Datamimic – don't let your coding agent invent its own test world
Datamimic has released its Community Edition, an open-source tool designed for deterministic synthetic data generation and PII-aware pseudonymization. The platform aims to provide regulated enterprises with governed workflows and secure test-data operations.
Why it matters
Synthetic data is increasingly critical for training AI agents and testing software in regulated industries like banking without compromising sensitive user information.
This repository contains the DATAMIMIC Community Edition (CE). MIT-licensed, Python-native, MCP-ready.
CE is fully usable standalone for deterministic synthetic data generation and PII-aware pseudonymization. The Enterprise Platform adds governed workflows, PII scanning, role-based access, audit logging, scheduling, multi-system execution, and the full operational layer that regulated enterprises require.
👉 Enterprise Platform: datamimic.io | 📘 Docs: docs.datamimic.io | 📅 Book a strategy call: datamimic.io/contact
🤖 AI agent? Start at AGENTS.md and use the project CLI: preserve new intent as model.dm.json , submit an early best attempt via datamimic scaffold ... --format json , repair from the structured issues, declare an expectation per stated requirement, and stop on verified=true . Existing raw XML uses lint plus bounded dry-run.
DATAMIMIC CE is the open-source deterministic data engine at the core of the DATAMIMIC Enterprise Platform. It is usable standalone for synthetic data generation and PII-aware pseudonymization in any local, CI, or agent-driven workflow.
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