8 patterns
Knowledge & Memory
Pull in outside or stored knowledge, decide when to fetch it, and check what comes back.
Patterns in Knowledge & Memory
Agentic RAG
Knowledge & Memory · 7 nodes · 3 LLM calls · branch · 2 scenarios
The agent decides whether to retrieve, from which source, and with what rewritten query, and repeats until it has enough context.
Corrective RAG
Knowledge & Memory · 8 nodes · 4 LLM calls · loop · branch · 2 scenarios
Grade the retrieved documents. If they're irrelevant, rewrite the query and fall back to web search before generating.
HyDE
Knowledge & Memory · 6 nodes · 2 LLM calls · parallel · 2 scenarios
The LLM writes a hypothetical answer passage. That passage is embedded instead of the query, and the real documents nearest to it are retrieved.
Multi-Query RAG
Knowledge & Memory · 8 nodes · 2 LLM calls · parallel · 1 scenario
Rewrite one question into several queries, retrieve for each in parallel, then fuse the ranked lists before generating.
Retrieve-and-Rerank
Knowledge & Memory · 9 nodes · 2 LLM calls · parallel · 2 scenarios
Cast a wide net with cheap keyword and vector search over context-prefixed chunks, then let a reranker keep only the best few.
GraphRAG
Knowledge & Memory · 10 nodes · 1 LLM call · 2 scenarios
Index a corpus as an LLM-built entity graph with pre-written community summaries, then map-reduce over those summaries to answer whole-corpus questions.
Memory Stream
Knowledge & Memory · 7 nodes · 3 LLM calls · loop · 2 scenarios
Log every observation with an importance score, retrieve by recency, importance and relevance, and periodically write reflections back into the stream.
Hierarchical Memory
Knowledge & Memory · 6 nodes · 1 LLM call · 2 scenarios
Treat the context window like RAM. The agent uses tool calls to page facts between core, recall and archival memory.