Vedana – open-source RAG over a knowledge graph
Knowledge graph + agents beats vector-only RAG for structured queries.
Federated, local-first search for an AI — one query across transcripts, files, knowledge graph, vector store, and the web, fused by trust-weighted RRF. Apache-2.0.
Trust-tier ranking beats naive RAG fusion when sources conflict.
AI developers building agent systems with memory and retrieval needs
LangChain · LlamaIndex · Mem0
Knowledge graph + agents beats vector-only RAG for structured queries.
Knowledge graphs beat vector similarity for structured relationship queries.
Single-file mmap storage plus an HNSW vector index and explicit graph edges is an elegant, practical combo — think "SQLite for agent memory" with CRC-32 crash recovery and zero-server convenience. The C++20 core + nanobind gives zero-copy NumPy views and GIL-free searches, and the claimed FAISS-like throughput makes this genuinely interesting for local setups; main caveat is build/toolchain friction and how rich the surrounding ecosystem becomes.
Embeddings + vector search on AI skill graph—but is this a demo or a product you can actually use?
Markdown knowledge graph replaces scattered PLAN.md files with structured protocol.
Federated memory for agents when LangMem and Mem0 already exist.