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9 starsC#

Retrievo – In-memory hybrid search for .NET AI agents

by ztq121121·Mar 4, 2026·3 points·0 comments

AI Analysis

●●●BangerSolve My ProblemBig Brain

Hybrid search in-process — BM25 + vectors + RRF, zero external DB, validated on BEIR benchmarks.

Strengths
  • BM25 + vector + RRF fusion in pure C# eliminates need for Weaviate, Qdrant, or Pinecone for local/edge scenarios up to 10k docs.
  • SIMD-accelerated brute-force vector math and BEIR benchmark validation show rigor; not just a wrapper.
  • Dual packages (core + Azure OpenAI provider) let you choose embedding sourcing; clean separation of concerns.
Weaknesses
  • 10k document limit caps use cases; scales only to local/edge, not production multi-tenant systems.
  • Competes with lightweight alternatives (LiteDB, SQLite vector extensions) for the 'no external service' crowd; positioning could be clearer.
Target Audience

.NET developers building AI agents and RAG systems

Similar To

Weaviate · Qdrant · LiteDB Vector

Post Description

Hey HN,

I’m building mem.net, a file-first agent memory framework for .NET. In production, it scales out to Azure Blob and Azure AI Search. But I wanted the local execution experience to accurately mirror those production hybrid search capabilities—without forcing developers to spin up a heavy vector DB.

To bridge that gap, I built Retrievo: a pure in-memory hybrid search engine written entirely in C#. I’ve now integrated it as the default local search provider for mem.net.

Under the hood of Retrievo:

- BM25 + Vector search + RRF fusion

- Entirely in-process (zero external infrastructure needed)

- Validated retrieval quality against standard BEIR benchmarks

The result is you get production-grade hybrid search characteristics entirely in-process.

Repos:

- https://github.com/TianqiZhang/Retrievo

- https://github.com/TianqiZhang/mem.net

Curious to hear how others are handling local hybrid search for agent architectures. Happy to answer questions on the C# implementation or the BEIR benchmarking!

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