Evidra — The research harness - Give your agents a problem worth staying with.

Hey Product Hunt! 👋 Research rarely follows a straight line. Ideas fail, evidence conflicts, and promising results don’t always survive a second experiment. Evidra is a research harness built to manage that process. Some of the techniques we’re bringing together: - Evolutionary search: maintain diverse candidate approaches, select promising ones, and exchange ideas between independent search groups. - Multi-agent exploration: research lanes investigate different formulations, share discoveries, and challenge each other’s conclusions. - Falsification memory: preserve failed hypotheses and negative evidence so future attempts can change course. - Adaptive allocation: use uncertainty, information gain, diversity, and remaining budget to decide what deserves the next experiment. - Evidence provenance: connect source excerpts, claims, hypotheses, runs, and decisions in a traceable research graph. - Critics and replication: review promising findings independently and use repeated experiments and statistical checks before accepting improvements. - Replay and harness evolution: compare alternative search policies using recorded trajectories, and evaluate changes to the harness itself through measured experiments. - Durable execution: checkpoint campaigns across sessions and use failure-specific recovery when experiments hit timeouts, crashes, or resource limits. The goal is one general-purpose harness for open-ended research and measurable challenges, with code execution, local or remote compute, and explicit budgets and permissions. Evidra is currently in alpha under active development. These mechanisms are at different stages of maturity; we’re still validating how well they work together on real problems. We’re not claiming proven superiority over other harnesses. We’d love feedback, contributions, and difficult problems to test against. Which capability would make the biggest difference to your work?