> ## Documentation Index > Fetch the complete documentation index at: https://docs.parallel.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create a Snapshot Monitor > Monitor any task output for material changes on a schedule
For AI agents: a documentation index is available at [https://docs.parallel.ai/llms.txt](https://docs.parallel.ai/llms.txt). The full text of all docs is at [https://docs.parallel.ai/llms-full.txt](https://docs.parallel.ai/llms-full.txt). You may also fetch any page as Markdown by appending `.md` to its URL or sending `Accept: text/markdown`.
A snapshot monitor watches the output of a Task Run on a schedule. Each execution re-runs the same task and compares the result against the previous snapshot. When the system detects a material change—new data, a removed field, a significant value shift—it fires a webhook. Snapshot monitors work with **any** task output—text or structured JSON, `base`, `core`, `pro`, `ultra`, or any other processor. They are especially powerful for **structured enrichment tasks**, where the JSON schema acts as a stable template the monitor can diff field-by-field across runs, surfacing precisely which properties changed and why. This gives you task cron with built-in deduplication and reasoning about what actually changed, at a fraction of the cost of running full Task API calls independently. **Typical use cases:** * Executive team or board changes at a company * Competitor pricing or product page updates * Regulatory filing status changes * Job posting additions or removals * Periodic deep-research briefs on a watchlist of companies, people, or topics ## Prerequisites Generate your API key on [Platform](https://platform.parallel.ai): ```bash theme={"system"} export PARALLEL_API_KEY="PARALLEL_API_KEY" ``` *** ## Step 1. Create a Task Run to establish a baseline A snapshot monitor requires a completed Task Run as its starting point. The run's input, processor, and output schema become the template the monitor re-executes on each tick. Any Task Run works—free-form text outputs are diffed as a whole, while structured JSON outputs are diffed field-by-field for the most precise change detection. The example below uses a **structured enrichment task** that extracts the executive team of a company. Structured outputs are recommended whenever you care about specific fields (prices, statuses, lists of entities) rather than narrative text: ```bash cURL theme={"system"} curl --request POST \ --url https://api.parallel.ai/v1/tasks/runs \ --header 'Content-Type: application/json' \ --header "x-api-key: $PARALLEL_API_KEY" \ --data '{ "input": "Who are the current C-suite executives at Acme Corp (acme.com)?", "processor": "base", "output_schema": { "type": "json", "json_schema": { "type": "object", "properties": { "executives": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "title": { "type": "string" } } } } } } } }' ``` **Response:** ```json theme={"system"} { "run_id": "taskrun_a1b2c3d4e5f6", "status": "queued" } ``` Wait for the run to complete (poll `GET /v1/tasks/runs/{run_id}` or use a webhook). Once `status` is `"completed"`, note the `run_id`—you'll use it in the next step. See [Task API Quickstart](/task-api/examples/task-enrichment) for full details on creating and polling task runs. ## Step 2. Create a snapshot monitor Create a snapshot monitor that re-runs the same task weekly and fires when the output changes: ```bash cURL theme={"system"} curl --request POST \ --url https://api.parallel.ai/v1/monitors \ --header 'Content-Type: application/json' \ --header "x-api-key: $PARALLEL_API_KEY" \ --data '{ "type": "snapshot", "frequency": "1w", "processor": "lite", "settings": { "task_run_id": "taskrun_a1b2c3d4e5f6" }, "webhook": { "url": "https://example.com/webhook", "event_types": ["monitor.event.detected"] }, "metadata": { "external_id": "acme-snapshot-001" } }' ``` ```python Python theme={"system"} import os from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) monitor = client.monitor.create( type="snapshot", frequency="1w", processor="lite", settings={"task_run_id": "taskrun_a1b2c3d4e5f6"}, webhook={ "url": "https://example.com/webhook", "event_types": ["monitor.event.detected"], }, metadata={"external_id": "acme-snapshot-001"}, ) print(f"Monitor ID: {monitor.monitor_id}") ``` ```typescript TypeScript theme={"system"} import Parallel from "parallel-web"; const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY }); const monitor = await client.monitor.create({ type: "snapshot", frequency: "1w", processor: "lite", settings: { task_run_id: "taskrun_a1b2c3d4e5f6" }, webhook: { url: "https://example.com/webhook", event_types: ["monitor.event.detected"], }, metadata: { external_id: "acme-snapshot-001" }, }); console.log(`Monitor ID: ${monitor.monitor_id}`); ``` **Response:** ```json theme={"system"} { "monitor_id": "monitor_c3d4e5f6a1b2", "type": "snapshot", "status": "active", "frequency": "1w", "processor": "lite", "settings": { "task_run_id": "taskrun_a1b2c3d4e5f6" }, "webhook": { "url": "https://example.com/webhook", "event_types": ["monitor.event.detected"] }, "metadata": { "external_id": "acme-snapshot-001" }, "created_at": "2025-04-23T20:21:48.037943Z" } ``` ## Step 3. Receive and retrieve events When the monitor detects a material change, your webhook receives: ```json theme={"system"} { "type": "monitor.event.detected", "timestamp": "2025-12-17T09:00:12.000000+00:00", "data": { "monitor_id": "monitor_c3d4e5f6a1b2", "event": { "event_group_id": "mevtgrp_9f8e7d6c5b4a3d2c1b0a" }, "metadata": { "external_id": "acme-snapshot-001" } } } ``` Fetch the events for that execution using the `event_group_id` as a query parameter: ```bash cURL theme={"system"} curl --request GET \ --url "https://api.parallel.ai/v1/monitors/${MONITOR_ID}/events?event_group_id=${EVENT_GROUP_ID}" \ --header "x-api-key: $PARALLEL_API_KEY" ``` ```python Python theme={"system"} import os from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) result = client.monitor.events( monitor_id, event_group_id=event_group_id, ) for event in result.events: print(event.changed_output.content) ``` ```typescript TypeScript theme={"system"} import Parallel from "parallel-web"; const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY }); const result = await client.monitor.events(monitorId, { event_group_id: eventGroupId, }); for (const event of result.events) { console.log(event.changed_output?.content); } ``` **Response:** ```json theme={"system"} { "events": [ { "event_id": "evt_9f8e7d6c5b4a", "event_group_id": "mevtgrp_9f8e7d6c5b4a3d2c1b0a", "event_date": "2025-12-17", "event_type": "snapshot", "changed_output": { "type": "json", "content": { "executives": [{ "name": "Jane Smith", "title": "CFO" }] }, "basis": [ { "field": "executives", "citations": [{ "url": "https://acme.com/press/leadership-update" }], "reasoning": "Official press release confirms the CFO transition from John Doe to Jane Smith.", "confidence": "high" } ] }, "previous_output": { "type": "json", "content": { "executives": [{ "name": "John Doe", "title": "CFO" }] } } } ] } ``` * `changed_output` contains only the fields that changed, each with `basis` (citations and reasoning). * `previous_output` contains the full output from the prior run for comparison. See [Research Basis](/task-api/guides/access-research-basis) for the full basis schema. ## Next Steps * **[Event Stream Quickstart](./monitor-quickstart)**: Track new events as they appear on the web. * **[Events](./monitor-events)**: Full event model and retrieval options. * **[Follow-up Tasks](./monitor-task)**: Trigger structured enrichment or deep research from a monitor event. * **[API Reference](/api-reference/monitor/create-monitor)**: Complete endpoint documentation.