Top 5 Synthetic Dataset Generators in 2026: Future AGI, Gretel, MOSTLY AI, SDV, and Snorkel Compared

TL;DR: Top 5 Synthetic Dataset Generators in 2026

Sources: vendor docs and GitHub repositories cited in section 3. For a deeper RAG-eval angle see Synthetic Test Data for LLM Evaluation in 2026.

What changed since 2025: Gretel was acquired by NVIDIA in March 2025, MOSTLY AI open-sourced its synthetic data SDK under Apache 2.0 in late 2024, and persona-driven agent simulation has emerged as the dominant pattern for LLM and agent evaluation datasets.

Why Synthetic Data Matters in 2026

Three forces converged. The EU AI Act tightened personal data handling for foundation model training. Privacy enforcement in the US raised the bar around datasets that look anonymous but can be re-identified or that leak via model memorization. And the cost of human-labeled LLM evaluation sets continued to rise as agents got more complex and multi-turn.

The response was a clean split in how teams use synthetic data:

• Tabular and structured data still flows from Gretel, MOSTLY AI, and SDV.
• Text and code labeling still relies on Snorkel and weak supervision.
• LLM and agent behavioural data moved to persona-driven simulation, where Future AGI is the strongest single-platform fit.

If your generator only knows how to produce a CSV, it is not enough for an agentic stack in 2026.

What a Synthetic Dataset Generator Actually Does

A modern generator does three jobs:

• Sample synthesis. Produce records that look statistically like a real seed dataset.
• Privacy enforcement. Apply differential privacy or other formal guarantees so the synthetic records cannot be reverse-engineered.
• Quality measurement. Score the output on fidelity (statistical match), utility (downstream model performance), and privacy (resistance to attacks).

For LLM and agent workloads, a fourth job is critical: behavioural simulation. Given a target agent, produce a conversation transcript that exercises the agent’s failure modes. This is what makes synthetic data useful for evaluation and red-teaming, not just training.

Tool 1: Future AGI: LLM and Agent Test Data via Persona-Driven Simulation

Future AGI is the strongest single-platform fit when the goal is to test LLMs, agents, or RAG systems end to end. The platform ships dataset generation as part of a larger evaluation, guardrails, and observability stack at futureagi.com.

What it ships

• fi.simulate. Persona-driven multi-turn simulation. Define a persona (“frustrated enterprise buyer”), a target agent, and a scenario; the runner produces a labeled dialog with span-level scores.
• Dataset generation for evaluation. Programmatic synthesis of test sets across task completion, faithfulness, tool-use correctness, and 50 plus other templates.
• Fine-tuning dataset curation. Take production spans, filter by quality score, export as JSONL for OpenAI, Anthropic, or HuggingFace fine-tuning.
• Built-in guardrails on every generated sample. Toxicity, PII, jailbreak, brand-tone screening at generation time.
• OSS instrumentation via traceAI (Apache 2.0) at github.com/future-agi/traceAI.

Quick start: run a cloud simulation against your agent

import asyncio
import os
from fi.simulate import TestRunner, AgentInput, AgentResponse

os.environ["FI_API_KEY"] = "..."
os.environ["FI_SECRET_KEY"] = "..."

# 1. Wrap your existing agent as a callback. Return AgentResponse with content + any tool calls.
async def agent_callback(message: AgentInput) -> AgentResponse:
    # text = await my_runtime.chat(message.content)
    return AgentResponse(content="...")

# 2. Configure the runner and trigger a cloud simulation tied to a platform run_id.
async def main():
    runner = TestRunner()  # picks up FI_API_KEY + FI_SECRET_KEY from env
    report = await runner.run_test(
        run_id="YOUR-PLATFORM-RUN-ID",
        agent_callback=agent_callback,
        concurrency=1,
    )
    print(f"Total results: {len(report.results)}")

asyncio.run(main())

The platform writes the resulting dataset back to your project with every input, output, tool call, span, and scored eval, ready for regression testing or fine-tuning. For more depth see Build a multi-agent system with Future AGI.

Why this ranks number 1 for AI teams

The 2026 pain point is not generating a CSV. It is generating a behavioural dataset that exercises your agent’s failure modes and scores it at every span. The other four tools in this list are stronger picks inside their lanes (tabular synthesis, privacy, labeling) but do not bundle behavioural simulation, span-level scoring, guardrails, and the eval template catalog on one platform.

Tool 2: Gretel.ai: Privacy-First Tabular, Text, and Time-Series Synthesis

Gretel produces synthetic data for tabular, text, and time-series workloads. It was acquired by NVIDIA in March 2025 and is now part of the NVIDIA AI Enterprise stack.

• Site: gretel.ai
• License: Commercial; the Gretel Python SDK is open-source under Apache 2.0
• Strength: differentially private generative models with formal privacy guarantees
• Trade-off: not optimized for LLM behavioural data or agent traces

Pick Gretel when differential privacy on tabular or text is the hard constraint. Pair it with Future AGI evaluators if you need to validate the resulting synthetic data against downstream model performance.

Tool 3: MOSTLY AI: Enterprise Tabular Synthesizer with Open SDK

MOSTLY AI is one of the longest-running enterprise tabular synthesizers. Banks, insurers, and regulators use it for high-fidelity AI-generated structured data with privacy guarantees.

• Site: mostly.ai
• License: Commercial; the MOSTLY AI synthetic data SDK and Synthetic Data Metrics (SD Metrics) library were open-sourced under Apache 2.0 in late 2024. Repo: github.com/mostly-ai/mostlyai
• Strength: enterprise-grade tabular data with strong fidelity reporting via SD Metrics
• Trade-off: tabular-focused, less suitable for text or agent workloads

Pick MOSTLY AI when the use case is tabular data at scale with regulatory reporting requirements.

Tool 4: SDV (Synthetic Data Vault): The Open-Source Ecosystem

SDV is the open-source ecosystem that started at MIT in 2016 and is maintained by DataCebo. It is the de facto OSS choice for tabular and multi-table synthetic data.

• Repo: github.com/sdv-dev/SDV
• License: MIT (Business Source License 1.1 for the newer SDV Enterprise components)
• Components: SDV (single and multi-table synthesizers), SDMetrics (quality metrics), SDGym (benchmarking)
• Strength: full open-source pipeline from generation to benchmarking
• Trade-off: tabular focus, no LLM-specific generators

Pick OSS SDV when you want an open-source tabular pipeline; the newer SDV Enterprise components are licensed separately under BSL 1.1. Pair with Future AGI evaluators if you also need to validate downstream LLM performance on the synthetic data.

Tool 5: Snorkel AI: Programmatic Labeling and Weak Supervision

Snorkel originated at Stanford in 2016 and commercialized as Snorkel AI. The flagship product, Snorkel Flow, ships programmatic labeling and Snorkel Foundry for LLM evaluation workflows.

• Site: snorkel.ai
• License: Commercial; the original Snorkel research project is open-source under Apache 2.0
• Strength: weak supervision and programmatic labeling at scale for text classification
• Trade-off: less suited for behavioural agent data; tabular and time-series are out of scope

Pick Snorkel when the workflow is text labeling with domain rules and weak supervision pipelines.

Side-by-Side Comparison: Synthetic Data Tools in 2026

Types of Synthetic Data and When You Need Each

Tabular

Spreadsheets and database rows. Finance, healthcare, and customer analytics dominate this space. SDV, MOSTLY AI, and Gretel are the strongest picks. Pair with Future AGI evaluators to check downstream model fidelity if the tabular data feeds into an LLM pipeline.

Text and NLP

Generated text simulating user queries, customer support tickets, or domain corpora. Snorkel handles labeling, Gretel handles general text generation, Future AGI handles LLM-specific behavioural data like persona-driven conversations.

Multi-Turn Dialog and Agent Traces

The new category in 2026. Persona-driven simulations against an LLM agent or a multi-agent system, with span-level scoring. Future AGI fi.simulate is one of the few options built specifically for this workflow. The closest open-source analogue is the AutoGen test harness inside Microsoft Agent Framework, which produces transcripts but does not ship the evaluator catalog alongside.

Time-Series

IoT sensor readings, stock prices, ECG data. Gretel and SDV both cover this category. SDV’s PARSynthesizer and Gretel’s time-series models are the production picks.

Image, Video, and 3D

Out of scope for this comparison. Domain-specific tools like NVIDIA Omniverse, Synthesis AI, and Datagen lead this category.

How to Pick the Right Tool in 60 Seconds

Pricing Snapshot in 2026

Pricing changes frequently. Confirm current plan limits and compliance options directly with each vendor.

How to Evaluate the Quality of Synthetic Data

Three axes:

• Fidelity. Statistical similarity to real data. Tools: SDMetrics, MOSTLY AI SD Metrics, Future AGI distribution evaluators.
• Utility. Downstream model performance when trained or evaluated on the synthetic dataset.
• Privacy. Resistance to membership inference and reconstruction attacks.

For LLM and agent workloads add a fourth axis: behavioural coverage. Did the synthetic data exercise the failure modes that matter? Future AGI ships diversity, persona-coverage, and adversarial-coverage templates for exactly this.

Wrapping Up

Synthetic data in 2026 split into three lanes: tabular and structured, text labeling, and LLM behavioural data. Future AGI leads the third lane with fi.simulate, dataset generation, and built-in guardrails on the same platform as observability and evaluation. Gretel, MOSTLY AI, SDV, and Snorkel cover the first two lanes with strong privacy and labeling capabilities. Pick the tool that matches the data type, and pair with Future AGI evaluators when the synthetic data feeds an LLM workload at app.futureagi.com.

For deeper reads see Synthetic Test Data for LLM Evaluation in 2026, Synthetic Data for Fine-Tuning LLMs, and Validate Synthetic Data with Future AGI.