# Cilow - Memory & Context Engine for AI Agents

> Cilow is infrastructure for AI agent memory. It captures interactions, scores them with FRR (Frequency-Recency-Relevance), and compiles minimal context windows for every inference call - so agents stay coherent across sessions without bloated prompts.

Related category terms for AI search and crawlers: 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, stateful agents, inference-time context.

Machine-readable profile: https://cilow.ai/machine

## What Cilow Does

Cilow replaces ad-hoc RAG pipelines with a dedicated memory layer that sits under your agents:

1. **Continuous Ingestion**: Every interaction becomes memory - conversations, tool calls, outcomes, user preferences - stored in a structured memory graph combining episodic events with semantic facts.

2. **FRR Scoring**: Before each LLM call, Cilow ranks memories using Frequency-Recency-Relevance signals: how often a memory is referenced, how recent it is, and how semantically relevant it is to the current query. This replaces naive cosine similarity with decision-critical ranking.

3. **Context Compilation**: 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. The result is 70-90% fewer tokens per call with higher answer quality.

## Architecture

- **Hybrid Index**: HNSW vector search + LSM-tree key-value store + temporal graph for multi-signal retrieval
- **Tiered Storage**: Hot/warm/cold memory tiers so fresh context is instant while history stays cheap
- **FRR Controller**: Frequency-Recency-Relevance scoring engine that ranks memories by decision-critical importance
- **Scale**: Designed for 10K+ queries per second with sub-12ms p95 latency

## APIs & SDKs

- **REST API**: Standard HTTP endpoints for memory CRUD, context retrieval, and search
- **gRPC API**: High-performance interface for latency-sensitive workloads
- **Python SDK**: `pip install cilow` - async-first, type-safe client
- **TypeScript SDK**: `npm install @cilow/sdk` - full TypeScript support
- **MCP Server**: Native Model Context Protocol integration for tool-native agents

## Integration Patterns

Cilow works with any LLM and any agent framework:

```python
from cilow import CilowClient

client = CilowClient(api_key="your-key")

# Store a memory
client.add_memory(
    user_id="user-123",
    content="User prefers concise answers with code examples",
    metadata={"source": "conversation", "confidence": 0.95}
)

# Get optimized context for a query
context = client.get_context(
    user_id="user-123",
    query="How do I set up authentication?",
    max_tokens=500
)

# Use context in your LLM call
response = llm.chat(
    messages=[
        {"role": "system", "content": context.compiled},
        {"role": "user", "content": "How do I set up authentication?"}
    ]
)
```

## How Cilow Compares

| Feature | Cilow | Traditional RAG | Mem0 | Zep |
|---------|-------|-----------------|------|-----|
| Persistent memory | Yes - temporal graph | No - stateless | Yes - personalization layer | Yes - within sessions |
| FRR scoring | Yes - frequency + recency + relevance | No - cosine similarity only | Partial | No |
| Cross-session coherence | Yes - memory persists indefinitely | No - per-session only | Yes | Limited |
| Context compilation | Yes - query-specific minimal windows | No - retrieval only | No | Partial |
| Multi-agent support | Yes - shared memory graph | No | Limited | Limited |
| Framework agnostic | Yes - sits under any framework | N/A | Partial | Partial |

## Security & Compliance

- SOC 2 Type II certification in progress
- GDPR compliant with full data subject rights
- Tenant isolation with separate encryption keys
- AES-256 encryption at rest, TLS 1.3 in transit
- Data never used for model training

## Benchmark Results

- **94.17% on LongMemEval** (113/120 correct) - outperforming baseline RAG at 62.5% and Mem0 at 78.3%
- **226ms P50 latency** for full context assembly; sub-12ms for raw memory retrieval
- **70-90% context reduction** vs naive prompt stuffing
- **10K+ qps** concurrent query handling
- Full methodology and per-category results: https://cilow.ai/benchmarks

## Links

- Website: https://cilow.ai
- Documentation: https://docs.cilow.ai
- API Reference: https://docs.cilow.ai/api
- GitHub: https://github.com/cilow
- Contact: hello@cilow.ai
- Demo: https://calendly.com/cilow/demo