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DataFoundry v0.1.0 Public Preview

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@iancaoo iancaoo released this 02 Jul 10:03
v0.1.0
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DataFoundry v0.1.0 Public Preview

DataFoundry is now open source.

This is the first public preview of DataFoundry: an open-source, enterprise-grade Data Agent workbench for trustworthy data analysis. It is built for teams that want AI to work with real datasources, files, knowledge, tools, and analysis artifacts without turning enterprise data tasks into an unsafe black box.

DataFoundry is not just a SQL chatbot. It combines unified semantics, read-only execution boundaries, audit/replay evidence, and Web/TUI/API surfaces so one natural-language question can become a governed, reproducible data task.

Why This Release Matters

Most data-agent demos look impressive because they reduce the workflow to:

prompt -> SQL -> answer

That is not enough for enterprise data work.

In real environments, the hard questions are different:

  • Does the agent understand business definitions such as GMV, retention, active user, or conversion?
  • Can it avoid guessing the wrong table, field, join path, or metric definition?
  • Can it query data without exposing credentials to the model or browser?
  • Can teams audit what SQL was generated, what tools were called, and why a conclusion was produced?
  • Can the result become a reusable table, chart, report, or file instead of disappearing into a chat transcript?

DataFoundry v0.1.0 is the first public version of our answer to those questions.

Highlights

Enterprise Data Agent Workbench

DataFoundry provides a dedicated workbench for data tasks instead of a generic coding-agent interface. The product surface is optimized for selecting datasources, inspecting schema, managing context, running analysis, reviewing evidence, and turning outputs into reusable assets.

The current release includes:

  • A Web workbench for interactive data-agent workflows.
  • A TUI for terminal-native data tasks.
  • A REST API for integration and embedding.
  • Shared runtime behavior across Web, TUI, and API surfaces.

28 Datasource Types Out Of The Box

DataFoundry is designed to meet existing enterprise data stacks where they already are.

The current public preview includes broad datasource coverage across analytical databases, operational databases, search systems, caches, and file-like sources, including PostgreSQL, MySQL, Snowflake, BigQuery, ClickHouse, MongoDB, Redis, Elasticsearch, CSV, XLSX, DuckDB, SQLite, and more.

This matters because data agents become useful only when they can connect to the systems teams already depend on. The goal is to reduce integration friction and get from first question to real analysis faster.

Unified Semantics And Context Organization

DataFoundry treats schema, metrics, field relationships, and business definitions as first-class context.

Instead of forcing the model to re-guess meaning from table names on every run, DataFoundry is designed around a semantic operating layer: business terms should resolve to approved tables, fields, joins, and metric definitions. This is the foundation for better accuracy, fewer wrong joins, and less definition drift.

In v0.1.0, the implemented base includes schema-first analysis, metadata/context organization, datasource configuration, and runtime context controls. The broader unified semantic layer is a strategic product direction and will continue to evolve in future releases.

Safe By Default: Read-Only Data Gateway

DataFoundry puts a controlled execution boundary between the model and enterprise data.

The Data Gateway owns datasource connections, SQL guardrails, read-only execution, row limits, timeouts, and field masking. Credentials and API keys are not passed through model messages, browser state, or forwarded props.

This is a core design choice: a data agent should not need direct credential visibility to answer data questions.

Audit, Replay, And Evidence Chain

A useful enterprise data agent must be explainable after the answer is produced.

DataFoundry persists the operational evidence behind a run:

  • SQL generated and executed.
  • Tool calls and tool results.
  • Event streams.
  • Session history.
  • Generated tables, charts, reports, and files.
  • Run identity and task state.

This gives teams a way to review, replay, continue, and hand off analysis work instead of trusting a one-shot black-box answer.

Complex Multi-Step Data Tasks

DataFoundry is built for more than direct single-table lookup.

The runtime is designed around multi-step analysis: selecting resources, inspecting schema, budgeting context, calling tools, executing read-only queries, checking intermediate results, and materializing outputs. This helps complex questions converge into usable conclusions rather than ending as partial SQL snippets.

Open Agent Ecosystem

DataFoundry builds on and connects with modern open-source agent infrastructure:

  • Mastra for agent runtime foundations.
  • AG-UI for event stream protocol integration.
  • CopilotKit for agent UX integration.
  • Ink for terminal UI.
  • MCP for tool ecosystem connectivity.

The product direction is open and composable: teams should be able to connect tools, skills, files, knowledge, and model providers without locking the data-agent workflow into one closed runtime.

Self-Hosted And Multi-Model

DataFoundry is intended to run inside your own environment.

The model side supports OpenAI-compatible providers, including GPT, Qwen, DeepSeek, and similar compatible services. This lets teams choose the right balance of security, cost, latency, and quality for each scenario.

What You Can Try Today

This public preview is best suited for:

  • Local evaluation.
  • Product demos.
  • Integration development.
  • Data-agent workflow exploration.
  • Testing datasource connection and schema inspection flows.
  • Trying Web, TUI, and API-based data task surfaces.

Start with the README and Quick Start:

Installation

The npm package is published as a public distribution anchor for the workbench:

npm view datafoundry@0.1.0

For normal usage, we recommend cloning the repository and following the Quick Start for the full workspace setup.

Known Boundaries

This is a public preview, not a production-stable v1.0.0.

The current release is suitable for local trials, demos, and integration development. Production deployments still need environment-specific design for:

  • Multi-tenant authentication and authorization.
  • Centralized secret management.
  • Deployment topology and service operations.
  • Observability, monitoring, and alerting.
  • Enterprise governance policies.
  • Production data-access review.

The npm package is not a stable SDK contract yet. It is published to reserve the public package name and provide a distribution anchor for the open-source workbench.

Community

We welcome feedback, issues, discussions, and real-world adoption notes.

Release Metadata

  • Version: v0.1.0
  • Status: Public Preview
  • License: Apache-2.0
  • Package: datafoundry@0.1.0
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