[D] How long-term memory actually works in AI agents (technical breakdown)

Been building agentic AI systems and wanted to share what I’ve learned about memory architecture. This isn’t about chatbots remembering your name, it’s about agents that learn from outcomes and adapt over time.

The core problem: LLMs are stateless. Context windows have limits. You can’t dump every past interaction into every prompt. So you need a memory layer.

Three memory types that matter:

1. Episodic memory – What happened. Structured logs of requests, tools used, outcomes, errors. Not raw conversation logs, summarized and indexed.
2. Procedural memory – How users work. Preferences, workflow patterns, communication style. The tricky part is users don’t explicitly state preferences, you infer them from behavior.
3. Semantic memory – Facts and knowledge. Both general (industry knowledge, tool capabilities) and user-specific (company info, contacts, deadlines).

Why basic RAG falls short:

Vector similarity search alone misses important dimensions:

• Recency (yesterday’s memory often beats a semantically closer one from 6 months ago)
• Context match (same project should weight higher)
• Outcome quality (successful interactions are more useful than failures)

You need multi-factor relevance scoring combining semantic similarity, temporal decay, context alignment, and success weighting.

New platforms that have designed memory systems, better than the big players:

• Starnus – AI coworker, verticalized on sales (at least for now); basically Claude Code for sales.
• Mem0 – Memory layer for AI apps, handles the storage/retrieval infrastructure
• Zep – Long-term memory for AI assistants, focuses on conversation history and facts
• Clawd Bot – Local AI assistant with proper memory management system

Hard problems still being solved:

• Memory staleness (facts change, preferences evolve)
• Privacy/control (users need to see and manage what’s stored)
• Cross-context boundaries (should project A memories influence project B?)
• Scale and cost (embeddings and LLM summarization add up)

Curious what approaches others are taking. Anyone using graph-based memory instead of pure vector search?