{"slug": "powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph", "title": "Powering Agentic Workflows with a Knowledge Graph for n8n and LangGraph", "summary": "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.", "body_md": "## Overview\n\nAgentic 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.\n\nThat'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.\n\n## 🛠️ What We'll Cover\n\n- The Fragmented Data Problem: Why agents hallucinate and stall when knowledge is spread across disconnected sources.\n- One Graph, Many Sources: Combining documents, APIs, and structured data into a unified knowledge graph with FalkorDB.\n- n8n in Action: Building a visual agentic workflow that queries FalkorDB for grounded, relationship-aware answers.\n- LangGraph/LangChain in Action: Orchestrating stateful agents in Python with FalkorDB as the persistent knowledge and memory layer.\n- Choosing Your Stack: Low-code speed vs. code-first control, and how the FalkorDB layer stays the same in both.\n- Live Demo: The same GraphRAG-powered workflow built end to end in each tool.\n\n## Author\n\n-\nGuy 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.", "url": "https://wpnews.pro/news/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph", "canonical_source": "https://www.falkordb.com/news-updates/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph/", "published_at": "2026-07-30 11:40:53+00:00", "updated_at": "2026-08-03 13:46:15.493302+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-tools", "ai-agents"], "entities": ["FalkorDB", "Guy Korland", "n8n", "LangGraph", "LangChain", "Redis", "Stor.ai", "GigaSpaces"], "alternates": {"html": "https://wpnews.pro/news/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph", "markdown": "https://wpnews.pro/news/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph.md", "text": "https://wpnews.pro/news/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph.txt", "jsonld": "https://wpnews.pro/news/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph.jsonld"}}