Powering Agentic Workflows with a Knowledge Graph for n8n and LangGraph

FalkorDB CEO Guy Korland will demonstrate how to power agentic workflows with a knowledge graph for n8n and LangGraph/LangChain, addressing the fragmented data problem by combining documents, APIs, and structured data into a unified graph. The session will include live builds in both orchestration stacks, showing how the FalkorDB layer remains consistent whether using low-code or code-first approaches.

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 framework like LangGraph/LangChain, your agents still face the same core problem: data scattered across documents, APIs, CRMs, and databases, with no unified view of how it all connects. That's where FalkorDB comes in. By combining your data sources into a single knowledge graph, FalkorDB gives your agents a fast, queryable layer of connected knowledge to reason over. In this session, we'll build agentic workflows live in both n8n and LangGraph/LangChain, each one grounded in the same FalkorDB-powered graph, so you can see how the knowledge layer stays consistent no matter which orchestration stack you choose. 🛠️ What We'll Cover - The Fragmented Data Problem: Why agents hallucinate and stall when knowledge is spread across disconnected sources. - One Graph, Many Sources: Combining documents, APIs, and structured data into a unified knowledge graph with FalkorDB. - n8n in Action: Building a visual agentic workflow that queries FalkorDB for grounded, relationship-aware answers. - LangGraph/LangChain in Action: Orchestrating stateful agents in Python with FalkorDB as the persistent knowledge and memory layer. - Choosing Your Stack: Low-code speed vs. code-first control, and how the FalkorDB layer stays the same in both. - Live Demo: The same GraphRAG-powered workflow built end to end in each tool. Author - Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds a PhD in Computer Science from Tel Aviv University and brings over 20 years of experience in database engineering. He previously led Redis’ incubation arm as SVP & CTO, oversaw platform architecture as GM & CTO at Stor.ai Self-Point , co-founded and served as CTO of Shopetti, and directed R&D as VP at GigaSpaces.