Content
# pgEdge AI DBA Workbench
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-alerter.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-client.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-collector.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-docker.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-docs.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-e2e.yml)
[](https://github.com/pgEdge/ai-dba-workbench/actions/workflows/ci-server.yml)
## Table of Contents
- [pgEdge AI DBA Workbench](#pgedge-ai-dba-workbench)
- [Using Binary Files to Install Workbench](#using-binary-files-to-install-workbench)
- [Building Workbench from Source](#building-workbench-from-source)
- Installing and Configuring pgEdge AI DBA Workbench:
- [Installation Overview](docs/getting-started/installation_overview.md)
- [Quick Start - Installing with Binaries](docs/getting-started/binary_install.md)
- [Building from Source Code](docs/getting-started/build_from_source.md)
- [Deploying with Docker](docs/getting-started/docker.md)
- Configuration Details:
- [Server](docs/getting-started/configuration/server.md)
- [Collector](docs/getting-started/configuration/collector.md)
- [Alerter](docs/getting-started/configuration/alerter.md)
- [Web Client](docs/getting-started/configuration/client.md)
- [Configuring systemd Services](docs/getting-started/configuration/configure_systemd.md)
- User Guide:
- [Using the Workbench](docs/user-guide/index.md)
- Monitoring Dashboards:
- [Dashboard Overview](docs/user-guide/dashboards/index.md)
- [Estate Dashboard](docs/user-guide/dashboards/estate.md)
- [Cluster Dashboard](docs/user-guide/dashboards/cluster.md)
- [Server Dashboard](docs/user-guide/dashboards/server.md)
- [Database Dashboard](docs/user-guide/dashboards/database.md)
- [Object Dashboard](docs/user-guide/dashboards/object.md)
- Alerts:
- [Understanding Alerts](docs/user-guide/alerts/index.md)
- [Alert Reference](docs/user-guide/alerts/rule-reference.md)
- [AI Alert Analysis](docs/user-guide/alerts/ai-analysis.md)
- [Blackout Management](docs/user-guide/blackouts.md)
- AI Features:
- [AI Overview](docs/user-guide/ai/overview.md)
- [Ask Ellie](docs/user-guide/ai/ask-ellie.md)
- [Connecting MCP Clients](docs/user-guide/ai/mcp-clients.md)
- [MCP Tools](docs/user-guide/mcp-tools.md)
- Administrator's Guide:
- [Overview](docs/admin-guide/index.md)
- Managing Users and Permissions:
- [Understanding the Workbench Permission Model](docs/admin-guide/managing-users-and-permissions/permission_model.md)
- [Account Management](docs/admin-guide/managing-users-and-permissions/accounts.md)
- [Group Management](docs/admin-guide/managing-users-and-permissions/groups.md)
- [Token Management](docs/admin-guide/managing-users-and-permissions/tokens.md)
- [Permission Management](docs/admin-guide/managing-users-and-permissions/permission_mgmt.md)
- [Enforcing the Password Policy](docs/admin-guide/password.md)
- [Connection Management](docs/admin-guide/connections.md)
- [TLS & Reverse Proxy](docs/admin-guide/tls-and-reverse-proxy.md)
- [Verifying the Health of Components](docs/admin-guide/verify_health.md)
- [Alert Rules & Thresholds](docs/admin-guide/alert-rules.md)
- [Notification Channels](docs/admin-guide/notification-channels.md)
- [Probe Configuration](docs/admin-guide/probes.md)
- REST API:
- [API Reference](docs/admin-guide/api/reference.md)
- [API Browser](docs/admin-guide/api/browser.md)
- [Server Information](docs/admin-guide/api/server-info.md)
- [Metrics API](docs/admin-guide/api/metrics.md)
- Developer's Guide:
- [Overview](docs/developer-guide/index.md)
- [Contributing](docs/developer-guide/contributing.md)
- Collector:
- [Architecture](docs/developer-guide/collector/architecture.md)
- [Schema Design](docs/developer-guide/collector/schema.md)
- [Schema Management](docs/developer-guide/collector/schema-management.md)
- [Scheduler](docs/developer-guide/collector/scheduler.md)
- [Probes](docs/developer-guide/collector/probes.md)
- [Adding Probes](docs/developer-guide/collector/adding-probes.md)
- [Probe Reference](docs/developer-guide/collector/probe-reference.md)
- [pg_settings Usage](docs/developer-guide/collector/pg-settings-usage.md)
- [Testing](docs/developer-guide/collector/testing.md)
- Alerter:
- [Architecture](docs/developer-guide/alerter/architecture.md)
- [Anomaly Detection](docs/developer-guide/alerter/anomaly-detection.md)
- [Adding Rules](docs/developer-guide/alerter/adding-rules.md)
- [Cron Expressions](docs/developer-guide/alerter/cron-expressions.md)
- [Testing](docs/developer-guide/alerter/testing.md)
- Server:
- [Architecture](docs/developer-guide/server/architecture.md)
- Client:
- [Architecture](docs/developer-guide/client/architecture.md)
- Design:
- [Node Role Probe](docs/developer-guide/design/node-role-probe.md)
- [Changelog](docs/changelog.md)
- [Issues](#issues)
- [Contributing](#contributing)
- [License](#license)
## pgEdge AI DBA Workbench
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. The Workbench watches every instance,
catches anomalies before they become outages, and walks through 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 DBA Workbench bridges database administration and AI reasoning; it
offers an extensible foundation for observability, troubleshooting, and
intelligent workflow creation across the pgEdge ecosystem.
The pgEdge AI DBA Workbench consists of four main components:
- The [Collector](collector/README.md) monitors PostgreSQL servers and
stores metrics in a centralized datastore.
- The [Server](server/README.md) provides MCP tools and resources for
interacting with PostgreSQL systems.
- The [Alerter](alerter/README.md) evaluates collected metrics against
thresholds and AI-powered anomaly detection to generate alerts.
- The [Client](client/README.md) provides a web-based user interface for
the AI DBA Workbench.
The Workbench can be:
* installed with [binary files](#using-binary-files-to-install-workbench) from the Github repo.
* built from [source code](#building-workbench-from-source) from the Github repo.
* deployed in a [Docker container](#using-docker-to-install-workbench).
* installed with [packages from the pgEdge](https://docs.pgedge.com/enterprise/) repository.
### Using Binary Files to Install Workbench
Pre-built binary files for Workbench are available from the pgEdge repo at:
[https://github.com/pgEdge/ai-dba-workbench/releases](https://github.com/pgEdge/ai-dba-workbench/releases).
The `Quick Start - Installing with Binaries` guide contains detailed
instructions for using the binary files to install and configure
[the Workbench](docs/getting-started/binary_install.md).
### Building Workbench from Source
The Workbench can be built from source for local development or to
produce custom binaries.
The `Quick Start - Building from Source` guide contains detailed
instructions for cloning the repository, satisfying build dependencies,
and compiling the Workbench:
[Building from Source](docs/getting-started/build_from_source.md).
### Using Docker to Install Workbench
Pre-built container images for Workbench are published to the GitHub
Container Registry for each release.
The `Quick Start - Docker Deployment` guide contains detailed
instructions for deploying the Workbench using Docker Compose:
[Docker Deployment](docs/getting-started/docker.md).
## Issues
To report an issue with the software, visit:
[GitHub Issues](https://github.com/pgEdge/ai-dba-workbench/issues)
## Contributing
We welcome your project contributions; for more information, see
[docs/developer-guide/contributing.md](docs/developer-guide/contributing.md).
For more information, visit [docs.pgedge.com](https://docs.pgedge.com).
## License
This project is licensed under the
[PostgreSQL License](LICENSE.md).
Connection Info
You Might Also Like
markitdown
MarkItDown-MCP is a lightweight server for converting URIs to Markdown.
markitdown
Python tool for converting files and office documents to Markdown.
Filesystem
Node.js MCP Server for filesystem operations with dynamic access control.
TrendRadar
TrendRadar: Your hotspot assistant for real news in just 30 seconds.
mempalace
The highest-scoring AI memory system ever benchmarked. And it's free.
mempalace
The highest-scoring AI memory system ever benchmarked. And it's free.