KORE
Agent-native ops kernel with 14 PIL-powered tools — route, compress, guard, memory, score, certify, optimizeprompt, and more.
Community: Submitted by a user or imported; check the owner before granting accessOnlineNo sign-inGlobalFreeCan modify data
What it can do
- Guard: Check AI output against source documents to detect hallucination. Returns safety score, flagged claims, and revision hints.
- Compress: Semantically compress text 3-5x without quality loss. Reduces token costs for LLM context windows.
- Route: Route a task to the cheapest LLM provider that meets quality threshold. Uses 11-provider fallback chain. Saves 60-90% on inference costs.
What data it sees
Do you need an account
No: the server works without sign-in
Agent-native ops kernel with 14 PIL-powered tools — route, compress, guard, memory, score, certify, optimize_prompt, and more.
PIPELINE (7cr = $0.007): route → compress → guard → memory → score → certify
PIL KERNEL: optimize_prompt uses a self-improving 45-template prompt archive invent: full invention chain with adversarial verification (15cr)
Free tier: 500 credits. No card needed. POST /v1/register → instant API key
Starter: $9 / 10,000 credits Pro: $49 / 100,000 credits
Server tool list (13)
Raw names from tools/list. Only developers need these.
| guard | Check AI output against source documents to detect hallucination. Returns safety score, flagged claims, and revision hints. |
| compress | Semantically compress text 3-5x without quality loss. Reduces token costs for LLM context windows. |
| route | Route a task to the cheapest LLM provider that meets quality threshold. Uses 11-provider fallback chain. Saves 60-90% on inference costs. |
| memory | Store or retrieve observations from cross-agent memory. Qdrant-backed with all-MiniLM embeddings. Network effect across all agents. |
| normalize | Detect prompt injections, normalize whitespace, strip noise from text. Returns risk score and blocked flag. |
| score | Rank multiple candidate outputs by heuristic quality. Evaluates length, code quality, structure, and keyword coverage. |
| split | Decompose a complex task into parallel subtasks. Supports 5 decomposition types: sequential, parallel, build, verify, document. |
| diff | Compute a meaning-preserving semantic diff between original and modified text using SequenceMatcher. |
| provenance | Generate EU AI Act compliant provenance certificate with HMAC-SHA256 hash chain. Certify claims with source attribution. |
| embed | Compute text embeddings via all-MiniLM-L6-v2 with LRU cache. Returns vector embeddings for RAG or similarity search. |
| sandbox | Execute Python code in a secure sandbox. 30-second timeout with automatic temp file cleanup. No Docker needed. |
| verify | Verify AI-generated answers against provided source documents. Returns groundedness score, risk level, and detailed issue list. |
| audit | Retrieve or verify the immutable HMAC-SHA256 chained audit trail. EU AI Act compliant. Tamper-evident logging for all API calls. |