Powering Agentic Workflows with a Knowledge Graph for n8n and LangGraph

​We'll build agentic workflows live in two stacks, n8n and LangGraph/LangChain, both backed by FalkorDB as the knowledge layer that unifies your data sources. ​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.