seedloom 0.1.2

AI-powered database seeding: introspects your schema and generates realistic, referentially-valid seed data with Claude, OpenAI, Gemini, or local models.

Seed your database, automated.

seedloom

AI-powered database seeding. seedloom connects to your PostgreSQL database, introspects the real schema (tables, columns, types, foreign keys, enums, constraints), and uses an LLM to generate realistic, referentially-valid seed data - then inserts it in the correct dependency order.

Works with Claude, OpenAI, Gemini, local Ollama models, and any OpenAI-compatible endpoint (Groq, Together, Fireworks, OpenRouter, DeepSeek, Mistral, LM Studio, vLLM, text-generation-webui) - see Providers.

No more hand-writing 50 fake users and hoping your orders table's user_id values actually exist.

Why not just use Faker?

Faker generates plausible-looking values per column, with no awareness of your schema's relationships. seedloom:

• Reads your actual schema (works with Prisma, Django, raw SQL migrations - anything)
• Resolves foreign key dependency order automatically (seeds users before orders before order_items)
• Constrains foreign-key columns to an enum of real, already-inserted parent IDs - the model structurally cannot invent a dangling reference
• Respects NOT NULL, UNIQUE, enum types, and column types when generating values
• Skips columns the DB fills in itself (SERIAL, gen_random_uuid(), now() defaults)
• Auto-fills image/avatar/logo/banner/video columns with real, working CDN URLs (picsum.photos, i.pravatar.cc, sample .mp4s) instead of letting the model invent broken links
• pgvector-aware: vector/halfvec/sparsevec columns are detected at introspection time (including their declared dimension) and filled with real, correctly-sized, unit-length random vectors - generated locally, not by asking an LLM to hand-write hundreds of floats
• Automatically excludes ORM/migration bookkeeping tables (SequelizeMeta, knex_migrations, alembic_version, django_migrations, __EFMigrationsHistory, etc.) from introspection - they're never seeded
• Swap the LLM provider with one flag or env var - cloud or fully local/free

Install

pip install seedloom[anthropic]   # or [openai], [gemini], [ollama], [all]

Or from source:

git clone https://github.com/therealonenak/seedloom
cd seedloom
pip install -e ".[all]"

Each provider is an optional extra so you only install the SDK you need. ollama has no cloud SDK - it just needs requests, already covered by that extra.

Setup

Set DATABASE_URL plus whichever provider you're using (or put them in a .env file in your working directory):

export DATABASE_URL="postgresql://user:pass@localhost:5432/mydb"
export SEEDLOOM_PROVIDER="anthropic"       # default; see Providers below
export ANTHROPIC_API_KEY="sk-ant-..."

Usage

# 1. Introspect your schema and cache it locally
seedloom init

# 2. Generate + insert seed data, in FK-safe order
seedloom run --rows 20

# Preview generated data without inserting anything
seedloom run --rows 5 --dry-run

# Only seed specific tables
seedloom run --rows 50 --tables users,products

# Override the provider/model for a single run
seedloom run --provider gemini --model gemini-2.5-flash --rows 20
seedloom run --provider ollama --model llama3.1 --rows 20

Try it against the example schema in examples/schema.sql on a scratch database.

pgvector

If the pgvector extension is installed and a table has a vector, halfvec, or sparsevec column, seedloom detects it during seedloom init (along with its declared dimension, e.g. vector(384)) and fills it with a random, unit-length vector at seed time - generated locally, not by the LLM. This is enough to exercise vector indexes and similarity queries (ORDER BY embedding <-> ...) against seed data, but the vectors carry no real semantic meaning - they aren't embeddings of the row's actual content.

Columns declared as a plain vector with no fixed dimension fall back to a default width (currently 256).

No separate install extra is needed - this works with pip install seedloom plus whichever provider extra you're already using.

Providers

Set SEEDLOOM_PROVIDER (or pass --provider) plus the matching API key env var. SEEDLOOM_MODEL overrides the default model for any provider.

Provider	SEEDLOOM_PROVIDER	API key env var	Notes
Anthropic (Claude)	anthropic	ANTHROPIC_API_KEY	default
OpenAI	openai	OPENAI_API_KEY
Google Gemini	gemini	GEMINI_API_KEY	official Gemini API; free tier is capped at 20 requests/day per model - schemas with many tables will likely exhaust it in one run
Ollama (local)	ollama	-	run any open-source model locally; needs ollama serve
Groq	groq	GROQ_API_KEY	free tier, serves open-source models fast
Together AI	together	TOGETHER_API_KEY	open-source models, free tier
Fireworks AI	fireworks	FIREWORKS_API_KEY	open-source models
OpenRouter	openrouter	OPENROUTER_API_KEY	many free :free-suffixed open-source models
DeepSeek	deepseek	DEEPSEEK_API_KEY
Mistral	mistral	MISTRAL_API_KEY
LM Studio (local)	lmstudio	-	local server, OpenAI-compatible
vLLM (local)	vllm	-	local server, OpenAI-compatible
text-generation-webui (local)	text_generation_webui	-	local server, OpenAI-compatible
Any OpenAI-compatible endpoint	openai_compatible	OPENAI_COMPATIBLE_API_KEY	set SEEDLOOM_BASE_URL

Ollama host defaults to http://localhost:11434; override with SEEDLOOM_HOST or --host. Custom/self-hosted OpenAI-compatible endpoints use SEEDLOOM_BASE_URL or --base-url.

A note on Google Antigravity: it's a desktop agentic IDE, not something with a public API for scripts like this to call. If you want Google's models programmatically, gemini (the official Gemini API) is the supported path - same models, real API key, free tier included.

How it works

1. Introspect - queries information_schema / pg_catalog to build a full picture of your schema: columns, types, nullability, uniqueness, enums, and foreign keys. ORM/migration bookkeeping tables are filtered out automatically.
2. Order - topologically sorts tables so parents are always seeded before their dependents (users → orders → order_items).
3. Generate - for each table, builds a JSON Schema describing exactly what a valid row looks like (including an enum of real parent-key values for any FK column) and asks the configured provider to fill it in via tool-use / structured output. Image/video columns are then populated with real, working CDN URLs rather than model-invented links.
4. Insert - batch-inserts the generated rows and tracks the primary keys the database assigns, so the next table's foreign keys always point at something real.

Rate limits

Cloud providers get automatic retry with exponential backoff on transient 429/rate-limit errors, honoring the provider's own suggested retry delay when it's present in the error. If a provider reports a daily quota is exhausted (e.g. Gemini's free-tier 20-requests/day cap), seedloom fails fast with a clear message instead of retrying for several minutes against a quota that won't reset - switch --provider/--model or re-run once it resets. Already-seeded tables are skipped automatically on the next run.

Limitations (v1)

• PostgreSQL only (MySQL/SQLite support welcome as a PR)
• Single-column primary keys only for FK-pool tracking (composite PKs insert fine, just aren't reused as FK sources yet)
• No deferred-constraint support for genuinely cyclic FK relationships between two tables
• No per-provider quota/cost estimation before a run - a large schema can burn through a free-tier daily quota in a single seedloom run

Contributing

PRs welcome - this was built as a learning project and is deliberately kept readable over clever. Good first contributions: MySQL/SQLite introspection, composite PK support, a --seed-from-existing mode that samples real rows for context instead of generating from scratch.

License

MIT