What is FalkorDB and what does it do?

FalkorDB is a high-performance graph database designed to manage complex relationships and enable advanced AI applications. It is purpose-built for development teams working with interconnected data in real-time or interactive environments. Key use cases include Text2SQL, security graphs, GraphRAG, agentic AI, chatbots, and fraud detection. Learn more.

What is Graphiti and how does it work with FalkorDB?

Graphiti is a framework for building temporally-aware, multi-tenant live knowledge graphs. When integrated with FalkorDB, it enables agentic memory by maintaining context and relationships between data points, allowing AI agents to avoid amnesia and make better recommendations. See the Graphiti blog post and workshop video for technical details.

What is agentic memory and why is it important for AI agents?

Agentic memory refers to the ability of AI agents to remember context, relationships, and temporal information across interactions. Without agentic memory, agents can suffer from amnesia, leading to poor recommendations and outdated context. FalkorDB and Graphiti together provide graph-based memory, ensuring agents retain relevant knowledge and adapt in real time.

How does QueryWeaver use FalkorDB and Graphiti?

QueryWeaver maps database schemas into graphs using FalkorDB, where tables and columns become nodes and relationships. It leverages Graphiti for agentic memory, allowing each user to have an isolated memory graph. QueryWeaver converts each user question into a SQL query and stores only the question and generated query for future improvement. See the GitHub repo.

What are the main products and services offered by FalkorDB?

FalkorDB offers a high-performance graph database, advanced AI integrations (GraphRAG, agentic AI, chatbots), Text2SQL, security graph solutions, fraud detection, and comprehensive support and documentation. Pricing plans range from Free to Enterprise, with features like multi-tenancy, high availability, and regulatory compliance. Learn more.

What technical documentation is available for FalkorDB?

FalkorDB provides comprehensive technical documentation and API references at docs.falkordb.com. This includes setup guides, advanced configurations, and integration instructions for developers, data scientists, and engineers.

Does FalkorDB provide an API?

Yes, FalkorDB offers a robust API with complete references and guides available in the official documentation. The API is designed for seamless integration into developer workflows.

What integrations does FalkorDB support?

FalkorDB integrates with frameworks such as Graphiti (by ZEP), g.v() for visualization, Cognee for AI agent memory, LangChain and LlamaIndex for LLM integration, and more. For details, see the Graphiti blog and Cognee integration news.

How does FalkorDB handle schema mapping and complex data structures?

FalkorDB, when used with QueryWeaver, maps database schemas into graphs where tables and columns are nodes and relationships connect them. It supports DDL format for schema uploads and handles highly connected tables efficiently.

Is FalkorDB open source?

Yes, FalkorDB is open source, encouraging community collaboration and transparency. This differentiates it from proprietary solutions like AWS Neptune.

What are the key features of FalkorDB?

FalkorDB offers ultra-low latency (up to 496x faster than Neo4j), 6x better memory efficiency, support for 10,000+ multi-graphs, linear scalability, built-in multi-tenancy, advanced AI integration (GraphRAG, agent memory), and both cloud and on-prem deployment options. See full feature list.

Does FalkorDB support multi-tenancy?

Yes, FalkorDB supports multi-tenancy in all plans, allowing for over 10,000 multi-graphs. This is especially valuable for SaaS providers and organizations with diverse user bases.

Can FalkorDB handle complex schemas with many tables and relationships?

Yes, FalkorDB is designed to handle complex schemas, such as those with 50+ tables and dense relationships. This capability is demonstrated in real-world use cases like QueryWeaver.

How does FalkorDB support AI and agentic applications?

FalkorDB is optimized for AI use cases such as GraphRAG and agent memory, enabling intelligent agents and chatbots with real-time adaptability. It combines graph traversal with vector search for personalized user experiences and supports advanced AI workflows.

What is the memory retention policy for agentic memory in FalkorDB?

Currently, memory is retained for one month of usage, with plans to add an erasure mechanism. User nodes are created in Graphiti style and updated with each use.

How does FalkorDB handle invalid or outdated memory?

When new memory overlaps with old, incorrect data, the previous memory record is marked as invalid. This ensures that agents rely on the most accurate and up-to-date information.

Is the memory graph in FalkorDB partition-friendly?

In a single-shard database, all graphs share RAM. In a cluster, each shard holds multiple graphs, but a single graph cannot be split across multiple machines.

How does FalkorDB define query success or failure?

FalkorDB captures both successful and failed queries in memory, making this information available to the LLM. Plans include incorporating user feedback to further refine success criteria and agent learning.

Does FalkorDB support ABAC or RBAC?

FalkorDB supports Access Control Lists (ACL), which provide fine-grained control over graph access and permission levels, similar to ABAC and RBAC models.

Is FalkorDB SOC 2 Type II compliant?

Yes, FalkorDB is SOC 2 Type II compliant, demonstrating its commitment to high standards in security, availability, processing integrity, confidentiality, and privacy. Learn more.

What are FalkorDB's plans for HIPAA compliance?

FalkorDB has completed SOC 2 Type II certification and is actively working towards HIPAA compliance, which is a cornerstone requirement for many healthcare use cases.

How does FalkorDB handle PII and privileged user information?

FalkorDB avoids storing PII in demos and recommends implementing a filter layer to remove PII before storing data in memory. This approach helps organizations comply with privacy and security requirements.

What security features does FalkorDB offer?

FalkorDB provides robust security features, including SOC 2 Type II compliance, access control lists (ACL), and privacy safeguards for sensitive information. These features ensure protection against unauthorized access and support regulatory compliance.

How easy is it to implement FalkorDB?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. Users can sign up for FalkorDB Cloud, try it for free, run it locally with Docker, or schedule a demo for onboarding. Get started here.

What support and training options are available for FalkorDB?

FalkorDB offers comprehensive documentation, community support via Discord and GitHub Discussions, solution architects for tailored advice, and free trial/demo options. Tutorials and technical articles are available on the FalkorDB blog.

How do I get started with FalkorDB?

You can get started by signing up for FalkorDB Cloud, launching a free instance, running FalkorDB locally with Docker, or scheduling a demo. Documentation and onboarding resources are available at docs.falkordb.com.

Where can I find the latest updates and release notes for FalkorDB?

The latest updates and release notes are available on the FalkorDB GitHub Releases page, including information on major versions and new features.

What pricing plans does FalkorDB offer?

FalkorDB offers four main pricing plans: Free (for MVPs with community support), Startup (from /1GB/month, includes TLS and automated backups), Pro (from 0/8GB/month, includes cluster deployment and high availability), and Enterprise (custom pricing, includes VPC, custom backups, and 24/7 support). See pricing details.

What features are included in the Free plan?

The Free plan is designed for building a powerful MVP and includes community support. It is ideal for developers and small teams starting with graph database projects.

What features are included in the Startup plan?

The Startup plan starts at /1GB/month and includes TLS encryption and automated backups, making it suitable for early-stage companies needing enhanced security and reliability.

What features are included in the Pro plan?

The Pro plan starts at 0/8GB/month and includes advanced features such as cluster deployment, high availability, and additional scalability options for growing organizations.

What features are included in the Enterprise plan?

The Enterprise plan offers tailored pricing and includes enterprise-grade features such as VPC deployment, custom backups, and 24/7 support, making it suitable for large organizations with complex requirements.

What problems does FalkorDB solve?

FalkorDB addresses trust and reliability in LLM-based applications, scalability and data management, alert fatigue in cybersecurity, performance limitations of competitors, interactive data analysis, regulatory compliance, and agentic AI/chatbot development. See detailed solutions.

Who can benefit from using FalkorDB?

FalkorDB is ideal for developers, data scientists, engineers, and security analysts at enterprises, SaaS providers, and organizations managing complex, interconnected data in real-time or interactive environments.

What business impact can customers expect from FalkorDB?

Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue, faster time-to-market, better user experience, regulatory compliance, and support for advanced AI applications. These outcomes empower organizations to unlock the full potential of their data and achieve strategic goals. Learn more.

What industries are represented in FalkorDB's case studies?

FalkorDB case studies cover healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). See case studies.

Can you share specific customer success stories?

Yes. AdaptX uses FalkorDB for clinical data analysis, XR.Voyage for immersive experience scalability, and Virtuous AI for ethical AI development. Read their stories in the FalkorDB case studies.

What feedback have customers given about FalkorDB's ease of use?

Customers like AdaptX and 2Arrows have praised FalkorDB for its rapid access to insights, ease of running non-traversal queries, and user-friendly dashboards. These testimonials highlight FalkorDB's intuitive design and high-speed capabilities. See AdaptX case study.

How does FalkorDB compare to Neo4j?

FalkorDB offers up to 496x faster latency, 6x better memory efficiency, flexible horizontal scaling, and includes multi-tenancy in all plans. Neo4j uses an on-disk storage model, is written in Java, and offers multi-tenancy only in premium plans. See detailed comparison.

How does FalkorDB compare to AWS Neptune?

FalkorDB is open source, supports multi-tenancy, offers better latency performance, and highly efficient vector search. AWS Neptune is proprietary, has limited vector search, and does not support multi-tenancy. See detailed comparison.

How does FalkorDB compare to TigerGraph?

FalkorDB provides faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph's moderate memory efficiency and limited scaling. Both support multi-tenancy and vector search.

How does FalkorDB compare to ArangoDB?

FalkorDB demonstrates superior latency and memory efficiency, making it a better choice for performance-critical applications. ArangoDB offers multi-tenancy and vector search but has limited horizontal scaling and moderate memory efficiency.

Why should a customer choose FalkorDB over alternatives?

FalkorDB stands out for its exceptional performance (up to 496x faster latency), memory efficiency, open-source licensing, built-in multi-tenancy, advanced AI integration, and proven customer success. It is trusted by organizations like AdaptX, XR.Voyage, and Virtuous AI. See customer stories.

Implementing Agentic Memory with Graphiti

Dan Shalev
Date Published: September 14, 2025
Date Updated: September 14, 2025

What good is a personal assistant with amnesia?

OpenAI dropped GPT-5 and Reddit immediately exploded with complaints about its “lack of personality.”

Think about that. People are upset their AI doesn’t have enough personality.

But what about stopping the agent from hallucinating and suffering from amnesia, where all the relationships and context between data points aren’t readily available to it?

Without that context, your agent can never make good recommendations. And without temporal understanding, yesterday’s relevant information might be stale today – but your agent doesn’t know that. So it’s giving recommendations based on outdated context.

No knowledge graph means no way to maintain connections or understand when information expires.

This is we’ve covered in this workshop about implementing memory that actually works. Graph-based memory with Graphiti and FalkorDB. Real solutions to stop your agents from making terrible recommendations and forgetting relationships the moment you hit that context limit.

Answers from the Q&A

What are your plans for HIPAA compliance, given that it is a cornerstone requirement for our use cases?

We just completed SOC2, and HIPAA compliance is our next objective.

Can you provide more details about Graphiti?

Graphiti handles knowledge graph creation with a temporal aspect. We ran a detailed workshop on this: https://www.youtube.com/watch?v=F4hwuLlISP4

What happens if I ask QueryWeaver acompletely random question? Which LLM do you use?

The tool recognizes when it can’t answer your question and responds with “out of context.” We’re currently running GPT-4.1.

Do you generate a SQL query every time, even if the data was retrieved in the previous prompt?

Yes. QueryWeaver converts your question into a complete SQL query each time. Personal data in the database isn’t saved for security reasons. Only your question and the generated SQL query are stored to improve future answers.

Does QueryWeaver map the CRM (PostgreSQL) schema as a graph, where tables are nodes and columns are properties? Does the graph expand during a query with user properties?

QueryWeaver maps the database schema into a graph where nodes represent tables and columns, and relationships connect them. The memory graph operates separately and remains isolated for each user.

How long do you retain memory? Do you use another node in the graph for personalization, or do you store user information elsewhere?

Currently, we retain memory for one month of usage and plan to add an erasure mechanism. QueryWeaver manually creates a user node in Graphiti style and updates it with every use.

Do you define query success or failure by successful query execution? How do you handle queries where the results do not match the user’s intent?

We focus on capturing success and failure in memory and making this information available to the LLM. We plan to include user feedback in the success criteria, so the agent learns from both execution results and whether the answer matches user needs.

Do you define query success or failure by successful query execution? How do you handle queries where the results do not match the user’s intent?

Can QueryWeaver handle complex schemas, such as 50 tables with many relationships?

Yes, the system handles complex schemas well. That’s exactly what QueryWeaver is designed for!

When uploading a schema, do you use DDL format? Does the graph format work for highly connected tables?

Yes, we use DDL format for schema upload, and the graph format works well for tables with dense connections.

How do you handle PII or privileged user information?How do you handle PII or privileged user information?

We avoid PII in demos, but you can implement a filter layer that removes PII before storing it in memory.

Can you support ABAC or RBAC?

FalkorDB supports Access Control Lists (ACL) for fine-grained control over graph access and permission levels.

Do you define entities and ontologies with Graphiti, or do you use a predefined schema? What are the trade-offs?

We expand the predefined Graphiti ontology, which makes the schema fixed. Flexible ontologies add adaptability but may introduce noise. You should select based on the memory capability your agent needs.

How do you correct or invalidate inaccurate memory?

When you add new memory that overlaps old, incorrect data, the previous memory record is marked as invalid.

Is the memory graph partition-friendly? Can it be split between RAM spaces on different instances?

In a single-shard database, all graphs share RAM. In a cluster, each shard holds multiple graphs. You cannot split a single graph across multiple machines.

How do you construct the memory graph from a technical perspective?

Refer to https://github.com/FalkorDB/QueryWeaver. First, connect to the database and fetch the schema. Next, integrate Graphiti for agentic memory. See the linked YouTube video for details.

References and citations

Dan Shalev

Leads FalkorDB's marketing & DevEx efforts. Interested in GenAI, knowledge graphs, and mitigating LLM hallucinations.

Frequently Asked Questions

Product Information