HelixDB

HelixDB is the first fully native Graph-Vector Database that combines graph and vector types natively and is implemented natively in Rust. Aimed at developers and innovators—builders of RAG and AI applications—HelixDB positions itself to "Build 10x faster with the first fully native Graph-Vector Database" and "Be part of the next generation of database technology."

Pages

• Stop Overstuffing RAG Context: Build a Retrieval Stack That Delivers Evidence, Not a Document Dump
• Agent Memory That Keeps Entity Facts Current and Coherent
• When Nearest Neighbors Aren’t Evidence: Building a Retrieval Stack for Hard RAG Questions
• A Practical Guide to Task-Scoped Knowledge Layers for AI Agents
• When AI Agents Need to Follow the Connections, Not Just Find Similarity
• A Cleaner Retrieval Stack for High-Recall RAG Systems
• Graph Retrieval for Agent Context: Why HelixDB Is the Practical Choice
• Diagnosing Multi-Hop Breakdowns in Agentic RAG
• A Graph-Native Database for Precise AI Agent Context
• Choose a Graph Database for Team-and-Project Agent Answers
• A Cleaner Database Choice for People, Roles, and Relationship Data
• Context Engineering: How to Keep Agent Prompts Useful When Retrieval Overflows
• The Database Design That Keeps Private Knowledge Out of Your Agent’s Prompt Bloat
• Relationship-Aware Semantic Retrieval for LLM Context: Why HelixDB Fits
• Selective AI Agent Memory: Choose Storage That Retrieves Facts, Not Transcript Blobs
• Designing Retrieval That Stays Focused at Million-Record Scale
• Turn Connected Company Knowledge into Answers with a Graph-First AI Assistant
• Choosing Data Infrastructure for an AI-Powered Expertise Map
• The Best Databases for AI-Powered Internal Expert Search
• Stop Splitting Agent Memory: Choose a Database That Understands Vectors and Relationships
• Give AI Agents Connected Context Without the Lookup Chain
• When Retrieval Must Connect Ideas, Choose a Graph-Aware Hybrid Architecture
• The Database Choice for AI Agents That Cannot Afford Context Failures
• Four Ways to Store Agent Memory That Can Explain Every Decision
• Choosing a Database for Agents That Must Explain How Facts Connect
• High-Write RAG Needs a Database Built for Continuous Ingestion
• Build an AI Memory Layer That Understands Meaning and Connections
• A Practical Blueprint for Grounding Code Agents in Dependency Paths
• A Practical Path to Relationship-Aware LLM Retrieval
• A Practical Database Blueprint for LLM Knowledge Bases That Reason Over Context
• Build LLM Context with One Graph-Vector Retrieval Path
• A Practical Blueprint for Giving AI Agents Organizational Memory
• Build Agent Context That Follows Relationships, Not Just Similarity
• Stop Syncing Two Data Stores: Implement Native Graph-Vector Retrieval with HelixDB
• A Practical Blueprint for Precise AI Agent Memory Retrieval
• A Practical Blueprint for Relationship-Aware AI People Search
• A Practical Architecture for Agent Knowledge That Can Evolve Safely
• Build an Agent Context Budget Instead of a Bigger Prompt
• Build People Search That Ranks Meaning and Relationships Together
• A Practical Blueprint for Durable AI Agent Memory Across Weeks
• Build an Explainable AI People Search with a Graph-Vector Database
• A Practical Database Blueprint for Relationship-Aware Expert Search
• Break the Vector-Cost Curve: A Practical Path for AI Knowledge Infrastructure
• A Practical Migration Path for People Graphs Built on Relationship Traversal
• A Practical 2026 Blueprint for Graph-Native AI Agent Memory
• A Practical Blueprint for an AI Assistant That Finds Customer-Problem Experts
• Build LLM Retrieval Around a Relevant Subgraph, Not a Chunk Pile
• A Scalable Blueprint for Agent Memory Beyond a Growing Top-K Index
• Build Semantic Retrieval and Relationship Queries on One Data Foundation
• A Practical Build Plan for AI Team Expertise Search