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What Crowkis does under the hood, one capability at a time. 75 articles.
Subscribe with RSSWhat is a semantic cache for LLMs? (and why exact-match caching fails)
A plain key-value cache misses the moment a prompt is reworded, and a raw vector cache can serve the wrong answer. A semantic cache understands meaning and structure, and only reuses when it's safe.
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MCP explained: giving models tools they can hold
MCP explained: giving models tools they can hold. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Semantic + structural cache matching: how it works and when to use it
Semantic + structural cache matching, matches questions by meaning (HNSW vectors) and by structure (12 intent-class templates), so paraphrases hit but a changed number, entity, or negation does not. Here's how Crowkis does it and why it matters for cost and safety.
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Your AI agent has amnesia. We gave it memory that stays in its lane.
Most AI agents forget everything the moment a session ends, then re-pay to relearn it. Crowkis gives agents durable, semantic memory, and keeps every tenant's memory strictly walled off from the next.
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Adaptive confidence thresholds: how it works and when to use it
Adaptive confidence thresholds, learns the right reuse bar per intent class with a feedback loop, and persists it across restarts, so it stops both over-serving and missing safe hits. Here's how Crowkis does it and why it matters for cost and safety.
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crowjudge: the bouncer that keeps your cache honest
A cache that serves a wrong answer is worse than no cache. Meet crowjudge, the second model that re-reads borderline matches and vetoes the ones that don't hold up.
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Confidence scoring on every hit: how it works and when to use it
Confidence scoring on every hit, returns a per-hit confidence score (a geometric mean of similarity, freshness, trust, validation, and domain accuracy) so you decide the bar reuse must clear. Here's how Crowkis does it and why it matters for cost and safety.
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Freshness control (TTL + webhooks): how it works and when to use it
Freshness control (TTL + webhooks), expires answers by query-type TTL, webhook invalidation, and version-aware recompute, so a cached price or status never goes quietly stale. Here's how Crowkis does it and why it matters for cost and safety.
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Smart semantic eviction: how it works and when to use it
Smart semantic eviction, scores what to keep by recency, frequency, isolation, and compute cost, so an expensive reasoning answer outranks a cheap, recently-hit triviality. Here's how Crowkis does it and why it matters for cost and safety.
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Semantic caching, explained without the jargon
If you're paying for an LLM and haven't met semantic caching yet, this is the five-minute version. No math, no buzzwords, just why it saves money and how it works.
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Cache invalidation for AI answers, done right
Cache invalidation for AI answers, done right. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Reasoning reuse (cache the chain of thought): how it works and when to use it
Reasoning reuse (cache the chain of thought), stores a chain-of-thought trace as a reusable step graph and replays it for the next query that shares its shape, at roughly 15% of the original token cost. Here's how Crowkis does it and why it matters for cost and safety.
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Long-term agent memory: how it works and when to use it
Long-term agent memory, gives agents durable, per-(agent, user) memory recalled by relevance blended with recency, so an assistant remembers across sessions. Here's how Crowkis does it and why it matters for cost and safety.
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Contradiction-aware memory consolidation: how it works and when to use it
Contradiction-aware memory consolidation, retires a fact when a new one contradicts it (kept for history, never deleted), so recall returns the current answer, not all the old ones. Here's how Crowkis does it and why it matters for cost and safety.
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Knowledge-graph memory: how it works and when to use it
Knowledge-graph memory, stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess. Here's how Crowkis does it and why it matters for cost and safety.
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LangChain's cache is exact-match. Here's the two-line upgrade.
LangChain's built-in caches are great until a user rephrases the question. Swap in a semantic cache and the paraphrases start hitting, without changing a line of your chains.
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Freshness vs speed: the LLM cache tradeoff
Freshness vs speed: the LLM cache tradeoff. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Bi-temporal (time-travel) memory: how it works and when to use it
Bi-temporal (time-travel) memory, answers what the agent believed at a past instant using validity windows, so you can reconstruct 'what did we know then?'. Here's how Crowkis does it and why it matters for cost and safety.
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Auto fact extraction from conversations: how it works and when to use it
Auto fact extraction from conversations, pulls durable facts out of a transcript deterministically, dropping questions, greetings, and filler, with no model call. Here's how Crowkis does it and why it matters for cost and safety.
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Why 'who wrote Hamlet' and 'today's stock price' can't share a TTL
A single time-to-live for every cached answer is a bug in disguise. Some facts are true for a decade; some are stale in minutes. Freshness has to know the difference.
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A cache hit is not the same as a correct answer
Most caches treat every hit as equally trustworthy, a binary yes. But LLM answers are probabilistic and time-sensitive. Crowkis scores its confidence before it serves.
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Evals without an LLM judge
Evals without an LLM judge. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.