Topic · 75 posts
Deep dives on every Crowkis capability: agent memory, reasoning reuse, guardrails, evals, RAG, prompt versioning, and the AI gateway.
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.
The expensive part of a hard answer is the thinking. Crowkis stores reasoning as a reusable step graph and replays it for the next question that shares its shape, at a fraction of the token cost.
If your corpus fits a cache, you don't need a separate vector database to do retrieval. CDOC adds documents with auto-chunking, filtered search, and reranking, all local.
The Crowkis binary doubles as an MCP server, so Claude Desktop, Claude Code, and any MCP-capable agent can check the cache before calling the model and store what they compute.
Point your existing OpenAI client at Crowkis and change nothing else. Repeated questions are served from cache with no upstream call, and you get retries and routing for free.
Embeddings usually mean an API key and a per-token bill. CEMBED turns text into vectors using the bundled local model, for free, with nothing leaving your machine.
MCP explained: giving models tools they can hold. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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.
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.
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.
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.
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.
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.
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.
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.
Cache invalidation for AI answers, done right. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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.
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.
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.
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.
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.
Freshness vs speed: the LLM cache tradeoff. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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.
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.
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.
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.
Evals without an LLM judge. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Model-free online evals (CEVAL), grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge. Here's how Crowkis does it and why it matters for cost and safety.
Prompt versioning and A/B testing at the data layer. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Prompt versioning and A/B testing (CPROMPT), versions prompts on every write, renders variables, and runs sticky per-user A/B splits, so you roll back or test without a code deploy. Here's how Crowkis does it and why it matters for cost and safety.
Chain-of-thought costs several times more tokens than the answer it produces, and it's usually thrown away. For structurally similar problems, most of that reasoning can be reused.
Streaming responses from cache without breaking UX. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Self-hosted RAG (CDOC), adds documents with auto-chunking and metadata, then runs filtered ANN search with optional reranking, no separate vector database. Here's how Crowkis does it and why it matters for cost and safety.
'What's 2+2?' needs a near-exact match to reuse safely. 'Give me creative ideas for X' can tolerate a loose one. A single global threshold guarantees you're too strict somewhere and too loose somewhere else.
Local offline embeddings (CEMBED), turns text into vectors with the bundled local model, no API key and no egress, with a micro-cache so repeats are free. Here's how Crowkis does it and why it matters for cost and safety.
Multi-turn session memory (CSESSION), keeps a bounded conversation buffer with both recent-window reads and semantic search across the whole chat. Here's how Crowkis does it and why it matters for cost and safety.
We gave agents tools, planning, and the ability to act. We forgot to give them a place to remember. That gap is why your agents feel brilliant and amnesiac at the same time.
Tool-result caching (CTOOLSET), caches a deterministic tool call keyed by tool plus exact args, so a swarm's duplicate lookups become one call. Here's how Crowkis does it and why it matters for cost and safety.
Multimodal caching (image + text), caches image-plus-text lookups, so a repeated vision question is a hit instead of an expensive re-run. Here's how Crowkis does it and why it matters for cost and safety.
Embeddings sound like math you need a PhD for. The core idea is simpler and more useful than that, and it's the reason a cache can tell that two different sentences mean the same thing.
Streaming response caching, serves cached answers chunk by chunk, so a hit feels like live typing and the seam between hit and miss disappears. Here's how Crowkis does it and why it matters for cost and safety.
Prompt injection rarely arrives in plain English. CGUARD normalizes the evasion first, whitespace, leetspeak, zero-width characters, then scans for jailbreaks, overrides, and system-prompt exfiltration.
OpenAI-compatible AI gateway, proxies /v1/chat/completions with a semantic cache in front, so you point your client's base URL at Crowkis and change nothing else. Here's how Crowkis does it and why it matters for cost and safety.
Multi-provider routing and fallback, load-balances and fails over across providers on error class, with retries using exponential backoff and jitter. Here's how Crowkis does it and why it matters for cost and safety.
The model's output is the other trust boundary. COUTCHECK scans responses for PII leakage and toxicity, and optionally validates JSON, returning a structured verdict you can act on.
Coding agents re-read the same schema, re-derive the same conventions, and re-ask the same architecture questions on every run. A shared memory turns that repeated context into a one-time cost.
LLM-as-judge is expensive and leaks your data. CEVAL ships nine deterministic evaluators, toxicity, PII, injection-safety, relevance, JSON validity and more, that score input/output pairs locally and track the results over time.
Golden answer pinning (CPIN), serves a human-approved answer verbatim for any phrasing of a question, with an audit trail of who approved it. Here's how Crowkis does it and why it matters for cost and safety.
Answer lineage and cascade purge (CSOURCE), ties answers to their source so that when a document changes, every answer built on it can be purged in one move. Here's how Crowkis does it and why it matters for cost and safety.
Prompts are production logic edited like sticky notes. CPROMPT gives them named templates, automatic versioning, rollback, variable rendering, and sticky weighted A/B splits, all surviving restart.
Natural-language cache invalidation (CINVALIDATE), purges entries whose meaning matches a plain-English instruction, previewing by default and only acting on COMMIT. Here's how Crowkis does it and why it matters for cost and safety.
Stale-while-revalidate (CSTALE), returns a cached answer past its TTL with a stale flag, so expiry is a snappy answer plus a refresh signal, not a cold miss. Here's how Crowkis does it and why it matters for cost and safety.
You don't always need a separate vector database to do retrieval. CDOC is a mini RAG store inside Crowkis, auto-chunking, metadata filtering, and optional cross-encoder reranking, sharing the cache's embedder.
Chat history is more than the last N turns. CSESSION stores a multi-turn buffer per session, bounded and TTL'd, with both recent-window reads and semantic search across the whole conversation.
For the questions where 'close enough' is unacceptable, pricing, legal, brand lines, CPIN serves a human-approved answer verbatim, records who approved it, and never lets the model improvise.
Most caches only remember good answers. Crowkis also remembers bad ones, flag a hallucinated or harmful response once, and CCHECKBAD catches every paraphrase of the question that would have reproduced it.
Cached answers derive from sources, a doc, a config, an API. CSOURCE ties answers to their origin so that when the source changes, every answer built on it can be purged in one move.
Agents call the same tools with the same arguments constantly. CTOOLSET and CTOOLGET cache tool results keyed by tool plus exact arguments, so a deterministic call runs once and serves many.
MCP server for AI apps, doubles as an MCP server over stdio, so Claude and any MCP-capable agent can check the cache and store what they compute. Here's how Crowkis does it and why it matters for cost and safety.
Sometimes you need to clear 'everything about the old pricing', a fuzzy, semantic set. CINVALIDATE takes a natural-language instruction, previews what it would purge, and only acts on COMMIT.
A hard TTL turns a one-second-expired answer into a full model call. CSTALE serves the cached answer past its TTL with a stale flag, so you choose freshness versus latency per request.
Token spend is usually a month-end surprise. CBUDGET tracks per-tenant token and dollar consumption in real time and surfaces alerts, so a runaway tenant is a notification, not a billing shock.
Point your existing OpenAI client at Crowkis and change nothing else. The gateway proxies /v1/chat/completions, serves semantic hits without an upstream call, and adds retries, routing, and rate limits.
Embeddings usually mean an API key and a per-token bill. CEMBED turns text into vectors using the bundled ONNX model, locally, for free, and caches repeats so the second call is instant.
Vision queries are expensive and repetitive, the same product photo, the same screenshot, asked about again and again. Crowkis caches image-plus-text lookups so a repeated visual question is a hit.
A cache that only says 'hit' or 'miss' makes you trust it blindly. Crowkis returns a confidence score per hit, a geometric mean of five signals, so you decide the bar reuse must clear.
A fixed similarity threshold is wrong the day after you set it. Crowkis uses a three-tier scheme, per-intent base, complexity adjustment, and an EMA feedback loop, that learns the right bar and persists it.
A semantic cache slowly accumulates near-duplicate answers. CDEDUP finds the clusters that mean the same thing and collapses them, reclaiming memory, and Crowkis is honest about its cost.
A cache of LLM traffic is a cache of whatever users typed, including PII. CPII reports what personal data is present and executes right-to-erasure, so compliance is a command, not a project.
Infrastructure you can't observe is infrastructure you don't trust. CINFO and the built-in dashboard expose hit rate, saved spend, safety blocks, memory pressure, and license state in real time.
A runaway agent or a noisy tenant can torch a budget in minutes. CKEYLIMIT sets per-tenant requests-per-minute and tokens-per-minute ceilings, enforced locally before the spend happens.
The reasoning is the expensive part of a hard answer. CTHINK stores a chain-of-thought trace as a reusable step graph; CREUSE fetches the matching plan for a new query at a fraction of the original token cost.
What is agent memory, and why your agents need it. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
RAG in production: the parts nobody warns you about. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Semantic caching, explained for engineers. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.