AgentsKB – 3.3k verified answers to stop agent hallucinations

Hacker News

Hey HN, I built AgentsKB after watching Claude/Cursor hallucinate Stripe API syntax for the 10th time in a week. The Problem: AI agents don't "remember" across sessions. You debug a tricky Next.js issue on Monday. Tuesday, same error, same web search loop, same wasted 30 minutes. The Solution: A curated knowledge base with 3,276 verified Q&As across 160 domains (PostgreSQL, Redis, Kafka, TypeScript, AWS, etc.). 99% confidence rating, 50ms query time. How it works: - Integrates via MCP (Model Context Protocol) into Claude Desktop/Code - Agent queries verified answers before guessing - No more "let me search the web for that" delays Tech stack: - MCP native (no plugins to manage) - Vector similarity search for atomic Q&As - Covers common pain points: JWT auth, Kubernetes configs, API design patterns, PostgreSQL quirks Current stats: - 3,276 Q&As - 160 domains - 73% atomic (single-concept answers) - 99% average authority score Why I built this: Every AI coding session wastes time re-teaching the agent things it "learned" yesterday. This gives agents persistent, verified memory. Try it: [Your URL] Looking for feedback on: 1. Which domains/libraries should I prioritize next? 2. How do you currently handle agent hallucinations? 3. Interest in self-hosted version for proprietary codebases?

Visit product View on Hacker NewsNov 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, apple

98%98% 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 · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

31%31% 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

24%24% 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.

Correct prediction on native model

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