How to Scrape DefiLlama Data: Complete Guide for 2026

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

TL;DR

To scrape DefiLlama, use a proxy-enabled API like AlterLab to bypass anti-bot protections and handle dynamic content. You can extract data using standard HTTP requests for static pages or use the Cortex AI extraction endpoint to convert HTML into structured JSON without writing CSS selectors.

Why collect finance data from DefiLlama?

DefiLlama is the primary source of truth for Total Value Locked (TVL) and DeFi protocol metrics. For engineers building financial tooling, automating this data collection is essential for:

– Market Research: Tracking the growth of specific ecosystems (e.g., Solana vs. Ethereum) in real-time. – Price Monitoring: Correlating TVL changes with token price movements for algorithmic trading signals. – Competitive Analysis: Monitoring new protocol launches and liquidity migrations across different chains.

Technical challenges

Finance platforms like DefiLlama implement protections to prevent server overload and unauthorized data harvesting. If you attempt to use a basic requests library in Python or axios in Node.js, you will likely encounter 403 Forbidden errors or CAPTCHAs.

These protections typically include:

1. TLS Fingerprinting: The server checks if the request comes from a real browser or a known scraping library.
2. IP Rate Limiting: Rapid requests from a single IP address are flagged and blocked.
3. JavaScript Requirements: Some data points are rendered client-side, meaning raw HTML requests return empty containers.

To handle these, you need a Smart Rendering API that can mimic human browser behavior and rotate high-quality residential proxies.

Quick start with AlterLab API

Before starting, follow the Getting started guide to configure your environment.

Python Implementation

Python is the standard for data pipelines. Use the alterlab SDK to handle the request logic.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://defillama.com/protocols")
print(response.text)

Node.js Implementation

For real-time dashboards or serverless functions, Node.js provides a non-blocking approach.

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://defillama.com/protocols");
console.log(response.text);

cURL Implementation

For quick testing or integration into shell scripts:

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://defillama.com/protocols"}'

Extracting structured data

Once you have the HTML, you need to isolate the data. Most DeFi tables use consistent class names, but these can change during site updates.

For a standard protocol list, you would target the <table> element and iterate through <tr> (rows) and <td> (cells).

Common target paths: – Protocol Name: .protocol-name or td:nth-child(1) – TVL Value: .tvl-value or td:nth-child(2) – Change %: .change-value or td:nth-child(3)

If the site uses a React-based frontend, the data is often embedded in a __NEXT_DATA__ script tag in JSON format. Parsing this script tag is significantly more reliable than parsing HTML.

Structured JSON extraction with Cortex

Writing CSS selectors is fragile. When the site updates its UI, your scraper breaks. AlterLab's Cortex AI removes this requirement by using LLMs to identify data points based on their meaning, not their location in the DOM.

You define a JSON schema, and Cortex returns typed data.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://defillama.com/protocols",
    schema={
        "type": "object",
        "properties": {
            "protocols": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "tvl": {"type": "number"},
                        "category": {"type": "string"}
                    }
                }
            }
        }
    }
)
print(result.data)  # Typed JSON output

Cost breakdown

Pricing depends on the complexity of the page. For DefiLlama, most public pages are handled by T2 or T3.

For detailed billing and plan options, visit AlterLab pricing.

Note on Auto-Escalation: You don't need to guess the tier. AlterLab starts at T1. If the request fails, it automatically promotes the request to T2, T3, and so on. You only pay for the tier that successfully returns the data.

Best practices

To maintain a healthy scraping pipeline and avoid blocks, follow these engineering standards:

1. Respect robots.txt Always check defillama.com/robots.txt. If a path is explicitly disallowed for all users, reconsider the necessity of scraping that specific endpoint.

2. Implement Exponential Backoff Do not retry failed requests immediately. Use a delay that increases with each failure (e.g., 1s, 2s, 4s, 8s) to avoid triggering security alarms.

3. Use User-Agent Rotation While AlterLab handles this automatically, if you are building custom logic, ensure your User-Agent string matches a modern browser (Chrome 120+).

4. Cache Your Results Finance data doesn't always change every second. Cache the response for 5–15 minutes to reduce costs and load on the target server.

Scaling up

When moving from a few hundred to millions of requests, the architecture must change.

Batching Instead of sequential requests, use asynchronous programming. In Python, use asyncio with httpx. In Node.js, use Promise.all() with a concurrency limit to avoid overwhelming your own local memory.

Scheduling For TVL tracking, use cron-based scheduling. Rather than keeping a script running 24/7, trigger a Lambda function or GitHub Action every hour to fetch the latest state.

Data Pipelines Push scraped data directly into a time-series database like InfluxDB or TimescaleDB. This allows you to perform window functions and trend analysis on the TVL data over time.

Key takeaways

– Use AlterLab to bypass anti-bot protections on DefiLlama. – Prefer Cortex AI extraction over CSS selectors for long-term stability. – Leverage auto-escalation to minimize costs per request. – Always prioritize rate limiting and robots.txt compliance.

For more specific implementation details, see our DefiLlama scraping guide.

AlterLab // Web Data, Simplified.