01 / Product

Name
Cilow
Tagline
Context engine for AI agents
Description
Cilow gives agents persistent memory without context window bloat, compiling the right history into every inference.
Category
AI Infrastructure / Developer Tools
Value Proposition
Give your agents a real memory. Build systems that compound: every session improves the next.
Footer Tagline
Memory infrastructure for AI agents

02 / How It Works

  1. #1 CAPTURE: 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.

  2. #2 RANK: 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.

  3. #3 COMPILE: 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.

03 / Features

  • -Long-term memory for AI agents
  • -FRR scoring (Frequency-Recency-Relevance)
  • -Optimized context window compilation
  • -Hybrid retrieval (vector + graph + temporal)
  • -REST and gRPC APIs
  • -Python and TypeScript SDKs
  • -Model Context Protocol (MCP) integration
  • -Multi-agent memory sharing
  • -Tiered storage (hot/warm/cold)
  • -SOC 2 and GDPR compliance

04 / Use Cases

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.

05 / FAQ

Q: What is Cilow?

A: 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.

Q: Why not just RAG?

A: RAG retrieves similar fragments. It can't decide what's current, what conflicts, or what actually belongs in the working set. Cilow handles all three.

Q: How is Cilow different from a vector DB or GraphRAG?

A: Vector DBs return similarity. GraphRAG adds relationships. Cilow goes further: ingestion, structuring, updating, conflict resolution, and context assembly in one layer. The goal isn't more data. It's the right data.

Q: What data can Cilow use?

A: Documents, chats, code, APIs, product data, tickets, notes, structured records. If your agent depends on it, Cilow turns it into context.

Q: Does it support continual learning?

A: Yes. Cilow updates context without retraining. As facts change, what the model sees changes - no weight updates, no brittle prompt hacks.

Q: How does it fit into my stack?

A: Cilow replaces your retrieval and context layer. One API in front of your models - no separate vector DB, search service, or RAG pipeline to maintain.

06 / Team

  • Stephen KeehnFounder & CEO
  • Mihir GuptaCo-Founder & CTO

07 / Pricing

Coming soon. Free tier available.

08 / Contact

Email: contact@cilow.ai

Structured Data (JSON-LD)

The full schema.org structured data embedded in this page.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://cilow.ai/#organization",
      "name": "Cilow",
      "url": "https://cilow.ai",
      "logo": "https://cilow.ai/logo-color.png",
      "sameAs": [
        "https://x.com/cilow_ai",
        "https://linkedin.com/company/cilow",
        "https://github.com/cilow"
      ],
      "founder": [
        {
          "@type": "Person",
          "name": "Stephen Keehn",
          "jobTitle": "Founder & CEO"
        },
        {
          "@type": "Person",
          "name": "Mihir Gupta",
          "jobTitle": "Co-Founder & CTO"
        }
      ],
      "contactPoint": {
        "@type": "ContactPoint",
        "email": "contact@cilow.ai",
        "contactType": "sales"
      }
    },
    {
      "@type": "WebSite",
      "@id": "https://cilow.ai/#website",
      "name": "Cilow",
      "url": "https://cilow.ai",
      "description": "Cilow is the memory and context engine for stateful AI agents. Persist long-term memory, compile smarter context windows, and ship coherent agents across sessions.",
      "publisher": {
        "@id": "https://cilow.ai/#organization"
      }
    },
    {
      "@type": "SoftwareApplication",
      "name": "Cilow",
      "alternateName": [
        "context engine",
        "context layer",
        "context layers",
        "context infrastructure",
        "AI context infrastructure",
        "memory layer",
        "AI memory layer",
        "agent memory layer",
        "LLM memory layer",
        "persistent context",
        "context assembly",
        "inference-time context",
        "stateful agents"
      ],
      "applicationCategory": "DeveloperApplication",
      "applicationSubCategory": "AI Infrastructure",
      "operatingSystem": "Web",
      "softwareVersion": "1.4.2",
      "datePublished": "2025-10-01",
      "description": "Cilow is the memory and context engine for stateful AI agents. Persist long-term memory, compile smarter context windows, and ship coherent agents across sessions.",
      "url": "https://cilow.ai",
      "keywords": "context engine, context layer, context layers, context infrastructure, AI context infrastructure, memory layer, AI memory layer, agent memory layer, LLM memory layer, persistent context, context assembly, inference-time context, stateful agents",
      "offers": {
        "@type": "Offer",
        "price": "0",
        "priceCurrency": "USD",
        "description": "Free developer tier available. Usage-based pricing for teams."
      },
      "featureList": [
        "Long-term memory for AI agents",
        "FRR scoring (Frequency-Recency-Relevance)",
        "Optimized context window compilation",
        "Hybrid retrieval (vector + graph + temporal)",
        "REST and gRPC APIs",
        "Python and TypeScript SDKs",
        "Model Context Protocol (MCP) integration",
        "Multi-agent memory sharing",
        "Tiered storage (hot/warm/cold)",
        "SOC 2 and GDPR compliance"
      ],
      "subjectOf": [
        "https://cilow.ai/machine",
        "https://cilow.ai/llms.txt",
        "https://cilow.ai/llms-full.txt"
      ]
    },
    {
      "@type": "HowTo",
      "name": "How Cilow Works",
      "description": "How Cilow captures, ranks, and compiles memory for AI agents",
      "step": [
        {
          "@type": "HowToStep",
          "name": "CAPTURE",
          "text": "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."
        },
        {
          "@type": "HowToStep",
          "name": "RANK",
          "text": "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."
        },
        {
          "@type": "HowToStep",
          "name": "COMPILE",
          "text": "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."
        }
      ]
    },
    {
      "@type": "FAQPage",
      "mainEntity": [
        {
          "@type": "Question",
          "name": "What is Cilow?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "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."
          }
        },
        {
          "@type": "Question",
          "name": "Why not just RAG?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "RAG retrieves similar fragments. It can't decide what's current, what conflicts, or what actually belongs in the working set. Cilow handles all three."
          }
        },
        {
          "@type": "Question",
          "name": "How is Cilow different from a vector DB or GraphRAG?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Vector DBs return similarity. GraphRAG adds relationships. Cilow goes further: ingestion, structuring, updating, conflict resolution, and context assembly in one layer. The goal isn't more data. It's the right data."
          }
        },
        {
          "@type": "Question",
          "name": "What data can Cilow use?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Documents, chats, code, APIs, product data, tickets, notes, structured records. If your agent depends on it, Cilow turns it into context."
          }
        },
        {
          "@type": "Question",
          "name": "Does it support continual learning?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Yes. Cilow updates context without retraining. As facts change, what the model sees changes - no weight updates, no brittle prompt hacks."
          }
        },
        {
          "@type": "Question",
          "name": "How does it fit into my stack?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Cilow replaces your retrieval and context layer. One API in front of your models - no separate vector DB, search service, or RAG pipeline to maintain."
          }
        }
      ]
    }
  ]
}