ai-dba-workbench

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SUMMARY

A PostgreSQL monitoring system built combining traditional and AI monitoring from the ground up.

README.md

pgEdge AI DBA Workbench

CI - Alerter
CI - Client
CI - Collector
CI - Docker
CI - Docs
CI - Server

The pgEdge AI DBA Workbench is a unified environment for monitoring
and management of any PostgreSQL v14+ instance, including Supabase
and Amazon RDS, with an optional AI agent.  It watches every instance,
catches anomalies before they become outages and walks through a
diagnosis and resolution step by step.

The Workbench combines a Model Context Protocol (MCP) Server with a
web-based user interface and data collector. Users can query,
analyze, and manage distributed clusters using natural language and
intelligent automation. The Workbench exposes pgEdge tools and data
sources such as Spock replication status, cluster configuration, and
operational metrics to language models.

The architecture supports switching between cloud-connected LLMs
like Claude and locally hosted models from Ollama. This design
ensures similar levels of functionality in air-gapped or secure
environments. The pgEdge AI Workbench bridges database
administration and AI reasoning; it offers an extensible foundation
for observability, troubleshooting, and intelligent workflow
creation across the pgEdge ecosystem.

Table of Contents

Components

The pgEdge AI DBA Workbench consists of four main components:

  • The Collector monitors PostgreSQL
    servers and stores metrics in a centralized datastore.
  • The Server provides MCP tools and
    resources for interacting with PostgreSQL systems.
  • The Alerter evaluates collected
    metrics against thresholds and AI-powered anomaly
    detection to generate alerts.
  • The Client provides a web-based user
    interface for the AI Workbench.

Documentation

Comprehensive documentation is available in the docs
directory:

Prerequisites

Before building the project, install the following tools:

Building

The project uses Makefiles for building and testing. All
components can be built from the top-level directory:

# Build all components
make all

# Build individual components
cd collector && make build

Testing

The project includes comprehensive unit tests for each component.

Run All Tests

# Run all sub-project tests
make test

# Run all sub-project tests with coverage
make coverage

# Run all sub-project tests with linting
make lint

# Run everything (all sub-project test-all)
make test-all

Run Tests for Individual Components

cd collector && make test

Environment Variables for Testing

  • TEST_AI_WORKBENCH_SERVER specifies the PostgreSQL
    connection string for the test database; the default is
    postgres://postgres@localhost:5432/postgres.
  • TEST_AI_WORKBENCH_KEEP_DB=1 preserves the test
    database after tests complete.

Available Make Targets

Each sub-project and the top-level Makefile support
the following targets:

  • all builds the project and is the default target.
  • test runs the test suite.
  • coverage runs tests with a coverage report.
  • lint runs the linter.
  • test-all runs tests, coverage, and the linter.
  • clean removes build artifacts.
  • killall kills any running processes.
  • help shows the available targets.

Getting Started

For information on getting started with each component,
refer to the following guides:

Deployment

For detailed installation, configuration, and usage
instructions, see the following documentation:

Issues

To report an issue with the software, visit:
GitHub Issues

Contributing

We welcome your project contributions; for more information, see
docs/developer-guide/contributing.md.

For more information, visit
docs.pgedge.com.

License

This project is licensed under the
PostgreSQL License.

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