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Weco AI

Weco is production-grade autoresearch: point it at your code and an evaluation metric, and an automated research loop (AIDE) searches, tests, and ships measurable improvements as auditable, version-controlled code.

For agents: Weco: production autoresearch tool that runs an automated AIDE tree-search loop to optimize code against an evaluation metric, with docs and quickstart.

autoresearchcode optimizationaide tree searchevaluation metricautomated experiments

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-25T12:42:46.094Z. Cached copy · Source on weco.ai

# Weco AI

> Weco is production-grade autoresearch: point it at your code and an evaluation metric, and an automated research loop (AIDE) searches, tests, and ships measurable improvements as auditable, version-controlled code.

Weco AI builds recursively self-improving AI. The `weco` CLI wraps the open-source AIDE agent with orchestration, steerability, and observability so teams can run the autoresearch loop in production — improving agent harnesses, prompts, ML models, and other performance-critical code against any metric you can measure.

## Product & Docs

- [Weco product overview](https://www.weco.ai/product): What Weco does, who it is for, and how the autoresearch loop works.
- [Full documentation](https://docs.weco.ai/): Install, quickstart, CLI reference, and guides.
- [weco CLI (GitHub)](https://github.com/WecoAI/weco-cli): The command-line tool for production-grade autoresearch.
- [AIDE (open source)](https://github.com/WecoAI/aideml): The research agent behind Weco — human-level performance on ML engineering tasks.

## Blog

- [AIDE²: The First Evidence of Recursive Self-Improvement](https://www.weco.ai/blog/first-evidence-of-recursive-self-improvement): We ran autoresearch on autoresearch: an outer loop rewriting its own research agent for 100 unattended steps. In eight days it discovered agents that beat our two years of hand-tuning on held-out benchmarks, and taught itself to reward hack less.
- [4 Levels of Recursive Self-Improvement](https://www.weco.ai/blog/4-levels-of-recursive-self-improvement): RSI is usually discussed as a binary. We grade it instead: four levels — delegation, net positive, ignition, inflection — each the necessary condition for the next, and precise enough to be falsifiable.
- [How Weco's Autoresearch Improved a Production Fraud Detection Pipeline](https://www.weco.ai/blog/autoresearch-fraud-detection-pipeline): Vardera pointed Weco's autoresearch at their production fraud detection pipeline — F1 up 17.7%, precision up 18.9% — with every change shipping as auditable, version-controlled code.
- [An AI Agent Became the #1 Contributor in OpenAI's Hiring Challenge](https://www.weco.ai/blog/parameter-golf-aiden): Aiden spent 22 days inside OpenAI's Parameter Golf and became the competition's most influential contributor by records, citations, and public signal quality.
- [SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents](https://www.weco.ai/blog/specbench): We built SpecBench to measure the gap between code that passes visible tests and code that actually satisfies the specification. Across 30 systems-level tasks, that gap is real, measurable, and grows with task complexity.
- [AutoResearch vs Classical Hyperparameter Tuning](https://www.weco.ai/blog/autoresearch-vs-classical-hpo): We ran a head-to-head comparison on NanoChat: Optuna vs AutoResearch. The agent wins on sample efficiency, cost efficiency, and generalization.
- [Weco Now Integrates with LangSmith](https://www.weco.ai/blog/weco-lan
…

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