feat(tracedetail): format raw Anthropic and Gemini bodies, skip embeddings (#13141)

<!--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/mocks-server/__mockdata__/alerts.ts

@@ -51,38 +51,3 @@ export const opsGeniePriorityDefaultValue =
 
 export const pagerDutySeverityTextDefaultValue =
 	'{{ (index .Alerts 0).Labels.severity }}';
-
-export const notificationChannelsV2 = [
-	{
-		id: '3',
-		name: 'dummy-channel',
-		displayName: 'Dummy-Channel',
-		kind: 'slack',
-		createdAt: '2023-08-09T04:45:19.239344617Z',
-		updatedAt: '2024-06-27T11:37:14.841184399Z',
-	},
-	{
-		id: '4',
-		name: 'oncall-pagerduty',
-		displayName: 'Oncall PagerDuty',
-		kind: 'pagerduty',
-		createdAt: '2024-02-03T04:45:19.239344617Z',
-		updatedAt: '2024-06-28T11:37:14.841184399Z',
-	},
-];
-
-export const notificationChannelV2 = {
-	id: '3',
-	name: 'dummy-channel',
-	displayName: 'Dummy-Channel',
-	createdAt: '2023-08-09T04:45:19.239344617Z',
-	updatedAt: '2024-06-27T11:37:14.841184399Z',
-	config: {
-		kind: 'slack',
-		spec: {
-			apiUrl: 'https://hooks.slack.com/services/dummy',
-			channel: '#dummy_channel',
-			sendResolved: true,
-		},
-	},
-};

frontend/src/mocks-server/handlers.ts

@@ -2,11 +2,7 @@ import { rest } from 'msw';
 
 import commonEnTranslation from '../../public/locales/en/common.json';
 import enTranslation from '../../public/locales/en/translation.json';
-import {
-	allAlertChannels,
-	notificationChannelsV2,
-	notificationChannelV2,
-} from './__mockdata__/alerts';
+import { allAlertChannels } from './__mockdata__/alerts';
 import { alertRulesFixture } from './__mockdata__/alert_rules';
 import { triggeredAlertsFixture } from './__mockdata__/triggered_alerts';
 import { billingSuccessResponse } from './__mockdata__/billing';
@@ -209,32 +205,6 @@ export const handlers = [
 	rest.get('http://localhost/api/v1/channels', (_, res, ctx) =>
 		res(ctx.status(200), ctx.json({ data: allAlertChannels, status: 'success' })),
 	),
-	rest.get('http://localhost/api/v2/notification_channels', (req, res, ctx) => {
-		const query = req.url.searchParams.get('query')?.toLowerCase() ?? '';
-		const kind = req.url.searchParams.get('kind');
-		const channels = notificationChannelsV2.filter(
-			(channel) =>
-				(!query || channel.displayName.toLowerCase().includes(query)) &&
-				(!kind || channel.kind === kind),
-		);
-		return res(
-			ctx.status(200),
-			ctx.json({
-				data: { channels, total: channels.length },
-				status: 'success',
-			}),
-		);
-	}),
-	rest.get('http://localhost/api/v2/notification_channels/:id', (_, res, ctx) =>
-		res(
-			ctx.status(200),
-			ctx.json({ data: notificationChannelV2, status: 'success' }),
-		),
-	),
-	rest.delete(
-		'http://localhost/api/v2/notification_channels/:id',
-		(_, res, ctx) => res(ctx.status(204)),
-	),
 	rest.get('http://localhost/api/v1/alerts', (_, res, ctx) =>
 		res(
 			ctx.status(200),

frontend/src/pages/AlertList/Channels/stories/Channels.stories.mocks.tsx

@@ -10,7 +10,7 @@ import { defineStoryMocks } from '@/storybook/controls/defineStoryMocks';
 
 import {
 	CHANNEL_MAX,
-	notificationChannelsResponse,
+	channelsResponse,
 } from '../../stories/__story_mockdata__/alerts';
 import { AlertListTabs } from '../../types';
 
@@ -27,8 +27,8 @@ export const channelsMocks = defineStoryMocks({
 	},
 	handlers: (values, response) => [
 		rest.get(
-			'http://localhost/api/v2/notification_channels',
-			response.json(() => notificationChannelsResponse(values.channels)),
+			'http://localhost/api/v1/channels',
+			response.json(() => channelsResponse(values.channels)),
 		),
 	],
 	config: () => ({ route: `/alerts?tab=${AlertListTabs.CHANNELS}` }),

frontend/src/pages/AlertList/NotificationChannels/NotificationChannels.module.scss

@@ -1,144 +0,0 @@
-.container {
-	--control-height: 2rem;
-
-	display: flex;
-	flex-direction: column;
-	height: calc(100vh - 62px);
-	min-height: 400px;
-
-	--combobox-trigger-height: var(--control-height);
-	--select-trigger-height: var(--control-height);
-	--input-height: var(--control-height);
-	--input-wrapper-height: var(--control-height);
-	--button-height: var(--control-height);
-}
-
-/* Title, count and filters sit on the page surface, with matching space above
-   and below before the table starts. */
-.headerBand {
-	display: flex;
-	flex-direction: column;
-	gap: var(--spacing-6);
-	flex-shrink: 0;
-	padding: var(--spacing-6) var(--spacing-8);
-}
-
-/* Title and count on the left, the create action at the far end, the way the
-   dashboards list opens. */
-.commandHeader {
-	display: flex;
-	align-items: center;
-	gap: 12px;
-}
-
-.headingBlock {
-	display: flex;
-	align-items: center;
-	gap: 10px;
-	min-width: 0;
-}
-
-.title {
-	margin: 0;
-	color: var(--l1-foreground);
-	font-size: var(--font-size-lg);
-	font-weight: var(--font-weight-medium);
-	line-height: 28px;
-	letter-spacing: -0.09px;
-	overflow: hidden;
-	text-overflow: ellipsis;
-	white-space: nowrap;
-}
-
-.countPill {
-	padding: 2px 9px;
-	border-radius: 999px;
-	background: var(--l2-background);
-	border: 1px solid var(--l2-border);
-	color: var(--l2-foreground);
-	font-size: var(--font-size-xs);
-	font-variant-numeric: tabular-nums;
-}
-
-.grow {
-	flex: 1;
-}
-
-/* Search and the type filter, the band's second row. */
-.filtersRow {
-	position: relative;
-	z-index: 10;
-	display: flex;
-	align-items: center;
-	gap: var(--spacing-4);
-	flex-wrap: wrap;
-}
-
-/* Spans the row, the way the dashboards filter bar does. The input's own
-   wrapper is `width: 100%`, so the basis has to be 0 or `auto` resolves to the
-   full row and wraps the type filter onto the next line. */
-.searchInput {
-	flex: 1 1 0;
-	min-width: 220px;
-}
-
-.kindSelect {
-	/* the trigger sizes itself to 100%, so the flex item has to be bounded */
-	flex: 0 0 180px;
-	width: 180px;
-	max-width: 180px;
-}
-
-/* The options panel is rendered inline (withPortal={false}) so it can be told to
-   match the trigger; Radix publishes that width at the popper layer. */
-.filtersRow :global([data-slot='select-content']) {
-	width: var(--radix-select-trigger-width);
-	min-width: var(--radix-select-trigger-width);
-}
-
-.commandHeader > button {
-	flex: 0 0 auto;
-}
-
-.searchIcon {
-	color: var(--l2-foreground);
-}
-
-.actionsHeader {
-	display: block;
-	text-align: right;
-}
-
-/* Left on the table's own defaults, the way Infrastructure Monitoring uses it:
-   transparent cells over the page background rather than a raised slab. */
-.tableContainer {
-	flex: 1;
-	min-height: 0;
-	display: flex;
-	flex-direction: column;
-
-	--tanstack-table-row-hover-bg: var(--l2-background-hover);
-	--tanstack-table-row-active-bg: var(--l2-background-active);
-	--tanstack-table-row-height: 42px;
-
-	--tanstack-cell-padding-top-override: 5px;
-	--tanstack-cell-padding-bottom-override: 5px;
-	--tanstack-cell-padding-left-override: 16px;
-	--tanstack-cell-padding-right-override: 16px;
-
-	--tanstack-table-row-odd-bg: color-mix(
-		in srgb,
-		var(--l1-foreground) 2%,
-		transparent
-	);
-	--tanstack-table-row-even-bg: color-mix(
-		in srgb,
-		var(--l1-foreground) 1%,
-		transparent
-	);
-}
-
-.paginationContainer {
-	padding-right: var(--spacing-12);
-	height: 62px;
-}

frontend/src/pages/AlertList/NotificationChannels/NotificationChannels.tsx

@@ -1,253 +0,0 @@
-import { useCallback, useEffect, useMemo, useState } from 'react';
-import { Plus, Search } from '@signozhq/icons';
-import { Button } from '@signozhq/ui/button';
-import { Input } from '@signozhq/ui/input';
-import { SelectSimple } from '@signozhq/ui/select';
-import { Typography } from '@signozhq/ui/typography';
-import logEvent from 'api/common/logEvent';
-import ErrorEmptyState from 'components/Alerts/ErrorEmptyState';
-import NoResultsEmptyState from 'components/Alerts/NoResultsEmptyState';
-import TanStackTable from 'components/TanStackTableView';
-import { useCalculatedPageSize } from 'components/TanStackTableView/useCalculatedPageSize';
-import { useTableParams } from 'components/TanStackTableView/useTableParams';
-import TextToolTip from 'components/TextToolTip';
-import { useUrlSearchState } from 'hooks/useUrlSearchState';
-import { parseAsString, useQueryState } from 'nuqs';
-import { useTimezone } from 'providers/Timezone';
-
-import ChannelActionsMenu from './components/ChannelActionsMenu/ChannelActionsMenu';
-import ChannelJsonDialog from './components/ChannelJsonDialog/ChannelJsonDialog';
-import {
-	KIND_FILTER_ALL,
-	KIND_FILTER_OPTIONS,
-	KIND_KEY,
-	SEARCH_KEY,
-} from './constants';
-import { useChannelDelete } from './hooks/useChannelDelete';
-import { useChannelFormView } from './hooks/useChannelFormView';
-import { useChannelsData } from './hooks/useChannelsData';
-import styles from './NotificationChannels.module.scss';
-import { getChannelColumns } from './table.config';
-import type { Channel } from './types';
-
-const QUERY_PARAMS_CONFIG = {
-	orderBy: 'orderBy',
-	page: 'page',
-	limit: 'limit',
-} as const;
-
-const DEFAULT_PAGE = 1;
-const DEFAULT_LIMIT = 10;
-
-function NotificationChannels(): JSX.Element {
-	const { formatTimezoneAdjustedTimestamp } = useTimezone();
-	const formView = useChannelFormView();
-
-	const { containerRef, calculatedPageSize } = useCalculatedPageSize({
-		rowHeight: 46,
-	});
-
-	const { orderBy, page, limit, setLimit, setPage } = useTableParams(
-		QUERY_PARAMS_CONFIG,
-		{
-			page: DEFAULT_PAGE,
-			limit: DEFAULT_LIMIT,
-			storageKey: 'notification-channels',
-			calculatedPageSize,
-			cleanupOnUnmount: true,
-		},
-	);
-
-	const resetPageOnSearch = useCallback((): void => {
-		setPage(1);
-	}, [setPage]);
-
-	const { searchText, debouncedSearch, handleSearchChange, clearSearch } =
-		useUrlSearchState(SEARCH_KEY, { onDebouncedChange: resetPageOnSearch });
-
-	const [kind, setKind] = useQueryState(
-		KIND_KEY,
-		parseAsString.withDefault(KIND_FILTER_ALL),
-	);
-
-	const handleKindChange = useCallback(
-		(value: string | string[]): void => {
-			const next = value as string;
-			void setKind(next === KIND_FILTER_ALL ? null : next);
-			setPage(1);
-		},
-		[setKind, setPage],
-	);
-
-	const { channels, total, isFetching, isError, refetch } = useChannelsData({
-		search: debouncedSearch,
-		kind,
-		orderBy,
-		page,
-		limit,
-	});
-
-	const deletion = useChannelDelete();
-	const [jsonChannel, setJsonChannel] = useState<Channel | null>(null);
-
-	useEffect(() => {
-		void logEvent('Alert Channel: Channel list page visited', {});
-	}, []);
-
-	const handleEdit = useCallback(
-		(channel: Channel, options?: { newTab?: boolean }): void => {
-			formView.openEdit(channel.id, options);
-		},
-		[formView],
-	);
-
-	const handleRowClick = useCallback(
-		(channel: Channel): void => formView.openEdit(channel.id),
-		[formView],
-	);
-
-	const handleRowClickNewTab = useCallback(
-		(channel: Channel): void => formView.openEdit(channel.id, { newTab: true }),
-		[formView],
-	);
-
-	const columns = useMemo(
-		() => getChannelColumns(formatTimezoneAdjustedTimestamp),
-		[formatTimezoneAdjustedTimestamp],
-	);
-
-	const columnsWithActions = useMemo(
-		() => [
-			...columns,
-			{
-				id: 'actions',
-				header: (): JSX.Element => (
-					<span className={styles.actionsHeader}>Actions</span>
-				),
-				accessorKey: 'id',
-				width: { fixed: '96px', ignoreLastColumnFill: true },
-				enableSort: false,
-				enableResize: false,
-				enableRemove: false,
-				enableMove: false,
-				pin: 'right' as const,
-				cell: ({ row }: { row: Channel }): JSX.Element => (
-					<ChannelActionsMenu
-						channel={row}
-						onOpen={handleEdit}
-						onDelete={deletion.request}
-						onViewJson={setJsonChannel}
-					/>
-				),
-			},
-		],
-		[columns, handleEdit, deletion.request],
-	);
-
-	const hasActiveFilters = searchText.length > 0 || kind !== KIND_FILTER_ALL;
-	const isEmpty = !isFetching && channels.length === 0;
-
-	const handleClearFilters = useCallback((): void => {
-		clearSearch();
-		void setKind(null);
-	}, [clearSearch, setKind]);
-
-	return (
-		<div className={styles.container}>
-			<header className={styles.headerBand}>
-				<div className={styles.commandHeader}>
-					<div className={styles.headingBlock}>
-						<Typography.Title className={styles.title}>
-							All notification channels
-						</Typography.Title>
-						<span className={styles.countPill}>{total}</span>
-					</div>
-					<div className={styles.grow} />
-					<TextToolTip
-						text="More details on how to configure notification channels"
-						url="https://signoz.io/docs/setup-alerts-notification/"
-						urlText="Learn More"
-					/>
-					<Button
-						variant="solid"
-						color="primary"
-						size="sm"
-						prefix={<Plus size={14} />}
-						onClick={formView.openCreate}
-						testId="channels-create"
-					>
-						New channel
-					</Button>
-				</div>
-
-				<div className={styles.filtersRow}>
-					<Input
-						containerClassName={styles.searchInput}
-						placeholder="Search by channel name"
-						value={searchText}
-						onChange={handleSearchChange}
-						suffix={<Search size={14} className={styles.searchIcon} />}
-						testId="channels-search"
-					/>
-					<SelectSimple
-						className={styles.kindSelect}
-						items={KIND_FILTER_OPTIONS}
-						value={kind}
-						onChange={handleKindChange}
-						withPortal={false}
-						testId="channels-kind-filter"
-					/>
-				</div>
-			</header>
-
-			<div ref={containerRef} className={styles.tableContainer}>
-				{isError ? (
-					<ErrorEmptyState
-						title="Failed to load notification channels"
-						onRefresh={refetch}
-					/>
-				) : isEmpty && hasActiveFilters ? (
-					<NoResultsEmptyState
-						title="No matching channels"
-						subtitle="No notification channels match your search. Try adjusting your search criteria."
-						onClear={handleClearFilters}
-						clearButtonText="Clear Search"
-					/>
-				) : (
-					<TanStackTable<Channel>
-						data={channels}
-						columns={columnsWithActions}
-						isLoading={isFetching}
-						getRowKey={(row): string => row.id}
-						getItemKey={(row): string => row.id}
-						columnStorageKey="notification-channels-columns"
-						enableQueryParams={QUERY_PARAMS_CONFIG}
-						onRowClick={handleRowClick}
-						onRowClickNewTab={handleRowClickNewTab}
-						pagination={{
-							total,
-							calculatedPageSize,
-							onLimitChange: setLimit,
-							showTotalCount: true,
-						}}
-						paginationClassname={styles.paginationContainer}
-						enableAlternatingRowColors
-						plainTextCellLineClamp={1}
-					/>
-				)}
-			</div>
-
-			{jsonChannel && (
-				<ChannelJsonDialog
-					channelId={jsonChannel.id}
-					channelName={jsonChannel.displayName}
-					onClose={(): void => setJsonChannel(null)}
-				/>
-			)}
-
-			{deletion.contextHolder}
-		</div>
-	);
-}
-
-export default NotificationChannels;

frontend/src/pages/AlertList/NotificationChannels/__tests__/NotificationChannels.test.tsx

@@ -1,100 +0,0 @@
-import { server } from 'mocks-server/server';
-import { screen, userEvent, waitFor } from 'tests/test-utils';
-
-import { renderChannels } from './renderChannels';
-
-const FIND = { timeout: 5000 };
-const TEST_TIMEOUT = 20000;
-
-describe('Notification channels list', () => {
-	beforeEach(() => {
-		// search, filter and page live in the url, so one test's filter would
-		// otherwise still be applied in the next
-		window.history.replaceState({}, '', '/');
-	});
-
-	afterEach(() => {
-		jest.restoreAllMocks();
-		server.resetHandlers();
-	});
-
-	it(
-		'heads the page with the channel count',
-		async () => {
-			renderChannels();
-
-			await expect(
-				screen.findByText('All notification channels', {}, FIND),
-			).resolves.toBeInTheDocument();
-			// the count comes from the API's total, not the loaded page
-			await expect(screen.findByText('2', {}, FIND)).resolves.toBeInTheDocument();
-		},
-		TEST_TIMEOUT,
-	);
-
-	it(
-		'lists every channel with its type',
-		async () => {
-			renderChannels();
-
-			await expect(
-				screen.findByText('Dummy-Channel', {}, FIND),
-			).resolves.toBeInTheDocument();
-			expect(screen.getByText('Oncall PagerDuty')).toBeInTheDocument();
-			// the type reads as plain text, no colour coding
-			expect(screen.getByText('Slack')).toBeInTheDocument();
-			expect(screen.getByText('PagerDuty')).toBeInTheDocument();
-		},
-		TEST_TIMEOUT,
-	);
-
-	it(
-		'filters server-side as the user types',
-		async () => {
-			renderChannels();
-
-			await screen.findByText('Dummy-Channel', {}, FIND);
-
-			await userEvent.type(screen.getByTestId('channels-search'), 'oncall');
-
-			await waitFor(() => {
-				expect(screen.queryByText('Dummy-Channel')).not.toBeInTheDocument();
-			}, FIND);
-			await expect(
-				screen.findByText('Oncall PagerDuty', {}, FIND),
-			).resolves.toBeInTheDocument();
-		},
-		TEST_TIMEOUT,
-	);
-
-	it(
-		'asks for confirmation before deleting, from the row menu',
-		async () => {
-			renderChannels();
-
-			await screen.findByText('Dummy-Channel', {}, FIND);
-			// the table paints a loading layer over the rows until the fetch settles
-			const user = userEvent.setup({ delay: null, pointerEventsCheck: 0 });
-			await user.click(screen.getByTestId('channel-actions-3'));
-			// Radix renders the menu into a portal once the trigger is activated
-			await screen.findByRole('menu', {}, FIND);
-			// the menu lives inside the row's clickable cell, so opening it must not
-			// also fire the row click and route to the edit screen
-			expect(window.location.pathname).not.toContain('/channels/edit/');
-
-			await user.click(
-				await screen.findByRole('menuitem', { name: 'Delete' }, FIND),
-			);
-
-			// the shared destructive confirmation, the one dashboards also use
-			await expect(
-				screen.findByText(
-					/Are you sure you want to delete the Dummy-Channel channel\?/,
-					{},
-					FIND,
-				),
-			).resolves.toBeInTheDocument();
-		},
-		TEST_TIMEOUT,
-	);
-});

frontend/src/pages/AlertList/NotificationChannels/__tests__/channelJson.test.ts

@@ -1,54 +0,0 @@
-import { AlertmanagertypesGettableNotificationChannelDTO } from 'api/generated/services/sigNoz.schemas';
-
-import {
-	channelToJson,
-	hasSecrets,
-	SECRET_PLACEHOLDER,
-} from '../utils/channelJson';
-
-const slackChannel = {
-	id: '1',
-	name: 'prod-alerts',
-	displayName: 'prod alerts',
-	createdAt: '2026-09-01T00:00:00Z',
-	updatedAt: '2026-09-01T00:00:00Z',
-	config: {
-		kind: 'slack',
-		spec: {
-			apiUrl: 'https://hooks.slack.com/services/T/B/SECRET',
-			channel: '#alerts',
-			sendResolved: true,
-		},
-	},
-} as AlertmanagertypesGettableNotificationChannelDTO;
-
-describe('channelToJson', () => {
-	it('hides the credential the API returns in the clear', () => {
-		const json = JSON.parse(channelToJson(slackChannel, false));
-
-		expect(json.config.spec.apiUrl).toBe(SECRET_PLACEHOLDER);
-		expect(json.config.spec.channel).toBe('#alerts');
-		expect(channelToJson(slackChannel, false)).not.toContain('SECRET');
-	});
-
-	it('reveals it only when asked', () => {
-		const json = JSON.parse(channelToJson(slackChannel, true));
-
-		expect(json.config.spec.apiUrl).toBe(
-			'https://hooks.slack.com/services/T/B/SECRET',
-		);
-	});
-
-	it('leaves the timestamps and id out, so the JSON can be reused', () => {
-		const json = JSON.parse(channelToJson(slackChannel, true));
-
-		expect(json).not.toHaveProperty('id');
-		expect(json).not.toHaveProperty('createdAt');
-		expect(json.name).toBe('prod-alerts');
-	});
-
-	it('knows which kinds carry a credential', () => {
-		expect(hasSecrets('slack')).toBe(true);
-		expect(hasSecrets('email')).toBe(false);
-	});
-});

frontend/src/pages/AlertList/NotificationChannels/__tests__/renderChannels.tsx

@@ -1,13 +0,0 @@
-import { VirtuosoMockContext } from 'react-virtuoso';
-import { render } from 'tests/test-utils';
-
-import NotificationChannels from '../NotificationChannels';
-
-/** The list is virtualized, so jsdom needs a viewport for rows to mount. */
-export function renderChannels(): ReturnType<typeof render> {
-	return render(
-		<VirtuosoMockContext.Provider value={{ viewportHeight: 800, itemHeight: 46 }}>
-			<NotificationChannels />
-		</VirtuosoMockContext.Provider>,
-	);
-}

frontend/src/pages/AlertList/NotificationChannels/components/ChannelActionsMenu/ChannelActionsMenu.module.scss

@@ -1,15 +0,0 @@
-.actions {
-	display: flex;
-	justify-content: flex-end;
-	align-items: center;
-	gap: 6px;
-}
-
-.actions button {
-	opacity: 0.7;
-	transition: opacity 0.15s ease;
-
-	&:hover {
-		opacity: 1;
-	}
-}

frontend/src/pages/AlertList/NotificationChannels/components/ChannelActionsMenu/ChannelActionsMenu.tsx

@@ -1,87 +0,0 @@
-import { useMemo } from 'react';
-import {
-	Braces,
-	Ellipsis,
-	PenLine,
-	SquareArrowOutUpRight,
-	Trash2,
-} from '@signozhq/icons';
-import { Button } from '@signozhq/ui/button';
-import { Dropdown, type DropdownItemType } from '@signozhq/ui/dropdown';
-
-import type { Channel } from '../../types';
-import styles from './ChannelActionsMenu.module.scss';
-
-interface ChannelActionsMenuProps {
-	channel: Channel;
-	onOpen: (channel: Channel, options?: { newTab?: boolean }) => void;
-	onDelete: (channel: Channel) => void;
-	onViewJson: (channel: Channel) => void;
-}
-
-function ChannelActionsMenu({
-	channel,
-	onOpen,
-	onDelete,
-	onViewJson,
-}: ChannelActionsMenuProps): JSX.Element {
-	const menuItems = useMemo<DropdownItemType[]>(
-		() => [
-			{
-				type: 'item',
-				value: 'open',
-				label: 'Edit',
-				prefix: <PenLine size={14} />,
-				onClick: (): void => onOpen(channel),
-			},
-			{
-				type: 'item',
-				value: 'open-new-tab',
-				label: 'Open in New Tab',
-				prefix: <SquareArrowOutUpRight size={14} />,
-				onClick: (): void => onOpen(channel, { newTab: true }),
-			},
-			{
-				type: 'item',
-				value: 'view-json',
-				label: 'View JSON',
-				prefix: <Braces size={14} />,
-				onClick: (): void => onViewJson(channel),
-			},
-			{ type: 'separator', value: 'before-delete' },
-			{
-				type: 'item',
-				value: 'delete',
-				label: 'Delete',
-				prefix: <Trash2 size={14} />,
-				danger: true,
-				onClick: (): void => onDelete(channel),
-			},
-		],
-		[channel, onOpen, onDelete, onViewJson],
-	);
-
-	return (
-		// the row itself opens the channel, so the actions keep their clicks
-		// eslint-disable-next-line jsx-a11y/click-events-have-key-events, jsx-a11y/no-static-element-interactions
-		<div
-			className={styles.actions}
-			onClick={(event): void => event.stopPropagation()}
-		>
-			<Dropdown items={menuItems} nativeButton align="end" side="bottom">
-				<Button
-					variant="outlined"
-					color="secondary"
-					size="sm"
-					icon
-					testId={`channel-actions-${channel.id}`}
-					aria-label={`Actions for ${channel.displayName}`}
-				>
-					<Ellipsis size={16} />
-				</Button>
-			</Dropdown>
-		</div>
-	);
-}
-
-export default ChannelActionsMenu;

frontend/src/pages/AlertList/NotificationChannels/components/ChannelJsonDialog/ChannelJsonDialog.module.scss

@@ -1,26 +0,0 @@
-.body {
-	display: flex;
-	flex-direction: column;
-	gap: var(--spacing-3);
-	height: 100%;
-	min-height: 420px;
-}
-
-.toolbar {
-	display: flex;
-	align-items: center;
-	gap: var(--spacing-2);
-	flex-wrap: wrap;
-}
-
-.spacer {
-	flex: 1 1 auto;
-}
-
-.editor {
-	flex: 1 1 auto;
-	min-height: 360px;
-	border: 1px solid var(--l2-border);
-	border-radius: var(--radius-2);
-	overflow: hidden;
-}

frontend/src/pages/AlertList/NotificationChannels/components/ChannelJsonDialog/ChannelJsonDialog.tsx

@@ -1,116 +0,0 @@
-import { useCallback, useEffect, useMemo, useState } from 'react';
-import { useCopyToClipboard } from 'react-use';
-import MEditor from '@monaco-editor/react';
-import { DrawerWrapper } from '@signozhq/ui/drawer';
-import { useGetNotificationChannel } from 'api/generated/services/channels';
-import ErrorEmptyState from 'components/Alerts/ErrorEmptyState';
-import Spinner from 'components/Spinner';
-import { READONLY_EDITOR_OPTIONS } from 'container/RolesSettings/monaco.config';
-import {
-	defineJsonEditorTheme,
-	JSON_EDITOR_THEME,
-} from 'lib/monaco/jsonEditorTheme';
-
-import { channelToJson, hasSecrets } from '../../utils/channelJson';
-import styles from './ChannelJsonDialog.module.scss';
-import ChannelJsonToolbar from './ChannelJsonToolbar';
-
-interface ChannelJsonDialogProps {
-	channelId: string;
-	channelName: string;
-	onClose: () => void;
-}
-
-function ChannelJsonDialog({
-	channelId,
-	channelName,
-	onClose,
-}: ChannelJsonDialogProps): JSX.Element {
-	const [copyState, copyToClipboard] = useCopyToClipboard();
-	const [copied, setCopied] = useState(false);
-	const [showSecrets, setShowSecrets] = useState(false);
-
-	const { data, isLoading, refetch } = useGetNotificationChannel({
-		id: channelId,
-	});
-	const channel = data?.data;
-
-	const json = useMemo(
-		() => (channel ? channelToJson(channel, showSecrets) : ''),
-		[channel, showSecrets],
-	);
-
-	useEffect(() => {
-		if (copyState.value) {
-			setCopied(true);
-			const timer = setTimeout(() => setCopied(false), 1500);
-			return (): void => clearTimeout(timer);
-		}
-		return undefined;
-	}, [copyState]);
-
-	const handleCopy = useCallback(
-		(): void => copyToClipboard(json),
-		[copyToClipboard, json],
-	);
-
-	const handleRefetch = useCallback((): void => {
-		void refetch();
-	}, [refetch]);
-
-	const handleToggleSecrets = useCallback(
-		(): void => setShowSecrets((value) => !value),
-		[],
-	);
-
-	const kindHasSecrets = channel ? hasSecrets(channel.config.kind) : false;
-
-	return (
-		<DrawerWrapper
-			open
-			onOpenChange={(isOpen): void => {
-				if (!isOpen) {
-					onClose();
-				}
-			}}
-			title={channelName}
-			subTitle="Channel JSON"
-			width="wide"
-			testId="channel-json-dialog"
-		>
-			{isLoading ? (
-				<Spinner tip="Loading channel..." />
-			) : !channel ? (
-				<ErrorEmptyState title="Failed to load channel" onRefresh={handleRefetch} />
-			) : (
-				<div className={styles.body}>
-					<ChannelJsonToolbar
-						kindHasSecrets={kindHasSecrets}
-						showSecrets={showSecrets}
-						onToggleSecrets={handleToggleSecrets}
-						copied={copied}
-						onCopy={handleCopy}
-					/>
-
-					<div className={styles.editor}>
-						<MEditor
-							value={json}
-							language="json"
-							options={READONLY_EDITOR_OPTIONS}
-							height="100%"
-							theme="vs-dark"
-							// the same theme the dashboard JSON drawer uses: it reads the
-							// page's own tokens, so it follows light and dark
-							onMount={(editor, monaco): void => {
-								defineJsonEditorTheme(monaco, editor.getContainerDomNode());
-								monaco.editor.setTheme(JSON_EDITOR_THEME);
-							}}
-						/>
-					</div>
-				</div>
-			)}
-		</DrawerWrapper>
-	);
-}
-
-export default ChannelJsonDialog;

frontend/src/pages/AlertList/NotificationChannels/components/ChannelJsonDialog/ChannelJsonToolbar.tsx

@@ -1,63 +0,0 @@
-import { Color } from '@signozhq/design-tokens';
-import { Check, Copy, Eye, EyeOff } from '@signozhq/icons';
-import { Button } from '@signozhq/ui/button';
-import { Typography } from '@signozhq/ui/typography';
-
-import styles from './ChannelJsonDialog.module.scss';
-
-interface ChannelJsonToolbarProps {
-	kindHasSecrets: boolean;
-	showSecrets: boolean;
-	onToggleSecrets: () => void;
-	copied: boolean;
-	onCopy: () => void;
-}
-
-function ChannelJsonToolbar({
-	kindHasSecrets,
-	showSecrets,
-	onToggleSecrets,
-	copied,
-	onCopy,
-}: ChannelJsonToolbarProps): JSX.Element {
-	return (
-		<div className={styles.toolbar}>
-			{kindHasSecrets && (
-				<Typography.Text color="muted" size="sm">
-					{showSecrets ? 'Credentials are visible.' : 'Credentials are hidden.'}
-				</Typography.Text>
-			)}
-			<span className={styles.spacer} />
-			{kindHasSecrets && (
-				<Button
-					variant="outlined"
-					color="secondary"
-					size="sm"
-					prefix={showSecrets ? <EyeOff size={14} /> : <Eye size={14} />}
-					onClick={onToggleSecrets}
-					testId="channel-json-toggle-secrets"
-				>
-					{showSecrets ? 'Hide credentials' : 'Show credentials'}
-				</Button>
-			)}
-			<Button
-				variant="outlined"
-				color="secondary"
-				size="sm"
-				prefix={
-					copied ? (
-						<Check size={14} color={Color.BG_FOREST_400} />
-					) : (
-						<Copy size={14} />
-					)
-				}
-				onClick={onCopy}
-				testId="channel-json-copy"
-			>
-				{copied ? 'Copied' : 'Copy'}
-			</Button>
-		</div>
-	);
-}
-
-export default ChannelJsonToolbar;

frontend/src/pages/AlertList/NotificationChannels/constants.ts

@@ -1,34 +0,0 @@
-import { AlertmanagertypesChannelKindDTO } from 'api/generated/services/sigNoz.schemas';
-
-export const SEARCH_KEY = 'search';
-export const KIND_KEY = 'kind';
-export const PAGE_KEY = 'page';
-
-export const PAGE_SIZE = 20;
-export const SEARCH_DEBOUNCE_MS = 300;
-
-export const CHANNEL_KIND_LABEL: Record<
-	AlertmanagertypesChannelKindDTO,
-	string
-> = {
-	[AlertmanagertypesChannelKindDTO.slack]: 'Slack',
-	[AlertmanagertypesChannelKindDTO.email]: 'Email',
-	[AlertmanagertypesChannelKindDTO.webhook]: 'Webhook',
-	[AlertmanagertypesChannelKindDTO.pagerduty]: 'PagerDuty',
-	[AlertmanagertypesChannelKindDTO.opsgenie]: 'Opsgenie',
-	[AlertmanagertypesChannelKindDTO.msteams]: 'MS Teams',
-	[AlertmanagertypesChannelKindDTO.googlechat]: 'Google Chat',
-	[AlertmanagertypesChannelKindDTO.jira]: 'Jira',
-	[AlertmanagertypesChannelKindDTO.jsmops]: 'JSM Ops',
-	[AlertmanagertypesChannelKindDTO.incidentio]: 'incident.io',
-};
-
-export const KIND_FILTER_ALL = 'all';
-
-export const KIND_FILTER_OPTIONS = [
-	{ value: KIND_FILTER_ALL, label: 'All types' },
-	...Object.entries(CHANNEL_KIND_LABEL).map(([value, label]) => ({
-		value,
-		label,
-	})),
-];

frontend/src/pages/AlertList/NotificationChannels/hooks/useChannelDelete.tsx

@@ -1,55 +0,0 @@
-import { useQueryClient } from 'react-query';
-import { Typography } from '@signozhq/ui/typography';
-import {
-	invalidateListNotificationChannels,
-	useDeleteNotificationChannel,
-} from 'api/generated/services/channels';
-import { useDeleteConfirm } from 'components/DeleteConfirmModal/useDeleteConfirm';
-import { useErrorModal } from 'providers/ErrorModalProvider';
-import { ReactNode } from 'react';
-import APIError from 'types/api/error';
-
-import type { Channel } from '../types';
-
-export interface ChannelDeleteState {
-	/** Must be rendered by the caller for the confirmation to appear. */
-	contextHolder: ReactNode;
-	request: (channel: Channel) => void;
-}
-
-/**
- * Deleting a channel goes through the shared destructive confirmation, the same
- * one dashboards and saved views use, so the wording and the danger button match.
- */
-export function useChannelDelete(onDeleted?: () => void): ChannelDeleteState {
-	const queryClient = useQueryClient();
-	const { showErrorModal } = useErrorModal();
-	const { contextHolder, confirmDelete } = useDeleteConfirm();
-	const { mutateAsync } = useDeleteNotificationChannel();
-
-	const request = (channel: Channel): void => {
-		confirmDelete({
-			title: (
-				<Typography.Title level={5}>
-					Are you sure you want to delete the {channel.displayName} channel?
-				</Typography.Title>
-			),
-			// Alert rules and routing policies reference a channel by name, so a
-			// delete can silence them.
-			content:
-				'Alert rules and routing policies that notify this channel will stop reaching it.',
-			// Returning the promise keeps Delete loading until the request settles.
-			onConfirm: async (): Promise<void> => {
-				try {
-					await mutateAsync({ pathParams: { id: channel.id } });
-					await invalidateListNotificationChannels(queryClient);
-					onDeleted?.();
-				} catch (error) {
-					showErrorModal(error as APIError);
-				}
-			},
-		});
-	};
-
-	return { contextHolder, request };
-}

frontend/src/pages/AlertList/NotificationChannels/hooks/useChannelFormView.ts

@@ -1,28 +0,0 @@
-import { generatePath } from 'react-router-dom';
-import ROUTES from 'constants/routes';
-import history from 'lib/history';
-import { openInNewTab } from 'utils/navigation';
-
-export interface ChannelFormView {
-	openCreate: () => void;
-	openEdit: (channelId: string, options?: { newTab?: boolean }) => void;
-}
-
-/** Create and edit both render on their own route. */
-export function useChannelFormView(): ChannelFormView {
-	const openEdit = (channelId: string, options?: { newTab?: boolean }): void => {
-		const path = generatePath(ROUTES.CHANNELS_EDIT, { channelId });
-
-		if (options?.newTab) {
-			openInNewTab(path);
-			return;
-		}
-
-		history.push(path);
-	};
-
-	return {
-		openCreate: (): void => history.push(ROUTES.CHANNELS_NEW),
-		openEdit,
-	};
-}

frontend/src/pages/AlertList/NotificationChannels/hooks/useChannelsData.ts

@@ -1,75 +0,0 @@
-import { useListNotificationChannels } from 'api/generated/services/channels';
-import {
-	AlertmanagertypesChannelKindDTO,
-	AlertmanagertypesChannelListOrderDTO,
-	AlertmanagertypesChannelListSortDTO,
-} from 'api/generated/services/sigNoz.schemas';
-import type { SortState } from 'components/TanStackTableView';
-
-import { KIND_FILTER_ALL } from '../constants';
-import type { Channel } from '../types';
-
-/** Table column ids the list API can order by. */
-const SORT_BY_COLUMN: Record<string, AlertmanagertypesChannelListSortDTO> = {
-	name: AlertmanagertypesChannelListSortDTO.name,
-	createdAt: AlertmanagertypesChannelListSortDTO.created_at,
-	updatedAt: AlertmanagertypesChannelListSortDTO.updated_at,
-};
-
-interface UseChannelsDataArgs {
-	search: string;
-	kind: string;
-	orderBy: SortState | null;
-	page: number;
-	limit: number;
-	enabled?: boolean;
-}
-
-export interface ChannelsData {
-	channels: Channel[];
-	total: number;
-	isFetching: boolean;
-	isError: boolean;
-	refetch: () => void;
-}
-
-/**
- * Search, kind filter, sort and paging all run on the API, so the table shows
- * one page at a time rather than filtering a full download in the browser.
- */
-export function useChannelsData({
-	search,
-	kind,
-	orderBy,
-	page,
-	limit,
-	enabled = true,
-}: UseChannelsDataArgs): ChannelsData {
-	const sort = orderBy ? SORT_BY_COLUMN[orderBy.columnName] : undefined;
-
-	const { data, isFetching, isError, refetch } = useListNotificationChannels(
-		{
-			limit,
-			offset: (page - 1) * limit,
-			...(sort
-				? {
-						sort,
-						order: orderBy?.order as AlertmanagertypesChannelListOrderDTO,
-					}
-				: {}),
-			...(search ? { query: search } : {}),
-			...(kind !== KIND_FILTER_ALL
-				? { kind: kind as AlertmanagertypesChannelKindDTO }
-				: {}),
-		},
-		{ query: { enabled, keepPreviousData: true } },
-	);
-
-	return {
-		channels: data?.data?.channels ?? [],
-		total: data?.data?.total ?? 0,
-		isFetching,
-		isError,
-		refetch,
-	};
-}

frontend/src/pages/AlertList/NotificationChannels/table.config.tsx

@@ -1,88 +0,0 @@
-import type { TableColumnDef } from 'components/TanStackTableView';
-import TanStackTable from 'components/TanStackTableView';
-import { DATE_TIME_FORMATS } from 'constants/dateTimeFormats';
-
-import { CHANNEL_KIND_LABEL } from './constants';
-import styles from './table.module.scss';
-import type { Channel } from './types';
-import { toRelativeTime } from './utils/relativeTime';
-
-export function getChannelColumns(
-	formatTimezoneAdjustedTimestamp: (date: string, format: string) => string,
-): TableColumnDef<Channel>[] {
-	const timestampCell = (row: Channel, value: string): JSX.Element => {
-		if (!value) {
-			return <TanStackTable.Text>-</TanStackTable.Text>;
-		}
-
-		return (
-			<TanStackTable.HoverTooltip
-				rowId={row.id}
-				title={formatTimezoneAdjustedTimestamp(
-					value,
-					DATE_TIME_FORMATS.DASH_DATETIME,
-				)}
-			>
-				{/* A plain inline-block hugs the text. TanStackTable.Text is a
-				    `-webkit-box` for line clamping, which fills the cell and leaves
-				    the arrow pointing at the cell's middle instead of the words. */}
-				<span className={styles.timeText}>{toRelativeTime(value)}</span>
-			</TanStackTable.HoverTooltip>
-		);
-	};
-
-	return [
-		{
-			id: 'name',
-			header: 'Channel',
-			accessorKey: 'displayName',
-			width: { default: '100%', min: '220px' },
-			enableSort: true,
-			enableResize: true,
-			enableRemove: false,
-			enableMove: false,
-			cell: ({ row }): JSX.Element => (
-				<TanStackTable.Text
-					title={row.displayName}
-					data-testid={`channel-row-${row.id}-name`}
-				>
-					{row.displayName}
-				</TanStackTable.Text>
-			),
-		},
-		{
-			id: 'kind',
-			header: 'Type',
-			accessorKey: 'kind',
-			width: { default: '150px', min: '110px' },
-			enableSort: false,
-			enableResize: true,
-			enableMove: false,
-			cell: ({ row }): JSX.Element => (
-				<TanStackTable.Text data-testid={`channel-row-${row.id}-kind`}>
-					{CHANNEL_KIND_LABEL[row.kind]}
-				</TanStackTable.Text>
-			),
-		},
-		{
-			id: 'createdAt',
-			header: 'Created At',
-			accessorKey: 'createdAt',
-			width: { default: '150px', min: '120px' },
-			enableSort: true,
-			enableResize: true,
-			enableMove: false,
-			cell: ({ row }): JSX.Element => timestampCell(row, row.createdAt),
-		},
-		{
-			id: 'updatedAt',
-			header: 'Updated At',
-			accessorKey: 'updatedAt',
-			width: { default: '150px', min: '120px' },
-			enableSort: true,
-			enableResize: true,
-			enableMove: false,
-			cell: ({ row }): JSX.Element => timestampCell(row, row.updatedAt),
-		},
-	];
-}

frontend/src/pages/AlertList/NotificationChannels/table.module.scss

@@ -1,7 +0,0 @@
-.timeText {
-	display: inline-block;
-	white-space: nowrap;
-	font-size: var(--tanstack-plain-cell-font-size, 14px);
-	line-height: var(--tanstack-plain-cell-line-height, 18px);
-	color: inherit;
-}

frontend/src/pages/AlertList/NotificationChannels/types.ts

@@ -1,4 +0,0 @@
-import { AlertmanagertypesListedNotificationChannelDTO } from 'api/generated/services/sigNoz.schemas';
-
-/** A channel as the list API returns it: identity and kind, but no config. */
-export type Channel = AlertmanagertypesListedNotificationChannelDTO;

frontend/src/pages/AlertList/NotificationChannels/utils/channelJson.ts

@@ -1,50 +0,0 @@
-import { AlertmanagertypesGettableNotificationChannelDTO } from 'api/generated/services/sigNoz.schemas';
-
-export const SECRET_PLACEHOLDER = '••••••••';
-
-/**
- * The spec fields the API marks `format: password`. A fetch by ID returns them
- * in the clear, so the JSON view hides them until asked.
- */
-const SECRET_FIELDS_BY_KIND: Record<string, readonly string[]> = {
-	slack: ['apiUrl'],
-	webhook: ['url', 'password', 'bearerToken'],
-	pagerduty: ['routingKey'],
-	opsgenie: ['apiKey'],
-	msteams: ['webhookUrl'],
-	googlechat: ['webhookUrl'],
-	jira: ['apiToken'],
-	jsmops: ['apiKey'],
-	incidentio: ['token'],
-};
-
-export function channelToJson(
-	channel: AlertmanagertypesGettableNotificationChannelDTO,
-	showSecrets: boolean,
-): string {
-	const { kind, spec } = channel.config;
-	const secrets = SECRET_FIELDS_BY_KIND[kind] ?? [];
-
-	const safeSpec = showSecrets
-		? spec
-		: Object.fromEntries(
-				Object.entries(spec).map(([key, value]) => [
-					key,
-					secrets.includes(key) && value ? SECRET_PLACEHOLDER : value,
-				]),
-			);
-
-	return JSON.stringify(
-		{
-			name: channel.name,
-			displayName: channel.displayName,
-			config: { kind, spec: safeSpec },
-		},
-		null,
-		2,
-	);
-}
-
-export function hasSecrets(kind: string): boolean {
-	return (SECRET_FIELDS_BY_KIND[kind] ?? []).length > 0;
-}

frontend/src/pages/AlertList/NotificationChannels/utils/relativeTime.ts

@@ -1,10 +0,0 @@
-import dayjs from 'dayjs';
-import relativeTimePlugin from 'dayjs/plugin/relativeTime';
-
-dayjs.extend(relativeTimePlugin);
-
-/** "2 months ago". The exact timestamp stays available on hover. */
-export function toRelativeTime(value: string | undefined): string {
-	const parsed = value ? dayjs(value) : null;
-	return parsed?.isValid() ? parsed.fromNow() : '-';
-}

frontend/src/pages/AlertList/index.tsx

@@ -4,6 +4,7 @@ import { Tabs, TabsProps } from 'antd';
 import ConfigureIcon from 'assets/AlertHistory/ConfigureIcon';
 import HeaderRightSection from 'components/HeaderRightSection/HeaderRightSection';
 import ROUTES from 'constants/routes';
+import AllAlertChannels from 'container/AllAlertChannels';
 import AllAlertRules from 'container/ListAlertRules';
 import { PlannedDowntime } from 'container/PlannedDowntime/PlannedDowntime';
 import RoutingPolicies from 'container/RoutingPolicies';
@@ -15,8 +16,6 @@ import AlertDetails from 'pages/AlertDetails';
 import ChannelsEdit from 'pages/ChannelsEdit';
 import ChannelsNew from 'pages/ChannelsNew';
 
-import NotificationChannels from './NotificationChannels/NotificationChannels';
-
 import { AlertListSubTabs, AlertListTabs } from './types';
 
 import './AlertList.styles.scss';
@@ -105,7 +104,7 @@ function AllAlertList(): JSX.Element {
 				<div className="alert-rules-container">
 					{isChannelsNew && <ChannelsNew />}
 					{isChannelsEdit && <ChannelsEdit />}
-					{!isChannelDetails && <NotificationChannels />}
+					{!isChannelDetails && <AllAlertChannels />}
 				</div>
 			),
 		},

frontend/src/pages/AlertList/stories/__story_mockdata__/alerts.ts

@@ -26,9 +26,6 @@ import {
 	type RuletypesAlertCompositeQueryDTO,
 	type RuletypesRuleConditionDTO,
 	type RuletypesRuleDTO,
-	type AlertmanagertypesChannelConfigDTO,
-	type AlertmanagertypesGettableNotificationChannelDTO,
-	type AlertmanagertypesListedNotificationChannelDTO,
 } from 'api/generated/services/sigNoz.schemas';
 import { NEW_ALERT_SCHEMA_VERSION } from 'types/api/alerts/alertTypesV2';
 import type { Channels } from 'types/api/channels/getAll';
@@ -707,22 +704,6 @@ const buildChannel = (index: number): Channels => {
 	};
 };
 
-const buildListedChannel = (
-	index: number,
-): AlertmanagertypesListedNotificationChannelDTO => {
-	const seed = CHANNEL_SEEDS[index % CHANNEL_SEEDS.length];
-
-	return {
-		id: String(index + 1),
-		name: seed.name,
-		displayName: seed.name,
-		kind: seed.type as AlertmanagertypesListedNotificationChannelDTO['kind'],
-		createdAt: ago((index + 10) * DAY),
-		updatedAt: ago((index + 1) * DAY),
-	};
-};
-
-/** The v2 list, which is what the channel pickers read. */
 export const channelsResponse = (
 	count: number,
 ): { status: string; data: Channels[] } => ({
@@ -730,97 +711,10 @@ export const channelsResponse = (
 	data: Array.from({ length: count }, (_unused, index) => buildChannel(index)),
 });
 
-/** The v2 list, which is what the redesigned channels list reads. */
-export const notificationChannelsResponse = (
-	count: number,
-): {
-	status: string;
-	data: {
-		channels: AlertmanagertypesListedNotificationChannelDTO[];
-		total: number;
-	};
-} => {
-	const channels = Array.from({ length: count }, (_unused, index) =>
-		buildListedChannel(index),
-	);
-
-	return { status: 'success', data: { channels, total: channels.length } };
-};
-
 /** Channel names the alert form and the routing policies pick from. */
 export const channelNames = (count: number): string[] =>
 	channelsResponse(count).data.map((channel) => channel.name);
 
-/**
- * The v2 spec each kind is fetched with, so the edit form renders populated
- * fields. Keys mirror the generated channel spec schemas.
- */
-const CHANNEL_SPECS: Record<ChannelType, Record<string, unknown>> = {
-	slack: {
-		apiUrl: 'https://hooks.slack.com/services/T000/B000/story-token',
-		channel: '#ops-alerts',
-		title: '[{{ .Status | toUpper }}] {{ .CommonLabels.alertname }}',
-		text: '{{ range .Alerts -}}*Alert:* {{ .Labels.alertname }}\n{{ end }}',
-		sendResolved: true,
-	},
-	webhook: {
-		url: 'https://example.com/story-hook',
-		username: 'story-user',
-		sendResolved: true,
-	},
-	pagerduty: {
-		routingKey: 'story-routing-key',
-		description: '{{ .CommonLabels.alertname }}',
-		severity: 'critical',
-		sendResolved: true,
-	},
-	opsgenie: {
-		apiKey: 'story-opsgenie-key',
-		message: '{{ .CommonLabels.alertname }}',
-		description: 'Story channel',
-		sendResolved: true,
-	},
-	email: {
-		to: 'oncall@story.signoz.io',
-		html: '<p>{{ .CommonLabels.alertname }}</p>',
-		sendResolved: true,
-	},
-	msteams: {
-		webhookUrl: 'https://story.webhook.office.com/webhookb2/story',
-		title: '[{{ .Status | toUpper }}] {{ .CommonLabels.alertname }}',
-		text: 'Story channel',
-		sendResolved: true,
-	},
-	googlechat: {
-		webhookUrl: 'https://chat.googleapis.com/v1/spaces/STORY/messages',
-		title: '[{{ .Status | toUpper }}] {{ .CommonLabels.alertname }}',
-		text: 'Story channel',
-		sendResolved: true,
-	},
-	jira: {
-		site: 'https://story.atlassian.net',
-		project: 'OPS',
-		issueType: 'Task',
-		email: 'story@signoz.io',
-		apiToken: 'story-jira-token',
-		summary: '{{ .CommonLabels.alertname }}',
-		sendResolved: true,
-	},
-	jsmops: {
-		apiKey: 'story-jsm-key',
-		message: '{{ .CommonLabels.alertname }}',
-		description: 'Story channel',
-		sendResolved: true,
-	},
-	incidentio: {
-		url: 'https://api.incident.io/v2/alert_events/http/story',
-		token: 'story-incidentio-token',
-		title: '{{ .CommonLabels.alertname }}',
-		sendResolved: true,
-	},
-};
-
-/** A single channel as the v2 API returns it, which the edit screen reads. */
 export const channelResponse = (
 	id: string,
 	type: ChannelType,
@@ -833,36 +727,6 @@ export const channelResponse = (
 	};
 };
 
-/** The v2 fetch by id, which is what the redesigned screens read. */
-export const notificationChannelResponse = (
-	id: string,
-	type: ChannelType,
-): {
-	status: string;
-	data: AlertmanagertypesGettableNotificationChannelDTO;
-} => {
-	const index = Math.max(
-		CHANNEL_SEEDS.findIndex((seed) => seed.type === type),
-		0,
-	);
-	const seed = CHANNEL_SEEDS[index];
-
-	return {
-		status: 'success',
-		data: {
-			id,
-			name: seed.name,
-			displayName: seed.name,
-			createdAt: ago((index + 10) * DAY),
-			updatedAt: ago((index + 1) * DAY),
-			config: {
-				kind: type,
-				spec: CHANNEL_SPECS[type],
-			} as unknown as AlertmanagertypesChannelConfigDTO,
-		},
-	};
-};
-
 export const CHANNEL_ACTION_OUTCOMES = ['succeeds', 'fails'] as const;
 
 export type ChannelActionOutcome = (typeof CHANNEL_ACTION_OUTCOMES)[number];

frontend/src/pages/DashboardPage/DashboardContainer/DashboardPageToolbar/JsonEditorDrawer/JsonEditorDrawer.tsx

@@ -12,10 +12,7 @@ import { DashboardDetailEvents } from 'pages/DashboardPage/constants/events';
 import { useCopyToClipboard } from 'react-use';
 import { toast } from '@signozhq/ui/sonner';
 
-import {
-	defineJsonEditorTheme,
-	JSON_EDITOR_THEME,
-} from 'lib/monaco/jsonEditorTheme';
+import { defineJsonEditorTheme, JSON_EDITOR_THEME } from './editorTheme';
 import styles from './JsonEditorDrawer.module.scss';
 import JsonEditorToolbar from './JsonEditorToolbar';
 import { useJsonEditor } from './useJsonEditor';

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(