DefiLlama Data API: Extract Structured JSON in 2026

TL;DR: To get structured DefiLlama data via API, use the AlterLab Extract API to send a URL and a JSON schema. This returns validated, typed JSON containing specific finance metrics like ticker, price, and market cap, bypassing the need for manual HTML parsing.

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Why use DefiLlama data?

DefiLlama is the industry standard for decentralized finance (DeFi) analytics. For data engineers and AI developers, accessing this information in a machine-readable format is critical for several high-stakes use cases:

• AI Training & RAG: Feeding real-time TVL (Total Value Locked) or protocol metrics into Large Language Models to power financial AI agents.
• Automated Analytics: Building dashboards that track protocol growth, volume shifts, and liquidity movements across different chains.
• Competitive Intelligence: Monitoring market cap fluctuations and volume trends to identify emerging protocols in the DeFi ecosystem.

What data can you extract?

When building a data pipeline for finance, you need more than raw HTML. You need specific, typed fields. Using a data API approach, you can target any publicly available metric on DefiLlama, including:

• ticker: The unique identifier for the asset or protocol.
• price: The current market price in USD.
• change_percent: The 24-hour price movement percentage.
• volume: The 24-hour trading volume.
• market_cap: The total market capitalization.

By defining these in a schema, you ensure your downstream database receives clean data every time.

The extraction approach

Traditionally, developers attempted to get this data using standard HTTP libraries (like requests in Python) combined with HTML parsers (like BeautifulSoup).

This approach is extremely fragile. Finance websites frequently update their DOM structure, class names, and nesting to optimize for performance or change their UI. A single class name change in a React component can break your entire data pipeline. Furthermore, modern finance sites use complex obfuscation and anti-bot measures that standard libraries cannot navigate.

A data API solves this by moving the complexity from your code to the infrastructure. Instead of writing logic to find a <div> with a specific class, you simply describe the data you want. The engine handles the rendering, the proxy rotation, and the parsing, returning only the clean JSON you requested.

Quick start with AlterLab Extract API

To begin, you'll need to follow our Getting started guide. Once you have your API key, you can use the extract endpoint to turn any public DefiLlama URL into a structured object.

Python Implementation

The Python SDK makes it easy to define a schema and receive typed data.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "ticker": {
      "type": "string",
      "description": "The asset ticker symbol"
    },
    "price": {
      "type": "string",
      "description": "Current price in USD"
    },
    "change_percent": {
      "type": "string",
      "description": "24h percentage change"
    },
    "volume": {
      "type": "string",
      "description": "24h trading volume"
    },
    "market_cap": {
      "type": "string",
      "description": "Total market cap"
    }
  }
}

result = client.extract(
    url="https://defillama.com/protocols/example",
    schema=schema,
)
print(result.data)

cURL Implementation

If you prefer working in the terminal or via shell scripts, use a simple POST request.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://defillama.com/protocols/example",
    "schema": {
      "type": "object",
      "properties": {
        "ticker": {"type": "string"},
        "price": {"type": "string"},
        "change_percent": {"type": "string"}
      }
    }
  }'

Define your schema

The power of the Extract API docs lies in the schema definition. You aren't just asking for "the price"; you are defining a contract.

When you provide a JSON schema, AlterLab uses LLM-powered extraction to map the visual elements of the page to your specific keys. If the website changes "Price: $1,200" to "$1,200 USD", a standard scraper breaks. A schema-based data API understands that both represent the same data point.

Handle pagination and scale

For production-grade pipelines, you rarely extract one page at a time. You likely need to monitor hundreds of protocols or thousands of tokens.

When scaling, you should implement an asynchronous job pattern. Instead of waiting for a synchronous response, you can dispatch multiple extraction requests and handle them as they complete. This is essential for maintaining high throughput without hitting local resource limits.

For large-scale operations, you can estimate your costs before running heavy batches. Use the estimation endpoint to preview the cost of a call before committing.

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

async def get_protocol_data(url, schema):
    # Using async for high-volume extraction
    return await client.extract_async(url=url, schema=schema)

async def main():
    schema = {"type": "object", "properties": {"ticker": {"type": "string"}}}
    urls = ["https://defillama.com/protocols/a", "https://defillama.com/protocols/b"]
    
    tasks = [get_protocol_data(url, schema) for url in urls]
    results = await asyncio.gather(*tasks)
    for r in results:
        print(r.data)

asyncio.run(main())

As your volume grows, keep an eye on your AlterLab pricing. We offer a transparent, pay-for-what-you-use model. There are no hidden fees or massive upfront commitments. You can set spend limits on your API keys to ensure your budget stays within your project's parameters.

Key takeaways

• Avoid fragile parsing: Stop writing regex and CSS selectors for finance sites.
• Use schemas: Define your data contract upfront to get reliable, typed JSON.
• Scale with async: Use asynchronous patterns to build high-throughput data pipelines.
• Automate everything: Combine extraction with scheduling to create self-updating datasets.

Hit reply if you have questions.

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