Show HN: Vector databases are the wrong primitive for AI agents
github.comHi HN,
We built ReasonDB because vector databases break down when agents need to reason over structured relationships.
ReasonDB combines: • knowledge graphs • reasoning queries • LLM-friendly APIs
Example:
agent.reason("Why did the refund fail?")
which traces relationships across events, policies and logs.
Repo: https://github.com/brainfish-ai/ReasonDB
Would love feedback. Benchmark We ran a small benchmark on a real-world insurance corpus:
• 4 policy documents
• ~1,900 hierarchical nodes
• 100 queries across 6 complexity tiers Comparing ReasonDB to a typical RAG pipeline (LangChain / LlamaIndex defaults): Metric ReasonDB Typical RAG
Pass rate 100% (12/12) 55–70%
Context recall 90% avg 60–75%
Median latency 6.1 s 15–45 s The key difference is that ReasonDB performs BM25 candidate selection + LLM-guided traversal, rather than flat chunk similarity. ⸻ Example reasoning case One query asked: “What conditions define recurrent disability?” The answer was split across two sections:
• disability definition clause
• policy schedule clause Flat chunk retrieval returned only the first section. ReasonDB followed the cross-reference extracted during ingestion, which raised recall from 67% → 100%.