> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Edge AI

## Edge AI & On-Device Memory

Cognee is bringing AI memory to the edge with **cognee-RS**, our Rust-based SDK designed for resource-constrained devices. Run the full memory pipeline (ingestion, semantic organization, retrieval) directly on-device, sub-100ms recall and data stay local.

## The Edge AI Opportunity

Picture this: Your smart glasses capture a conversation during a run, instantly recall your to-do list, and feed you directions - all offline, with zero data uploaded. Or your smart-home hub analyzes your evening routine, suggests energy optimizations for better sleep, and monitors wellness patterns without sending a single byte to the cloud.

This is the future and the promise of edge AI memory.

## cognee-RS: Rust-Powered Memory for Devices

cognee-RS is our experimental Rust SDK. It is a port of cognee's proven memory architecture to edge devices like phones, smartwatches, glasses, and smart-home hubs.

It combines:

* A lean retrieval engine optimized for constrained resources
* Support for on-device LLMs
* Seamless hybrid switching to cloud when needed
* Full multimodal support (text, images, audio)

### Core Capabilities

**Fully Offline Operation**
Run with Phi-4-class LLMs and local embeddings—no internet required for queries or retrieval. Toggle to hosted models with a single config flag when you have connectivity and need more power.

**High Accuracy**
We're targeting 90%+ answer accuracy, matching our Python SDK. The local semantic layer ensures retrieval fidelity even with smaller models. Graph-aware retrieval boosts accuracy 15-25% through structural cues.

**Hybrid Execution**
Route tasks intelligently: local for embeddings, cloud for heavy entity extraction, or split dynamically based on connectivity, battery, and latency requirements.

**Multimodal Fusion**
Handles text, images, audio, and sensor data. Real-time fusion from device inputs (mic + camera) creates holistic context that a cloud-only approach can't match.

**Resource Orchestration**
Dynamic scheduling caps memory and CPU usage. Heavy processing doesn't interrupt core device functions—retrieval stays prioritized while batch ingestion happens during idle time.

## Use Cases: Where Edge Memory Excels

### Personal Voice Assistants

Smart earbuds and wearables that remember your conversations, preferences, and context—without uploading your private discussions to the cloud.

> "What did Sarah say about the project deadline during our walk yesterday?"

Local conversation memory enables instant recall. Sync only opt-in summaries, never raw audio.

### Smart Home & Wellness

Baby monitors, vital-sign wearables, and home hubs that analyze patterns locally—complying with GDPR and HIPAA by design.

* Sleep pattern analysis without cloud dependency
* Anomaly detection that works during internet outages
* Behavioral insights that stay on your network

Your health data stays yours.

### Robotics & Autonomous Systems

Drones, robots, and autonomous vehicles need real-time memory access for navigation and decision-making—especially in dead zones.

```
Robot enters new environment
    │
    ▼
cognee-RS builds local context map
    │
    ▼
Real-time retrieval: "Have I seen this obstacle type before?"
    │
    ▼
Decision without connectivity delay
```

No connectivity? No problem. Local context drives decisions.

### Industrial IoT

Factory-floor sensors, offline kiosks, and field equipment often operate in network-constrained environments.

Edge AI enables:

* 24/7 local reasoning without persistent connection
* Anomaly detection at the source
* Bandwidth savings—only critical events sync to cloud
* Continued operation during network outages

## Trade-Offs and Mitigations

Edge isn't effortless. Smaller models have tighter context windows. Devices have limited compute and battery budgets. Complex reasoning may exceed local capabilities.

cognee-RS addresses these constraints:

| Challenge              | Mitigation                               |
| ---------------------- | ---------------------------------------- |
| Limited context window | Graph-aware retrieval for precision      |
| Complex reasoning      | Hybrid execution—offload when needed     |
| Battery constraints    | Dynamic scheduling, idle-time processing |
| Storage limits         | Semantic compression, smart eviction     |
| Model size             | Support for Phi-4 class, upgradeable     |

cognee-RS is currently experimental. Early conversations with partners are giving promising results.

## The Vision: Memory Everywhere

The future isn't cloud-only AI. It's AI that runs where you are: on your phone, your glasses, your watch, your car. AI that remembers your context without uploading your life to someone else's servers.

cognee-RS is how we get there: the same semantic memory layer that powers enterprise deployments, compiled to run on the devices in your pocket.

Privacy-first. Real-time. Offline-capable. Memory-enabled.

***