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AI · December 10, 2025

SO

Skald – open-source context layer API that runs in your VPC

Hey HN! We built an open source context layer to power AI agents and apps even in air-gapped infra setups. TL;DR: Use our API (or one of the 6 SDKs) to push context into the Skald platform, and get semantic search and AI chat out of the box. We’ve seen companies spend months building a context layer system internally, only for it to have subpar performance and require active maintenance. Skald gives you the plumbing to get started really fast when building context-aware agents and AI apps (customers have gone to prod in a day with us) but is still highly extensible and configurable to fit…

What it does

In the maker’s words, at launch

Hey HN! We built an open source context layer to power AI agents and apps even in air-gapped infra setups. TL;DR: Use our API (or one of the 6 SDKs) to push context into the Skald platform, and get semantic search and AI chat out of the box. We’ve seen companies spend months building a context layer system internally, only for it to have subpar performance and require active maintenance. Skald gives you the plumbing to get started really fast when building context-aware agents and AI apps (customers have gone to prod in a day with us) but is still highly extensible and configurable to fit individual needs. Our core is entirely MIT-licensed and you can even run it with a fully self-hosted stack (document parsing, chunking, vector search, LLM, etc) in your VPC. We think solutions in this space must be open source, and not just “open source but bring 6 API keys to get started”. Our customers have used Skald to quickly build features for their own users, as well as for internal tooling. The self-hosted MIT version is completely fully-featured, so much so that it’s what we run on our Cloud offering today. We do also have an enterprise license for larger customers that runs on their infra and has a few enterprise-focused features on top of the MIT version. Keen to hear your feedback and I’ll be around to answer questions! GitHub: https://github.com/skaldlabs/skald

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