Financial Data APIs for AI Agents: Where a Scraping API Fits (2026)

Search "best financial data API for developers" and you land on Polygon.io, Alpha Vantage, Finnhub, Twelve Data and similar vendors, whose entire pitch is licensed, low-latency market data for trading systems. This guide is not a claim to compete with them on that. It is for a different builder: someone wiring an AI agent, a research tool or a fintech side project that needs to pull several kinds of public financial data, reason across them, and cite where each number came from. For that job, the question is not "how fast is the quote" but "how many sources do I have to integrate, authenticate and pay for separately".

What the real-time specialists do better

Credit where it is due, because choosing wrong here is expensive:

• Licensed, low-latency feeds. Polygon.io and Databento sell direct exchange data with millisecond-level guarantees. A public-page API does not replicate that and should not pretend to.
• Deep historical tick data. Backtesting a strategy needs years of tick-by-tick history, cleaned for splits and corporate actions. That is a specialist product, priced and engineered for it.
• SLAs and support for production trading. If a feed going down costs money in real time, you want a vendor whose business is that guarantee.
• Fundamentals with a warranty. Alpha Vantage, Finnhub and EODHD publish normalized fundamentals with documented methodologies. They are the right call when a dashboard's numbers must be defensible to a client.

If any of that is your requirement, stop here and pick a specialist. The rest of this guide is for everyone else.

What an agent actually asks

Watch a research agent work through "should I be worried about Company X" and the tool calls fall into four groups:

A price feed answers the first row. The other three are where an agent spends its reasoning, and where a broad public-data API earns its place next to the licensed vendor rather than instead of it.

What to look for in a broader financial data API

• Use case first: an execution system or backtest needs a licensed specialist; an agent or research tool that reads snapshots and filings can use a broader API.
• Source coverage: equities, crypto, filings and prediction markets under one key, or one vendor per source with four integrations to maintain?
• Filings depth: full-text search and parsed insider and institutional data, or only company lookup and headline financials?
• Agent surface: is there an MCP server or tool definitions, or do you wrap every REST call yourself?
• Pricing shape: per-seat subscription for one data source, or usage-based credits across all of them, billed only on success?
• A free tier large enough to test the actual fields on the actual companies before committing.

The landscape

Where Crawlora is genuinely differentiated: SEC EDGAR

Most financial-data APIs stop at price and fundamentals. Crawlora's SEC platform covers the filings layer with ten endpoints:

Full-text search across every EDGAR filing, which several incumbents gate behind a paid tier:

curl -s "https://api.crawlora.net/api/v1/sec/full-text-search" \
  -H "x-api-key: $CRAWLORA_API_KEY" \
  --data-urlencode "q=\"supply chain disruption\"" \
  --data-urlencode "forms=10-K,10-Q" \
  --data-urlencode "from=2026-01-01"

Parsed insider transactions from Forms 3, 4 and 5: who at a company bought or sold, and when:

curl -s "https://api.crawlora.net/api/v1/sec/insider?ticker=AAPL&limit=10" \
  -H "x-api-key: $CRAWLORA_API_KEY"

13F institutional holdings: what a fund manager holds, sorted by value:

curl -s "https://api.crawlora.net/api/v1/sec/institutional-holdings?cik=0001067983&limit=50" \
  -H "x-api-key: $CRAWLORA_API_KEY"

Two cautions from our own data studies, because they change how an agent should read these endpoints. Insider buying is a noisier signal than it looks: a large share of open-market "purchases" in Form 4 data are option exercises and plan purchases, not conviction buys (the insider-buying mirage). And 13F filings cap what they reveal: they cover long positions above the reporting threshold, quarterly, with a 45-day lag, so they describe the past, not the present (what the 13F position cap hides). Build those caveats into the agent's prompt, not just its footnotes.

A worked agent loop

Here is the shape of a "brief me on this company" tool chain using one key:

import requests

BASE = "https://api.crawlora.net/api/v1"
H = {"x-api-key": "YOUR_API_KEY"}

def get(path, **params):
    r = requests.get(f"{BASE}/{path}", headers=H, params=params, timeout=60)
    r.raise_for_status()
    return r.json()["data"]

ticker = "AAPL"
quote    = get(f"google/finance/quote/{ticker}:NASDAQ")                       # price context
sections = get("sec/filing/sections", ticker=ticker, items="1A")             # latest 10-K risk factors
insiders = get("sec/insider", ticker=ticker, limit=20)                       # who is buying or selling
holders  = get("sec/institutional-holdings", cik="0000320193", limit=20)     # 13F holders, by CIK
events   = get("kalshi/events", status="open", limit=20)                     # what the market is betting on

Every call is the same header, the same base URL, the same credit pool, and the same shape of error handling. The same endpoints are exposed as tools on Crawlora's hosted MCP server, so an agent framework can call them without wrapping REST yourself. Parameter names per endpoint are in the API docs; the snippet above shows the shape, not a copy-paste contract.

The rest of the coverage

• Yahoo Finance and Google Finance: ticker quotes, historical OHLCV, financials, analyst ratings, dividends, splits, options chains, market movers and news as normalized JSON. Snapshot data from public pages with the delays those pages show, not a licensed feed. The Google Finance API guide covers that platform's 20 endpoints.
• CoinGecko: coin and market rows, global charts, trending coins, exchanges, categories. Documented as public market data, not for trading execution.
• Kalshi and Polymarket: event and market odds, order books, price history. A demand signal most financial APIs do not carry at all; our biggest prediction-market reversals study shows how quickly those odds can move.

When to use a real-time specialist instead

• You are executing trades and a stale price costs real money.
• You need tick-level historical data for backtesting.
• Your product is single-source (just equities, or just crypto) and a dedicated vendor's deeper feature set beats breadth.
• Your numbers must carry a data licence and a methodology document a client can audit.

Many teams run both: a licensed feed for the price, and a broad API for the filings, holdings and odds the agent reasons over.

Stocks, crypto, SEC filings, and prediction markets in one key

Credit-based pricing across every source, billed only on success, exposed as REST and as MCP tools. 2,000 free credits a month, no card.

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Sources

Related reading

• Google Finance API Guide: the Google Finance endpoints in depth.
• How to Scrape Yahoo Finance: ticker history, financials and options data.
• How to Scrape SEC EDGAR: the filings platform step by step.
• How to Scrape CoinGecko: crypto market data in detail.
• What is actually in SEC EDGAR: what the corpus contains, by form type.