Xata Agent is an open-source AI assistant built to serve as a site reliability engineer for PostgreSQL databases. It constantly monitors logs and performance metrics, capturing signals such as slow queries, CPU and memory spikes, and abnormal connection counts, to detect emerging issues before they escalate into outages. Drawing on a curated collection of diagnostic playbooks and safe, read-only SQL routines, the agent provides concrete recommendations and can even automate routine tasks, such as vacuuming and indexing. By encapsulating years of operational expertise and pairing it with modern large language model (LLM) capabilities, Xata Agent reduces the burden on database administrators and empowers development teams to maintain high performance and availability without requiring deep Postgres specialization.
Under the hood, Xata Agent is implemented as a Next.js application utilizing the Vercel AI SDK and is written primarily in TypeScript. The repository is organized as a monorepo, with dedicated directories for the database agent frontend (‘apps/dbagent’), shared libraries (‘packages’), configuration files, and Docker assets. This layout streamlines the contribution process: after installing Node via the included ‘.nvmrc’ file, a developer runs ‘pnpm install’ to pull dependencies, sets up a local PostgreSQL instance using Docker Compose, defines LLM credentials in a ‘.env.local’ file, applies database migrations, and launches the development server. This turnkey developer experience makes it straightforward to iterate on both the user interface and the agent’s diagnostic logic.
Deploying the Xata Agent in production follows similar, straightforward steps. The team publishes Docker images for both the agent service and its companion PostgreSQL database, and provides a ‘docker-compose.yml’ example. Operators configure a small set of environment variables, such as the public URL and API keys for their chosen LLM provider, in an ‘.env.production’ file. Then, a single command boots up the entire stack:
docker-compose upAfter a brief startup phase, the agent’s web interface appears at the specified address, guiding users through database onboarding, credential configuration, and initial health checks. This self-hosted model strikes a balance between autonomy and control, allowing teams to audit every component, integrate the agent with internal monitoring pipelines, and still benefit from community-driven enhancements.
Below is an illustrative snippet of a ‘docker-compose.yml’ configuration for self-hosting:
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/datavolumes: db_data:For local development, the workflow looks like:
# Switch Node versioncd apps/dbagentnvm use# Install dependenciespnpm install# Copy example environmentcp .env.local.example .env.local# Start development serverpnpm devIn ‘.env.local’, developers supply the credentials for their LLMs and define where the frontend should connect:
OPENAI_API_KEY=sk-your-openai-keyANTHROPIC_API_KEY=ak-your-anthropic-keyPUBLIC_URL=http://localhost:3000A core design principle of Xata Agent is extensibility. The agent avoids hallucinations by adhering to a fixed set of human-written playbooks and non-destructive tools. Playbooks are plain English files that specify step-by-step instructions, whereas tools are TypeScript functions that encapsulate database queries or cloud-provider API calls. Integrations—such as Slack and AWS RDS—plug into the system via configuration and UI widgets, enabling the addition of new data sources and notification channels with minimal effort.
Key functionalities of Xata Agent include:
- Proactive monitoring: Continuously watch logs and metrics, including CPU usage, memory pressure, and query latency, to flag anomalies early. Configuration tuning: Suggest adjustments to Postgres settings such as ‘shared_buffers’ and ‘work_mem’ based on workload characteristics. Performance troubleshooting: Investigate slow queries, identify missing indexes, and recommend indexing strategies. Safe diagnostics: Execute read-only SQL against system views (‘pg_stat_statements’, ‘pg_locks’) to gather context without risking data integrity. Cloud integration: Pull logs and metrics directly from managed services like RDS and Aurora via CloudWatch. Alerting and notifications: Send real-time alerts to Slack channels when critical thresholds are crossed. LLM flexibility: Support multiple inference engines, including OpenAI, Anthropic, and Deepseek, so organizations can optimize for security and cost. Playbook customization: Define new troubleshooting flows in plain English to capture proprietary best practices. MCP server capability: Act as a Model Context Protocol server, enabling other agents to call its tools over the network. Approval workflows and eval-testing: Plan to introduce governance controls for sensitive operations and automated validation of agent recommendations.
Developers can author new tools by exporting simple TypeScript functions. For example, a tool to fetch the five slowest queries might look like:
// packages/db-tools/src/tools/checkSlowQueries.tsimport { 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 };}Then register it so the agent can call it:
// apps/dbagent/src/server/tools.tsimport { 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); },});Playbooks tie together tools into a coherent diagnostic flow. Below is an excerpt from a YAML-style playbook for investigating slow queries:
# configs/playbooks/investigate_slow_queries.playbook.yamlname: Investigate Slow Queriesdescription: 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."To integrate with Slack, one can leverage the built-in Slack adapter:
// packages/integrations/src/slackAdapter.tsimport { 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}', });}This modular architecture, where tools, playbooks, and integrations are loosely coupled, ensures that extending the agent to support new workflows or platforms requires minimal boilerplate. For example, adding Google Cloud SQL support only involves implementing a new integration that fetches metrics via Google’s monitoring APIs and wiring it into the UI as a configuration step.
Xata Agent’s roadmap reflects its commitment to evolving enterprise observability. Short-term plans include custom playbooks, which empower teams to encode domain-specific recovery procedures, and Model Context Protocol (MCP) support, allowing other agents to call Xata’s tools over the network. Mid-term enhancements include evaluation and testing harnesses to benchmark the accuracy of agent advice against historical incidents and approval workflows for potentially sensitive operations. A managed cloud edition is also in development, promising one-click integrations with popular monitoring stacks and simplified onboarding for teams without self-hosting infrastructure.
A carefully engineered system prompt drives the orchestration layer that ties language models to these playbooks and tools. As highlighted in a recent commentary on AI-agent design, the agent is instructed to “Provide clear, concise, and accurate responses to questions. Use the provided tools to get context from the PostgreSQL database to answer questions. When asked why a query is slow, call the explainQuery tool and also consider the table sizes. During the initial assessment, use the getTablesAndInstanceInfo, getPerformanceAndVacuumSettings, and getPostgresExtensions tools. When asked to run a playbook, use the getPlaybook tool to get the playbook contents. Then use the contents of the playbook as an action plan. Execute the plan step by step. This prompt-driven architecture, which pairs LLM flexibility with deterministic tool use, demonstrates a novel “playbook” pattern for safe and reliable AI operations.
By codifying best practices into reproducible playbooks, Xata Agent standardizes incident response and lowers the barrier for junior engineers to troubleshoot complex database issues. Teams leveraging the agent gain a single source of truth for operational procedures, reducing human error and enabling on-call rotations where less experienced staff can confidently handle alerts. Whether self-hosted or provided as a managed service, Xata Agent invites community contributions, peer review, and collaborative governance, ensuring that the collective expertise of the open source community continually enhances the agent’s capabilities.
In conclusion, Xata Agent represents a significant advance in database observability and autonomous troubleshooting. Its combination of an extensible TypeScript monorepo, human-written playbooks, safe SQL tools, and flexible LLM integration positions it as a practical solution for modern DevOps teams. As organizations increasingly seek to automate complex infrastructure tasks, Xata Agent stands out by augmenting human expertise rather than attempting to replace it, providing clear, actionable insights and automations that help maintain PostgreSQL performance and reliability at scale.
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