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Kapso is a team of long-running agents that optimize AI and Data systems, and learn from every experience. Give it an objective and it builds the best solution and deploys it.
It learns from its own work, keeping what moved the metric and dropping what did not, each lesson backed by the evidence that earned it. And it learns from the public state of the art, so the field's best practices are always at hand.
Optimize CUDA kernels for GPU performance. Cut a PyTorch model's wall-clock and memory without changing the numerics. Deliver a prediction model end to end, from data prep to a deployable artifact. Evolve the harness around a model: prompts, decoding, scoring. Set agents to improving agents until the metric climbs. Works with Claude Code and Codex.
Proven in public: #1 open-source on MLE-Bench, #1 on ALE-Bench, ahead of the best tabular foundation model on RelBench, above every human at IOAI 2026.
Docs: docs.leeroo.com/docs.
Source: github.com/Leeroo-AI/kapso.
Features
- Long-running agents that optimize AI and Data systems
- Learns from every experience: keeps what moved the metric, backed by evidence
- Learns from repositories and papers into a searchable knowledge graph
- Works with Claude Code and Codex
- Ships the winner: local, Docker or Modal deployment
- Benchmarked in public: MLE-Bench, ALE-Bench, RelBench, IOAI 2026
