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Openground, on-device RAG pipeline with hybrid search for coding agents

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Openground, on-device RAG pipeline with hybrid search for coding agents

Hi HN! tldr: openground lets you give controlled access to documentation to AI agents. Everything happens on-device. I'm sharing my initial release of openground, an opensource and completely on-device RAG tool that let's you give controlled documentation access to your coding agents. Solutions like Context7 MCP provide a likely source of truth for docs, but their closed-source ingestion and querying pose security/privacy risks. openground aims to give users full control over what content is available to agents and how it is ingested. Find a documentation source (git repo or sitemap), add it to openground via the CLI, and openground will use a local embedding model and vector db (lancedb) to store your docs. You can then use the CLI to install the MCP server to your agent to allow the agent to query the docs via hybrid BM25 full-text and vector search. Again, this is an initial release, so it is pretty barebones. Upcoming features I am working on: - specific library version handling (it currently only supports latest versions) - docs "registry" to allow pushing and pulling of documentation embeddings within organizations to S3 - lighter-weight package Suggestions and PRs welcome! I'll also be around for discussion.

Visit productView on Hacker NewsDec 29, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, organizations · Missing: reddit linkedin, podcasting, created
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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