Best AI memory tools for agents

Agents that remember beat agents that don't. These are the memory backends we recommend for production agent workflows.

Quick answer

Best overall:
Mem0
Best vector DB:
Pinecone
Best self-host:
Qdrant
Best budget option:
Supabase pgvector

What to avoid: Rolling your own memory in raw Postgres without pgvector or a vector store — retrieval falls off a cliff.

Last verified: July 2026

Comparison matrix

Mem0
Score 9.0

Agent episodic memory

API
Yes
Free tier
Yes
Pricing
From $19/mo
Strength
Purpose-built for agent memory
Limitation
Newer than raw vector DBs
Verified
2026-07
Pinecone
Score 9.0

Managed vector DB

API
Yes
Free tier
Yes
Pricing
Starter free
Strength
Serverless, fast retrieval
Limitation
Costs jump at scale
Verified
2026-07
Qdrant
Score 8.8

Self-hosted vector DB

API
Yes
Free tier
Yes
Pricing
OSS + cloud
Strength
Great perf, self-host option
Limitation
You own ops
Verified
2026-07
Supabase pgvector
Score 8.4

Cheapest useful memory

API
Yes
Free tier
Yes
Pricing
Free tier
Strength
Postgres you already have
Limitation
Lower recall vs dedicated vector DBs
Verified
2026-07

When to use these

Use for chat agents with long memory, RAG over user data, personalization and session recall.

FAQ

Do I need a vector DB or just Postgres?

For simple RAG, pgvector is fine. For agent episodic memory across sessions, prefer Mem0, Pinecone or Qdrant.

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