen_ai.input.messages
+			name: "FlattenedMessages_RawRequestIgnored",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":     "system",
+				"llm.input_messages.0.message.content":  "You are concise.",
+				"llm.input_messages.1.message.role":     "user",
+				"llm.input_messages.1.message.content":  "Say hello in five words.",
+				"llm.output_messages.0.message.role":    "assistant",
+				"llm.output_messages.0.message.content": "Hello! How are you today?",
+				"llm.finish_reason":                     "stop",
+				GenAIInputMessages:                      `{"messages": [{"role": "system", "content": "You are concise."}, {"role": "user", "content": "Say hello in five words."}], "model": "gpt-4o-mini", "max_tokens": 50, "temperature": 0.2}`,
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Say hello in five words."}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello! How are you today?"}], "finish_reason": "stop"}]`,
+		},
+		{
+			// openinference capture, ChatCompletion span: message.tool_calls and llm.finish_reason
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/tool_calling.md#L37
+			name: "ToolCalls_ToolCallParts_FinishReasonFromLLMKey",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                       "user",
+				"llm.input_messages.0.message.content":                                    "What is the weather in Bengaluru in celsius?",
+				"llm.output_messages.0.message.role":                                      "assistant",
+				"llm.output_messages.0.message.tool_calls.0.tool_call.id":                 "call_MdYKTRrkshuBG9W5oBXYZaRc",
+				"llm.output_messages.0.message.tool_calls.0.tool_call.function.name":      "get_current_weather",
+				"llm.output_messages.0.message.tool_calls.0.tool_call.function.arguments": `{"city":"Bengaluru","unit":"c"}`,
+				"llm.finish_reason": "tool_calls",
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "What is the weather in Bengaluru in celsius?"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "get_current_weather", "id": "call_MdYKTRrkshuBG9W5oBXYZaRc", "arguments": {"city": "Bengaluru", "unit": "c"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// openinference capture, the follow-up ChatCompletion span: tool role messages carry the result
+			// in message.content with their tool_call_id
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/llm_spans.md#L50
+			name: "ToolRoleMessages_ToolCallResponseParts",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                      "user",
+				"llm.input_messages.0.message.content":                                   "What's the weather in Paris and in London? Use the tool for each.",
+				"llm.input_messages.1.message.role":                                      "assistant",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.id":                 "call_SW4hst7nzysgvWSAz4OdyK9D",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.function.name":      "get_current_weather",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.function.arguments": `{"city": "Paris"}`,
+				"llm.input_messages.1.message.tool_calls.1.tool_call.id":                 "call_si8wiETYGpooMB7gMXSgMQpk",
+				"llm.input_messages.1.message.tool_calls.1.tool_call.function.name":      "get_current_weather",
+				"llm.input_messages.1.message.tool_calls.1.tool_call.function.arguments": `{"city": "London"}`,
+				"llm.input_messages.2.message.role":                                      "tool",
+				"llm.input_messages.2.message.tool_call_id":                              "call_SW4hst7nzysgvWSAz4OdyK9D",
+				"llm.input_messages.2.message.content":                                   `{"city": "Paris", "temp_c": 18, "summary": "Clear"}`,
+				"llm.input_messages.3.message.role":                                      "tool",
+				"llm.input_messages.3.message.tool_call_id":                              "call_si8wiETYGpooMB7gMXSgMQpk",
+				"llm.input_messages.3.message.content":                                   `{"city": "London", "temp_c": 18, "summary": "Clear"}`,
+				"llm.output_messages.0.message.role":                                     "assistant",
+				"llm.output_messages.0.message.content":                                  "The current weather is as follows:\n\n- **Paris**: 18°C, Clear\n- **London**: 18°C, Clear",
+				"llm.finish_reason":                                                      "stop",
+			},
+			wantInput: `[
+			  {"role": "user", "parts": [{"type": "text", "content": "What's the weather in Paris and in London? Use the tool for each."}]},
+			  {"role": "assistant", "parts": [
+			    {"type": "tool_call", "name": "get_current_weather", "id": "call_SW4hst7nzysgvWSAz4OdyK9D", "arguments": {"city": "Paris"}},
+			    {"type": "tool_call", "name": "get_current_weather", "id": "call_si8wiETYGpooMB7gMXSgMQpk", "arguments": {"city": "London"}}
+			  ]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_SW4hst7nzysgvWSAz4OdyK9D", "response": {"city": "Paris", "temp_c": 18, "summary": "Clear"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_si8wiETYGpooMB7gMXSgMQpk", "response": {"city": "London", "temp_c": 18, "summary": "Clear"}}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "The current weather is as follows:\n\n- **Paris**: 18°C, Clear\n- **London**: 18°C, Clear"}], "finish_reason": "stop"}]`,
+		},
+		{
+			// openinference capture, vision call; base64 trimmed. An image as a data URL:
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/multimodal_attributes.md#L47
+			name: "ContentsTextAndImage_TextAndBlobParts",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                       "user",
+				"llm.input_messages.0.message.contents.0.message_content.type":            "text",
+				"llm.input_messages.0.message.contents.0.message_content.text":            "What animal is in this image? One sentence.",
+				"llm.input_messages.0.message.contents.1.message_content.type":            "image",
+				"llm.input_messages.0.message.contents.1.message_content.image.image.url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBD",
+				"llm.output_messages.0.message.role":                                      "assistant",
+				"llm.output_messages.0.message.content":                                   "The animal in the image is a duck.",
+				"llm.finish_reason":                                                       "stop",
+			},
+			wantInput: `[{"role": "user", "parts": [
+			  {"type": "text", "content": "What animal is in this image? One sentence."},
+			  {"type": "blob", "modality": "image", "mime_type": "image/jpeg", "content": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBD"}
+			]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "The animal in the image is a duck."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// openinference-openai-agents capture, Response span: one output message per item, an empty one
+			// for a reasoning item without a summary
+			name: "ResponsesOutputItems_AssistantMessagesFolded_EmptyDropped",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                  "system",
+				"llm.input_messages.0.message.content":                               "You solve problems step by step and give a final answer.",
+				"llm.input_messages.1.message.role":                                  "user",
+				"llm.input_messages.1.message.content":                               "A bat and a ball cost $1.10. How much is the ball?",
+				"llm.output_messages.0.message.role":                                 "assistant",
+				"llm.output_messages.1.message.role":                                 "assistant",
+				"llm.output_messages.1.message.contents.0.message_content.type":      "text",
+				"llm.output_messages.1.message.contents.0.message_content.text":      "Final Answer: The ball costs $0.05.",
+				"llm.output_messages.2.message.role":                                 "assistant",
+				"llm.output_messages.2.message.tool_calls.0.tool_call.id":            "call_1",
+				"llm.output_messages.2.message.tool_calls.0.tool_call.function.name": "done",
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You solve problems step by step and give a final answer."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "A bat and a ball cost $1.10. How much is the ball?"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "text", "content": "Final Answer: The ball costs $0.05."},
+			  {"type": "tool_call", "name": "done", "id": "call_1"}
+			]}]`,
+		},
+		{
+			// openinference capture, the ChatCompletion span of a failed call
+			name: "InputOnly_NoOutput",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":    "user",
+				"llm.input_messages.0.message.content": "this should fail",
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "this should fail"}]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// the strands processor keeps the SDK's gen_ai.system_instructions on the span and inserts the same
+			// prompt as message 0
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L152
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L293
+			name: "StrandsSystemInstructionsRepeated_OneSystemMessage",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":     "system",
+				"llm.input_messages.0.message.content":  "You are concise.",
+				"llm.input_messages.1.message.role":     "user",
+				"llm.input_messages.1.message.content":  "hi",
+				"llm.output_messages.0.message.role":    "assistant",
+				"llm.output_messages.0.message.content": "Hello.",
+				GenAISystemInstructions:                 `[{"type": "text", "content": "You are concise."}]`,
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "hi"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello."}]}]`,
+		},
+		{
+			// pipecat writes the request as llm.input_messages and the streamed reply as output.value alone,
+			// which the mapper moves onto gen_ai.output.messages
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-pipecat/src/openinference/instrumentation/pipecat/_attributes.py#L430
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-pipecat/src/openinference/instrumentation/pipecat/_observer.py#L628
+			name: "OutputKeysAbsent_RawOutputFormatted_Warns",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":    "user",
+				"llm.input_messages.0.message.content": "hi",
+				GenAIOutputMessages:                    "Hello there.",
+			},
+			warnings:   []string{"bare assistant text, role assumed", "output formatted as text, input as openinference"},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "hi"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello there."}]}]`,
+		},
+		{
+			// message.role left unset by some instrumentors: tool_call_id, tool_calls or neither decide it,
+			// as the collector-contrib normalizer does
+			// https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/fc79b95297776dec6c47490cd6f6026b942bf6fb/processor/genainormalizerprocessor/internal/openinference/messages.go#L473
+			name: "MissingRole_InferredFromToolFields",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.content":                                   "Weather in Oslo?",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.id":                 "call_9",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.function.name":      "get_weather",
+				"llm.input_messages.1.message.tool_calls.0.tool_call.function.arguments": `{"city":"Oslo"}`,
+				"llm.input_messages.2.message.tool_call_id":                              "call_9",
+				"llm.input_messages.2.message.content":                                   "4C, cloudy",
+			},
+			wantInput: `[
+			  {"role": "user", "parts": [{"type": "text", "content": "Weather in Oslo?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_9", "arguments": {"city": "Oslo"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_9", "response": "4C, cloudy"}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// message_content.type left unset on text items by some instrumentors, as the collector-contrib
+			// normalizer reads them
+			// https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/fc79b95297776dec6c47490cd6f6026b942bf6fb/processor/genainormalizerprocessor/internal/openinference/messages.go#L285
+			name: "UntypedContentWithText_TextPart",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                            "user",
+				"llm.input_messages.0.message.contents.0.message_content.text": "Summarise this.",
+				"llm.input_messages.0.message.contents.1.message_content.text": "Second block.",
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "Summarise this."}, {"type": "text", "content": "Second block."}]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// an image given by URL
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/multimodal_attributes.md#L42
+			name: "ImageURL_UriPart",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                       "user",
+				"llm.input_messages.0.message.contents.0.message_content.type":            "image",
+				"llm.input_messages.0.message.contents.0.message_content.image.image.url": "https://example.com/duck.jpg",
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "uri", "modality": "image", "uri": "https://example.com/duck.jpg"}]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// audio by URL with its transcript, or as the data URI built from a Chat Completions input_audio
+			// block; the transcript has no part to go to
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/multimodal_attributes.md#L55
+			name: "Audio_UriAndBlobParts_TranscriptDropped",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                              "user",
+				"llm.input_messages.0.message.contents.0.message_content.type":                   "audio",
+				"llm.input_messages.0.message.contents.0.message_content.audio.audio.url":        "https://example.com/hello.mp3",
+				"llm.input_messages.0.message.contents.0.message_content.audio.audio.transcript": "Hello, how are you?",
+				"llm.input_messages.0.message.contents.1.message_content.type":                   "audio",
+				"llm.input_messages.0.message.contents.1.message_content.audio.audio.url":        "data:audio/wav;base64,UklGRg==",
+			},
+			wantInput: `[{"role": "user", "parts": [
+			  {"type": "uri", "modality": "audio", "uri": "https://example.com/hello.mp3"},
+			  {"type": "blob", "modality": "audio", "mime_type": "audio/wav", "content": "UklGRg=="}
+			]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// reasoning items; id, signature, data and encrypted_content have no field on the reasoning part
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/llm_spans.md#L181
+			name: "Reasoning_ReasoningParts_ProviderKeysDropped",
+			attributes: map[string]any{
+				"llm.output_messages.0.message.role":                                         "assistant",
+				"llm.output_messages.0.message.contents.0.message_content.type":              "reasoning",
+				"llm.output_messages.0.message.contents.0.message_content.text":              "The ball must cost less than a dollar.",
+				"llm.output_messages.0.message.contents.0.message_content.signature":         "EuYBCkQYAiJA",
+				"llm.output_messages.0.message.contents.1.message_content.type":              "reasoning",
+				"llm.output_messages.0.message.contents.1.message_content.data":              "EmwKAhgBEgy3",
+				"llm.output_messages.0.message.contents.2.message_content.type":              "reasoning",
+				"llm.output_messages.0.message.contents.2.message_content.id":                "rs_abc123",
+				"llm.output_messages.0.message.contents.2.message_content.encrypted_content": "gAAAAA==",
+				"llm.output_messages.0.message.contents.3.message_content.type":              "text",
+				"llm.output_messages.0.message.contents.3.message_content.text":              "The ball costs 5 cents.",
+			},
+			wantInput: `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "reasoning", "content": "The ball must cost less than a dollar."},
+			  {"type": "reasoning", "content": ""},
+			  {"type": "reasoning", "content": ""},
+			  {"type": "text", "content": "The ball costs 5 cents."}
+			]}]`,
+		},
+		{
+			// a tool call repeated as an ordered tool_use content item; message.tool_calls stays the one
+			// source. llm.finish_reason is Anthropic's stop_reason as the instrumentation passes it through
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/tool_calling.md#L63
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-anthropic/src/openinference/instrumentation/anthropic/_wrappers.py#L938
+			name: "ToolUseBesideToolCalls_OneToolCallPart",
+			attributes: map[string]any{
+				"llm.output_messages.0.message.role":                                      "assistant",
+				"llm.output_messages.0.message.contents.0.message_content.type":           "text",
+				"llm.output_messages.0.message.contents.0.message_content.text":           "Let me check.",
+				"llm.output_messages.0.message.contents.1.message_content.type":           "tool_use",
+				"llm.output_messages.0.message.contents.1.tool_call.id":                   "toolu_01",
+				"llm.output_messages.0.message.contents.1.tool_call.function.name":        "get_weather",
+				"llm.output_messages.0.message.contents.1.tool_call.function.arguments":   `{"city": "Paris"}`,
+				"llm.output_messages.0.message.tool_calls.0.tool_call.id":                 "toolu_01",
+				"llm.output_messages.0.message.tool_calls.0.tool_call.function.name":      "get_weather",
+				"llm.output_messages.0.message.tool_calls.0.tool_call.function.arguments": `{"city": "Paris"}`,
+				"llm.finish_reason": "tool_use",
+			},
+			wantInput: `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "text", "content": "Let me check."},
+			  {"type": "tool_call", "name": "get_weather", "id": "toolu_01", "arguments": {"city": "Paris"}}
+			], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// a content type the spec does not name is kept as sent
+			name: "UnknownContentType_GenericPart",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":                                            "user",
+				"llm.input_messages.0.message.contents.0.message_content.type":                 "document",
+				"llm.input_messages.0.message.contents.0.message_content.document.document.id": "file_1",
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "document", "document": {"document": {"id": "file_1"}}}]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// the legacy function_call keys
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/semantic_conventions.md#L111
+			name: "LegacyFunctionCall_ToolCallPart",
+			attributes: map[string]any{
+				"llm.output_messages.0.message.role":                         "assistant",
+				"llm.output_messages.0.message.function_call_name":           "multiply",
+				"llm.output_messages.0.message.function_call_arguments_json": `{"x": 2}`,
+			},
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "multiply", "arguments": {"x": 2}}]}]`,
+		},
+		{
+			// a tool result with message.name naming the function
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/llm_spans.md#L50
+			name: "ToolResultWithName_NameKept",
+			attributes: map[string]any{
+				"llm.input_messages.0.message.role":         "tool",
+				"llm.input_messages.0.message.name":         "multiply",
+				"llm.input_messages.0.message.tool_call_id": "call_62136355",
+				"llm.input_messages.0.message.content":      "2001",
+			},
+			wantInput:  `[{"role": "tool", "name": "multiply", "parts": [{"type": "tool_call_response", "id": "call_62136355", "response": "2001"}]}]`,
+			wantOutput: `[]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_openrouter.go

@@ -0,0 +1,47 @@
+package aiobservabilitytypes
+
+import "strings"
+
+// Format: the {completion, reasoning, tools, rawRequest} answer object OpenRouter's broadcast writes as
+// gen_ai.completion on its LLM Generation spans, beside the OpenAI chat {messages} request it writes as
+// gen_ai.prompt; the span mapper moves both onto gen_ai.*.messages. A tool call arrives as the tool name
+// followed by its JSON arguments in completion, so it is split against the names in tools.
+// https://openrouter.ai/docs/guides/features/broadcast/otel-collector
+
+func isOpenRouterCompletion(m map[string]any) bool {
+	_, ok := m["completion"].(string)
+	return ok
+}
+
+func (f *formatting) openRouterCompletion(m map[string]any) OutputMessages {
+	msg := OutputMessage{Role: RoleAssistant, Parts: Parts{}}
+	if reasoning := stringOf(m["reasoning"]); reasoning != "" {
+		msg.Parts = append(msg.Parts, part(ReasoningPart{Type: PartTypeReasoning, Content: reasoning}))
+	}
+	completion := stringOf(m["completion"])
+	if call, ok := openRouterToolCall(completion, m["tools"]); ok {
+		msg.Parts = append(msg.Parts, call)
+		msg.FinishReason = finishReason("tool_calls")
+	} else if completion != "" {
+		msg.Parts = append(msg.Parts, part(textPart(completion)))
+	}
+	return OutputMessages{msg}
+}
+
+func openRouterToolCall(completion string, tools any) (Part, bool) {
+	list, _ := tools.([]any)
+	for _, item := range list {
+		tool, _ := item.(map[string]any)
+		fn, _ := tool["function"].(map[string]any)
+		name := stringOf(fn["name"])
+		if name == "" || !strings.HasPrefix(completion, name) {
+			continue
+		}
+		args, ok := decode(strings.TrimPrefix(completion, name)).(map[string]any)
+		if !ok {
+			continue
+		}
+		return part(ToolCallRequestPart{Type: PartTypeToolCall, Name: name, Arguments: args}), true
+	}
+	return Part{}, false
+}

pkg/types/aiobservabilitytypes/message_format_openrouter_test.go

@@ -0,0 +1,35 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Captured cases are gen_ai.input.messages and gen_ai.output.messages as the collector stored them for
+// the SigNoz/scripts openrouter-gateway capture, LLM Generation spans.
+func TestFormat_OpenRouter(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// the completion is the tool name followed by the JSON arguments
+			name:       "CompletionIsToolNameAndArguments_ToolCallPart",
+			input:      `{"messages":[{"role":"user","content":"What's the weather in Bengaluru? Use the tool."}]}`,
+			output:     `{"completion":"get_current_weather{\"city\":\"Bengaluru\"}","reasoning":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather for a city.","parameters":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}}}],"rawRequest":{"model":"openai/gpt-4o-mini","max_tokens":80,"tool_choice":"auto"}}`,
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "What's the weather in Bengaluru? Use the tool."}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "get_current_weather", "arguments": {"city": "Bengaluru"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			name:   "CompletionWithReasoning_ReasoningThenText",
+			input:  `{"messages":[{"role":"system","content":"You are concise."},{"role":"user","content":"Say hello in five words."}]}`,
+			output: `{"completion":"Hello there, how are you?","reasoning":"A greeting of exactly five words.","tools":[],"rawRequest":{"model":"openai/gpt-4o-mini"}}`,
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Say hello in five words."}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "reasoning", "content": "A greeting of exactly five words."}, {"type": "text", "content": "Hello there, how are you?"}]}]`,
+		},
+		{
+			// a completion that only starts with a tool name stays text
+			name:       "CompletionStartsWithToolNameButNoArguments_Text",
+			output:     `{"completion":"get_current_weather is the tool to call.","reasoning":null,"tools":[{"type":"function","function":{"name":"get_current_weather"}}]}`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "get_current_weather is the tool to call."}]}]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_semconv.go

@@ -6,15 +6,20 @@ import (
 )
 
 // Format: the OTel shape itself, [{role, parts}].
-// Written by: litellm, langchain, openllmetry, portkey, traceloop LLM spans.
-// Decoding is the genai types. Repairs: "text" used for "content" on text parts; OpenAI content
-// blocks left inside parts.
-// Not yet: "thinking" for "reasoning"; "result" or "output" for "response" on tool call responses.
+// Written by: litellm, langchain, openllmetry, portkey, traceloop LLM spans, pydantic-ai.
+// Decoding is the genai types. Repairs: "text" used for "content" on text parts; OpenAI content blocks
+// left inside parts; tool call arguments as a JSON string and "result" for "response", as pydantic-ai
+// writes them:
+// https://github.com/pydantic/pydantic-ai/blob/9ac853bb84ffed7ba0edccb026c5b4f3228c1de7/pydantic_ai_slim/pydantic_ai/messages.py#L3130
+// https://github.com/pydantic/pydantic-ai/blob/9ac853bb84ffed7ba0edccb026c5b4f3228c1de7/pydantic_ai_slim/pydantic_ai/messages.py#L1724
+// The choice finish_reason as the provider spells it, as litellm copies it:
+// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2822
+// Not yet: "thinking" for "reasoning".
 
 // semconv decodes messages that already follow the schema through the genai types. A part of a
 // type the schema does not name is a provider content block, which contentPart converts.
 func (f *formatting) semconv(list []any) OutputMessages {
-	data, err := json.Marshal(semconvTextKey(list))
+	data, err := json.Marshal(semconvRepairs(list))
 	if err != nil {
 		f.warn("messages could not be encoded: %s", err)
 		return OutputMessages{genericMessage(list)}
@@ -28,6 +33,9 @@ func (f *formatting) semconv(list []any) OutputMessages {
 		if msgs[i].Parts == nil {
 			msgs[i].Parts = Parts{}
 		}
+		if msgs[i].FinishReason != nil {
+			msgs[i].FinishReason = finishReason(msgs[i].FinishReason.StringValue())
+		}
 		for j, p := range msgs[i].Parts {
 			if block, ok := p.Value.(GenericPart); ok {
 				msgs[i].Parts[j] = contentPart(map[string]any(block))
@@ -37,9 +45,9 @@ func (f *formatting) semconv(list []any) OutputMessages {
 	return msgs
 }
 
-// semconvTextKey copies the text and reasoning parts some SDKs write with "text" in place of the
-// schema's "content". The input is left untouched, it is also the span's raw attribute.
-func semconvTextKey(list []any) []any {
+// semconvRepairs copies the parts whose keys SDKs misspell into the schema's spelling. The input
+// is left untouched, it is also the span's raw attribute.
+func semconvRepairs(list []any) []any {
 	out := make([]any, 0, len(list))
 	for _, item := range list {
 		m, ok := item.(map[string]any)
@@ -50,13 +58,8 @@ func semconvTextKey(list []any) []any {
 		}
 		fixedParts := make([]any, 0, len(parts))
 		for _, p := range parts {
-			part, ok := p.(map[string]any)
-			typ := stringOf(part["type"])
-			if ok && (typ == "text" || typ == "reasoning") && part["content"] == nil && part["text"] != nil {
-				fixed := maps.Clone(part)
-				fixed["content"] = fixed["text"]
-				delete(fixed, "text")
-				p = fixed
+			if part, ok := p.(map[string]any); ok {
+				p = semconvRepairPart(part)
 			}
 			fixedParts = append(fixedParts, p)
 		}
@@ -66,3 +69,24 @@ func semconvTextKey(list []any) []any {
 	}
 	return out
 }
+
+func semconvRepairPart(part map[string]any) map[string]any {
+	fixed := maps.Clone(part)
+	switch stringOf(part["type"]) {
+	case "text", "reasoning":
+		if part["content"] == nil && part["text"] != nil {
+			fixed["content"] = fixed["text"]
+			delete(fixed, "text")
+		}
+	case "tool_call":
+		if args, ok := part["arguments"].(string); ok {
+			fixed["arguments"] = decode(args)
+		}
+	case "tool_call_response":
+		if _, ok := part["response"]; !ok && part["result"] != nil {
+			fixed["response"] = part["result"]
+			delete(fixed, "result")
+		}
+	}
+	return fixed
+}

pkg/types/aiobservabilitytypes/message_format_semconv_test.go

@@ -157,3 +157,34 @@ func TestFormat_Semconv(t *testing.T) {
 		},
 	})
 }
+
+func TestFormat_Semconv_Repairs(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// pydantic-ai keeps string tool call arguments as sent, writes the tool result under result, and the
+			// instructions as a gen_ai.system_instructions text part:
+			// https://github.com/pydantic/pydantic-ai/blob/9ac853bb84ffed7ba0edccb026c5b4f3228c1de7/pydantic_ai_slim/pydantic_ai/messages.py#L3130
+			// https://github.com/pydantic/pydantic-ai/blob/9ac853bb84ffed7ba0edccb026c5b4f3228c1de7/pydantic_ai_slim/pydantic_ai/messages.py#L1724
+			// https://github.com/pydantic/pydantic-ai/blob/9ac853bb84ffed7ba0edccb026c5b4f3228c1de7/pydantic_ai_slim/pydantic_ai/models/instrumented.py#L292
+			name:       "PydanticAI_ArgumentsDecoded_ResultBecomesResponse_InstructionsLead",
+			input:      `[{"role":"user","parts":[{"type":"text","content":"Weather in Paris?"}]},{"role":"assistant","parts":[{"type":"tool_call","id":"call_stub1","name":"get_weather","arguments":"{\"city\": \"Paris\"}"}],"finish_reason":"tool_call"},{"role":"user","parts":[{"type":"tool_call_response","id":"call_stub1","name":"get_weather","result":{"city":"Paris","temp_c":18}}]}]`,
+			output:     `[{"role":"assistant","parts":[{"type":"text","content":"Paris is clear at 18C."}],"finish_reason":"stop"}]`,
+			attributes: map[string]any{GenAISystemInstructions: `[{"type":"text","content":"Use the tool."}]`},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "Use the tool."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Weather in Paris?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_stub1", "arguments": {"city": "Paris"}}]},
+			  {"role": "user", "parts": [{"type": "tool_call_response", "id": "call_stub1", "response": {"city": "Paris", "temp_c": 18}}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Paris is clear at 18C."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// litellm copies the choice finish_reason into the semconv message as OpenAI spells it:
+			// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2822
+			name:       "LiteLLMFinishReasonSpelling_Normalised",
+			output:     `[{"role": "assistant", "parts": [], "finish_reason": "tool_calls"}]`,
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [], "finish_reason": "tool_call"}]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_test.go

@@ -112,6 +112,16 @@ So, the ball costs 5 cents.`,
 			wantInput:  `[{"role": "", "parts": [{"type": "generic", "content": "{\"city\":\"Paris\",\"temp_c\":21}"}]}]`,
 			wantOutput: `[]`,
 		},
+		{
+			// openinference capture, CreateEmbeddings span: the mapper moved the request and the vectors onto
+			// the message attributes and copied the span kind onto gen_ai.operation.name
+			name:       "EmbeddingsOperation_EmptyLists",
+			input:      `{"input": ["observability is the new monitoring"], "model": "text-embedding-3-small"}`,
+			output:     `{"data":[{"embedding":[-0.0039,0.0203],"index":0}],"model":"text-embedding-3-small"}`,
+			attributes: map[string]any{GenAIOperationName: "EMBEDDING", GenAIRequestModel: "text-embedding-3-small"},
+			wantInput:  `[]`,
+			wantOutput: `[]`,
+		},
 		{
 			name:       "NoMessageAttributes_EmptyLists",
 			attributes: map[string]any{GenAIRequestModel: "gpt-4o", GenAIToolName: "get_weather"},
@@ -120,3 +130,108 @@ So, the ball costs 5 cents.`,
 		},
 	})
 }
+
+func TestIndexed(t *testing.T) {
+	testCases := []struct {
+		name string
+		doc  map[string]any
+		want []map[string]any
+	}{
+		{
+			name: "NumericKeys_SortedNumerically",
+			doc:  document(map[string]any{"llm.input_messages.10.message.role": "user", "llm.input_messages.2.message.role": "system", "llm.input_messages.x.message.role": "skipped"}),
+			want: []map[string]any{{"message": map[string]any{"role": "system"}}, {"message": map[string]any{"role": "user"}}},
+		},
+		{
+			name: "StructuredList_KeptInOrder",
+			doc:  map[string]any{"llm": map[string]any{"input_messages": []any{map[string]any{"message": "b"}, map[string]any{"message": "a"}, "not an object"}}},
+			want: []map[string]any{{"message": "b"}, {"message": "a"}},
+		},
+		{name: "Absent_Empty", doc: map[string]any{}, want: []map[string]any{}},
+	}
+	for _, testCase := range testCases {
+		t.Run(testCase.name, func(t *testing.T) {
+			assert.Equal(t, testCase.want, indexed(testCase.doc, openInferenceInputMessages))
+		})
+	}
+}
+
+func TestFormat_SystemInstructions(t *testing.T) {
+	input := `[{"role":"user","parts":[{"type":"text","content":"hi"}]}]`
+	runFormatCases(t, []formatCase{
+		{
+			// the langchain instrumentation moves system messages out of the history into gen_ai.system_instructions,
+			// a JSON list of parts:
+			// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/span_utils.py#L304
+			name:       "ListOfParts_LeadsInput",
+			input:      input,
+			attributes: map[string]any{GenAISystemInstructions: `[{"type": "text", "content": "You are concise."}]`},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "hi"}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// the OpenInference strands processor keeps gen_ai.system_instructions on the span and inserts the
+			// same prompt as message 0
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L152
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/python/instrumentation/openinference-instrumentation-strands-agents/src/openinference/instrumentation/strands_agents/processor.py#L293
+			name:       "SameTextLeadsInput_Skipped",
+			input:      `[{"role":"system","parts":[{"type":"text","content":"You are concise."}]},{"role":"user","parts":[{"type":"text","content":"hi"}]}]`,
+			attributes: map[string]any{GenAISystemInstructions: `[{"type": "text", "content": "You are concise."}]`},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "hi"}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			name:       "BareString_TextPart",
+			input:      input,
+			attributes: map[string]any{GenAISystemInstructions: "be brief"},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "be brief"}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "hi"}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// a create_agent span carries instructions alone
+			name:       "NoMessages_Nothing",
+			attributes: map[string]any{GenAISystemInstructions: "be brief"},
+			wantInput:  `[]`,
+			wantOutput: `[]`,
+		},
+	})
+}
+
+func TestFormat_FinishReasons(t *testing.T) {
+	output := `[{"role":"assistant","parts":[{"type":"text","content":"a"}],"finish_reason":"stop"},{"role":"assistant","parts":[{"type":"text","content":"b"}]}]`
+	runFormatCases(t, []formatCase{
+		{
+			name:       "List_OverridesMessages",
+			output:     output,
+			attributes: map[string]any{GenAIResponseFinishReasons: []any{"tool_calls", "length"}},
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "a"}], "finish_reason": "tool_call"}, {"role": "assistant", "parts": [{"type": "text", "content": "b"}], "finish_reason": "length"}]`,
+		},
+		{
+			name:       "LengthMismatch_LeavesMessages_Warns",
+			output:     output,
+			attributes: map[string]any{GenAIResponseFinishReasons: []any{"stop"}},
+			warnings:   []string{"gen_ai.response.finish_reasons has 1 entries for 2 output messages"},
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "a"}], "finish_reason": "stop"}, {"role": "assistant", "parts": [{"type": "text", "content": "b"}]}]`,
+		},
+		{
+			// llm.finish_reason copied by the mapper as a bare string, in the Vercel AI SDK spelling:
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-finish-reason.ts
+			name:       "BareString_SingleChoice",
+			output:     `{"choices":[{"finish_reason":"stop","message":{"role":"assistant","content":"hi"}}]}`,
+			attributes: map[string]any{GenAIResponseFinishReasons: "tool-calls"},
+			wantInput:  `[]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "hi"}], "finish_reason": "tool_call"}]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_tool.go

@@ -1,12 +1,15 @@
 package aiobservabilitytypes
 
 // Format: gen_ai.tool.call.arguments and gen_ai.tool.call.result on an execute_tool span, each an
-// object or its JSON string (open-telemetry/semantic-conventions-genai@06ec68e, model/gen-ai/registry.yaml).
+// object or its JSON string:
+// https://github.com/open-telemetry/semantic-conventions-genai/blob/06ec68e722c45a7218e23ea1bc1339fe4e21ecae/model/gen-ai/registry.yaml#L545
 // Written by: opentelemetry-instrumentation-langchain, traceloop @tool, OpenInference TOOL spans and
 // Vercel ai.toolCall.* through the span mapper.
-// Envelopes unwrapped: LangChain's callback payloads, {input_str, tags, metadata, inputs, kwargs} and
-// {output, kwargs} (openllmetry callback_handler.py on_tool_start/on_tool_end), and the traceloop
-// decorator's {args, kwargs} (traceloop-sdk decorators/base.py _handle_span_input).
+// Envelopes unwrapped: LangChain's on_tool_start and on_tool_end payloads, {input_str, tags, metadata,
+// inputs, kwargs} and {output, kwargs}:
+// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py#L837
+// The traceloop decorator's {args, kwargs}:
+// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/traceloop-sdk/traceloop/sdk/decorators/base.py#L169
 
 // tool renders the call as an assistant tool_call and the result as a tool tool_call_response, both
 // carrying gen_ai.tool.call.id so the pair can be matched.

pkg/types/aiobservabilitytypes/message_format_tool_test.go

@@ -7,8 +7,9 @@ import "testing"
 func TestFormat_Tool(t *testing.T) {
 	runFormatCases(t, []formatCase{
 		{
-			// langchain capture, execute_tool get_weather span; opentelemetry-instrumentation-langchain
-			// callback_handler.py on_tool_start/on_tool_end envelopes
+			// gen_ai.tool.call.arguments and gen_ai.tool.call.result written by the langchain instrumentation itself,
+			// wrapped in its on_tool_start and on_tool_end payloads:
+			// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py#L837
 			name: "LangChainEnvelopes_InputsAndOutputUnwrapped",
 			attributes: map[string]any{
 				GenAIToolName:          "get_weather",
@@ -19,8 +20,9 @@ func TestFormat_Tool(t *testing.T) {
 			wantOutput: `[{"role": "tool", "parts": [{"type": "tool_call_response", "response": {"city": "Bengaluru", "temp_c": 18, "summary": "Clear"}}]}]`,
 		},
 		{
-			// openllmetry capture, get_weather.tool span; traceloop-sdk decorators/base.py
-			// _handle_span_input {args, kwargs} and the result encoded as a JSON string twice
+			// traceloop.entity.input and traceloop.entity.output from the @tool decorator, copied by the mapper;
+			// the tool returned a JSON string, which json.dumps encoded again:
+			// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/traceloop-sdk/traceloop/sdk/decorators/base.py#L169
 			name: "TraceloopDecorator_KwargsUnwrapped_ResultDecodedTwice",
 			attributes: map[string]any{
 				GenAIToolName:          "get_weather",
@@ -31,8 +33,8 @@ func TestFormat_Tool(t *testing.T) {
 			wantOutput: `[{"role": "tool", "parts": [{"type": "tool_call_response", "response": {"city": "Bengaluru", "temp_c": 28, "summary": "Partly cloudy"}}]}]`,
 		},
 		{
-			// a decorated function returning None is json.dumps(None); the openllmetry capture shows it on
-			// workflow spans, with this positional-args input
+			// traceloop.entity.output of a @tool returning None, json.dumps(None), copied by the mapper:
+			// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/traceloop-sdk/traceloop/sdk/decorators/base.py#L185
 			name: "TraceloopDecorator_PositionalArgsOnly_ListKept",
 			attributes: map[string]any{
 				GenAIToolName:          "lookup",
@@ -52,8 +54,8 @@ func TestFormat_Tool(t *testing.T) {
 			wantOutput: `[]`,
 		},
 		{
-			// openinference-openai-agents capture, get_weather TOOL span: input.value and output.value
-			// copied by the mapper (Arize-ai/openinference@12f2cc03, spec/traces.md)
+			// input.value and output.value of an OpenInference TOOL span, copied by the mapper:
+			// https://github.com/Arize-ai/openinference/blob/12f2cc035edfb00b60b656d977f07abfb7a69900/spec/traces.md
 			name: "OpenInferenceToolSpan_PlainJSONAndText",
 			attributes: map[string]any{
 				GenAIToolName:          "get_weather",
@@ -64,8 +66,8 @@ func TestFormat_Tool(t *testing.T) {
 			wantOutput: `[{"role": "tool", "parts": [{"type": "tool_call_response", "response": "27C, partly cloudy, light breeze from the south."}]}]`,
 		},
 		{
-			// vercel capture, ai.toolCall span: ai.toolCall.{id,name,args,result} copied by the mapper
-			// (https://ai-sdk.dev/docs/ai-sdk-core/telemetry)
+			// ai.toolCall.{id,name,args,result} of a Vercel ai.toolCall span, copied by the mapper:
+			// https://ai-sdk.dev/docs/ai-sdk-core/telemetry
 			name: "VercelToolCall_IDOnBothParts",
 			attributes: map[string]any{
 				GenAIToolName:          "get_current_weather",

pkg/types/aiobservabilitytypes/message_format_traceloop.go

@@ -0,0 +1,87 @@
+package aiobservabilitytypes
+
+// Format: the flattened gen_ai.prompt.N.* and gen_ai.completion.N.* keys of openllmetry's instrumentations
+// from before their semconv rewrite, role, content, tool_call_id, tool_calls.M.{id,name,arguments} and
+// finish_reason, with list content as its JSON:
+// https://github.com/traceloop/openllmetry/blob/93429cfb9767e6b51db6cb35f6b70f758117669a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/__init__.py#L159
+// https://github.com/traceloop/openllmetry/blob/af487056a35625a2cff1c17ac83c2c0d90a02bca/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/chat_wrappers.py#L408
+// https://github.com/traceloop/openllmetry/blob/af487056a35625a2cff1c17ac83c2c0d90a02bca/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/chat_wrappers.py#L460
+// Written by: crewai through the openai instrumentation 0.45.6, mistralai 0.61.0 (https://github.com/traceloop/openllmetry/blob/4643b8826a6e6a4ce1968178d6ac9ca3ce607e2e/packages/opentelemetry-instrumentation-mistralai/opentelemetry/instrumentation/mistralai/__init__.py#L83),
+// litellm beside its semconv value.
+
+const (
+	traceloopPrompts     = "gen_ai.prompt"
+	traceloopCompletions = "gen_ai.completion"
+)
+
+func (f *formatting) traceloop() bool {
+	prompts := indexed(f.attributes, traceloopPrompts)
+	completions := indexed(f.attributes, traceloopCompletions)
+	if len(prompts) == 0 && len(completions) == 0 {
+		return false
+	}
+	f.out.Input = inputMessages(f.traceloopMessages(prompts))
+	f.out.Output = f.traceloopMessages(completions)
+	return true
+}
+
+// completionToolCalls fills an output message left without parts from the gen_ai.completion.N keys
+// litellm writes beside its semconv value, whose parts never carry the tool call:
+// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2365
+// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2770
+func (f *formatting) completionToolCalls() {
+	for i := range f.out.Output {
+		if len(f.out.Output[i].Parts) == 0 {
+			f.out.Output[i].Parts = append(f.out.Output[i].Parts, traceloopToolCalls(indexedAt(f.attributes, traceloopCompletions, i))...)
+		}
+	}
+}
+
+func (f *formatting) traceloopMessages(buckets []map[string]any) OutputMessages {
+	out := make(OutputMessages, 0, len(buckets))
+	for i, m := range buckets {
+		msg := OutputMessage{Role: role(stringOf(m["role"])), Parts: Parts{}, Name: optionalString(m["name"]), FinishReason: finishReason(stringOf(m["finish_reason"]))}
+		content, hasContent := m["content"]
+		if id := stringOf(m["tool_call_id"]); msg.Role == RoleTool && id != "" {
+			msg.Parts = Parts{part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: &id, Response: decode(content)})}
+			out = append(out, msg)
+			continue
+		}
+		if hasContent {
+			msg.Parts = append(msg.Parts, traceloopContent(content)...)
+		}
+		msg.Parts = append(msg.Parts, traceloopToolCalls(m)...)
+		if len(msg.Parts) == 0 {
+			f.warn("message %d has no content", i)
+		}
+		out = append(out, msg)
+	}
+	return out
+}
+
+// traceloopContent reads a content value, a string or the JSON of an OpenAI content block list.
+func traceloopContent(content any) Parts {
+	if blocks, ok := decode(content).([]any); ok {
+		parts := make(Parts, 0, len(blocks))
+		for _, block := range blocks {
+			parts = append(parts, contentPart(block))
+		}
+		return parts
+	}
+	if text := stringOf(content); text != "" {
+		return Parts{part(textPart(text))}
+	}
+	return nil
+}
+
+// traceloopToolCalls reads tool_calls.M.{id,name,arguments} and litellm's function_call object.
+func traceloopToolCalls(m map[string]any) Parts {
+	var parts Parts
+	for _, call := range indexed(m, "tool_calls") {
+		parts = append(parts, part(ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(call["name"]), ID: optionalString(call["id"]), Arguments: decode(call["arguments"])}))
+	}
+	if call, ok := m["function_call"].(map[string]any); ok {
+		parts = append(parts, part(ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(call["name"]), Arguments: decode(call["arguments"])}))
+	}
+	return parts
+}

pkg/types/aiobservabilitytypes/message_format_traceloop_test.go

@@ -0,0 +1,106 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Captured cases are the gen_ai.prompt.N.* and gen_ai.completion.N.* keys as the collector stored
+// them for the SigNoz/scripts crewai, mistral and litellm captures.
+func TestFormat_Traceloop(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// crewai capture, openai.chat span of a tool round, as the openai instrumentation 0.45.6 wrote
+			// prompts and completions before its semconv rewrite
+			// https://github.com/traceloop/openllmetry/blob/af487056a35625a2cff1c17ac83c2c0d90a02bca/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/chat_wrappers.py#L408
+			name: "ToolRound_ToolCallAndToolMessages",
+			attributes: map[string]any{
+				"gen_ai.prompt.0.role":                   "system",
+				"gen_ai.prompt.0.content":                "You are Weather Reporter. Call get_weather and summarise the result.",
+				"gen_ai.prompt.1.role":                   "user",
+				"gen_ai.prompt.1.content":                "Current Task: What's the weather in Bengaluru?",
+				"gen_ai.prompt.2.role":                   "assistant",
+				"gen_ai.prompt.2.tool_calls.0.id":        "call_bbVgWPe8AMBol7JlAhDkA6R7",
+				"gen_ai.prompt.2.tool_calls.0.name":      "get_weather",
+				"gen_ai.prompt.2.tool_calls.0.arguments": `{"city":"Bengaluru"}`,
+				"gen_ai.prompt.3.role":                   "tool",
+				"gen_ai.prompt.3.tool_call_id":           "call_bbVgWPe8AMBol7JlAhDkA6R7",
+				"gen_ai.prompt.3.content":                `{"city": "Bengaluru", "temp_c": 18, "summary": "Clear"}`,
+				"gen_ai.completion.0.role":               "assistant",
+				"gen_ai.completion.0.finish_reason":      "stop",
+				"gen_ai.completion.0.content":            "The current weather in Bengaluru is clear with a temperature of 18°C.",
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are Weather Reporter. Call get_weather and summarise the result."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Current Task: What's the weather in Bengaluru?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "get_weather", "id": "call_bbVgWPe8AMBol7JlAhDkA6R7", "arguments": {"city": "Bengaluru"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_bbVgWPe8AMBol7JlAhDkA6R7", "response": {"city": "Bengaluru", "temp_c": 18, "summary": "Clear"}}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "The current weather in Bengaluru is clear with a temperature of 18°C."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// crewai capture, openai.chat span answering with two tool calls
+			// https://github.com/traceloop/openllmetry/blob/af487056a35625a2cff1c17ac83c2c0d90a02bca/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/chat_wrappers.py#L460
+			name: "CompletionToolCalls_ToolCallParts",
+			attributes: map[string]any{
+				"gen_ai.prompt.0.role":                       "user",
+				"gen_ai.prompt.0.content":                    "What's the weather in Paris and in London?",
+				"gen_ai.completion.0.role":                   "assistant",
+				"gen_ai.completion.0.finish_reason":          "tool_calls",
+				"gen_ai.completion.0.tool_calls.0.id":        "call_1",
+				"gen_ai.completion.0.tool_calls.0.name":      "get_weather",
+				"gen_ai.completion.0.tool_calls.0.arguments": `{"city":"Paris"}`,
+				"gen_ai.completion.0.tool_calls.1.id":        "call_2",
+				"gen_ai.completion.0.tool_calls.1.name":      "get_weather",
+				"gen_ai.completion.0.tool_calls.1.arguments": `{"city":"London"}`,
+			},
+			wantInput: `[{"role": "user", "parts": [{"type": "text", "content": "What's the weather in Paris and in London?"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "tool_call", "name": "get_weather", "id": "call_1", "arguments": {"city": "Paris"}},
+			  {"type": "tool_call", "name": "get_weather", "id": "call_2", "arguments": {"city": "London"}}
+			], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// mistral capture, mistralai.chat span: the instrumentation writes the role for every message but
+			// drops a content that is not a plain string
+			// https://github.com/traceloop/openllmetry/blob/4643b8826a6e6a4ce1968178d6ac9ca3ce607e2e/packages/opentelemetry-instrumentation-mistralai/opentelemetry/instrumentation/mistralai/__init__.py#L75
+			// https://github.com/traceloop/openllmetry/blob/4643b8826a6e6a4ce1968178d6ac9ca3ce607e2e/packages/opentelemetry-instrumentation-mistralai/opentelemetry/instrumentation/mistralai/__init__.py#L88
+			name: "RoleWithoutContent_EmptyMessageWarns",
+			attributes: map[string]any{
+				"gen_ai.prompt.0.role":              "user",
+				"gen_ai.completion.0.role":          "assistant",
+				"gen_ai.completion.0.finish_reason": "stop",
+				"gen_ai.completion.0.content":       "Observability is the ability to understand a system's internal state by analyzing its external outputs.",
+			},
+			warnings:   []string{"message 0 has no content"},
+			wantInput:  `[{"role": "user", "parts": []}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Observability is the ability to understand a system's internal state by analyzing its external outputs."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// a vision prompt: list content is written as its JSON, the OpenAI content block list
+			// https://github.com/traceloop/openllmetry/blob/af487056a35625a2cff1c17ac83c2c0d90a02bca/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/chat_wrappers.py#L431
+			name: "ContentBlockList_TextAndBlobParts",
+			attributes: map[string]any{
+				"gen_ai.prompt.0.role":    "user",
+				"gen_ai.prompt.0.content": `[{"type":"text","text":"What animal is this?"},{"type":"image_url","image_url":{"url":"data:image/jpeg;base64,/9j/4AAQ"}}]`,
+			},
+			wantInput: `[{"role": "user", "parts": [
+			  {"type": "text", "content": "What animal is this?"},
+			  {"type": "blob", "modality": "image", "mime_type": "image/jpeg", "content": "/9j/4AAQ"}
+			]}]`,
+			wantOutput: `[]`,
+		},
+		{
+			// litellm capture, litellm_request span: the semconv output has no part for the tool call, the
+			// gen_ai.completion.0.function_call keys beside it still carry it
+			// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2365
+			// https://github.com/BerriAI/litellm/blob/9d72bae336ec0d14c41651adcb0d2e1149721f46/litellm/integrations/opentelemetry.py#L2770
+			name: "SemconvEmptyParts_ToolCallFromCompletionKeys",
+			attributes: map[string]any{
+				GenAIInputMessages:                            `[{"role": "user", "parts": [{"type": "text", "content": "What is the weather in Bengaluru in celsius?"}]}]`,
+				GenAIOutputMessages:                           `[{"role": "assistant", "parts": [], "finish_reason": "tool_calls"}]`,
+				"gen_ai.completion.0.function_call.name":      "get_current_weather",
+				"gen_ai.completion.0.function_call.arguments": `{"city":"Bengaluru"}`,
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "What is the weather in Bengaluru in celsius?"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "get_current_weather", "arguments": {"city": "Bengaluru"}}], "finish_reason": "tool_call"}]`,
+		},
+	})
+}

pkg/types/aiobservabilitytypes/message_format_vercel.go

@@ -0,0 +1,187 @@
+package aiobservabilitytypes
+
+import (
+	"encoding/base64"
+	"strings"
+)
+
+// Format: the AI SDK's legacy telemetry attributes. ai.prompt.messages on the doGenerate and doStream
+// spans is the provider prompt, ai.prompt on the parent generateText and streamText spans is {system,
+// prompt} or {messages}, ai.response.text and ai.response.toolCalls hold the answer:
+// https://ai-sdk.dev/docs/ai-sdk-core/telemetry
+// The prompt parts in the provider's v1 spelling, image with mimeType, tool-call args and tool-result
+// result, or v2, file with mediaType, tool-call input and tool-result output:
+// https://github.com/vercel/ai/blob/47bd16dd50283c82a5fef4c839d7f4102c45f5b9/packages/provider/src/language-model/v1/language-model-v1-prompt.ts#L120
+// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L93
+// The span mapper moves ai.prompt.messages and one of the response keys onto gen_ai.*.messages; the other
+// response key and ai.prompt are read where they are.
+// Written by: the Vercel AI SDK, Mastra.
+// Handled: text, reasoning, tool-call, tool-result, image and file parts as a data URL, a URL, bare base64
+// or a serialised Buffer; ai.response.finishReason.
+
+const (
+	vercelPrompt           = "ai.prompt"
+	vercelOperation        = "ai.operationId"
+	vercelResponseText     = "ai.response.text"
+	vercelResponseCalls    = "ai.response.toolCalls"
+	vercelResponseFinished = "ai.response.finishReason"
+)
+
+func (f *formatting) vercel() bool {
+	if attribute(f.attributes, vercelOperation) == nil && attribute(f.attributes, vercelPrompt) == nil && attribute(f.attributes, "ai.response") == nil {
+		return false
+	}
+	switch input := decode(attribute(f.attributes, GenAIInputMessages)).(type) {
+	case []any:
+		f.out.Input = inputMessages(vercelMessages(input))
+	case nil:
+		if prompt, ok := decode(attribute(f.attributes, vercelPrompt)).(map[string]any); ok {
+			f.out.Input = inputMessages(vercelParentPrompt(prompt))
+		}
+	default:
+		f.out.Input = inputMessages(OutputMessages{genericMessage(input)})
+		f.warn("input is not a message list, kept as generic")
+	}
+
+	text, calls := attribute(f.attributes, vercelResponseText), decode(attribute(f.attributes, vercelResponseCalls))
+	switch output := decode(attribute(f.attributes, GenAIOutputMessages)).(type) {
+	case string:
+		text = output
+	case []any:
+		calls = output
+	}
+	msg := OutputMessage{Role: RoleAssistant, Parts: Parts{}, FinishReason: finishReason(stringOf(attribute(f.attributes, vercelResponseFinished)))}
+	if s := stringOf(text); s != "" {
+		msg.Parts = append(msg.Parts, part(textPart(s)))
+	}
+	if list, ok := calls.([]any); ok {
+		for _, item := range list {
+			if call, ok := item.(map[string]any); ok {
+				msg.Parts = append(msg.Parts, vercelToolCall(call))
+			}
+		}
+	}
+	if len(msg.Parts) > 0 {
+		f.out.Output = OutputMessages{msg}
+	}
+	return true
+}
+
+// vercelParentPrompt reads the parent span's ai.prompt: the system prompt with either a bare prompt or
+// the messages.
+func vercelParentPrompt(prompt map[string]any) OutputMessages {
+	var msgs OutputMessages
+	if parts := systemParts(prompt["system"]); len(parts) > 0 {
+		msgs = append(msgs, OutputMessage{Role: RoleSystem, Parts: parts})
+	}
+	if messages, ok := prompt["messages"].([]any); ok {
+		return append(msgs, vercelMessages(messages)...)
+	}
+	if text := stringOf(prompt["prompt"]); text != "" {
+		msgs = append(msgs, OutputMessage{Role: RoleUser, Parts: Parts{part(textPart(text))}})
+	}
+	return msgs
+}
+
+func vercelMessages(list []any) OutputMessages {
+	out := make(OutputMessages, 0, len(list))
+	for _, item := range list {
+		m, ok := item.(map[string]any)
+		if !ok {
+			out = append(out, genericMessage(item))
+			continue
+		}
+		msg := OutputMessage{Role: role(stringOf(m["role"])), Parts: Parts{}}
+		switch content := m["content"].(type) {
+		case string:
+			if content != "" {
+				msg.Parts = append(msg.Parts, part(textPart(content)))
+			}
+		case []any:
+			for _, item := range content {
+				msg.Parts = append(msg.Parts, vercelPart(item))
+			}
+		}
+		out = append(out, msg)
+	}
+	return out
+}
+
+func vercelPart(item any) Part {
+	p, ok := item.(map[string]any)
+	if !ok {
+		return contentPart(item)
+	}
+	switch stringOf(p["type"]) {
+	case "text":
+		return part(textPart(stringOf(p["text"])))
+	case "reasoning":
+		return part(ReasoningPart{Type: PartTypeReasoning, Content: stringOf(p["text"])})
+	case "redacted-reasoning":
+		return part(ReasoningPart{Type: PartTypeReasoning})
+	case "tool-call":
+		return vercelToolCall(p)
+	case "tool-result":
+		response := p["result"]
+		if output, ok := p["output"].(map[string]any); ok {
+			response = output["value"]
+		}
+		return part(ToolCallResponsePart{Type: PartTypeToolCallResponse, ID: optionalString(p["toolCallId"]), Response: response})
+	case "image":
+		mime := stringOf(p["mimeType"])
+		if mime == "" {
+			mime = stringOf(p["mediaType"])
+		}
+		if media, ok := vercelMedia(p["image"], mime, ModalityImage); ok {
+			return media
+		}
+	case "file":
+		mime := stringOf(p["mediaType"])
+		if mime == "" {
+			mime = stringOf(p["mimeType"])
+		}
+		if media, ok := vercelMedia(p["data"], mime, modalityOf(mime)); ok {
+			return media
+		}
+	}
+	return part(GenericPart(p))
+}
+
+// vercelToolCall reads {toolCallId, toolName, input} or the v1 {toolCallId, toolName, args}; the
+// arguments may be a JSON string:
+// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L126
+// https://github.com/vercel/ai/blob/47bd16dd50283c82a5fef4c839d7f4102c45f5b9/packages/provider/src/language-model/v1/language-model-v1-prompt.ts#L175
+func vercelToolCall(call map[string]any) Part {
+	args := call["input"]
+	if args == nil {
+		args = call["args"]
+	}
+	return part(ToolCallRequestPart{Type: PartTypeToolCall, Name: stringOf(call["toolName"]), ID: optionalString(call["toolCallId"]), Arguments: decode(args)})
+}
+
+// vercelMedia reads a data URL, a URL, bare base64 or a serialised Node Buffer {type: Buffer,
+// data: [bytes]}, which is how the parent span's ai.prompt carries an image given as bytes.
+func vercelMedia(value any, mimeType string, modality Modality) (Part, bool) {
+	switch v := value.(type) {
+	case string:
+		if v == "" {
+			return Part{}, false
+		}
+		if strings.Contains(v, "://") || strings.HasPrefix(v, "data:") {
+			return part(mediaPart(v, modality)), true
+		}
+		return part(BlobPart{Type: PartTypeBlob, Modality: modality, Content: v, MimeType: optionalString(mimeType)}), true
+	case map[string]any:
+		bytes, ok := v["data"].([]any)
+		if !ok {
+			return Part{}, false
+		}
+		raw := make([]byte, 0, len(bytes))
+		for _, b := range bytes {
+			n, _ := b.(float64)
+			raw = append(raw, byte(n))
+		}
+		return part(BlobPart{Type: PartTypeBlob, Modality: modality, Content: base64.StdEncoding.EncodeToString(raw), MimeType: optionalString(mimeType)}), true
+	}
+	return Part{}, false
+}

pkg/types/aiobservabilitytypes/message_format_vercel_test.go

@@ -0,0 +1,159 @@
+package aiobservabilitytypes
+
+import "testing"
+
+// Captured cases are the ai.* attributes as the collector stored them for the SigNoz/scripts vercel and
+// mastra captures, the AI SDK v4 shapes; the v5 cases follow the provider's v2 prompt types at the
+// linked lines.
+func TestFormat_Vercel(t *testing.T) {
+	runFormatCases(t, []formatCase{
+		{
+			// vercel capture, ai.generateText.doGenerate span after a tool round: v1 tool-call args and
+			// tool-result result
+			// https://github.com/vercel/ai/blob/47bd16dd50283c82a5fef4c839d7f4102c45f5b9/packages/provider/src/language-model/v1/language-model-v1-prompt.ts#L175
+			name: "V4ToolRound_ToolCallAndResultParts",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.generateText.doGenerate",
+				"ai.response.finishReason": "stop",
+				GenAIInputMessages:         `[{"role":"user","content":[{"type":"text","text":"What is the weather in Paris and in London, in celsius? Use the tool for each."}]},{"role":"assistant","content":[{"type":"tool-call","toolCallId":"call_isBPb3o3cqhY8OpugQntLOgy","toolName":"get_current_weather","args":{"city":"Paris","unit":"c"}},{"type":"tool-call","toolCallId":"call_sH9xd5EWFkiSaWmYvg4CeWTl","toolName":"get_current_weather","args":{"city":"London","unit":"c"}}]},{"role":"tool","content":[{"type":"tool-result","toolCallId":"call_isBPb3o3cqhY8OpugQntLOgy","toolName":"get_current_weather","result":{"city":"Paris","unit":"c","temperature":18,"condition":"clear"}},{"type":"tool-result","toolCallId":"call_sH9xd5EWFkiSaWmYvg4CeWTl","toolName":"get_current_weather","result":{"city":"London","unit":"c","temperature":18,"condition":"clear"}}]}]`,
+				GenAIOutputMessages:        "The current weather is as follows:\n\n- **Paris**: 18°C, clear\n- **London**: 18°C, clear",
+			},
+			wantInput: `[
+			  {"role": "user", "parts": [{"type": "text", "content": "What is the weather in Paris and in London, in celsius? Use the tool for each."}]},
+			  {"role": "assistant", "parts": [
+			    {"type": "tool_call", "name": "get_current_weather", "id": "call_isBPb3o3cqhY8OpugQntLOgy", "arguments": {"city": "Paris", "unit": "c"}},
+			    {"type": "tool_call", "name": "get_current_weather", "id": "call_sH9xd5EWFkiSaWmYvg4CeWTl", "arguments": {"city": "London", "unit": "c"}}
+			  ]},
+			  {"role": "tool", "parts": [
+			    {"type": "tool_call_response", "id": "call_isBPb3o3cqhY8OpugQntLOgy", "response": {"city": "Paris", "unit": "c", "temperature": 18, "condition": "clear"}},
+			    {"type": "tool_call_response", "id": "call_sH9xd5EWFkiSaWmYvg4CeWTl", "response": {"city": "London", "unit": "c", "temperature": 18, "condition": "clear"}}
+			  ]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "The current weather is as follows:\n\n- **Paris**: 18°C, clear\n- **London**: 18°C, clear"}], "finish_reason": "stop"}]`,
+		},
+		{
+			// vercel capture, ai.streamText.doStream span: the mapper moved ai.response.toolCalls onto the output
+			// and left the empty ai.response.text behind
+			name: "V4ToolCalls_ToolCallParts_EmptyTextIgnored",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.streamText.doStream",
+				"ai.response.finishReason": "tool-calls",
+				"ai.response.text":         "",
+				GenAIInputMessages:         `[{"role":"user","content":[{"type":"text","text":"What is the weather in Bengaluru in celsius?"}]}]`,
+				GenAIOutputMessages:        `[{"type":"tool-call","toolCallId":"call_l0ycKcmt1uPCdGWR4SkwoLBv","toolName":"get_current_weather","args":{"city":"Bengaluru","unit":"c"}}]`,
+			},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "What is the weather in Bengaluru in celsius?"}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "get_current_weather", "id": "call_l0ycKcmt1uPCdGWR4SkwoLBv", "arguments": {"city": "Bengaluru", "unit": "c"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// vercel capture, ai.generateText parent span: ai.prompt is unmapped, the answer text was moved
+			name: "ParentPromptWithSystem_SystemAndUserMessages",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.generateText",
+				"ai.prompt":                `{"system":"You are concise.","prompt":"Say hello in five words."}`,
+				"ai.response.finishReason": "stop",
+				GenAIOutputMessages:        "Hello! How are you today?",
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are concise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Say hello in five words."}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Hello! How are you today?"}], "finish_reason": "stop"}]`,
+		},
+		{
+			// vercel capture, ai.generateText parent span of a vision call: the image is a serialised Node Buffer
+			name: "ParentPromptMessages_BufferImage_BlobPart",
+			attributes: map[string]any{
+				"ai.operationId":    "ai.generateText",
+				"ai.prompt":         `{"messages":[{"role":"user","content":[{"type":"text","text":"What animal is in this image? One sentence."},{"type":"image","image":{"type":"Buffer","data":[137,80,78,71]},"mediaType":"image/png"}]}]}`,
+				GenAIOutputMessages: "A duck.",
+			},
+			wantInput: `[{"role": "user", "parts": [
+			  {"type": "text", "content": "What animal is in this image? One sentence."},
+			  {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "iVBORw=="}
+			]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "A duck."}]}]`,
+		},
+		{
+			// the v2 prompt after a tool round: tool-call input and tool-result output
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L126
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L161
+			name: "V5ToolRound_InputAndOutputSpellings",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.generateText.doGenerate",
+				"ai.response.finishReason": "stop",
+				GenAIInputMessages:         `[{"role":"system","content":"Use the tool."},{"role":"user","content":[{"type":"text","text":"Weather in Paris?"}]},{"role":"assistant","content":[{"type":"tool-call","toolCallId":"call_1","toolName":"getWeather","input":{"city":"Paris"}}]},{"role":"tool","content":[{"type":"tool-result","toolCallId":"call_1","toolName":"getWeather","output":{"type":"json","value":{"city":"Paris","tempC":18,"summary":"Clear"}}}]}]`,
+				GenAIOutputMessages:        "Paris is clear at 18C.",
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "Use the tool."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "Weather in Paris?"}]},
+			  {"role": "assistant", "parts": [{"type": "tool_call", "name": "getWeather", "id": "call_1", "arguments": {"city": "Paris"}}]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_1", "response": {"city": "Paris", "tempC": 18, "summary": "Clear"}}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "text", "content": "Paris is clear at 18C."}], "finish_reason": "stop"}]`,
+		},
+		{
+			// ai.response.toolCalls in the v2 spelling, the arguments under input; the text the mapper left
+			// behind in ai.response.text comes first
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L126
+			name: "V5ToolCallsBesideText_TextThenToolCall",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.streamText.doStream",
+				"ai.response.finishReason": "tool-calls",
+				"ai.response.text":         "Let me check.",
+				GenAIInputMessages:         `[{"role":"user","content":[{"type":"text","text":"Weather in Paris? Use the tool."}]}]`,
+				GenAIOutputMessages:        `[{"toolCallId":"call_2","toolName":"getWeather","input":{"city":"Paris"}}]`,
+			},
+			wantInput: `[{"role": "user", "parts": [{"type": "text", "content": "Weather in Paris? Use the tool."}]}]`,
+			wantOutput: `[{"role": "assistant", "parts": [
+			  {"type": "text", "content": "Let me check."},
+			  {"type": "tool_call", "name": "getWeather", "id": "call_2", "arguments": {"city": "Paris"}}
+			], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// the v2 file part, data as bare base64 or a URL with its mediaType, a reasoning part, and a tool
+			// result given as text
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L93
+			// https://github.com/vercel/ai/blob/a5e92fe3c692f8e152fe1eb9ef50a4e734ad0ffb/packages/provider/src/language-model/v2/language-model-v2-prompt.ts#L187
+			name: "V5FileParts_BlobsByMediaType_URLAsUri",
+			attributes: map[string]any{
+				"ai.operationId":   "ai.generateText.doGenerate",
+				GenAIInputMessages: `[{"role":"user","content":[{"type":"text","text":"What is in these?"},{"type":"file","mediaType":"image/png","data":"iVBORw0KGgo="},{"type":"file","mediaType":"application/pdf","filename":"a.pdf","data":"JVBERi0xLjQgc3R1Yg=="},{"type":"file","mediaType":"audio/wav","data":"https://example.com/a.wav"},{"type":"reasoning","text":"thinking"}]},{"role":"tool","content":[{"type":"tool-result","toolCallId":"call_1","toolName":"read","output":{"type":"text","value":"done"}}]}]`,
+			},
+			wantInput: `[
+			  {"role": "user", "parts": [
+			    {"type": "text", "content": "What is in these?"},
+			    {"type": "blob", "modality": "image", "mime_type": "image/png", "content": "iVBORw0KGgo="},
+			    {"type": "blob", "modality": "document", "mime_type": "application/pdf", "content": "JVBERi0xLjQgc3R1Yg=="},
+			    {"type": "uri", "modality": "audio", "uri": "https://example.com/a.wav"},
+			    {"type": "reasoning", "content": "thinking"}
+			  ]},
+			  {"role": "tool", "parts": [{"type": "tool_call_response", "id": "call_1", "response": "done"}]}
+			]`,
+			wantOutput: `[]`,
+		},
+		{
+			// mastra capture, ai.generateText.doGenerate span: the v4 shapes with the arguments as a JSON string
+			name: "MastraToolCalls_ToolCallPart",
+			attributes: map[string]any{
+				"ai.operationId":           "ai.generateText.doGenerate",
+				"ai.response.finishReason": "tool-calls",
+				GenAIInputMessages:         `[{"role":"system","content":"You are a weather assistant. When asked about weather, call get-weather once per city and summarise."},{"role":"user","content":[{"type":"text","text":"What is the weather in Bengaluru?"}]}]`,
+				GenAIOutputMessages:        `[{"toolCallType":"function","toolCallId":"call_526phoykytobsvLouzES805J","toolName":"getWeather","args":"{\"city\":\"Bengaluru\"}"}]`,
+			},
+			wantInput: `[
+			  {"role": "system", "parts": [{"type": "text", "content": "You are a weather assistant. When asked about weather, call get-weather once per city and summarise."}]},
+			  {"role": "user", "parts": [{"type": "text", "content": "What is the weather in Bengaluru?"}]}
+			]`,
+			wantOutput: `[{"role": "assistant", "parts": [{"type": "tool_call", "name": "getWeather", "id": "call_526phoykytobsvLouzES805J", "arguments": {"city": "Bengaluru"}}], "finish_reason": "tool_call"}]`,
+		},
+		{
+			// vercel capture, ai.generateText span of a failed call
+			name:       "PromptOnlyNoResponse_InputOnly",
+			attributes: map[string]any{"ai.operationId": "ai.generateText", "ai.prompt": `{"prompt":"this should fail"}`},
+			wantInput:  `[{"role": "user", "parts": [{"type": "text", "content": "this should fail"}]}]`,
+			wantOutput: `[]`,
+		},
+	})
+}

tests/integration/tests/tracedetail/02_thread.py

@@ -21,17 +21,25 @@ def test_thread_returns_gen_ai_spans_in_order(
     seed_attribute_evolution("traces", ATTRIBUTE_JSON_ROLLOUT_TIME)
     now = datetime.now(tz=UTC).replace(microsecond=0)
     trace_id = TraceIdGenerator.trace_id()
-    root_id, first_llm_id, tool_id, agent_id, second_llm_id, third_llm_id = (TraceIdGenerator.span_id() for _ in range(6))
+    root_id, first_llm_id, tool_id, agent_id, second_llm_id, legacy_llm_id, third_llm_id, flattened_llm_id, responses_llm_id, langchain_llm_id, openrouter_llm_id, vercel_llm_id, anthropic_llm_id, gemini_llm_id = (TraceIdGenerator.span_id() for _ in range(14))
     resources = {"service.name": "tracedetail-thread"}
     first_input = json.dumps([{"role": "user", "parts": [{"type": "text", "content": "weather in Bangalore?"}]}])
-    first_output = json.dumps([{"role": "assistant", "parts": [{"type": "tool_call", "id": "call_1", "name": "get_weather", "arguments": {"city": "Bangalore"}}], "finish_reason": "tool_call"}])
+    first_output = json.dumps([{"role": "assistant", "parts": [{"type": "tool_call", "id": "call_1", "name": "get_weather", "arguments": {"city": "Bangalore"}}]}])
+    first_instructions = json.dumps([{"type": "text", "content": "Answer with the tool."}])
     second_input = json.dumps([{"role": "tool", "content": "sunny", "tool_call_id": "call_1"}])
 
     insert_traces(
         [
             Traces(timestamp=now - timedelta(seconds=10), duration=timedelta(seconds=9), trace_id=trace_id, span_id=root_id, name="POST /chat", kind=TracesKind.SPAN_KIND_SERVER, resources=resources, attribute_write_mode="json_only"),
             Traces(
-                timestamp=now - timedelta(seconds=8), trace_id=trace_id, span_id=first_llm_id, parent_span_id=root_id, name="chat gpt-4o", resources=resources, attributes={"gen_ai.request.model": "gpt-4o", "gen_ai.input.messages": first_input, "gen_ai.output.messages": first_output}, attribute_write_mode="json_only"
+                timestamp=now - timedelta(seconds=8),
+                trace_id=trace_id,
+                span_id=first_llm_id,
+                parent_span_id=root_id,
+                name="chat gpt-4o",
+                resources=resources,
+                attributes={"gen_ai.request.model": "gpt-4o", "gen_ai.system_instructions": first_instructions, "gen_ai.input.messages": first_input, "gen_ai.output.messages": first_output, "gen_ai.response.finish_reasons": json.dumps(["tool_calls"])},
+                attribute_write_mode="json_only",
             ),
             Traces(
                 timestamp=now - timedelta(seconds=6),
@@ -45,7 +53,122 @@ def test_thread_returns_gen_ai_spans_in_order(
             ),
             Traces(timestamp=now - timedelta(seconds=5), trace_id=trace_id, span_id=agent_id, parent_span_id=root_id, name="invoke_agent planner", resources=resources, attributes={"gen_ai.agent.name": "planner"}, attribute_write_mode="json_only"),
             Traces(timestamp=now - timedelta(seconds=4), trace_id=trace_id, span_id=second_llm_id, parent_span_id=root_id, name="chat gpt-4o", resources=resources, attributes={"gen_ai.request.model": "gpt-4o", "gen_ai.input.messages": second_input}, attribute_write_mode="json_only"),
+            Traces(
+                timestamp=now - timedelta(seconds=3),
+                trace_id=trace_id,
+                span_id=legacy_llm_id,
+                parent_span_id=root_id,
+                name="openai.chat",
+                resources=resources,
+                attributes={"gen_ai.request.model": "gpt-4o", "gen_ai.prompt.0.role": "user", "gen_ai.prompt.0.content": "and tomorrow?", "gen_ai.completion.0.role": "assistant", "gen_ai.completion.0.content": "Also sunny.", "gen_ai.completion.0.finish_reason": "stop"},
+                attribute_write_mode="json_only",
+            ),
             Traces(timestamp=now - timedelta(seconds=2), trace_id=trace_id, span_id=third_llm_id, parent_span_id=root_id, name="chat gpt-4o", resources=resources, attributes={"gen_ai.request.model": "gpt-4o", "gen_ai.output.messages": "It is sunny in Bangalore."}, attribute_write_mode="json_only"),
+            Traces(
+                timestamp=now - timedelta(seconds=1),
+                trace_id=trace_id,
+                span_id=flattened_llm_id,
+                parent_span_id=root_id,
+                name="ChatCompletion",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "gpt-4o",
+                    "llm.input_messages.0.message.role": "user",
+                    "llm.input_messages.0.message.content": "thanks",
+                    "llm.output_messages.0.message.role": "assistant",
+                    "llm.output_messages.0.message.content": "You're welcome.",
+                    "llm.finish_reason": "stop",
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now,
+                trace_id=trace_id,
+                span_id=responses_llm_id,
+                parent_span_id=root_id,
+                name="responses gpt-4o",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "gpt-4o",
+                    "gen_ai.input.messages": json.dumps({"model": "gpt-4o", "instructions": "Be brief.", "input": [{"role": "user", "content": "bye"}]}),
+                    "gen_ai.output.messages": json.dumps({"object": "response", "status": "completed", "output": [{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "Bye!"}]}]}),
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now + timedelta(seconds=1),
+                trace_id=trace_id,
+                span_id=langchain_llm_id,
+                parent_span_id=root_id,
+                name="ChatOpenAI",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "gpt-4o",
+                    "gen_ai.input.messages": json.dumps({"messages": [[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "messages", "HumanMessage"], "kwargs": {"content": "see you", "type": "human"}}]]}),
+                    "gen_ai.output.messages": json.dumps(
+                        {"generations": [[{"text": "See you!", "generation_info": {"finish_reason": "stop"}, "type": "ChatGeneration", "message": {"lc": 1, "type": "constructor", "id": ["langchain", "schema", "messages", "AIMessage"], "kwargs": {"content": "See you!", "type": "ai"}}}]]}
+                    ),
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now + timedelta(seconds=2),
+                trace_id=trace_id,
+                span_id=openrouter_llm_id,
+                parent_span_id=root_id,
+                name="LLM Generation",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "openai/gpt-4o-mini",
+                    "gen_ai.input.messages": json.dumps({"messages": [{"role": "user", "content": "one word"}]}),
+                    "gen_ai.output.messages": json.dumps({"completion": "Yes.", "reasoning": None, "rawRequest": {"model": "openai/gpt-4o-mini"}}),
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now + timedelta(seconds=3),
+                trace_id=trace_id,
+                span_id=vercel_llm_id,
+                parent_span_id=root_id,
+                name="ai.generateText.doGenerate",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "gpt-4o-mini",
+                    "ai.operationId": "ai.generateText.doGenerate",
+                    "ai.response.finishReason": "stop",
+                    "gen_ai.input.messages": json.dumps([{"role": "user", "content": [{"type": "text", "text": "last one"}]}]),
+                    "gen_ai.output.messages": "Done.",
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now + timedelta(seconds=4),
+                trace_id=trace_id,
+                span_id=anthropic_llm_id,
+                parent_span_id=root_id,
+                name="messages claude-sonnet-4-5",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "claude-sonnet-4-5",
+                    "gen_ai.input.messages": json.dumps({"system": "Be terse.", "messages": [{"role": "user", "content": "ping"}]}),
+                    "gen_ai.output.messages": json.dumps({"type": "message", "role": "assistant", "content": [{"type": "text", "text": "pong"}], "stop_reason": "end_turn"}),
+                },
+                attribute_write_mode="json_only",
+            ),
+            Traces(
+                timestamp=now + timedelta(seconds=5),
+                trace_id=trace_id,
+                span_id=gemini_llm_id,
+                parent_span_id=root_id,
+                name="generateContent gemini-2.5-flash",
+                resources=resources,
+                attributes={
+                    "gen_ai.request.model": "gemini-2.5-flash",
+                    "gen_ai.input.messages": json.dumps({"contents": [{"role": "user", "parts": [{"text": "ping"}]}]}),
+                    "gen_ai.output.messages": json.dumps({"candidates": [{"content": {"role": "model", "parts": [{"text": "pong"}]}, "finishReason": "STOP"}]}),
+                },
+                attribute_write_mode="json_only",
+            ),
         ]
     )
 
@@ -54,10 +177,10 @@ def test_thread_returns_gen_ai_spans_in_order(
     assert response.status_code == HTTPStatus.OK, response.text
 
     thread = response.json()["data"]
-    assert [span["span_id"] for span in thread["spans"]] == [first_llm_id, tool_id, agent_id, second_llm_id, third_llm_id]
+    assert [span["span_id"] for span in thread["spans"]] == [first_llm_id, tool_id, agent_id, second_llm_id, legacy_llm_id, third_llm_id, flattened_llm_id, responses_llm_id, langchain_llm_id, openrouter_llm_id, vercel_llm_id, anthropic_llm_id, gemini_llm_id]
     assert "nextCursor" not in thread
 
-    first, tool, agent, input_only, output_only = thread["spans"]
+    first, tool, agent, input_only, legacy, output_only, flattened, responses, langchain, openrouter, vercel, anthropic, gemini = thread["spans"]
     assert agent["attributes"]["gen_ai.agent.name"] == "planner"
     assert agent["formattedInput"] == []
     assert agent["formattedOutput"] == []
@@ -73,7 +196,7 @@ def test_thread_returns_gen_ai_spans_in_order(
     assert output_only["attributes"]["gen_ai.output.messages"] == "It is sunny in Bangalore."
 
     tool_call = {"type": "tool_call", "name": "get_weather", "id": "call_1", "arguments": {"city": "Bangalore"}}
-    assert first["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "weather in Bangalore?"}]}]
+    assert first["formattedInput"] == [{"role": "system", "parts": [{"type": "text", "content": "Answer with the tool."}]}, {"role": "user", "parts": [{"type": "text", "content": "weather in Bangalore?"}]}]
     assert first["formattedOutput"] == [{"role": "assistant", "parts": [tool_call], "finish_reason": "tool_call"}]
     assert first["formatterWarnings"] == []
     assert tool["formattedInput"] == [{"role": "assistant", "parts": [tool_call]}]
@@ -83,6 +206,24 @@ def test_thread_returns_gen_ai_spans_in_order(
     assert output_only["formattedInput"] == []
     assert output_only["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "It is sunny in Bangalore."}]}]
     assert output_only["formatterWarnings"] == ["bare assistant text, role assumed"]
+    assert legacy["attributes"]["gen_ai.prompt.0.content"] == "and tomorrow?"
+    assert legacy["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "and tomorrow?"}]}]
+    assert legacy["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "Also sunny."}], "finish_reason": "stop"}]
+    assert flattened["attributes"]["llm.input_messages.0.message.content"] == "thanks"
+    assert flattened["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "thanks"}]}]
+    assert flattened["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "You're welcome."}], "finish_reason": "stop"}]
+    assert responses["formattedInput"] == [{"role": "system", "parts": [{"type": "text", "content": "Be brief."}]}, {"role": "user", "parts": [{"type": "text", "content": "bye"}]}]
+    assert responses["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "Bye!"}], "finish_reason": "stop"}]
+    assert langchain["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "see you"}]}]
+    assert langchain["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "See you!"}], "finish_reason": "stop"}]
+    assert openrouter["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "one word"}]}]
+    assert openrouter["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "Yes."}]}]
+    assert vercel["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "last one"}]}]
+    assert vercel["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "Done."}], "finish_reason": "stop"}]
+    assert anthropic["formattedInput"] == [{"role": "system", "parts": [{"type": "text", "content": "Be terse."}]}, {"role": "user", "parts": [{"type": "text", "content": "ping"}]}]
+    assert anthropic["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "pong"}], "finish_reason": "stop"}]
+    assert gemini["formattedInput"] == [{"role": "user", "parts": [{"type": "text", "content": "ping"}]}]
+    assert gemini["formattedOutput"] == [{"role": "assistant", "parts": [{"type": "text", "content": "pong"}], "finish_reason": "stop"}]
 
 
 def test_thread_paginates_with_cursors(