Effect-log – Semantic crash recovery for AI agent side effects

Your AI agent sends an email in step 3, crashes before step 4 records it, restarts, and sends the email again. This is the exactly-once side effect problem.

effect-log is an embeddable Rust library (with Python bindings) that solves this with two ideas:

1. Every tool declares an *effect kind* at registration: ReadOnly, IdempotentWrite, Compensatable, IrreversibleWrite, or ReadThenWrite.

2. A write-ahead log records intent before execution and completion after. On recovery, the effect kind drives the strategy — reads replay for fresh data, idempotent writes safely retry, irreversible writes return sealed results (never re-execute), and unknown states escalate to human review.

The entire recovery logic is a pure function that fits on one screen.

I'd love feedback on: Is five effect kinds the right number? Are there tool types that don't fit? What failure modes have you hit with agents taking real-world actions?