Cache LangGraph in your customer support bot with Crowkis
Building customer support bots on LangGraph? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Notes from the nest · 980 posts
Engineering notes written by the people building Crowkis. Comparisons, use cases, economics, internals, security, operations, and nothing written just to rank.
Building customer support bots on LangGraph? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Durable, per-user memory for LangGraph agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
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.
Users put personal data in prompts whether you like it or not. The cache's job is a full lifecycle: keep it out of shared entries, find it on demand, erase it provably.
Building education tutors on LangChain.js? Add a semantic cache so students asking the same concepts thousands of times stop costing full price.
Building HR assistants on Ollama? Add a semantic cache so the same policy questions from every employee stop costing full price.
Building onboarding assistants on Semantic Kernel? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Building coding assistants on LangGraph? Add a semantic cache so the same explanations and boilerplate reasoning, dozens of times a day stop costing full price.
Add a semantic cache to LlamaIndex so repeated and reworded questions are served for free, no rewrite, self-hosted.
The fastest disk read is the one that never happens. A few bits per key let Crowkis skip files that can't contain your answer, at a 1% false-positive cost we chose on purpose.
Every developer on your team asks the assistant the same questions about the same codebase. With Crowkis behind MCP, the second ask is free for everyone.
Building multi-agent systems on LangChain.js? Add a semantic cache so a swarm of agents asking overlapping questions stop costing full price.
Building IT helpdesk bots on Ollama? Add a semantic cache so the same tickets and fixes, endlessly stop costing full price.
Building meeting-notes summarizers on Semantic Kernel? Add a semantic cache so similar summaries requested repeatedly stop costing full price.
Building RAG document search on LangGraph? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Durable, per-user memory for LlamaIndex agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
Slice the traffic, compare against cached baselines, promote or retreat, model upgrades as a controlled experiment with the cache as your measuring instrument.
Every team has a runaway-loop story that ends with a shocking invoice. Per-key budgets with hard TPM and dollar walls end the genre.
Crowkis is a cache with a vector index, not a vector database, but it should still hold up at scale. We indexed 100K and 1M vectors and measured build time, search latency, and recall. Including where dedicated vector DBs still win.
Building voice assistants on LangChain.js? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.