timescale

timescale / ghostgres

Public

There are no dumb queries, only dumb databases

ghostgres.com
17
1
100% credibility
Found Apr 01, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Ghostgres is a playful PostgreSQL-compatible service that lets users query in natural language by routing requests to AI models from OpenAI or Anthropic.

How It Works

1
😩 Feeling frustrated with database chores

You hear about Ghostgres, a fun way to chat with your database instead of wrestling with tricky instructions.

2
🧠 Link up your smart AI helper

You prepare a clever AI service like one from OpenAI or Anthropic so it can understand your questions.

3
🔌 Connect your database tool

Open your favorite database app and point it to Ghostgres online or on your computer, sharing your AI details to make the magic happen.

4
Ask away in everyday words
🌐
Use the ready online version

Everything works instantly over the internet without setup.

🏠
Run it on your own computer

Start it locally for private fun with a quick launch.

5
✨ See smart results appear

Your questions turn into neat tables of answers, no errors or fuss, just pure imagination.

🎉 Database dreams come true

Now you explore data effortlessly, feeling like a wizard with no maintenance headaches.

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Star Growth

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AI-Generated Review

What is ghostgres?

Ghostgres is a Go-built Postgres wire-compatible server that replaces SQL with natural language queries powered by OpenAI or Anthropic LLMs. Connect via psql using your provider API key and model name—like `postgres://openai:sk-...@localhost/gpt-4o`—then fire off questions ending in a semicolon, such as "What is the best database?". There are no dumb questions or table errors; it maintains per-connection chat history and returns structured Postgres results, solving the pain of rigid SQL syntax and maintenance.

Why is it gaining traction?

It stands out by dropping in anywhere Postgres works, no schema needed—just your imagination and an API key. Custom prompts, TLS encryption, and options like `reasoning_effort=high` let you tune LLM output, while local runs via `go run` make prototyping instant. Devs love the zero-ops hook: query freely without "github there are no checks for this commit" style failures.

Who should use this?

AI experimenters building LLM demos, backend devs mocking APIs for frontend tests, or data scientists prototyping natural language analytics. Ideal for hackathons where you need quick "dumbledore there is light" insights without spinning up real DBs, but skip for production queries.

Verdict

Fun proof-of-concept at 17 stars and 1.0% credibility—docs are solid for basics, but expect immaturity like limited error handling. Try locally for sparks, not scale; watch for growth if LLM-DB hype sticks. (187 words)

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