Recall is Bittensor subnet 31 (SN31), a project in the Search and Information Retrieval lane. Community-owned RAG, ranked by competition
A subnet that turns retrieval-augmented generation into an open contest: nodes compete to serve the best search-and-answer pipeline, and validators score who actually retrieves the right thing.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is Recall (SN31)?
Recall is a Bittensor subnet (SN31) built around retrieval-augmented generation, or RAG: the technique of answering a question by first fetching relevant documents and then generating a response grounded in them, with citations. According to its on-chain identity description, miners serve embedding models, vector search, and LLM inference, while validators independently evaluate retrieval accuracy and answer quality.
In plain English: It's like Perplexity, but the search-and-answer engine is run by a competing network of nodes instead of a single company, and the best pipeline wins the queries.
Centralized reference points: Perplexity, managed RAG stacks
How miners and validators work
Like every Bittensor subnet, SN31 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners serve the retrieval and generation pipeline: embedding models, vector search, and LLM inference, per the subnet's on-chain description.
- Validators check retrieval accuracy and answer quality, scoring which pipelines actually surface and use the right information.
Mechanism and design
Per the subnet's on-chain identity description, miners run the full pipeline: embedding models to represent queries and documents, vector search to find candidates, and LLM inference to produce the answer. Validators independently evaluate retrieval accuracy and answer quality, and the subnet routes queries toward the pipelines that score best. In Bittensor terms, validators set weights based on those quality evaluations, and emissions follow the weights, so the strongest retrieval-and-answer pipeline earns the most.
On market footing, the alpha token trades near 0.00476 TAO with a market value around 7,065 TAO. Its emission share sits near half a percent of network emissions, backed by roughly 1,773 TAO of root TAO in the pool. Net inflows over the past week have been positive and price is up about 26% on the week, though it is roughly flat to slightly lower over the past month. The pool is on the thinner side, so position sizing matters.
A note on sourcing: the mechanism above comes entirely from the subnet's on-chain identity description, which is the authoritative current claim from the slot owner. A separate one-line summary mirrored in a third-party database still describes an older, unrelated theme; that is stale data and the on-chain identity is what governs here.
Why the project exists
- The problem it solves: Language models answer from frozen training data and confidently invent things they do not know. RAG grounds answers in freshly retrieved sources with citations. Recall's stated goal is to make that retrieval-and-generation pipeline an open competition rather than a closed product.
- The opportunity: Retrieval is the backbone of most useful AI systems today. A continuously improving, citation-backed engine that anyone can plug into is a large surface to aim at.
- The Bittensor advantage: Open competition routes user queries to the top-performing pipeline and keeps pressure on every node to improve, instead of locking users to one vendor's retrieval quality.
- Traction signals: On-chain, the alpha token has positive net inflows over the past week and rose roughly 26% in that window. Beyond the on-chain identity description and market data, the slot does not list a public website, repository, or social account, so independent verification of the implementation is limited for now.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on Recall · SN31 rather than trusting any static screenshot in a blog post.
Risk factors
- Limited public verification: The on-chain identity does not register a website, repository, or documentation, so the mechanism is known only from a short on-chain description. Independent review of the actual implementation is not currently possible from public sources.
- Crowded category: Retrieval and search is contested both inside Bittensor, where several subnets target search and information retrieval, and outside it, where well-funded products like Perplexity already serve RAG at scale.
- Concentration and liquidity: A Gini coefficient near 0.76 points to concentrated ownership or stake distribution, and with only about 1,773 TAO of root TAO backing the pool, large positions can move price and incur slippage on entry or exit.
- Early execution: This is an early-stage subnet whose public footprint is mostly its on-chain identity and market data. Execution against the RAG goal is unproven from what is publicly verifiable.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like Recall for thematic upside (and higher variance). If you use TaoScale, you can size SN31 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN31 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does Recall do on Bittensor?
It's like Perplexity, but the search-and-answer engine is run by a competing network of nodes instead of a single company, and the best pipeline wins the queries.
What is the SN31 alpha token?
When you stake TAO into subnet 31, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research Recall before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN31. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of Recall (SN31) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
