Graphiti integrates FalkorDB for sub-millisecond multi-agent knowledge graphs

Integration delivers temporal memory architecture with sub-10ms query performance for production agent deployments

Tel Aviv - July 3, 2025

Multi-agent AI systems require specialized memory architectures that maintain temporal context across concurrent agent sessions. Zep's Graphiti framework now provides native FalkorDB integration, addressing performance and isolation requirements in production AI agent deployments through purpose-built graph database architecture.

FalkorDB's sparse matrix representation reduces multi-hop reasoning queries from seconds to sub-10ms response times, enabling real-time agent interactions across millions of nodes. Graphiti's temporal knowledge graph architecture processes both unstructured conversational data and structured business information through episodic memory, with each fact including valid_at and invalid_at timestamps to track relationship evolution over time. Enterprise deployments demonstrate consistent performance gains across customer service agent fleets and enterprise knowledge management systems, where multiple agents require concurrent access to shared knowledge repositories while maintaining strict tenant isolation boundaries.

The integration addresses agent knowledge conflicts through FalkorDB's multi-tenant design, providing dedicated graph instances per agent while sharing compute resources and eliminating infrastructure duplication costs.
Organizations deploy specialized agents across departments-HR, legal, engineering-accessing overlapping but restricted knowledge domains without cross-contamination risks. FalkorDB's architecture supports over 10,000 isolated graph instances within single deployments, maintaining compliance boundaries while enabling authorized cross-department knowledge sharing through controlled access patterns.

Customer service implementations benefit from agent-specific context preservation during concurrent customer interactions, with each agent maintaining customer-specific memory while accessing shared product catalogs and policy databases without privacy violations.
Access integration documentation and performance benchmarks through the Graphiti repository to evaluate architectural improvements for your multi-agent deployments.

"FalkorDB's multi-tenant architecture directly addresses production deployment challenges we've encountered in enterprise environments. The sub-10ms query performance enables real-time agent interactions while tenant isolation prevents the data conflicts that typically emerge at scale", said Guy Korland, CEO of FalkorDB in a recent press briefing.

FalkorDB Inc., 850 New Burton Road, Suite 201 City of Dover, Country of Kent Delaware 199404

PRESS CONTACT
Dan Shalev, Marketing & Product
dan.shalev@falkordb.com
(972)51 558 4042

FalkorDB is a next-generation graph database designed to optimize large language models (LLMs) through its unique GraphRAG technology. Founded by ex-Redis engineers, it uses sparse matrices and GraphBLAS for efficient graph traversal and algebraic querying. FalkorDB supports vector similarity search and integrates seamlessly with AI frameworks like LangChain and LlamaIndex. Its ultra-low-latency architecture enables real-time decision-making by combining knowledge graphs with vector databases, reducing hallucinations in AI outputs. Additionally, FalkorDB offers multi-tenant capabilities and supports the Bolt protocol for easy migration from Neo4j. This makes it ideal for CTOs, Chief Architects, and AI/ML Engineers who need scalable solutions for high-performance Retrieval-Augmented Generation (RAG) applications. With its distributed architecture and in-memory processing, FalkorDB ensures fast query execution even with massive datasets, making it a preferred choice for enterprise-scale AI deployments.

This release was published on openPR.