When to use it
==============

``llama-index-agensgraph`` is for teams building retrieval and RAG applications **in
LlamaIndex** that want AgensGraph behind LlamaIndex's standard index abstractions.

**Good fits**

- **Property-graph RAG.** Build a ``PropertyGraphIndex`` from documents with
  ``SchemaLLMPathExtractor``, then answer questions with combined graph + vector retrieval, or
  with AgensGraph-dialect Text2Cypher, run read-only by ``SafeTextToCypherRetriever``.
- **Vector RAG.** Back a ``VectorStoreIndex`` with pgvector and query it through any LlamaIndex
  query engine or retriever.
- **Precise, filtered retrieval.** Constrain results with LlamaIndex ``MetadataFilters`` (all
  14 operators, plus AND/OR/NOT and nested groups), index the keys you filter on, and combine
  semantic and keyword search with reciprocal-rank-fusion hybrid mode, filtered or not.
- **Mixed-retrieval apps.** Route a single question between a graph index and a vector index,
  sharing one ``AgensEngine`` pool, with a ``RouterQueryEngine`` or an agent.
- **Async services.** Use the async methods (``aquery``, ``async_add``, ``aupsert_nodes``,
  ``avector_query``, …), which do not block the event loop, for concurrent workloads.

**How it compares**

- Pick **llama-index-agensgraph** when your application is built on LlamaIndex.
- Pick **LangChain** if you build on LangChain instead — it also adds chat history and a
  LangGraph checkpointer.
- Pick **LightRAG** or **cognee** for a turnkey graph-RAG or memory pipeline rather than
  assembling indices and retrievers yourself.

**Consider an alternative when**

- You want an agent such as Claude Desktop to use AgensGraph through tools with no application
  code — use the **MCP servers**.
- You only need raw Cypher access without LLM features — the ``agensgraph-python`` driver is
  enough.