How do Ergo oracle pools work?

Oracle pools aggregate data from multiple independent providers. Each oracle submits data, and smart contracts calculate the median or weighted average to produce a reliable feed resistant to manipulation.

What data can oracles provide?

Oracles can provide any external data: price feeds, weather data, sports results, IoT sensor readings, or any API data. The oracle pool framework is flexible enough to support any data type.

How are oracle operators incentivized?

Oracle operators earn rewards for providing accurate and timely data. They must stake collateral that can be slashed for malicious behavior, creating economic incentives for honest operation.

Can I create my own oracle?

Yes! The Oracle Core framework allows anyone to create custom oracles. You can connect any data source and define your own consensus mechanisms and incentive structures.

How reliable are oracle price feeds?

Ergo's oracle pools use multiple independent data sources and consensus mechanisms to ensure reliability. Historical data shows 99.9%+ uptime with accurate price feeds updated every epoch.

Oracles & Data Feeds

Bringing real-world data on-chain

Decentralized oracle pools provide external data for DeFi & prediction markets.

Oracle Core Documentation

Quick Start

Oracle Core

Documentation

Developer Docs

Oracle Features

Reliable external data for smart contracts

Decentralized Oracle Pools

Multiple data providers ensure reliability and prevent manipulation

On-Chain Aggregation

Smart contracts aggregate and validate data from multiple sources

Price Feeds

Real-time price data for DeFi protocols and prediction markets

Custom Data Sources

Connect any external API or data source to the blockchain

Consensus Mechanisms

Various consensus models for different data reliability requirements

Incentive Alignment

Economic incentives ensure accurate and timely data provision

Oracle Architecture

How oracle pools work on Ergo

Oracle System

Ergo's oracle system provides reliable external data:

Decentralized data collection
On-chain aggregation and validation
Economic incentives for accuracy
Flexible consensus mechanisms
Support for any data type

Oracle Comparison: Ergo vs Leading Alternatives

Six different approaches: eUTXO pools (Ergo), off-chain reporting (Chainlink), pull feeds (Pyth), hybrid models (RedStone), permissionless bonds (Tellor), and optimistic assertions (UMA).

Dimension	Ergo	Chainlink	Pyth	RedStone	Tellor	UMA
Update Model	Push pools on eUTXO; epoch-based publishing	Push feeds with Off-Chain Reporting (OCR)	Pull/on-demand price feeds	Hybrid: Push/Pull/X models	Permissionless reporters with bonds	Optimistic assertions with disputes
Aggregation Method	On-chain pool logic (boxes) + off-chain agents	Off-chain committee → single on-chain submit	Pyth program + confidence; dApp commits on demand	Push on-chain; Pull/X signed bundles in tx	On-chain consensus via economic incentives	Accepted unless disputed; DVM arbitrates
Who Pays Updates	Pool treasury pays rewards to reporters	Operator set; gas costs amortized	Consumer/updater pays tx fees on demand	Push: provider pays; Pull/X: tx sender pays	Reporters pay bonds; rewards in TRB	Asserter posts; participants fund disputes
Update Frequency	Configurable per pool (minutes/blocks)	Infrequent batched; high off-chain frequency	Very high off-chain; on-chain when consumed	Push: periodic; Pull/X: on demand	Request/reward-driven; variable timing	Fast if undisputed; slower when escalated
Permissions Model	Community-defined pools/reporters	Curated operator set per feed	Approved publishers; open reads	Signed by providers; open consumption	Fully permissionless participation	Open roles (asserter/disputer)
Data Types	Prices; extensible to events via scripts	Prices, VRF, Automation, Functions, CCIP	Primarily prices (crypto/FX/equities/commodities)	Prices, RWA data; automation hooks	Flexible (prices/events) via query spec	General truths: prices, events, KPIs
Primary Use Cases	Ergo DeFi (SigmaUSD), protocol metrics	General DeFi feeds, randomness, upkeep	Perp DEX/derivatives, high-frequency pricing	EVM rollups, cost-sensitive apps, RWA	Censorship-resistant feeds, open data	Prediction markets, insurance, non-standard data
Key Limitations	Need disciplined reporters; stale data risk	Service cost; curated operators dependency	Must handle confidence intervals; updater dependency	Signature validation complexity; bundle availability	Latency variance; dispute economics sensitivity	Trust window pre-dispute; arbitration delays

Strong advantages

Mixed/moderate

Limitations/trade-offs

Ergo-specific features

Note: For production integrations add safety belts — averaging windows, deviation thresholds, signature/source checks, fallback feeds, and circuit breakers on anomalies. Each oracle model has unique trade-offs between decentralization, latency, cost, and data quality.