The context engineengine for AI agents
Give agents persistent memory without blowing up context windows. Cilow compiles relevant history into minimal context.
CursorCursorThe missing piece for AI agents.
AI agents can reason. They can generate. They can even use tools.
┃ But they can't remember.
Every conversation resets.
Every context window fills with noise.
Every query returns documents, not decisions.
Broken context is why agents hallucinate, forget, and stall.
Most agent failures are not model failures. They are context failures: context rot, hallucination, weak personalization, and no compounding.
Context rot
Stale facts accumulate without retiring. The more outdated context a model processes, the worse it reasons. Conflicting information doesn't cancel out. It compounds into hallucination.
Retrieval is not relevance
More context isn't better context. Flooding a model with similar-but-wrong text makes it reason worse, not better.
Every session starts cold
Every session resets completely. No memory of preferences, prior decisions, or what worked. Agents guess at continuity they don't have.
No continual learning
Nothing writes back. Fix a behavior today and it's gone tomorrow. Without memory of what worked, the same failures repeat indefinitely.
Same model. Different context. Different outcome.
Without a context layer, agents ask for what they should already know. With Cilow, they act on history, timing, and prior decisions.
This is not a memory trick.
It is context infrastructure.
Cilow builds the right context
for every inference.
Capture what happens, not just what's said.
Cilow captures every important event, including queries, tool calls, outcomes, and user reactions, then writes them into a structured memory graph combining episodic events with semantic facts.
Long-term memory across sessions, channels, and tools.
Rank what matters right now.
Before each LLM call, Cilow ranks which memories matter using signals like recency, semantic similarity, causal role, and past usage to cut context size without sacrificing accuracy.
Smarter context windows ranked by relevance, not just similarity.
Compile a minimal, sharp context window.
Cilow assembles a query-specific context window with short summaries, key facts, and critical examples, then writes the interaction back so the agent improves over time.
Infra-native: tiered storage, hybrid retrieval, production-ready APIs.
Less noise. Better recall. More reliable decisions.
All of the above.
In a few lines of code.
No matter the use case, Cilow provides context to any layer of the context stack. Works with AI SDK, OpenAI SDK, Python, Typescript, Claude agents SDK, and many others.
1import Cilow from 'cilow';2 3const client = new Cilow();4 5await client.memories.add({6 containerTag: userId,7 content: "user booked flight F91098",8});9 10const result = await client.memories.search({11 containerTag: userId,12 q: "flight preferences?"13});14 15// "Usually flies Frontier, prefers16// morning departures, ~$100 budget"Where teams start with Cilow
Customer support agents
Give every agent a shared memory of prior tickets, preferences, and resolutions across channels so they don't ask the same questions twice.
Product and growth copilots
Let internal copilots remember experiments, shipping decisions, and user feedback over quarters so they can make recommendations grounded in history.
DevOps and reliability agents
Correlate incidents over time, learn playbooks that worked, and let agents spot patterns before they show up in dashboards.
Multi-agent systems
Give every agent in a swarm shared context. One agent's discoveries become another's working memory without re-ingesting the same data.
FAQ
Cilow is a context engine for AI. It replaces fragmented retrieval stacks with a single system that ingests data, structures it, keeps it current, and serves the right context at inference time.
Try Cilow.
Give your agents real memory.