FreshContext
Context judgment for AI agents and RAG
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FreshContext - Context judgment for AI agents and RAG

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FreshContext is context judgment infrastructure for AI agents, RAG systems, and retrieval workflows. Instead of only retrieving more sources, it evaluates caller-provided candidate context for freshness, provenance, confidence, utility, and source profile, then returns decision-ready output: cite, verify, refresh, background, watch, or exclude. The current front door is evaluate_context, with demos, trust gates, and reference adapters included.

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Hi Product Hunt — I’m Immanuel, solo builder of FreshContext. FreshContext started from a problem I kept seeing in AI systems: retrieval often brings back context, but the model still needs to know whether that context should actually be trusted, cited, refreshed, verified, backgrounded, watched, or excluded. FreshContext is built as a context judgment layer between retrieval and reasoning. The simple version: candidate context in decision-ready context out The current public release includes: - evaluate_context as the generic front door - Source Profiles - freshness, provenance, confidence, and utility evaluation - decision-ready outputs - BYOC demos - arXiv signal-to-decision proof - trust/release gates - reference adapters as proof surfaces Important boundary: FreshContext does not claim that freshness equals truth. It also does not fetch, crawl, browse, scrape, or read folders through evaluate_context. The caller brings candidate context; FreshContext judges what should happen to it before it reaches the model. I’d really value feedback on the category framing: does “context judgment layer” make sense for the gap between retrieval and reasoning?