How do I build memory for AI agents that run on mobile or edge devices?

AI agents running on mobile or edge devices face constraints that cloud-hosted agents do not: limited storage, intermittent connectivity, battery sensitivity, and privacy requirements that may prohibit sending user data to external servers. Memory architecture for edge AI requires a local-first approach to storage and retrieval.

The constraints

• Storage: Mobile vector stores must stay compact — typically under 50MB for a single user's memory. A typical user accumulates 50-500 memory entries after months of use, which at ~1,500 bytes per entry is 75KB-750KB. Manageable on any modern device.
• Embedding models: Calling an external embedding API requires network and adds latency. On-device embedding models — quantized MiniLM-L6 at ~22MB, nomic-embed-text quantized — run in 10-50ms with no network dependency.
• Connectivity: Memory operations must function offline. Sync with cloud backup when connectivity is available.
• Privacy: Many edge AI applications are explicitly local-first because the user does not want data sent to external servers.

On-device memory stack with Mem0

from mem0 import Memory

local_config = {
    "vector_store": {
        "provider": "sqlite",
        "config": {
            "db_path": "/data/user/memory.db"  # On-device path
        }
    },
    "embedder": {
        "provider": "ollama",
        "config": {
            "model": "nomic-embed-text",  # Runs locally
            "base_url": "http://localhost:11434"
        }
    },
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "llama3.2:1b",  # Local extraction model
            "base_url": "http://localhost:11434"
        }
    }
}

memory = Memory.from_config(local_config)

# All operations run on-device — no network required
memory.add([{"role": "user", "content": "I prefer dark mode and use an iPhone 15"}],
           user_id="local_user")

results = memory.search("device preferences", user_id="local_user")
# Runs entirely on-device

Sync strategy

For applications that want cloud backup or cross-device sync, implement a two-tier approach: primary storage on-device for low-latency offline access, background sync to Mem0's managed cloud platform when connectivity is available. Sync should be differential — only uploading new or modified entries — to minimize battery and bandwidth impact.