Agents That Remember Everything
Store, search, and recall context across sessions in <5ms. Semantic memory is built in — no external vector DB, no extra vendor, no glue code.
HOW IT WORKS
SQL + Vector in One Query
Agent memories are stored as structured rows with embedded vectors. The semantic_match() function enables hybrid queries — join structured data with semantic context in a single round-trip. Memories persist across sessions, are namespaced per agent, and support TTL-based expiry.
-- Recall relevant past resolutions
SELECT t.ticket_id, t.resolution,
semantic_rank(m.embedding,
'billing dispute') AS relevance
FROM tickets t
JOIN_VECTOR agent_memories m
ON semantic_match(m.embedding,
'billing dispute resolution', 0.75)
WHERE t.customer_id = 'cust_12345'
ORDER BY relevance DESC
LIMIT 5;WHY IT MATTERS
Why Agents Need This
No More Amnesia
Without persistent memory, every agent session starts from zero. Context is rebuilt, questions are re-asked, and users repeat themselves. Memory fixes this.
Semantic, Not Keyword
Agents need to recall by meaning, not exact strings. semantic_match() finds relevant memories even when the wording is different.
Cross-Agent Sharing
Memories are namespaced but shareable. A support agent can access resolution history from a billing agent — building organizational knowledge.
COMPARISON
Legacy Approach vs ANDI
Legacy Approach
ANDI Approach
ANDI SUBSTRATE
Explore the Full Platform
Agent-Native Data Infrastructure.
Production-Ready Today.
Persistent memory. Isolated state. Verifiable reasoning. Through standard SQL.