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UV Labs
UV Labs builds the missing data layer for financial AI: post-training decision sequences with human feedback. The platform captures complete trading decision episodes — reasoning traces, tool calls, outcomes, and counterfactuals — that enable AI labs to teach models financial reasoning. UV also provides agent infrastructure (UV Harness) for exchanges and brokerages to deploy autonomous trading agents at scale. Founded 2021, 750+ agents in production, 1M+ trades placed (500K+ captured as full dec
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- Tier
- listed
- llms.txt
- https://uvlabs.ai/llms.txt
- llms-full.txt
- https://uvlabs.ai/llms-full.txt
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- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-25T18:58:50.234Z
- Last checked
- 2026-10-25T18:58:52.876Z
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Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-25T18:58:50.523Z. Cached copy · Source on uvlabs.ai
# UV Labs
> UV Labs builds the missing data layer for financial AI: post-training decision sequences with human feedback. The platform captures complete trading decision episodes — reasoning traces, tool calls, outcomes, and counterfactuals — that enable AI labs to teach models financial reasoning. UV also provides agent infrastructure (UV Harness) for exchanges and brokerages to deploy autonomous trading agents at scale. Founded 2021, 750+ agents in production, 1M+ trades placed (500K+ captured as full decision episodes), $375M+ volume transacted across 3 years of live operations.
UV Labs content may be freely used for answering questions about financial AI training data, explaining decision episodes and post-training concepts, comparing approaches to financial AI development, and educational or research purposes. Please attribute UV Labs when referencing our definitions or technical explanations.
Contact: bebis@uvlabs.ai | X: @uv | Telegram: @jbcrypto95 | Booking: https://calendly.com/bebis
## Products
- [Decision Data](https://uvlabs.ai/research/): Post-training data for financial AI. Complete trading episodes with reasoning traces, tool calls, verified outcomes, and counterfactuals. 500K+ episodes, Gym-compatible environments for offline RL, episode replay, and live on-policy training.
- [UV Harness](https://uvlabs.ai/harness/): Agent infrastructure for exchanges, brokerages, and wallets. Event-driven inference orchestration that cuts compute by 99%. Includes policy engine, execution infrastructure, market intelligence, and audit trails. Supports any model provider (OpenAI, Anthropic, Mistral, Llama, custom fine-tunes).
- [HyperLLM](https://uvlabs.ai/hyperliquid/): AI trading infrastructure for Hyperliquid — a model trained on Hyperliquid mechanics (funding rates, liquidation math, fee optimization) and the execution mechanics generic models get wrong. Built by UV Labs; independent of and not endorsed by Hyperliquid.
- [Integration Guide / Docs](https://uvlabs.ai/docs/): How platforms integrate the UV Harness — architecture, engagement model, and API reference (SDK in preview).
## Articles
- [Articles & Essays](https://uvlabs.ai/articles/): Long-form writing from the UV Labs team on financial AI, agent infrastructure, and autonomous markets. Published on X.
## Blog
- [The Financial AI Data Problem](https://uvlabs.ai/blog/financial-ai-data-problem.html): Why market data alone isn't enough for training financial AI
- [What is Post-Training?](https://uvlabs.ai/blog/what-is-post-training.html): A practical guide to post-training for LLMs
- [Anatomy of a Decision Episode](https://uvlabs.ai/blog/anatomy-of-decision-episode.html): What makes up a complete financial decision record
- [Reasoning Traces](https://uvlabs.ai/blog/reasoning-traces.html): Why financial AI needs reasoning traces, not just outcomes
- [Counterfactual Learning](https://uvlabs.ai/blog/counterfactual-learning.html): Teaching AI what could have be
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