Alternatives
Products that do what Local Search Agent – RAG replacement, no embeddings, free tier does
- 1AE
2021 · ninfex.com
- 2PA
2016 · peekier.com
- 3HC
2021 · github.com
- 4WA
2020 · wiby.org
- 5AS
2017 · webtigerteam.com
- 6IM
When your embedding provider is good, but could be better for your use-case.
2024 · zoplabs.com
- 7AS
2020 · kuurio.com
- 8IW
2020 · searchcommons.org
- 9TA
In this post, we document the results of some experiments comparing vanilla Graph RAG (just a single pass of text2cypher) vs. a router agent Graph RAG approach that can call vector search tools alongside text2cypher. The routing agent uses an LLM to decide which vector search tool to call, depending on the terms identified in the question, and it works quite well. The results show that recent frontier LLMs like `gpt-4.1` and the trusty workhorse `gemini-2.0-flash` produce great quality Cypher reliably and reproducibly, with some prompt engineering to ensure that the graph schema is formatted…
2025 · blog.kuzudb.com
- 10AS
Apr 2026 · github.com
- 11LA
2024 · twitter.com
- 12AO
2022 · github.com
- 13LP
2014 · linkwok.com
- 14MA
2019 · github.com
- 15SF
2016 · solveforall.com
- 16RF
A scalable centralized embeddings platform for efficient embedding and retrieval to build RAG applications faster
2024 · github.com
- 17PD
2021 · presearch.org
- 18PR
Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com
- 19DS
2016 · duosear.ch
- 20MO
A small demo for a Metarank open-source project I'm maintaining.
2023 · demo.metarank.ai
- 21MM
2015 · louisdickinson.com
- 22WB
Hi HN, Our research team just released the best performing and most efficient reranker out there, and it's available now as an open weight model on HuggingFace. Reranker v2 was designed specifically for agentic RAG, supports instruction following (our v1 was the first to introduce this), and is multilingual. Along with this, we're also open source our eval set, which allows you to reproduce our benchmark results. By releasing these datasets, we are also advancing instruction-following reranking evaluation, where high-quality benchmarks are currently limited. Please give it a try and let us…
2025 · huggingface.co
- 23CA
Jun 2026 · github.com
- 24PS
2020 · private.sh
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