How to Give AI Agents Long-Term Memory:
5 Best Practices for 2026

Implement "Selective Remembering"

Don't store every word of a transcript. It creates noise. Use a "Summarizer" step before calling POST /v1/memory/remember.

Use Metadata for Hard-Filtering

While semantic search is powerful, sometimes you need exact filters. The metadata field lets you scope recalls to a specific user, session, or category without relying on similarity alone.

# Best practice: tag memories with structured metadata
payload = {
    "content": "User prefers dark mode.",
    "metadata": {
        "user_id": "user_123",
        "type": "UI_PREFERENCE"
    }
}

"Recall" Before "Act"

Always hit GET /v1/memory/recall before the agent generates a response. This "Reflective" step ensures the LLM is grounded in past facts, reducing hallucinations and contradictions.

Manage Memory Decay

Not all memories are permanent. Implement "forget" logic for outdated information using DELETE /v1/memory/forget/:id.

If a user updates their email address, the agent should delete the old record to avoid conflicting context. Stale memories are worse than no memories — they cause confident hallucinations.

Multi-Turn Retrieval

For complex queries, don't just search once. If the first recall doesn't provide enough context, have the agent reason about what else it needs and perform a second, more specific search.

# Round 1: broad recall
initial = recall("user project details")

# Round 2: agent reasons about gaps, searches again
if "deadline" not in str(initial):
    followup = recall("project deadline and milestones")
    context = initial + followup
else:
    context = initial

response = llm(system_prompt + context + user_query)