← Back to feed

Connecting My LangGraph AI Agent to Postgres

L3 · BuilderTutorials & GuidesTowards Data Science· 8/28/2026

Provides practical implementation guidance for building production-ready AI agents with database integration.

AI Summary

Shows how to connect a LangGraph to Postgres for persistent state management and database operations in a customer service booking system.

Excerpt

How to run the backend locally with Docker or in the cloud The post Connecting My LangGraph AI Agent to Postgres appeared first on Towards Data Science.

Read Original
0 upvotes · 0 downvotes · 1 min read

Related Articles

L2 · PractitionerTutorials & GuidesTowards Data Science
Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working

When fuzzy matching algorithms like Damerau-Levenshtein fail to distinguish typos from legitimately similar product identifiers, requiring alternative approaches for entity reconciliation.

L4 · DeveloperTutorials & GuidesTowards Data Science
Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply

This article explains three key Graph Neural Network architectures - GCN, MPNN, and GAT - with practical applications in molecular science, social networks, and traffic analysis.

L3 · BuilderTutorials & GuidesTowards Data Science
A Practical Introduction to PySpark Window Functions

PySpark window functions enable aggregations like rankings and running totals while preserving individual row details.

L3 · BuilderTutorials & GuidesTowards Data Science
A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

Explains how to make RAG systems provide defensible 'I don't know' responses with four specific evidence types to verify non-existence of information.

L4 · DeveloperTutorials & GuidesTowards Data Science
Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks

A practical guide to implementing Bayesian Neural Networks in Python that provides uncertainty quantification instead of just point predictions.