The common memory pipeline burns three LLM calls to store one fact and dumps a fat slice of the store on read. Collapse writes to a single ADD-only call, store agent inferences as first-class memories, and recall with fused multi-signal scoring that returns only what the turn needs.
Cut Agent-Memory Tokens With Single-Pass Writes and Multi-Signal Recall
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🔎Retrieval & RAG
Eliminates the embedding API call and vector search on cache hits. The saved cost tracks your hit rate; it cuts embedding/retrieval spend, not the tokens sent to the LLM.
Cache the Context, Not Just the Answer
Cache the retrieved chunk set keyed by a normalized query, so popular or repeated questions skip the embedding call and vector search and reuse the same context block instead of rebuilding it every time.
🔎Retrieval & RAG
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Stop Pasting Whole Documents: Retrieve the 3 Chunks That Actually Answer the Question
Dumping a full PDF or knowledge base into every prompt bills you for thousands of tokens the model never needed. Retrieve only the passages relevant to the question instead.
🔎Retrieval & RAG
20-40% fewer retrieved tokens at equal accuracy
Chunk on Structure, Not Character Count, So You Retrieve Fewer (and Smaller) Chunks
Naive fixed-length chunking splits ideas mid-sentence, forcing you to retrieve more chunks (and more overlap) to capture one answer. Chunk on semantic boundaries to send fewer tokens per query.