Allora Forge Competitions

Allora Forge (opens in a new tab) is the Allora Network's model competition platform: the hub where ML practitioners build, test, and deploy machine learning models against real-world data — competing for ALLO rewards while building an on-chain track record.

Forge runs competitions on an ongoing basis. Each competition targets one live topic on the Allora Network, carries its own ALLO prize pool, and moves through upcoming → active → ended as its start and end dates pass. Browse forge.allora.network (opens in a new tab) for the competitions that are open right now.

How a competition works

A competition ranks workers by their performance on its underlying topic. Topics run a continuous cycle:

1. Submission window opens — the network polls all registered workers on the topic for an inference.
2. Workers respond with a prediction; predictions lock when the window closes.
3. Evaluation period runs for the topic's time horizon (for example, 8 hours).
4. Scores are revealed — workers are ranked by loss against the ground truth, and rewards are distributed.

The cycle repeats every epoch, so a competition is not a one-shot submission: your model keeps predicting for the duration of the competition window, and the leaderboard reflects its live, cumulative performance.

Scoring

Scoring happens on-chain. Each epoch, the network compares every submitted inference against the topic's ground truth and ranks workers by loss.

The Forge Builder Kit (opens in a new tab) mirrors this methodology off-chain: its PerformanceEvaluator grades your model against Allora's scoring methodology before you deploy. It scores seven metrics — directional accuracy (plus its confidence-interval lower bound and p-value), Pearson correlation (plus its p-value), weighted-RMSE improvement, and CZAR improvement — each with a pass/fail threshold, and maps the composite to a letter grade from A+ to F. A higher grade means better generalization and a higher expected score on the network.

From testnet to mainnet

Workers start on testnet to establish a track record, then graduate to mainnet, where top performers earn ALLO token rewards.

Your Forge dashboard tracks this progression as Mainnet Readiness: a set of per-worker criteria with an eligibility threshold. Meet enough criteria and the worker becomes eligible for mainnet promotion; until then the dashboard shows which criteria are still in progress.

Compete

1. Create a Forge account. Sign up at forge.allora.network (opens in a new tab) and connect a wallet to access your dashboard.
2. Register. Competition participation requires registering and getting whitelisted; the Forge site links to the registration form.
3. Build and deploy a worker on the competition's topic. The fastest path is the Forge Builder Kit (opens in a new tab), which takes you from historical data to a deployed worker — or follow the price prediction worker walkthrough to do it with the Python SDK directly.
4. Link your worker to your Forge account. The builder kit's device flow signs with your on-disk worker key and links it to your account in the browser — your mnemonic never leaves your machine. Linked workers show up in your dashboard with their balance, earnings, and activity.
5. Track your standing. Forge shows per-topic leaderboards, the competitions you're in, and your workers' scores; the Allora Explorer (opens in a new tab) has the underlying on-chain detail.

Build with the Forge Builder Kit

The Allora Forge Builder Kit (opens in a new tab) handles everything between your model and the network:

• Workflow API — backfill historical data, engineer features, and build training datasets
• Evaluation — grade your model against Allora's scoring methodology before deploying
• Deployment tooling — wallet creation, faucet funding, and worker lifecycle management
• Monitoring dashboard — web UI with submission history, on-chain scores, and live logs
• Topic discovery — query all live topics on testnet and mainnet

If you previously built models with the deprecated offchain node or Model Development Kit (MDK), see the migration guide.

Next

• Pick a topic to compete on: existing topics
• Deploy your first worker: build a price prediction worker
• Coming from the offchain node or MDK: migrate to the Python SDK + Builder Kit