datnguye/dbterd — Generate the ERD as a code from dbt artifacts

Description and use

**Brief Summary (2‑3 sentences)**
*datnguye/dbterd* is an open‑source Python tool that generates Entity‑Relationship Diagrams (ERDs) directly from dbt artifacts, turning data‑model metadata into reusable code. It streamlines the addition of AI‑enabled data pipelines by letting teams prototype RAG, agent‑based workflows, or model‑evaluation tooling without building a dbt‑to‑ERD bridge from scratch. With active maintenance, a solid star count, and recent releases, it is ready for pilot‑level production use.

**Value**

• **Accelerates AI feature prototyping:** By automatically converting dbt models into ERD code, developers can quickly visualize and manipulate data relationships, a prerequisite for building retrieval‑augmented generation (RAG) systems or AI agents that need a clear schema.
• **Reduces duplicate effort:** Eliminates the manual, error‑prone step of hand‑crafting schema definitions for downstream AI tooling, ensuring consistency between dbt transformations and AI components.
• **Integrates with existing stacks:** Offers a CLI/SDK that can be invoked in CI pipelines or notebooks, making it easy to embed into data‑engineering or MLOps workflows.

**Practical Adoption Path**

1. **Evaluate locally:** Clone the repo, run the CLI against a recent dbt manifest.json to generate an ERD and inspect the output.
2. **Integrate into CI/CD:** Add a step in the dbt build pipeline that runs dbterd and stores the generated ERD artefacts (e.g., as GraphViz files or Python classes) for downstream consumption.
3. **Hook into AI components:** Feed the generated schema into LLM prompts, RAG index builders, or custom agents that need a structured view of the data model.
4. **Monitor & iterate:** Use the provided logging/metadata hooks to verify that schema changes in dbt are reflected in the ERDs, updating prompts or index configurations as needed.

**Production Readiness**

• **Activity & community:** 330 stars, 34 forks, recent commits (as of 2026‑06‑24), and a well‑documented Python codebase indicate an active project.
• **Stability:** The CLI/SDK surface is stable, with clear versioning and dependency management, suitable for inclusion in production pipelines.
• **Risk considerations:** No major licensing or security red flags have been identified, but a final review of the open‑source license (MIT/Apache) and a security audit of third‑party dependencies is advisable before full‑scale rollout.

Overall, *dbterd* offers a high‑impact, low‑friction way to bridge dbt metadata and AI‑driven data products, making it a strong candidate for early‑stage pilots and, with due diligence, for production deployment.

Problem

datnguye/dbterd helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

Generate the ERD as a code from dbt artifacts

Source