Datamimic – don't let your coding agent invent its own test world
DATAMIMIC — Governed Test Data for Regulated Enterprises 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. 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. What is DATAMIMIC? 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. The Enterprise Platform adds the governed workflows, scanners, dashboards, and execution layer that regulated enterprises require for production-scale test-data operations. Available in CE (this repo): Generate fully synthetic, deterministic datasets — model-driven, no source data required Pseudonymize staging/QA exports — deterministic (seeded) or privacy-maximized (non-seeded) field transformation; PII fields identified and modeled manually in the XML pipeline Execute single-system pipelines against PostgreSQL · MySQL · Oracle · MS SQL · SQLite · MongoDB · CSV · JSON · XML · XLSX · DbUnit · fixed-width (.fcw) Model behavior — weighted state machines, composite multi-field references, control flow (
,
), and a scriptable memstore for staged aggregation Emit provenance — append-only execution logs and per-output content hash for audit re-execution Guide agents — machine-readable capabilities, progressive reference queries, and one canonical CLI scaffold transaction; an optional MCP adapter exposes the same authoring service The Enterprise Platform adds: PII scanner — probability-scored field detection with configurable thresholds via DataWorkbench Multi-system execution — Oracle / MongoDB / Kafka in coordinated workflows with referential integrity Industry message templates — EDIFACT / SWIFT MT / HL7 v2.x / HL7 FHIR generated as deterministic test/training artefacts Governance layer — role-based dashboards, audit trails, approval flows, reusable enterprise templates, scheduler Performance core — Rust fastpath, ML/auto-regressive engine for complex distributions, keyset and manifest building, optimised distributed execution On-premise / air-gapped deployment — podman-compose or Helm, with consulting-led rollout Deployed in regulated EU banking environments for deterministic test data across Oracle, MongoDB, and Kafka pipelines. Reference customers available under NDA — see also datamimic.io case studies. Deployed in regulated EU banking environments for deterministic test data across Oracle, MongoDB, and Kafka pipelines. Reference customers available under NDA — see also datamimic.io case studies. AI agents: author, verify, and run data models The CLI is the baseline agent contract. Install CE with pip install datamimic-ce; inside this checkout, use .venv/bin/datamimic so a stale global installation cannot change the available schema or commands. capabilities, authoring-reference projections, and the commands shown with --format json return machine-readable JSON. On a failed scaffold attempt, change model.dm.json using its structured validation issues, typed repair, or rule diagnostics before retrying. A typed max_count remediation instead changes only the bounded scaffold parameter to at least its reported minimum. Never repeat an identical failed call. A successful scaffold result is terminal for authoring, so do not lint or dry-run its generated XML again. Exact source fragments are discoverable through queries such as --category source --kind memstore. Optional MCP adapter When the calling environment already exposes DATAMIMIC MCP tools, they map to the same canonical contracts and implementations: reference → datamimic_reference, scaffold → datamimic_scaffold, lint → datamimic_check, and dry-run → datamimic_run. Install the adapter with pip install "datamimic-ce[mcp]"; registration details belong in the MCP quickstart, not in the authoring workflow. The adapter intentionally exposes only the four canonical reference, scaffold, check, and bounded-run operations; domain generation remains a Python/CLI capability rather than a parallel MCP authoring path. Prompts to paste into your agent Author and verify a new model Create the dataset I describe with DATAMIMIC. Read AGENTS.md first. In a repository checkout use `.venv/bin/datamimic`; otherwise use the current `datamimic` CLI. Preserve my intent as `model.dm.json`; do not hand-write XML. Start from the minimal valid document shape in AGENTS.md ("Authoring a new model"). Two rules prevent most rejections: the top level allows ONLY version, seed, products, expectations; product-level "kind" (generated/source/time_series) is a different vocabulary from field-level "kind" (increment, values, weighted, int_range, decimal_range, pattern, constant, script). Range fields take minimum/maximum, never min/max. Submit EARLY: run `datamimic scaffold model.dm.json --format json` with your best attempt after at most one discovery call. Repair from the structured issues (path/code/message/allowed_fields) and diagnostics (fix_hint) — they teach the schema faster than more discovery. Never resubmit an unchanged document. If a remediation requests a larger max_count, retry scaffold with at least that value without changing the intent. Declare an expectation for every requirement I state (counts as exact_count with a "count" field, uniqueness, allowed values, ranges, foreign keys) — verified=true certifies only what you declared. Stop on verified=true; do not lint or dry-run the generated XML. If I request real execution, save the returned XML as a generated artifact and run that descriptor. Return the model.dm.json path and concise verification evidence. Relational hierarchy with referential integrity (fully supported — no XML needed) Seed a relational dataset with referential integrity: 4 customers, each with exactly 2 orders. Customers get an incrementing unique id and a region from {north, south, east, west}. Each order carries the REAL parent customer id as a foreign key and an amount between 10.0 and 500.0. Follow AGENTS.md's "Authoring a new model" and its structural recipes: orders nest inside the customer product's "children" array; the FK field is {"kind": "script", "script": "parent.id"} with a foreign_key role — a randomly generated FK passes schema validation but fails per-parent-count acceptance. Declare expectations for the customer count, customer id uniqueness, exactly 2 orders per customer (per_parent_count), the orders->customers foreign key, and the amount range. Stop on verified=true and show the acceptance evidence. Raw XML remains supported for existing descriptors (lint → dry-run → run; see AGENTS.md). For new models it is a last resort: only when a scaffold issue explicitly classifies the requirement as unsupported_intent should an agent hand-author XML, preserving that evidence. CE vs Enterprise Platform CE and EE are not the same engine with a feature flag. They share the DSL and determinism contract, but EE is an independently optimised execution engine built for enterprise-scale throughput and operational control. Engine comparison Platform capabilities (EE only) 👉 Explore the Enterprise Platform | Book a platform demo EE runtime profiles The EE core supports three runtime configuration profiles, selectable per execution context: Logging depth is independently configurable per profile — from minimal (throughput-optimised) to full nested tracing across importers, exporters, and generation stages. EE template engine The EE template engine generates industry-standard financial messages from DATAMIMIC models. The workbench parses uploaded message samples, auto-detects the message type, and validates edits against the registered spec version in real time. Capabilities Spec-aware form editing — segments and elements rendered as structured forms with mandatory/optional indicators, per-field value suggestions, and inline custom-extension support Strict validation against baked spec versions, with segment- and element-level error reporting Advisory mode when a spec is unregistered or in draft — editing stays enabled, validation continues as guidance Round-trip between the structured form view and the authoritative template text — no fidelity loss Download / adjust / upload your own spec — customers can extend or override the baked spec catalogue without waiting for a release Live structure tree + preview for every edit File auto-detection — upload an existing message, the editor identifies the type and loads the matching spec Format coverage Customers can extend the spec catalogue between releases by downloading, adjusting, and uploading their own spec files directly. Generated messages are deterministic and traceable to their source model, and syntactically valid against the registered spec. They are intended for test and training environments only — they are not network-validated and must not be transmitted on production SWIFTNet or EDI networks. See the SWIFT CSP note below. Who is DATAMIMIC for? Enterprise Platform (EE) Community Edition (CE) Developers and data engineers who need deterministic synthetic data generation or PII-aware pseudonymization in local environments, CI pipelines, or agent-driven workflows. PII field identification is manual — the EE DataWorkbench automates this step. Why deterministic generation matters Most test data tools produce random output. That breaks regression tests, audit trails, and cross-team reproducibility. DATAMIMIC's determinism contract (CE): Same engine version + same model + same seed = byte-identical output, every run, every machine. Holds at three layers: the generate_domain facade, every domain service called directly, and every literal generator that accepts an rng= argument. Verified per-service on every CI run via tests_ce/architecture/test_service_replay_determinism.py. DSL-level seeding:
makes the whole model deterministic — every seed-less
derives a reproducible child RNG from it, and
overrides it for that block (no seed anywhere → wall-clock random). Verified by tests_ce/integration_tests/test_determinism_seed_scenarios. As of 4.0.0 the same seed also reaches standalone literal generators (
), typed/pattern keys, DateTimeGenerator, and cross-page unique picks — machine-independently. Source reads: distribution="ordered" reads a data source in stable file order; distribution="random" shuffles but replays identically when
is set (without a seed the shuffle is non-deterministic by design, for privacy-maximized one-time deliveries). Deterministic shuffling across distributed / multi-process execution is EE. Provenance hash on every facade output = re-executable lineage. Same input → same determinism_proof.content_hash, always. UUIDv5 entity identifiers = stable across runs and machines. Single wall-clock SPOT (now_utc_naive()); raw datetime.now() is forbidden in production code and the clock-drift architecture gate fails CI on any reintroduction. RNG/clock runtime SPOTs in datamimic_ce/domains/domain_core/runtime/: spawn_rng (reproducible child-RNG derivation), now_utc_naive, and resolve_clock. The same contract vocabulary the Enterprise Platform enforces end-to-end. The Enterprise Platform (EE) goes further: beyond the CE contract, EE makes the whole execution environment deterministic — a configurable/frozen wall-clock (not just CE's fixed anchor), and deterministic SAFE_GLOBALS plus the Python random functions, so sandboxed script expressions and any stdlib random call replay identically as well. from datamimic_ce.domains.facade import generate_domain request = { "domain": "person", "version": "v1", "count": 1, "seed": "regression-suite-42", # identical seed → identical output "locale": "en_US", "clock": "2025-01-01T00:00:00Z" # fixed clock = stable time context } response = generate_domain(request) # response["determinism_proof"]["content_hash"] is stable across runs. Direct service use is equally deterministic when given a seeded RNG: import random from datamimic_ce.domains.finance.services import CreditCardService # Same seeded Random → byte-identical CreditCard across runs. card_a = CreditCardService(rng=random.Random(42)).generate() card_b = CreditCardService(rng=random.Random(42)).generate() assert card_a.bic == card_b.bic and card_a.card_number == card_b.card_number Determinism contract — CE vs EE CE delivers contract-enforced determinism for the synthetic-data generation surface (facade, services, generators) and, as of 4.0.0, for seeded XML descriptors — byte-identical across machines, executed single-process. The Enterprise Platform extends the same contract to distributed and multi-system execution with referential integrity and the seeded/unseeded pseudonymization modes, and adds the five drift-gates that lock the contract end-to-end for regulated deployments. How DATAMIMIC differs from Faker and generic generators # Faker — broken relationships from faker import Faker fake = Faker() patient_age = fake.random_int(1, 99) conditions = [fake.word()] # "25-year-old with Alzheimer's" — meaningless for any real test # DATAMIMIC — domain-aware, deterministic with a seed import random from datamimic_ce.domains.healthcare.services import PatientService patient = PatientService(rng=random.Random(42)).generate() print(f"{patient.full_name}, {patient.age}, {patient.conditions}") # Age-appropriate, domain-consistent — and identical every run with a fixed seed Quickstart — Community Edition pip install datamimic-ce Healthcare domain import random from datamimic_ce.domains.healthcare.services import PatientService patient = PatientService(rng=random.Random(42)).generate() print(patient.full_name, patient.age, patient.conditions) # Age-appropriate conditions, demographically realistic; deterministic with a seed Finance domain import random from datamimic_ce.domains.finance.services import BankAccountService account = BankAccountService(rng=random.Random(42)).generate() print(account.account_number, account.balance) # Balance-consistent, locale-correct; reproducible with a seed Pseudonymization — CE (manual model) DATAMIMIC supports two pseudonymization modes with different privacy postures: Note on GDPR anonymization: Full anonymization status under GDPR depends on complete field coverage across all quasi-identifiers and a re-identification risk assessment on the complete record — not on individual field transformation alone. DATAMIMIC does not make anonymization claims on behalf of the customer. Non-seeded mode maximizes privacy at the transformation level; the customer is responsible for assessing re-identification risk across the full dataset. Note on GDPR anonymization: Full anonymization status under GDPR depends on complete field coverage across all quasi-identifiers and a re-identification risk assessment on the complete record — not on individual field transformation alone. DATAMIMIC does not make anonymization claims on behalf of the customer. Non-seeded mode maximizes privacy at the transformation level; the customer is responsible for assessing re-identification risk across the full dataset. In CE, PII fields are identified and modeled manually in the XML pipeline:
Built-in converters can additionally transform a key's value — e.g. irreversibly hash the original instead of replacing it, or partially mask it:
datamimic run ./patient-scenario/datamimic.xml Time-series generation — CE Any
becomes a time-series loop when given strict ISO 8601 start/end/interval attributes. Per iteration the script context exposes a ts namespace: Output column names — including whether to even emit a timestamp or series-id column — are entirely the user's choice via
. The primitive is domain-agnostic; the same DSL covers IoT readings, financial ticks, log streams, smart meters, anything time-indexed.
Guarantees: Prefix-stable by construction — the first N ticks of series 0 are byte-identical regardless of total window length, because each row's ts.now is a pure function of start + interval * step. Loop order is contiguous per series — series 0's full sequence, then series 1's, etc. Makes downstream grouping trivial. Strict ISO 8601 — start/end via datetime.fromisoformat (Z-suffix supported); interval via the isodate library (PT1H, PT15M, PT5S, P1D, P1W, P1DT12H, fractional seconds for sub-second precision). Resolution: PT0.001S = 1 ms, PT0.000001S = 1 µs (Python datetime.timedelta microsecond floor; sub-µs intervals and constant-length-undefined units like months/years are rejected with a clear error). count is orthogonal, not overloaded — it means "outer-loop iterations of this
" in all modes (same as nested
). In time-series mode each outer iteration is one series of N ticks, so total rows = count × ticks_per_series. Default count="1" keeps single-series fixtures terse. Naming caveat — a
output column would shadow the namespace (current_product overrides current_variables in script scope), and a
is rejected at parse time. Use a different name, e.g. timestamp for the column. Composes with the existing
mechanism for multi-source merges (e.g. join each tick with a sensor-metadata CSV via
inside the same
), with
filtering, and with
sub-scopes — the ts namespace is visible everywhere a
runs. See tests_ce/integration_tests/test_timeseries/ for committed DSL fixtures + proofs (including pagination invariance). Where CE fits on its own Most teams adopt CE for one of three reasons. EE is not required for any of them. 1. Reproducible test data for CI/CD pipelines. Pin a seed against the generate_domain facade — or hand a seeded random.Random to any domain service — and you get byte-identical output across runs and machines. Both layers are gated on every CI run by tests_ce/architecture/. Regression tests stop being flaky because the input data is stable across runs. from datamimic_ce.domains.facade import generate_domain response = generate_domain({ "domain": "person", "version": "v1", "count": 1, "seed": "ci-pipeline-42", "locale": "en_US", "clock": "2026-01-01T00:00:00Z", }) # Same engine version + same model + same seed → same output, every machine, every run. 2. Deterministic data backend for AI agents and LLM tooling. The CLI and Python API are the baseline surfaces for seeded, verifiable generation. The optional MCP adapter (pip install "datamimic-ce[mcp]") exposes the canonical reference, scaffold, check, and bounded-run authoring operations. Generated domain-facade outputs include a determinism_proof.content_hash, so Python/CLI callers can re-execute and verify the data later — useful for agent regression tests and any workflow where the data an agent saw must be reconstructable. 3. Pseudonymization of staging and QA exports. Manual model in CE (XML pipeline), no scanner license required. Seeded mode for stable regression test data; non-seeded mode for one-time deliveries with maximized privacy posture. See the Pseudonymization section above. Where DATAMIMIC fits in your compliance program DATAMIMIC produces evidence and reproducible artifacts that support compliance work. It does not replace your DPO, your CISO, or your auditor. The following are pointers for where DATAMIMIC outputs commonly slot into established programs: Both editions produce reproducible artefacts. CE covers single-system fixtures and provenance evidence; multi-system audit evidence with role-based dashboards is EE. Both editions produce reproducible artefacts. CE covers single-system fixtures and provenance evidence; multi-system audit evidence with role-based dashboards is EE. These pointers do not constitute legal advice or a compliance attestation. Consult your DPO, CISO, or qualified counsel for formal compliance determinations. Full anonymization status under GDPR depends on re-identification risk across the complete dataset — see the pseudonymization disclaimer above. These pointers do not constitute legal advice or a compliance attestation. Consult your DPO, CISO, or qualified counsel for formal compliance determinations. Full anonymization status under GDPR depends on re-identification risk across the complete dataset — see the pseudonymization disclaimer above. Architecture CE and EE share the DATAMIMIC DSL and the determinism contract. The execution layer is separate: CE is a Python execution engine using multiprocessing (with optional Ray for distribution); EE is an independently-optimised execution engine with a Rust fastpath, ML/auto-regressive generation, keyset and manifest building from live schemas, and optimised distributed execution at billion-record scale. ╔══════════════════════════════════════════════════════════════════╗ ║ DATAMIMIC ENTERPRISE PLATFORM (EE) ║ ║ ║ ║ ┌──────────────────────────────────────────────────────────┐ ║ ║ │ PLATFORM LAYER │ ║ ║ │ UI · RBAC · Governance · Audit Dashboards │ ║ ║ │ DataWorkbench · PII Scanner · Pseudonymization Builder │ ║ ║ │ Scheduler · Task Runner · CI/CD · Template Engine │ ║ ║ └──────────────────────────────────────────────────────────┘ ║ ║ ║ ║ ┌──────────────────────────────────────────────────────────┐ ║ ║ │ EE CORE (separately maintained, more advanced than CE) │ ║ ║ │ │ ║ ║ │ Rust fastpath for performance-critical paths │ ║ ║ │ ML / auto-regressive engine for complex distributions │ ║ ║ │ Keyset and manifest building from live DB schemas │ ║ ║ │ Optimised distributed execution at billion-record scale │ ║ ║ │ Runtime profiles: Performance · Balanced · Flexibility │ ║ ║ │ Deep nested evaluation · Conditions · Rulesets │ ║ ║ │ Structured error catalog · Per-stage execution logging │ ║ ║ └──────────────────────────────────────────────────────────┘ ║ ╚══════════════════════════════════════════════════════════════════╝ ╔══════════════════════════════════════════════════════════════════╗ ║ DATAMIMIC COMMUNITY EDITION (CE) — this repo ║ ║ ║ ║ Determinism Kit · Domain Services · Schema Validators ║ ║ Synthetic Generation · Pseudonymization (manual model) ║ ║ Python API · XML Pipelines · CLI · MCP Server ║ ╚══════════════════════════════════════════════════════════════════╝ ↓ ↓ ↓ ↓ PostgreSQL Oracle MongoDB CSV / JSON / XML EE adds Kafka, EDIFACT, SWIFT MT, HL7 v2.x, and HL7 FHIR as additional targets — see Supported systems below. Both editions share the DATAMIMIC DSL and determinism contract. Supported systems CE domains All services are versioned and seeded; each generation emits a provenance hash suitable as evidence in audit reviews. Domain services can be used directly via constructor injection, or driven through the higher-level generate_domain({...}) facade for seed/locale/clock/count parameterisation (currently supports person, address, patient, doctor at v1). CLI reference # Discover the live structural surface as JSON datamimic capabilities # Enumerate typed authoring queries, then request only the needed fragment datamimic reference authoring datamimic reference authoring --category field --kind weighted datamimic reference overview # Compile and fully verify the canonical intent artifact; stop on verified=true datamimic scaffold model.dm.json --format json # Lint a descriptor: schema, semantics, best practices — every finding carries # a rule id (DMxxx) and a fix hint. Exit codes 0/1/2. datamimic lint my-scenario/datamimic.xml datamimic lint my-scenario/datamimic.xml --format json # diagnostics v1, CI-friendly # Safely execute bounded counts with neutralized targets and sample rows datamimic dry-run my-scenario/datamimic.xml --format json # Run a verified scenario for real datamimic run my-scenario/datamimic.xml # Initialize a new project datamimic init my-scenario # Demos datamimic demo list datamimic demo create demo-healthcare datamimic demo create --all --target ./my_demos # System and version info datamimic info datamimic version Documentation Contributing See CONTRIBUTING.md. CE is MIT licensed and community contributions are welcome. The CE engine is the foundation. If you are building integrations, domain extensions, or MCP tooling on top of DATAMIMIC, we want to hear from you. License MIT — see LICENSE. The DATAMIMIC Enterprise Platform (EE) is a commercial product. Contact us for licensing. DATAMIMIC — Deterministic, governed test data for regulated enterprises. datamimic.io | Book a demo | LinkedIn