the context enginefor AI
Cilow gives agents persistent memory without context window bloat, compiling the right history into every inference.
CursorMistralNotionOpenAILinearHugging FaceVercelGeminiSlackClaude CodeLangChainClaudeGitHubGoogle DriveCursorMistralNotionOpenAILinearHugging FaceVercelGeminiSlackClaude CodeLangChainClaudeGitHubGoogle DriveCursorMistralNotionOpenAILinearHugging FaceVercelGeminiSlackClaude CodeLangChainThe 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 The more outdated context a model processes, the worse it reasons.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.
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.
Lessnoise.Betterrecall.Morereliabledecisions.
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
Coding agents
Give Cursor, Claude Code, and copilots a memory of your repo, conventions, and prior decisions so they stop relearning the same codebase every session.
Company brain
One shared memory across docs, tickets, decisions, and Slack so every internal agent answers from the same source of truth - and stays current as the company changes.
Customer support agents
Shared memory of prior tickets, preferences, and resolutions across channels so agents don't ask the same questions twice.
Multi-agent systems
One agent's discoveries become another's working memory. No re-ingesting the same data, no fragmented context across the swarm.
FAQ
A context engine for AI agents. It replaces vector DBs, search pipelines, and RAG glue with one system that ingests data, structures it, keeps it current, and serves the right context at inference time.
Try Cilow.
Give your agents real memory.