Continual Learning: Learn from Agent Experience

Run continual learning live in production, with algorithms that experiment with new context and steering in real-time.

Ship software that learns from experience. No code changes — just point your LLM calls through the proxy.

Start Continual Learning API / Docs

How It Works

Drop-in proxy with reward feedback

Swap the base URL. Define rewards. MIPRO experiments with prompts and context while your system runs.

- Updated prompts available via low-latency proxy or HTTP
- Continual updates as traffic distribution shifts
- Ontology-aware for the hardest problems

Integration Guide Python SDK

Ontology-Aware

MIPRO builds and uses an ontology automatically

As rollouts run, MIPRO collects evidence and synthesizes a knowledge graph — strategies that work, causal patterns, and performance insights. It then reads that ontology back to guide future prompt search.

- Builds a graph of candidates, strategies, and causal links from traces
- Reads the graph back to steer context selection and prompt mutations
- Fire-and-forget evidence collection — never blocks inference

Ontology Guide API / Docs

Ready To Ship

Start learning from experience.

Free tier available. Paid plans are for training compute. Inference is billed at provider rates.

Start Continual Learning Book Demo