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Crypto Trading Tools · OBOL rating

DefiLlama review

The most trustworthy dataset in DeFi, maintained in the open, given away for free.

9.2/10
Exceptional
#1 of 15 crypto trading toolsChecked August 2026
The short answer

DefiLlama is the reference dataset for DeFi: it publishes how every figure is calculated, flags double-counted liquidity explicitly, takes no payment to list or flatter a protocol, and gives the whole thing away free. Its adapters are community-maintained, so a protocol's numbers are only as current as its adapter.

Best forHonest, free DeFi data

Score breakdown

Category rubric →
  1. Data accuracyweight 25%
    9.5avg 8.1
  2. Feature depthweight 20%
    8.0avg 8.1
  3. Value for moneyweight 20%
    10.0avg 7.6
  4. Coverageweight 15%
    9.5avg 8.3
  5. Data handling & securityweight 10%
    9.5avg 8.2
  6. Usabilityweight 10%
    8.0avg 8.0

this service category average across 15Score = Σ mark × weight. Best in category on the evidence we can verify. Methodology.

The evidence behind each mark

Scoring rules →
CriterionMarkWhat we foundSourceChecked
Data accuracy9.5not sourced yetnot sourced yet—
Feature depth8.0not sourced yetnot sourced yet—
Value for money10.0not sourced yetnot sourced yet—
Coverage9.5not sourced yetnot sourced yet—
Data handling & security9.5not sourced yetnot sourced yet—
Usability8.0not sourced yetnot sourced yet—

What we liked, and what fell short

Liked
  • Open methodology with double-counting explicitly handled
  • Covers essentially every chain and protocol of consequence
  • No account, no wallet connection, no tracking required
Fell short
  • Interface density assumes you know what you are looking for
  • Depends on adapters that individual contributors maintain

Why the marks landed where they did

In a sector where most data vendors sell numbers whose construction they will not explain, DefiLlama publishes the code that produces each figure. That is the entire reason it became the shared reference point: when two people argue about a protocol's TVL, they can read the adapter and settle it.

Why the methodology matters

Total value locked is trivially inflatable. Count a liquid staking token in the LST protocol, again in the lending market it collateralises, and again in the DEX pool it sits in, and one deposit becomes three. DefiLlama tracks and flags that double counting explicitly, offers toggles to exclude it, and separates borrowed value from deposited value. No paid competitor is as careful, and several are deliberately less so.

Coverage and tools

Every chain of consequence, thousands of protocols, plus yield aggregation, stablecoin supply tracking, bridge volumes, DEX volumes, protocol revenue and unlock schedules. The yield section is particularly useful because it shows the composition of an advertised APY — base yield versus emissions — which is the distinction most yield dashboards obscure.

Limitations

The interface is utilitarian and assumes you know what you are looking for. Adapters are maintained by contributors, so a protocol that changes its contracts can report stale figures until someone updates the code. Treat any single number as verifiable rather than verified, and read the adapter when it matters.

Who should use it

Anyone researching DeFi, which is everyone in this sector. It replaces most of what paid analytics platforms charge for, and where it does not, it tells you why.

FAQ

Is DefiLlama really free?
Yes. The dashboards are free and open, with paid API tiers for developers who need high-volume programmatic access.
What is double-counted TVL?
The same underlying deposit counted in several protocols — for example a staked-ETH token counted in the LST protocol, the lending market it collateralises and the DEX pool it sits in. DefiLlama flags it and lets you exclude it.
How accurate is DefiLlama?
Its methodology is open and generally rigorous, but adapters are community-maintained, so figures for a protocol that recently changed contracts can lag until the adapter is updated.
Can protocols pay to be listed favourably?
No. Listing and ranking are not for sale, which is a large part of why the dataset is trusted.