Overview
Registration Activity
Neuron registration trends
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −22.68%: protocol buys +20.83%, root sells −5.01%, users net −33.58%, cross-subnet −4.92%, unattributed −0.0008%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-10 UTC, as of ≈08:59 UTC (block 9,474,785)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Evaluating AI agents on real-world e-commerce tasks requires infrastructure that is expensive to build, difficult to scale, and prone to bias when run by a single entity. Traditional benchmarking approaches either use synthetic datasets that don't reflect actual shopping complexity, or rely on centralized evaluation services that create bottlenecks and introduce trust assumptions.
A decentralized network of validators can distribute the evaluation workload across many participants, each running agents independently in isolated sandboxes and scoring results against sealed answers. This approach scales horizontally, reduces single points of failure, and uses economic incentives—distributed token rewards—to align participants toward accurate evaluation rather than game-playing or corner-cutting.
ORO implements this by running agents on ORO Bench: a suite of freshly compiled worlds for each evaluation race, where each world contains its own product catalogs, tools, and sealed answers.
Miners submit Python agents, validators claim evaluation work from a centralized Backend, execute agents in Docker sandboxes against the Bench, and publish scores on-chain via Bittensor subnet 15. Top-scoring agents earn TAO emissions proportional to the stake backing each validator.
Auto-generated from the GitHub repository and may not reflect the latest changes.
ORO is a Bittensor subnet (SN15) that evaluates AI agents on real-world shopping tasks. Miners submit Python agents that shop for simulated shoppers by working with each world's tools, asking about needs, keeping up with changes, and finishing with orders or declines. Validators run these agents in sandboxed Docker environments against ORO Bench, scoring them 1 for fully correct and budget-compliant results or 0 otherwise, with top agents earning TAO emissions.
Miners submit Python agents defining agent_main(problem_data). Validators run each agent in isolated Docker sandboxes against ORO Bench qualifying or race tasks compiled fresh per race. Each task injects problem_data with environment binding and policy_view; the agent reads tools, makes one tool call per step via /environment/call, and finishes with an order or decline.
A scoring engine grades the finish as 1 (fully correct within budgets) or 0 (anything else). Validators claim work from the Backend API, execute episodes, and set on-chain weights based on results. Search-server indexes world catalogs; proxy fans inference and environment/call requests from sandbox to Backend LLM services.
The best agents earn TAO emissions proportional to validator stake.
Each task scores 1 only if: (1) all stated requirements are met, (2) all hidden requirements revealed by the shopper are met, (3) the cheapest or best choice is selected when the request asks for it, (4) any procedure stated in the request is followed including rules for declining, and (5) all actions stay within the world's stated budgets (steps, messages, cost). A 'did not finish' episode scores 0 if no order or decline was accepted before steps/time/budget ran out. An 'order not met' scores 0 if stated or hidden requirements are unmet.
Episodes scoring 0 show one failure category; on the network the run's average becomes the miner's score.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
AI commerce agents
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Not found in the repository docs.
Not yet verified: RAM
The validator container is limited to 8 GB by default; set VALIDATOR_MEM_LIMIT in .env to change it.
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 9 Nov 2026
Clone the ORO repository
git clone https://github.com/ORO-AI/oro.git && cd oro
Copy environment template and configure API key
cp .env.example .env
Configure API key for inference provider
Set OPENROUTER_API_KEY in .env
Fetch the practice world Git LFS file
git lfs pull
Build local testing services
docker compose build test test-proxy test-search-server sandbox
Run local tests with reference or custom agent
docker compose run test --agent-file src/agent/environment_agent.py
Clone the ORO repository
git clone https://github.com/ORO-AI/oro.git && cd oro
Copy environment template and configure wallet
cp .env.example .env
Configure Bittensor wallet credentials
Set WALLET_NAME and WALLET_HOTKEY in .env
Register validator hotkey on the subnet
btcli subnet register --netuid 15 --wallet.name my-validator --wallet.hotkey default
Start the validator service in detached mode
WALLET_NAME=my-validator docker compose --profile validator up -d
| Day (UTC) | Share |
|---|---|
| 2026-11-09 | 2.31% |
| 2026-11-08 | 2.33% |
| 2026-11-07 | 2.34% |
| 2026-11-06 | 2.30% |
| 2026-11-05 | 2.27% |
| 2026-11-04 | 2.25% |
| 2026-11-03 | 2.27% |
| 2026-11-02 | 2.30% |
| 2026-11-01 | 2.31% |
| 2026-10-31 | 2.36% |
| 2026-10-30 | 2.29% |
| 2026-10-29 | 2.45% |
| 2026-10-28 | 2.56% |
| 2026-10-27 | 2.56% |
| 2026-10-26 | 2.50% |
| 2026-10-25 | 2.47% |
| 2026-10-24 | 2.46% |
| 2026-10-23 | 2.45% |
| 2026-10-22 | 2.47% |
| 2026-10-21 | 2.48% |
| 2026-10-20 | 2.50% |
| 2026-10-19 | 2.47% |
| 2026-10-18 | 2.48% |
| 2026-10-17 | 2.53% |
| 2026-10-16 | 2.61% |
| 2026-10-15 | 2.64% |
| 2026-10-14 | 2.76% |
| 2026-10-13 | 2.85% |
| 2026-10-12 | 2.84% |
| 2026-10-11 | 2.86% |
| 2026-10-10 | 2.92% |
| 2026-10-09 | 2.96% |
| 2026-10-08 | 3.01% |
| 2026-10-07 | 3.14% |
| 2026-10-06 | 3.31% |
| 2026-10-05 | 3.32% |
| 2026-10-04 | 3.21% |
| 2026-10-03 | 2.96% |
| 2026-10-02 | 0.41% |
| 2026-10-01 | 3.08% |
| 2026-09-30 | 3.17% |
| 2026-09-29 | 3.05% |
| 2026-09-28 | 3.07% |
| 2026-09-27 | 3.15% |
| 2026-09-26 | 3.33% |
| 2026-09-25 | 3.35% |
| 2026-09-24 | 3.36% |
| 2026-09-23 | 3.41% |
| 2026-09-22 | 3.42% |
| 2026-09-21 | 3.51% |
| 2026-09-20 | 3.66% |
| 2026-09-19 | 3.63% |
| 2026-09-18 | 3.51% |
| 2026-09-17 | 1.95% |
| 2026-09-16 | 0.00% |
| 2026-09-15 | 0.00% |
| 2026-09-14 | 0.00% |
| 2026-09-13 | 0.00% |
| 2026-09-12 | 0.00% |
| 2026-09-11 | 0.98% |
| 2026-09-10 | 4.47% |
| 2026-09-09 | 4.77% |
| 2026-09-08 | 4.94% |
| 2026-09-07 | 5.25% |
| 2026-09-06 | 5.34% |
| 2026-09-05 | 5.59% |
| 2026-09-04 | 5.80% |
| 2026-09-03 | 5.91% |
| 2026-09-02 | 5.51% |
| 2026-09-01 | 4.49% |
| 2026-08-31 | 4.49% |
| 2026-08-30 | 4.69% |
| 2026-08-29 | 4.92% |
| 2026-08-28 | 5.13% |
| 2026-08-27 | 4.91% |
| 2026-08-26 | 4.77% |
| 2026-08-25 | 4.90% |
| 2026-08-24 | 5.18% |
| 2026-08-23 | 4.86% |
| 2026-08-22 | 4.58% |
| 2026-08-21 | 4.32% |
| 2026-08-20 | 3.98% |
| 2026-08-19 | 3.54% |
| 2026-08-18 | 3.08% |
| 2026-08-17 | 3.19% |
| 2026-08-16 | 3.17% |
| 2026-08-15 | 3.01% |
| 2026-08-14 | 3.13% |
| 2026-08-13 | 3.04% |
| 2026-08-12 | 3.14% |