Follow-up Tasks

Monitor events can feed directly into a Task API call to go deeper on any detected signal. Use follow-up tasks to enrich a signal with structured fields or launch a full research report—without any manual handoff. The flow is the same in both cases: receive a webhook, fetch the event, pass its output.content as the input and its event_id as previous_interaction_id to a new Task Run. Passing event_id as previous_interaction_id carries the monitor event’s full context forward into the Task run, preserving provenance through every step of the chain. See Interactions for details.

Scenario 1: Structured Enrichment on a Detected Event

Use this when you want to extract machine-readable fields from a detected event—for example, pulling company, amount, and round from a funding announcement.

# 1. Fetch the event
EVENT=$(curl --silent \
  --url "https://api.parallel.ai/v1/monitors/${MONITOR_ID}/events?event_group_id=${EVENT_GROUP_ID}" \
  --header "x-api-key: $PARALLEL_API_KEY")

OUTPUT=$(echo $EVENT | jq -r '.events[0].output.content')
EVENT_ID=$(echo $EVENT | jq -r '.events[0].event_id')

# 2. Enrich with structured extraction
curl --request POST \
  --url https://api.parallel.ai/v1/tasks/runs \
  --header 'Content-Type: application/json' \
  --header "x-api-key: $PARALLEL_API_KEY" \
  --data @- <<EOF
{
  "input": "$OUTPUT",
  "processor": "base",
  "previous_interaction_id": "$EVENT_ID",
  "output_schema": {
    "type": "json",
    "json_schema": {
      "type": "object",
      "properties": {
        "company": { "type": "string", "description": "Company that raised funding" },
        "amount_usd_millions": { "type": "number", "description": "Amount raised in USD millions" },
        "round": { "type": "string", "description": "Funding round, e.g. Series A" },
        "lead_investor": { "type": "string", "description": "Lead investor name" }
      }
    }
  }
}
EOF

import os
from parallel import Parallel

client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

# 1. Fetch the event
result = client.monitor.events(
    monitor_id,
    event_group_id=event_group_id,
)
event = result.events[0]
output_content = event.output.content
event_id = event.event_id

# 2. Enrich with structured extraction
task_run = client.task_run.create(
    input=output_content,
    processor="base",
    previous_interaction_id=event_id,
    task_spec={
        "output_schema": {
            "type": "json",
            "json_schema": {
                "type": "object",
                "properties": {
                    "company": {"type": "string", "description": "Company that raised funding"},
                    "amount_usd_millions": {"type": "number", "description": "Amount raised in USD millions"},
                    "round": {"type": "string", "description": "Funding round, e.g. Series A"},
                    "lead_investor": {"type": "string", "description": "Lead investor name"},
                },
            },
        }
    },
)
run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output.content)

import Parallel from "parallel-web";

const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY });

// 1. Fetch the event
const result = await client.monitor.events(monitorId, {
  event_group_id: eventGroupId,
});
const event = result.events[0];
const outputContent = event.output?.content;
const eventId = event.event_id;

// 2. Enrich with structured extraction
const taskRun = await client.taskRun.create({
  input: outputContent,
  processor: "base",
  previous_interaction_id: eventId,
  task_spec: {
    output_schema: {
      type: "json",
      json_schema: {
        type: "object",
        properties: {
          company: { type: "string", description: "Company that raised funding" },
          amount_usd_millions: { type: "number", description: "Amount raised in USD millions" },
          round: { type: "string", description: "Funding round, e.g. Series A" },
          lead_investor: { type: "string", description: "Lead investor name" },
        },
      },
    },
  },
});

// wait for task to complete and log result
const runResult = await client.taskRun.result(taskRun.run_id, { timeout: 3600 });
console.log(runResult.output.content);

See Task Enrichment for full details on structured extraction, output schemas, and polling.

Scenario 2: Deep Research on a Detected Event

Use this when you want a comprehensive report on a detected signal—for example, analyzing the strategic implications of a regulatory ruling or a competitor announcement.

# 1. Fetch the event
EVENT=$(curl --silent \
  --url "https://api.parallel.ai/v1/monitors/${MONITOR_ID}/events?event_group_id=${EVENT_GROUP_ID}" \
  --header "x-api-key: $PARALLEL_API_KEY")

OUTPUT=$(echo $EVENT | jq -r '.events[0].output.content')
EVENT_ID=$(echo $EVENT | jq -r '.events[0].event_id')

# 2. Launch deep research
curl --request POST \
  --url https://api.parallel.ai/v1/tasks/runs \
  --header 'Content-Type: application/json' \
  --header "x-api-key: $PARALLEL_API_KEY" \
  --data @- <<EOF
{
  "input": "Research the following event in depth and summarize its strategic implications: $OUTPUT",
  "processor": "ultra",
  "previous_interaction_id": "$EVENT_ID"
}
EOF

import os
from parallel import Parallel

client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

# 1. Fetch the event
result = client.monitor.events(
    monitor_id,
    event_group_id=event_group_id,
)
event = result.events[0]
output_content = event.output.content
event_id = event.event_id

# 2. Launch deep research
task_run = client.task_run.create(
    input=f"Research the following event in depth and summarize its strategic implications: {output_content}",
    processor="ultra",
    previous_interaction_id=event_id,
)
print(f"Run ID: {task_run.run_id}")
# Poll for result or use a webhook
run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output.content)

import Parallel from "parallel-web";

const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY });

// 1. Fetch the event
const result = await client.monitor.events(monitorId, {
  event_group_id: eventGroupId,
});
const event = result.events[0];
const outputContent = event.output?.content;
const eventId = event.event_id;

// 2. Launch deep research
const taskRun = await client.taskRun.create({
  input: `Research the following event in depth and summarize its strategic implications: ${outputContent}`,
  processor: "ultra",
  previous_interaction_id: eventId,
});

console.log(`Run ID: ${taskRun.run_id}`);
// wait for task to complete and log result
const runResult = await client.taskRun.result(taskRun.run_id, { timeout: 3600 });
console.log(runResult.output.content);

Deep research runs are asynchronous. Poll GET /v1/tasks/runs/{run_id} or use a Task webhook to receive the completed report. See Deep Research for full details.

Interactions: How previous_interaction_id chains context across API calls.
Task Enrichment: Structured data extraction at scale.
Deep Research: Multi-step research reports from natural language prompts.
Research Basis: Citations and reasoning on Task API outputs.
Events: Event model and retrieval options.

Getting started

Web Tools

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Developer Tools

Platform Integrations

Data Integrations

Additional Resources

Scenario 1: Structured Enrichment on a Detected Event

Scenario 2: Deep Research on a Detected Event

​Scenario 1: Structured Enrichment on a Detected Event

​Scenario 2: Deep Research on a Detected Event

​Related

Scenario 1: Structured Enrichment on a Detected Event

Scenario 2: Deep Research on a Detected Event

Related