How Kognite actually behaves
A real, end-to-end memory-QA harness — the same shape as LoCoMo / LongMemEval. Facts are ingested, then every question is answered using only what Kognite retrieves from the live API. We publish what we measure, including the gaps.
End-to-end: memory formation + retrieval
The full pipeline as a real agent uses it: every conversation is ingested through the live /api/memories/ingest — raw episodes plus LLM-extracted atomic facts (coreference resolved, relative time converted to absolute dates) — then each of the 1,542 questions is answered from what the live API retrieves (deepseek-chat answers). Graded by GPT-4o — the judge model behind Mem0's published LoCoMo results (Mem0 66.9%, Mem0g 68.4%) — so this number is directly comparable to theirs.
Per-category bars are GPT-4o-judged. Memory formation is what moves this number: storing the same turns verbatim scores 54.9% on the identical harness and judge (deepseek-chat) — the +16.5pt delta is same-judge. Retrieval is deterministic across both judged runs (identical recall). Runs: July 15, 2026 / July 15, 2026.
Retrieval engine in isolation
The same turns stored verbatim (formation off) — this isolates hybrid retrieval, and it is the corpus the Mem0 head-to-head below runs on. Recall@5 is defined over verbatim memories (a gold turn appears in the top-5), so it is reported for this mode.
Per conversation
Every conversation in the corpus, showing both what retrieval found and what the agent then answered. The distance between the two dots is where answers are lost after the right memory was already returned.
Kognite vs Mem0
Same harness, apples-to-apples on LoCoMo (1,542 questions): identical corpus, the same bge-small embedder, the same deepseek-chat judge, the same recall method. Only the retrieval engine differs — Kognite's hybrid + graph + rerank vs Mem0's vector search (Mem0 OSS, infer=False).
By category
Methodology
1542 questions across 10 multi-session conversations (5882 memories ingested). For each question we retrieve top-5 from the live API, count a recall hit when a gold supporting fact is in the results, then an LLM answers from the retrieved context and a second LLM grades it against the gold answer.
Hybrid retrieval: Postgres full-text (BM25-ish) + self-hosted vector embeddings, reciprocal-rank fused, on the live /api/memories endpoint. Numbers reflect the deployed system as of the run date.
Two configurations are shown. End-to-end ingests raw conversation turns through the live formation pipeline (episodes + extracted atomic facts) and reports answer accuracy — the number that reflects what the product actually does; it is graded twice (GPT-4o and deepseek-chat) because LoCoMo scores are judge-sensitive and cross-system comparison requires the same judge. Retrieval in isolation stores the turns verbatim, which keeps recall@5 well-defined (gold turn in the top-5) and the corpus identical to the Mem0 comparison. Extracted facts rephrase their source turns, so turn-level recall is not a meaningful metric for the end-to-end run — accuracy is.