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LLM claims validated against real data — never invented. Kremis ingests EAV signals, builds a deterministic graph, classifies every response: FACT (direct edge), INFERENCE (derived path), or UNKNOWN (not in data). No confidence scores. No probabilistic gaps. Built in pure Rust (no async, no floats in core), with ACID persistence via redb, HTTP API, CLI, and MCP bridge for Claude/Cursor. Apache 2.0. v0.17.8 — alpha, functional, tested. 354 tests passing.
I built Kremis because RAG retrieves the right documents but the LLM still invents details. I needed a way to check each claim against real data — not with similarity scores, but with a binary answer: is this in my data or not?
The core loop:
1. Ingest signals as EAV triples (entity, attribute, value)
2. Kremis builds a weighted graph from co-occurrence
3. Query → every result tagged as FACT, INFERENCE, or UNKNOWN
No embeddings in core. No floating-point. Same input, same output.
What's working today:
- kremis-core (pure Rust, no async, no network)
- HTTP API (axum) + CLI (clap)
- MCP bridge — 9 tools, works in Claude Desktop and Cursor
- ACID persistence via redb
- Docker image
The main friction is EAV ingestion: you structure your data as signals before querying. Is that too rigid? I'd like to hear.
Demo in the repo (Python, stdlib only):
python examples/demo_honesty.py
Docs: https://kremis.mintlify.app
Background story: https://dev.to/tykolt/i-spent-mo...
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About kremis on Product Hunt
“Every LLM claim traced to real data — or rejected”
kremis was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #104 on the daily leaderboard. LLM claims validated against real data — never invented. Kremis ingests EAV signals, builds a deterministic graph, classifies every response: FACT (direct edge), INFERENCE (derived path), or UNKNOWN (not in data). No confidence scores. No probabilistic gaps. Built in pure Rust (no async, no floats in core), with ACID persistence via redb, HTTP API, CLI, and MCP bridge for Claude/Cursor. Apache 2.0. v0.17.8 — alpha, functional, tested. 354 tests passing.
kremis was featured in Open Source (68.4k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.3k followers) on Product Hunt. Together, these topics include over 168.3k products, making this a competitive space to launch in.
Who hunted kremis?
kremis was hunted by TyKolt. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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TyKolt