Every AI agent today faces the same problem: they wake up with amnesia. Each session starts from zero unless you manually feed context back in.

The common workarounds — stuffing a MEMORY.md file into the system prompt, or using conversation history — have real limits:

• Context windows are finite. Even with 200K tokens, loading everything isn’t scalable.
• Keyword search fails. Grepping a markdown file for “database” won’t find “I prefer Postgres over MySQL.”
• No cross-session persistence. Switch devices or restart, and the context is gone.
• No importance weighting. A casual preference and a critical correction get the same treatment.

Semantic Memory: Search by Meaning

Persistent semantic memory solves this by storing information as vector embeddings. Instead of matching keywords, you search by meaning.

Ask for “database preferences” and you’ll find memories about PostgreSQL, Neon, and that time you said “never use MongoDB for this project” — even if none of them contain the word “database.”

What Good Memory Looks Like

A proper memory system for AI agents should:

1. Persist across sessions — survive restarts, device switches, and context resets
2. Support semantic search — find by meaning, not just keywords
3. Weight by importance — corrections matter more than casual mentions
4. Decay naturally — stale memories should fade unless reinforced
5. Work without setup — no databases to provision, no infrastructure to manage

Getting Started

With MemoClaw, adding persistent memory to your agent takes one command:

memoclaw init

That’s it. Your agent can now store and recall memories with semantic search, importance scoring, and natural decay — no signup, no API keys, no infrastructure.

Try it with the 100 free calls every wallet gets.