Context and memory for LLMs and agents
GraphRAG
Ground LLM answers in your knowledge graphAgentic AI
Agents grounded in how your domain connectsAgentic memory
Queryable memory that carries context across sessionsChatbots
Context-aware chatbots with long-term memoryProven patterns on connected data
Fraud detection
Trace rings of accounts, devices and transactions liveCybersecurity
Assets, findings and threats queried as one graphSupply chain
See how one supplier delay ripples downstreamReal-time recommendations
Suggestions based on how things actually relate
GraphRAG-SDK · FalkorDB · Entity resolution Entity resolution: how to make your knowledge graph accurate at scale Apple Inc., AAPL and Apple are one company,
How to test GraphRAG retrieval before you ship: build a production-grade test set, measure entity and relationship recall, and separate retrieval failures from reasoning failures.
FalkorDB · engineering notes GraphRAG × n8n · build log Complete build log Ask your handbook anything: docs become a team brain The whole machine,
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
Cortex Agents · FalkorDB · Snowflake Native App Your Snowflake tables, now a graph you can talk to Graph questions, who’s connected to whom, through
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.
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.
Learn how to implement Model Context Protocol agent memory using Graphiti MCP Server with FalkorDB for isolated, persistent context and multi-tenant knowledge graphs in production AI systems.
