Most writing about graph databases and GraphRAG is either marketing copy or a research paper. This blog sits in between: engineering deep dives on graph query performance, knowledge-graph modeling, agent memory and retrieval-augmented generation, written by the team building FalkorDB.
80,000 lines of Rust, 357 merged pull requests, 1,585 TCK scenarios and 1,322 flow tests green. FalkorDB’s new engine is here, and this is the
https://www.youtube.com/watch?v=LF8EqPHK16k Overview Agentic workflows are only as smart as the data behind them. Whether you orchestrate with a low-code platform like n8n or a code-first
Author Guy Korland Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds
Preventing LLM hallucinations in a RAG system is usually treated as a model problem. It is not. An LLM that hallucinates isn’t broken. It’s doing
Hackathon Recap Memory meets motion: the FalkorDB standouts On August 3, 2026, builders gathered at Frontier Tower in San Francisco for Memory Meets Motion, an
Your AI agent ships. Testing looks fine. Then production hits: multi-turn conversations degrade, the agent contradicts itself across sessions, and accuracy on anything relational collapses.
You’re probably sitting on connected data right now. The real question is whether your team can turn it into decisions. A fraud analyst sees accounts,
FalkorDB · engineering notes GraphRAG × n8n · build log Complete build log Ask your handbook anything: docs become a team brain The whole machine,
80,000 lines of Rust, 357 merged pull requests, 1,585 TCK scenarios and 1,322 flow tests green. FalkorDB’s new engine is here, and this is the
Your support team sees one version of a customer in Salesforce, another in HubSpot, and a third in the ERP. The damage shows up long
Some problems announce themselves as graph problems. Most don’t. A scheduler that can’t double-book reviewers, a fraud queue that keeps sending two analysts after the
https://www.youtube.com/watch?v=LF8EqPHK16k Overview Agentic workflows are only as smart as the data behind them. Whether you orchestrate with a low-code platform like n8n or a code-first
A late shipment lands on your desk and three systems tell three different stories. ERP says it left on time. The warehouse system says the
You’re probably staring at a bot that looks fine in a demo and then falls apart the moment real users ask messy questions, switch topics
You have probably already done the easy part. The browser shows a lock, the API gateway has a certificate, and the compliance checklist says TLS
10 Top Python Graph Libraries: A 2026 Guide You’re staring at a graph-shaped problem, and the first choice is already awkward. The data looks relational,
You’re trying to answer a simple question in SQL, then the query starts growing teeth. A product team wants to find usernames that start with
You’re probably dealing with the same kind of mess most AI teams hit early on. A workflow works in staging, then a vendor changes a
How SERGAS Group cut legal escalations ~50% by shipping a FalkorDB knowledge graph as a Claude Skill, with cited answers in under a minute.
Orders are piling up, dispatch is juggling driver calls, and the route plan that looked fine at 6 a.m. is already wrong by 8. One
Fraud is no longer a marginal checkout problem. As a baseline benchmark, the Cybersource Global Fraud Report 2024 found that global eCommerce merchants were losing
You usually notice dependency ordering only when it breaks. A build fails because one package now depends on a library that has not been installed
GraphRAG · FalkorDB From documents to answers: a knowledge graph you can question Upload your documents, watch a knowledge graph build itself in real time,
Cortex Agents · FalkorDB · Snowflake Native App Your Snowflake tables, now a graph you can talk to Graph questions, who’s connected to whom, through
The FalkorDB LangChain integration lets Python developers turn a low-latency graph database into the retrieval backbone of an LLM application. Instead of relying on vector
The Problem Nobody Talks About At a large retail enterprise, data engineers make changes to pipelines every day. A new transformation, a refactored join, a
Highlights ActiveGraph keeps two distinct storage layers, an event store that is the durable source of truth, and a graph store that is a live
Author Guy Korland Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds
Talk to most AI agents and you are talking to a goldfish. Three seconds of memory, then the conversation ends and everything you said is
Most graph database tutorials start with toy examples, three nodes, two edges, a single MATCH clause. Then you hit production, where queries span dozens of
TL;DR: Vector search retrieves code that looks similar, not code that is actually connected. A code graph models functions, files, and classes as nodes and
Most retrieval systems in production today rely on a single search modality, and each one fails in predictable ways. Keyword search misses semantic intent. Vector
You have a graph with fifty million nodes representing users, products, transactions, and the invisible threads connecting them all. Someone asks: “Find every user who
AI agents that forget everything between invocations are operationally useless for complex workflows. Multi-step reasoning, personalized recommendations, and enterprise knowledge retrieval all demand persistent, structured
A single bloated super node can turn a sub-millisecond graph traversal into a multi-second catastrophe. That’s the reality facing AI engineering teams who adopt graph
A single graph traversal that takes 200 milliseconds instead of 2 milliseconds can destroy the responsiveness of an entire AI pipeline. That gap matters more
You have a social network with ten million users, and someone asks: “Show me all friends-of-friends who also attended the same university and work in
FalkorDB adds MaxFlow, Harmonic Centrality, and programmable UDF traversal. These three graph database algorithms expand what you can compute, optimize, and automate directly inside the graph layer.
Evaluating FalkorDB as a Snowflake Native App for relationship-heavy workloads. Our tests show Cypher’s [:ROUTE*N] pattern simplifies complex pathfinding, maintaining query conciseness and performance where SQL requires escalating self-joins, often leading to timeouts at deeper traversals.
Stop re-indexing entire corpora. This summary explains how CocoIndex and FalkorDB enable real-time, incremental knowledge graph updates for production-grade AI agents.
FalkorDB GraphRAG SDK 1.0 is a production-ready, LLM-agnostic framework for building knowledge graph pipelines that ranked #1 on GraphRAG-Bench Novel and Medical datasets.
New FalkorDB browser features let developers save frequent queries, copy connection strings for any architecture, see node and edge stats instantly, and customize result tables—cutting friction in GraphRAG development.
The FalkorDB webinar demonstrates graph algorithms for cybersecurity: ransomware spread simulation, Log4Shell dependency tracing, and max-flow data leakage analysis, showing practical UDF and flow network applications.
Securin optimized their AI security agents by migrating from leading graph database provider to FalkorDB, achieving 0.3s latencies on complex 7-hop queries and eliminating a 25% query failure rate.
FalkorDB on Snowflake lets you query warehouse tables as native graphs with Cypher, SPCS-backed compute pools, reference binding security, and explicit lifecycle control for production workloads.
Context graphs transform AI agents from stateless responders into context-aware systems with institutional memory. Learn how to capture the “why” behind decisions using FalkorDB’s graph technology.
mem0-falkordb gives AI agents persistent graph memory via FalkorDB — replacing flat vector retrieval with entity-relationship traversal, per-user graph isolation, and sub-140ms query performance.
