Dragon Brain v1.1.0 — 100% on LongMemEval
100% recall@5 on LongMemEval (ICLR 2025) — the industry-standard benchmark for AI memory systems. 500 questions. 6 categories. No LLM required. Fully local.
Benchmark Results
| Metric | Score |
|---|---|
| recall_any@5 | 100.0% |
| recall_any@10 | 100.0% |
| recall_all@5 | 99.9% |
| recall_all@10 | 100.0% |
Dragon Brain is the only system to achieve 100% R@5 without LLM reranking and with a knowledge graph architecture.
Full results: RESULTS.md
What's New in v1.1.0
Retrieval Pipeline
- 6-channel parallel retrieval — vector + FTS5 + entity + temporal + relational + associative
- Weighted RRF fusion — per-channel weights set by intent classifier, k=35, PIT normalization
- Soft intent routing — all channels fire on every query (no hard gate)
- Cross-encoder reranking — ms-marco-MiniLM on GPU/CPU (optional, not needed for 100%)
New Capabilities
- FTS5 lexical search — SQLite BM25 catches keyword matches embeddings miss
- Entity extraction — spaCy NER for graph-native entity-first retrieval
- SUPERSEDES energy valves — directional energy flow drains stale knowledge
- Temporal date parsing — automatic date extraction and hard filtering
- diff_knowledge_state — time-travel diff between two graph snapshots
- Semantic Radar dashboard — visual discovery of missing relationships
- Observation-aware embeddings — entity vectors include observation content
Quality
- 1,281 tests (was 1,166), 0 failures
- 34 MCP tools (was 33)
Install
pip install dragon-brain
docker compose up -dReproduce the Benchmark
python -m benchmarks.longmemeval.runner --dataset oracle