Context engine for AI agents
Give your agents persistent memory without bloating every prompt. Cilow compiles relevant history into minimal context.
Works with
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.
Where agents break without memory.
Four failure modes hiding in every agent stack.
Your agent forgets you exist
Every session starts from zero. Preferences, past decisions, prior mistakes — all gone. Users repeat themselves endlessly.
Context decays mid-conversation
Long threads push critical facts out of the window. By turn 15, your agent is hallucinating against its own earlier answers.
Prompts are doing memory's job
Teams stuff history into system prompts. Costs balloon, latency spikes, and the signal-to-noise ratio collapses.
Retrieval doesn't understand time
RAG returns similar text, not what happened last. It can't distinguish yesterday's decision from last year's draft.
more details?”
details you're referring to?”
◆ Q3 call — chose Enterprise tier
◆ Oct slack — Sarah flagged budget freeze
◆ Last wk — freeze lifted, demo req'd
They're ready to close. Revised SOW attached.”
That's a memory problem.
What Cilow actually does
under the hood.
Cilow is a memory layer and context engine that sits under your agents. It captures, ranks, and compiles the smallest, sharpest context window for every inference request.
Capture what happens, not just what’s said.
Cilow captures every important event — queries, tool calls, outcomes, user reactions — and 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.
User is a frequent Frontier flyer who prioritizes low cost and early morning departures.
Compile a minimal, sharp context window.
Cilow assembles a query-specific context window — 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.
The result: your AI gets smarter with every interaction. No retraining. No manual curation. Just continuous learning built into the infrastructure.
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 Frontier F91098",8});9 10const result = await client.memories.search({11 containerTag: userId,12 q: "what are the flight preferences?"13});14 15// "User 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.
Research and knowledge agents
Track hypotheses, sources, and dead ends in long-running projects so agents don't rediscover the same ideas every week.
Give your agents a real memory.
Build systems that compound: every session improves the next.