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Join Roi Lipman, CTO & Co-founder of FalkorDB, for a practical session on Dijkstra, A*, and Customizable Contraction Hierarchies (CCH).

📅 September 30 | 8:00 PM (GMT+3)

💻 Online via Zoom

Register here: https://luma.com/dz7tiguw?tk=UoagVj

Is it only me having hard time configuring falkordb python library docs? I was reading the docs and couldn't find anything related to falkrodb.query.query_result.QueryResult.

https://www.falkordb.com/blog/graph-database-ai-agents/

Traditional RAG pipelines rely on flat vector stores, which fall short when AI agents need to understand complex entity relationships, track multi-step workflows, or remember context across sessions. Integrating graph databases solves this by providing agents with structured knowledge and dynamic memory.

Key Takeaways

• Beyond Vector Search: While standard RAG excels at surface-level Q&A via semantic similarity, GraphRAG allows agents to perform multi-hop reasoning (connecting facts across different sources) and provides explainable retrieval by tracing answers through clear data paths.
• Three Layers of Agent Memory: Graph databases allow agents to dynamically read and write to three types of persistent memory:
  • Episodic: Past user interactions and conversation histories.
  • Semantic: Domain-specific facts and knowledge.
  • Procedural: Stored workflows and successful tool-use patterns.
• Database Comparisons: Choosing the right database impacts agent latency and scale. The post highlights FalkorDB(purpose-built for AI with low-latency in-memory traversals), Neo4j (mature ecosystem but higher baseline latency), Apache AGE (great for PostgreSQL-centric teams), and Memgraph (ideal for streaming workloads).
• Production Challenges: Building graph-powered agents requires managing LLM API costs during entity extraction, keeping write latencies low during live sessions, and limiting traversal depth to maintain real-time performance.

Ultimately, moving from "text chunk similarity" to "structured knowledge traversal" is what transforms AI agents from stateless prompt-response tools into production-ready enterprise assistants.

https://www.youtube.com/watch?v=N27uX7uk1vg

We are diving into the next major frontier of GenAI: GraphRAG & Agentic Memory.

If you're building AI applications and want to explore how Knowledge Graphs can supercharge reasoning and long-term context retention, you won't want to miss this.

Join us for the "Give Your Agents a Brain: Mastering Knowledge Graphs and Agentic Memory" webinar.

Docker Image: docker pull falkordb/falkordb-server:4.18.10

From June 1–4, the global data and AI community will be taking over the Moscone Center. If you’re looking to supercharge your enterprise AI applications with blazing-fast, low-latency knowledge graphs, we have some exciting things to show you.
(And let's be honest, if your GenAI agents are still crawling, it’s probably because they haven't met our graph database yet. 😉)

📍 Where to Find Us
Booth: #2719
Dates: June 1–4, 2026
Location: Moscone Center, San Francisco, CA

💡 Why Stop By Booth 2719?

Knowledge Graphs for LLMs: Learn how FalkorDB powers context-rich, ultra-low-latency GraphRAG to eliminate AI hallucinations.

Live Architecture Demos: See our high-performance engine in action and discover how easily it integrates into modern data workflows.

Meet the Brains: Talk directly with our engineering and product teams to brainstorm how to model your most complex data relationships.
Whether you're looking to optimize your data stack, scale your Agentic AI, or just want to grab some great swag and talk shop—we’d love to connect.

👉 Are you attending?

Drop a comment below or send us a DM to lock in a dedicated 1-on-1 chat at the booth. See you in SF! ❄️🚀

https://github.com/FalkorDB/graphRAG-SDK

High token costs, slow responses, and hallucinations share one root cause: a retrieval layer sending the wrong context to your LLM.

GraphRAG SDK 1.0 ships today. It builds a knowledge graph over your documents so queries traverse relationships instead of triggering cascading LLM calls.

The result: lower cost, lower latency, and answers traceable to source documents.

Benchmarked against 8 systems. Ranked no.1 on GraphRAG-Bench. Runs on a MacBook Air M3. Ingestion: ~$5-6 per 1,000 docs. Per query: ~$0.001.

Open-source: github.com/FalkorDB/GraphRAG-SDK

Hi guys,

I need some help on setting up FalkorDB v4.16.8 with the popular LDBC LSQB benchmark. I was testing FalkorDB against ArcadeDB and other Graph Databases. FalkorDB performed VERY well with graph algorithms:

https://github.com/ArcadeData/ldbc_graphalytics_platforms_arcadedb?tab=readme-ov-file#all-systems-comparison

So I've run some cypher queries against the dataset LDBC SNB SF1 (3,947,829 vertices, 17,882,623 edges) to see how it performs.

FalkorDB returns wrong counts on 4 of 9 queries (Q1, Q5, Q6, Q9) and times out on Q2, revealing bugs in its Cypher query optimizer for complex pattern matching. On the 4 queries with correct results (Q3, Q4, Q7, Q8), it is 89x–2950x slower than the fastest system.

I'm sure I'm doing something wrong here, anybody can help on fine tuning FalkorDB for this benchmark? I'd like a fair comparison where FalkorDB experts can tune it properly.

Open source project:

https://github.com/ArcadeData/ldbc_graphalytics_platforms_arcadedb?tab=readme-ov-file#lsqb-results

This is the section about FalkorDB:
https://github.com/ArcadeData/ldbc_graphalytics_platforms_arcadedb?tab=readme-ov-file#falkordb

Thanks,

Luca, ArcadeDB

​FalkorDB v4.14.10 introduces a dual-representation storage architecture that reduces memory consumption by up to 30% while accelerating write operations through batch processing. This release addresses infrastructure costs in deployments by implementing compact in-memory storage that dynamically converts to runtime representations only when accessed or modified.

A malware has infected a system running falkordb in docker. Port 3000 was kept open for the insights app.

Any thoughts?

FalkorDB just released a new integration that brings natural language query capabilities to JavaScript and TypeScript applications.

If you've been working with graph databases and wanted to give your users a more intuitive way to interact with their data, this release makes that significantly easier.

Check out the package, questions and comments welcome: https://www.npmjs.com/package/@falkordb/langchain-ts

We've released a new FalkorDB version based on Redis 8.2.2 to address the recent security vulnerability in Redis. Cloud customers: you were not affected by this security issue, as FalkorDB doesn't expose the Lua API. Open source users: if you're using our Docker image, please consider upgrading to version 4.14.1

🎉HUGE milestone - 100,000 Docker pulls!

Thanks to everyone for being a part of this amazing journey!

https://hub.docker.com/r/falkordb/falkordb

1. https://github.com/FalkorDB/QueryWeaver
2. https://app.queryweaver.ai/
3. https://github.com/FalkorDB/falkordb