# Analyst Agent — Autonomous Research & Monitoring (`fuze/analyst-agent`) Actor

Give it a question; get a structured, cited analyst-grade report. Plans sub-questions, researches via live web search, synthesizes with source URLs. Monitor a topic for changes. Callable by any AI agent. Actively maintained. · More no-code data tools → datatools-hub.vercel.app

- **URL**: https://apify.com/fuze/analyst-agent.md
- **Developed by:** [Fredrik Hansson](https://apify.com/fuze) (community)
- **Categories:** AI, Agents
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $154.00 / 1,000 research report delivereds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Analyst Agent — Autonomous Research & Monitoring

Give it a question (or a company, market or topic) and get a **structured, cited analyst-grade report**. It plans sub-questions, researches each via live web search, and synthesizes the findings with **source URLs on every claim**. Point it at a previous run and it reports what changed = recurring monitoring. **Callable by any AI agent** via Apify's MCP server.

Unlike a generic chat "deep research" answer, this returns a **stable, reliable output schema** with per-claim citations that your agents and pipelines can depend on.

![Analyst Agent — Autonomous Research & Monitoring — input and sample output](https://datatools-hub.vercel.app/actor-shots/analyst-agent.png)

### 🔗 Pair it with your scraper

Running **[Google News Scraper (`easyapi/google-news-scraper`)](https://apify.com/easyapi/google-news-scraper)**, **[Yahoo Finance Scraper (`architjn/yahoo-finance`)](https://apify.com/architjn/yahoo-finance)** or any topic/company scraper to collect raw material? This is the layer that reads it and answers the actual question — ask about the company, market or topic you just scraped and get a structured, cited report your agents and pipelines can depend on. Works alongside any news, finance or web scraper.

### Quick start

1. **Fill in the input** — set the fields in the input tab (prefilled with a working example).
2. **Click Start** — the Actor runs and writes one record per item to the dataset.
3. **Get your data** — export to **JSON, CSV or Excel**, or pull it via the API.

### What it does

1. **Plans** the question into focused sub-questions (depth controls how many)
2. **Researches** each via live web search, collecting source URLs
3. **Synthesizes** a structured report: executive summary, sections (with sources), key findings (with sources), key players, risks, conclusion
4. **Monitors** — with a baseline, reports what's new/changed since the last run on the same question

### Input

| Field | Type | Description |
|---|---|---|
| `query` | string | The research question / company / market / topic. Required. |
| `depth` | string | `brief` (3), `standard` (5) or `deep` (8 sub-questions). |
| `baselineDatasetId` | string | A previous run's dataset ID → report what changed. |

```json
{ "query": "What is the competitive landscape for AI note-taking apps in 2026?", "depth": "standard" }
```

### Output

One report record: the structured `report` (summary / sections / findings / players / risks / conclusion), the full `sources[]` list, the sub-questions, and `changesSinceBaseline`.

### Pricing (pay-per-event — charged only on a delivered report)

| Event | When | Price |
|---|---|---|
| `research-delivered` | per report (brief / standard depth) | $1.50 |
| `deep-report-delivered` | per report (deep depth) | $6.00 |
| `monitoring-update` | per re-run that finds changes vs. baseline | $0.50 |

### Who it's for

Founders, consultants, analysts, marketers — and AI agents that need a finished cited report as a tool.

### Notes

- Requires an OpenAI API key (`OPENAI_API_KEY`).
- Listed as an MCP tool on Apify, so any agent can call it and get a reliable structured report back.

### Related actors

- [Equity Analyst — AI Investment Research Agent](https://apify.com/fuze/equity-analyst) — Pulls primary SEC EDGAR data (XBRL financials + filings + 10-K narrative) and writes a
- [Argus — Autonomous Intelligence Agent](https://apify.com/fuze/argus) — Describe a mission in plain English; Argus plans what to watch, gathers from public
- [Google News Scraper](https://apify.com/fuze/google-news-scraper) — Search Google News for any topic and get the latest articles — title, source, date, link

# Actor input Schema

## `query` (type: `string`):

The question, company, market or topic to research. Example: 'What is the competitive landscape for AI note-taking apps in 2026?'

## `depth` (type: `string`):

brief = 3 sub-questions, standard = 5, deep = 8 (more sources, deeper synthesis, higher charge).

## `baselineDatasetId` (type: `string`):

Optional. A previous run's dataset ID — results then report what CHANGED since then on the same question.

## `model` (type: `string`):

OpenAI model for planning, web-search research and synthesis. gpt-4o-mini is the cost-effective default.

## Actor input object example

```json
{
  "query": "Summarize NVIDIA's latest quarter: revenue, margins, key risks and competitive position.",
  "depth": "standard",
  "model": "gpt-4o-mini"
}
```

# Actor output Schema

## `report` (type: `string`):

The research report record in the default dataset.

# 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 = {
    "query": "Summarize NVIDIA's latest quarter: revenue, margins, key risks and competitive position."
};

// Run the Actor and wait for it to finish
const run = await client.actor("fuze/analyst-agent").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 = { "query": "Summarize NVIDIA's latest quarter: revenue, margins, key risks and competitive position." }

# Run the Actor and wait for it to finish
run = client.actor("fuze/analyst-agent").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 '{
  "query": "Summarize NVIDIA'\''s latest quarter: revenue, margins, key risks and competitive position."
}' |
apify call fuze/analyst-agent --silent --output-dataset

```

## MCP server setup

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

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/0FhFvPSu5GPzN46WO/builds/KQoPrc5koBRgE7dag/openapi.json