Xata Agent: The Open-Source AI Tool Revolutionizing PostgreSQL Monitoring and DevOps Automation

Proactive PostgreSQL Monitoring with Xata Agent

Xata Agent is an open-source AI assistant designed to act as a site reliability engineer focused on PostgreSQL databases. It continuously monitors logs and performance metrics such as slow queries, CPU and memory spikes, and abnormal connection counts. This enables early detection of emerging issues before they escalate into outages.

Automated Troubleshooting and Recommendations

The agent leverages a curated set of diagnostic playbooks and safe, read-only SQL routines to provide concrete recommendations. It can also automate routine tasks like vacuuming and indexing, reducing manual intervention and maintaining optimal database health.

Modern Tech Stack and Developer Experience

Built as a Next.js application using the Vercel AI SDK and primarily written in TypeScript, Xata Agent is organized as a monorepo. It includes separate directories for the database agent frontend, shared libraries, configuration, and Docker assets. Setting up the development environment involves installing Node via the provided '.nvmrc' file, running 'pnpm install' to fetch dependencies, launching a local PostgreSQL instance through Docker Compose, configuring LLM credentials in '.env.local', applying database migrations, and starting the development server. This streamlined workflow facilitates easy iteration on both UI and diagnostic logic.

Easy Deployment and Self-Hosting

Production deployment uses Docker images for both the agent and PostgreSQL database, with a sample 'docker-compose.yml' provided. Operators configure environment variables such as the public URL and LLM API keys in an '.env.production' file. Starting the stack requires just a single command:

docker-compose up

After startup, the web interface guides users through onboarding, credential setup, and initial health checks. This self-hosted approach balances control with flexibility, enabling integration with internal monitoring pipelines and community-driven improvements.

Example Docker-Compose Configuration

version: '3.8'
services:
  xata-agent:
    image: xataio/agent:latest
    environment:
      PUBLIC_URL: http://localhost:8080
      OPENAI_API_KEY: your_openai_api_key_here
# Optional additional providers:
#      ANTHROPIC_API_KEY: your_anthropic_api_key_here
#      DEEPSEEK_API_KEY: your_deepseek_api_key_here
    ports:
      - "8080:8080"
  postgres:
    image: postgres:14
    environment:
      POSTGRES_USER: agent_user
      POSTGRES_PASSWORD: secure_password
      POSTGRES_DB: agent_db
    volumes:
      - db_data:/var/lib/postgresql/data
 
volumes:
  db_data:

Development Workflow

# Switch Node version
cd apps/dbagent
nvm use
 
# Install dependencies
pnpm install
 
# Copy example environment
cp .env.local.example .env.local
 
# Start development server
pnpm dev

In '.env.local', developers define their LLM credentials and frontend connection URL:

OPENAI_API_KEY=sk-your-openai-key
ANTHROPIC_API_KEY=ak-your-anthropic-key
PUBLIC_URL=http://localhost:3000

Extensibility through Tools and Playbooks

Xata Agent ensures reliability by using human-written playbooks and safe, non-destructive tools. Playbooks are plain English instructions that guide diagnostic flows, while tools are TypeScript functions encapsulating database queries or API calls. Integrations with platforms like Slack and AWS RDS extend functionality with minimal configuration.

Key features include:

• Proactive monitoring of logs and metrics to detect anomalies early.
• Configuration tuning suggestions for PostgreSQL settings.
• Performance troubleshooting with analysis of slow queries and indexing recommendations.
• Safe diagnostics using read-only SQL queries.
• Cloud service integration for metrics collection.
• Real-time alerting through Slack notifications.
• Support for multiple LLM providers for flexibility in cost and security.
• Customizable playbooks and diagnostic flows.
• MCP server capabilities enabling networked tool access.
• Planned approval workflows and evaluation testing for governance and quality assurance.

Sample Tool Implementation

// packages/db-tools/src/tools/checkSlowQueries.ts
import { Pool } from 'pg';
import { ToolResult } from 'xata-agent';
 
export async function checkSlowQueries(pool: Pool): Promise<ToolResult> {
  const result = await pool.query('
    SELECT query, total_time, calls
    FROM pg_stat_statements
    ORDER BY total_time DESC
    LIMIT 5;
  ');
  return { rows: result.rows };
}

Registering the tool:

// apps/dbagent/src/server/tools.ts
import { defineTool } from 'xata-agent';
import { checkSlowQueries } from 'db-tools';
 
defineTool('checkSlowQueries', {
  description: 'Retrieve the top five slowest queries from pg_stat_statements',
  execute: async ({ dbPool }) => {
    return await checkSlowQueries(dbPool);
  },
});

Diagnostic Playbook Example

# configs/playbooks/investigate_slow_queries.playbook.yaml
name: Investigate Slow Queries
description: Steps to identify and resolve performance bottlenecks caused by slow queries.
steps:
  - tool: getTablesAndInstanceInfo
    description: "Gather table sizes and database instance details."
  - tool: checkSlowQueries
    description: "List the top slow queries to pinpoint hotspots."
  - tool: suggestIndexes
    description: "Generate index recommendations for queries exceeding thresholds."
  - tool: evaluateVacuumStats
    description: "Check vacuum statistics to determine if table bloat is impacting performance."
  - tool: notifySlack
    description: "Alert the team in Slack if queries exceed critical latency."

Slack Integration

// packages/integrations/src/slackAdapter.ts
import { SlackAdapter } from 'xata-agent/integrations';
 
const slack = new SlackAdapter({ webhookUrl: process.env.SLACK_WEBHOOK_URL });
 
export async function notifySlack({ message }: { message: string }) {
  await slack.send({
    channel: process.env.SLACK_CHANNEL,
    text: `Xata Agent Alert: ${message}`,
  });
}

Roadmap and Future Enhancements

The project plans to add custom playbooks for domain-specific recovery, MCP support for networked tool calls, evaluation and testing frameworks for recommendation accuracy, approval workflows for sensitive operations, and a managed cloud edition with simplified onboarding and one-click integrations to popular monitoring stacks.

Collaborative and Safe AI Operations

Xata Agent’s architecture pairs large language models with deterministic tools via a prompt-driven orchestration layer. This approach minimizes hallucinations and maintains safety by relying on human-written playbooks and fixed diagnostic procedures. It standardizes incident response, lowers barriers for junior engineers, and promotes community collaboration through open source contributions and governance.

Xata Agent delivers a practical solution that augments human expertise with AI-driven insights and automation to maintain PostgreSQL database reliability and performance at scale.