Hands-On with Context Graphs: Build an Agentic Memory Layer with TigerGraph

TigerGraph’s tutorial presents a graph-native memory architecture for AI agents that addresses the limitations of stateless conversation histories and vector similarity retrieval by storing persistent entities, relationships, documents, sessions, queries, and events in a context graph. Using TigerGraph Savanna and the TigerGraph MCP Server, agents can retrieve relationship-aware context before responding, then update the graph afterward with extracted entities, linked documents, events, and co-occurring concepts. The workflow covers designing a schema, configuring the MCP connection, implementing context-reading and interaction-writing functions, and combining them into a read–reason–write agent loop. This approach enables agents to track changes across sessions, answer relational and temporal questions, provide more personalized and traceable responses, and avoid repeatedly rediscovering information. The framework-independent MCP integration supports tools such as LangChain, LlamaIndex, OpenAI Agents SDK, and direct LLM APIs, while the same shared graph pattern can extend to multi-agent systems and domains including enterprise knowledge, supply chain operations, customer intelligence, fraud investigation, and IT operations.

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