When to use it#

cognee-agensgraph is for teams that want an AI-memory layer — add data, cognify it into a knowledge graph plus embeddings, then search it many ways — backed by AgensGraph.

Good fits

  • Persistent agent memory. Give an assistant a memory it builds up over time from mixed sources and recalls on demand.

  • One memory, many query styles. Answer the same memory with graph-grounded completion, chain-of-thought reasoning, plain vector RAG, raw entity-relation triplets, document summaries, natural-language → Cypher, or hand-written Cypher.

  • Domain-aligned graphs. Supply an ontology so extraction follows your own vocabulary (EntityType nodes and ontology_valid flags), keeping the graph consistent with your domain.

  • Code knowledge graphs. Turn a Python repository into a graph of modules, classes, and functions and search it with SearchType.CODE.

  • Inspecting and visualizing. Read the graph directly through get_graph_engine() — metrics, traversal, raw Cypher — or render it to an interactive HTML file.

How it compares

  • Pick cognee when you want a memory layer with many ways to query and inspect it, plus ontology-typed extraction and code graphs.

  • Pick LightRAG if your focus is document graph RAG with dual-level retrieval and multi-tenancy.

  • Pick LangChain or LlamaIndex when you want to assemble retrieval inside one of those frameworks.

Consider an alternative when

  • You want an agent to use AgensGraph directly through tools — use the MCP servers.

  • You need fine-grained control over chunking, extraction, and retrieval — a framework build gives you more control.