prest/prest

PostgreSQL ➕ REST, low-code, simplify and accelerate development, ⚡ instant, realtime, high-performance on any Postgres application, existing or new, MCP server

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Summary Information

Updated 14 minutes ago
Added to GitGenius on September 14th, 2026
Created on November 22nd, 2016
Open Issues & Pull Requests: 156 (+0)
GitHub issues: Enabled
Number of forks: 320
Total Stargazers: 4,617 (+0)
Total Subscribers: 75 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 39.3 days
Mean response time: 376.3 days
90th percentile: 2005.4 days
Tracked items: 27

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 10% of issues opened in the past year have been closed. Three people close 100% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 16
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 1,143 days
Stale 30+ days: 15
Stale 90+ days: 12

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (6)
  • product/api-server (5)
  • feature/request (4)
  • question (4)
  • deployment/docker (3)
  • docs (3)
  • feature (2)
  • help wanted (2)

Detailed Description

pREST is a low-code API server that instantly exposes REST and Model Context Protocol endpoints on top of existing or new PostgreSQL databases without requiring hand-written backend code.

The tool solves the problem of building backend APIs for Postgres applications by automatically generating CRUD operations, custom SQL routes, authentication, and access control lists. It works by connecting directly to a Postgres database and exposing its tables and custom queries through REST endpoints and an MCP interface, eliminating the need to write boilerplate backend logic. The project supports Postgres version 9.5 and higher and includes features like JWT authentication, read-only MCP endpoints, and multi-database support.

Teams should adopt pREST when they want to accelerate development by skipping backend scaffolding, particularly for new applications or when adding APIs to existing Postgres databases. It suits projects where rapid iteration matters more than custom business logic in the API layer. The tool is self-hosted and can be deployed via Docker, Homebrew, or as a Go binary, making it flexible for different deployment scenarios. It also integrates with AI clients through its MCP endpoint, enabling AI assistants to query the database directly.

The project maintains a formal contribution process with a contributor license agreement. Documentation is comprehensive and centralized, covering configuration, API reference, deployment options including Heroku, and integration with AI tools. The codebase emphasizes security in query handling, with built-in screening of interpolated values and support for parameterized queries to prevent SQL injection, while also providing helpers for binding user-supplied values safely.