# Deep Research Actor (`imdigitalashish/deep-research-actor`) Actor

Actor

- **URL**: https://apify.com/imdigitalashish/deep-research-actor.md
- **Developed by:** [Ashish Kumar Verma](https://apify.com/imdigitalashish) (community)
- **Categories:** AI, Agents
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Deep Research Actor

An AI-powered deep research service that performs comprehensive research on any query using multiple AI agents. Deployed as an Apify Actor with a REST API.

### Overview

This actor orchestrates multiple specialized AI agents to conduct thorough research on any topic. It automatically:

1. **Plans** - Creates a structured report outline with sections and key questions
2. **Researches** - Runs concurrent iterative research loops for each section
3. **Synthesizes** - Combines findings into a polished, comprehensive markdown report

### API Endpoints

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/` | GET | Health check and API info |
| `/health` | GET | Health check |
| `/research` | POST | Start a new research job |
| `/research` | GET | List all research jobs |
| `/research/{job_id}/status` | GET | Get job status |
| `/research/{job_id}/result` | GET | Get markdown result |
| `/research/{job_id}/logs` | GET | Get logs (supports long polling) |

### Quick Start

#### Start a Research Job

```bash
curl -X POST http://localhost:8080/research \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the latest developments in quantum computing?"}'
```

**Response:**

```json
{
  "job_id": "abc123",
  "status": "pending",
  "message": "Research job started. Use /research/{job_id}/status to check progress."
}
```

### Configuration

#### Request Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `query` | string | required | The research question or topic |
| `max_iterations` | int | 5 | Maximum research iterations per section |
| `max_time_minutes` | int | 10 | Maximum time per section |

# Actor input Schema

## `port` (type: `integer`):

Port for the HTTP server (default: 8080)

## Actor input object example

```json
{
  "port": 8080
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("imdigitalashish/deep-research-actor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("imdigitalashish/deep-research-actor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call imdigitalashish/deep-research-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,imdigitalashish/deep-research-actor"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Ttcd49c1DlhsqmLEs/builds/Jc4jt4hnH6PXIl6PZ/openapi.json