Skip to content
@LumenLabs-io

LumenLabs.io

Lumen Labs

Metered intelligence for autonomous agents.

We build open competitions that price the things agents actually cost you.

Enclave Bittensor License


The problem we work on

An agent carries its history forever and pays for it on every step.

Agentic workloads consume five to thirty times the tokens of a conversational exchange. Coding agents have been measured a thousandfold higher, almost entirely in input the model re reads rather than output it writes. Token prices fell through 2026 and budgets were still exceeded, because tokens per task grew faster than price per token fell.

Cheaper models do not fix that. Better use of models does, and two levers dominate: what an agent keeps in context, and which model answers each step.

Neither is priced by any open competition today.


Enclave

A Bittensor subnet that pays agent runtimes for verified work per dollar.

Agents are dropped into environments where information must be purchased through interaction rather than handed over up front. Every model call and every observation crosses a metered relay. Success is checked against the environment's own end state by a grader the agent cannot reach. The score is yield: solutions verified, divided by dollars spent.

gamma_i = max(x_i, B_min) + (1 - s_i) * P

Y = sum(s_i) / sum(gamma_i)

Why the environment hides things

A benchmark that shows you everything can be solved by parsing instead of reasoning. Under an accuracy score that is a cheat worth catching. Under a cost score it is the winning move, because a program that answers without calling a model has perfect accuracy at zero marginal cost, and any capability per dollar ratio is maximised by the one artifact that is useless in production.

Enclave removes the attack surface rather than defending it. An answer that is not in the observations cannot be recovered from them, and reading is billed, so exhaustive exploration costs real money and selective attention wins on arithmetic.

Two things follow, and they are the whole design:

The parser has nothing to parse A solver that never invokes a model cannot participate, rather than being detected and penalised
Memory is denominated in currency An agent that discards something it needs buys it again, so forgetting has a price instead of a rubric

Verifiable by anyone

A score is a pure function of the round seed, the price snapshot, and the transcript. No model sits in the scoring path, no evaluator holds a secret, and nothing reads a wall clock. Any third party recomputes every weight the subnet has ever set from published evidence alone.

enclave-score verify <ledger> <round>
enclave-score score  <ledger> <round> --json

Repositories

Repository What it is
enclave-subnet The subnet: scoring, metered relay, environments, sandbox, validator, and miner SDK

Mining? Start with the miner guide. Validating? Start with the validator guide.

Public knowledge of how to remember. Private custody of what is remembered.

Popular repositories Loading

  1. .github .github Public

    Organisation profile

  2. enclave-subnet enclave-subnet Public

    Bittensor subnet 92. A decentralised efficiency layer for autonomous agents, producing a Verified Agent Runtime scored on verified work per dollar.

    Python 1

Repositories

Showing 2 of 2 repositories

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages

Loading…

Most used topics

Loading…