Agentic retrieval (concept)¶
Agentic retrieval is iterative, tool-driven retrieval. A large language model (LLM) agent plans steps, issues search tool calls, fuses candidates, and ranks documents until it has enough context. One-pass retrieval sends a single static query through dense or hybrid search and returns chunk-level hits.
NeMo Retriever Library includes a first-class agentic query path. retriever query --agentic, POST /v1/query with agentic=true, and the agentic_query Model Context Protocol (MCP) tool run a Reason and Act (ReAct) loop over the same LanceDB table that one-pass retrieval uses. You do not have to implement the agent loop in application code.
The agentic path returns ranked document IDs rather than text-enriched chunks. Local CLI and harness runs default to an in-process vLLM agent LLM. Retriever Service requires a remote OpenAI-compatible chat-completions endpoint. A self-hosted vLLM-backed NIM must enable automatic tool choice and a tool-call parser. Helm answer_llm does not turn those options on by default.
For commands, service configuration, request and response contracts, and failure behavior, refer to Workflow: Agentic retrieval.