Build agents that remember what matters
Practical guides for teams putting long-term memory into production: how the API works, how memory differs from RAG, how to connect Vercel AI SDK, CrewAI and LangGraph, and how to test isolation before an agent remembers a real customer.
Read these four first
The shortest path from “our agent forgets” to a system that writes, retrieves, corrects and deletes durable memory.
Vercel AI SDK memory without stuffing the transcript
Add durable Vercel AI SDK memory with scoped retrieval, streaming-safe writes, and a TypeScript pattern for user and tenant context. Includes tests and limits.
Memory vs fine-tuning: where should AI agents learn?
Compare memory and fine-tuning for AI agents: changing facts, behavior, cost, privacy, retraining, and a decision framework for choosing or combining both.
Best AI memory tools for agents in 2026
Compare Mem0, Zep, Letta, Cognee, Supermemory, LangMem and AI Memory on retrieval, governance, deployment, pricing and portability as of October 2026.
Multi-tenant AI memory without cross-customer leaks
Design multi-tenant AI memory with tenant scopes, query-time isolation, deletion, audit, noisy-neighbor controls, and tests that catch cross-customer leaks.
22 guides, 5 ways into memory
Start with the concept, compare the market, or begin with the workflow you need to ship this quarter.
Start here
The core concepts behind a production memory layer, from the first API call to the difference between a document index and a memory of the user.
Compare
Evidence-led comparisons of the memory products teams most often evaluate, with attention to pricing, deployment and what it takes to leave.
- A Mem0 alternative built for portability, not lock-in
- A Zep alternative when you want a memory layer, not a graph platform
- A Letta alternative for teams that want memory, not a new agent OS
- Self-hosted memory without giving up the managed API
- A Cognee alternative for teams that need portable memory
- A Supermemory alternative for application memory
Integrate
Framework-neutral patterns for putting durable memory around the agent runtime you already use, without turning every conversation into a permanent transcript.
Evaluate
Decision frameworks and test plans for quality, portability, governance, cost and the tenant boundaries that must hold before production.
Use cases
Two places where memory changes the product rather than the prompt: coding agents that carry project decisions, and support agents that carry the customer relationship.
How to tell real memory from a longer prompt
A memory layer should reduce the context you send and improve what the agent does next. This three-step test exposes the difference quickly.
Start with one durable fact
Pick a fact the agent should know on the next session, such as a plan change, a preference or a decision. Write it once and retrieve it after the original transcript is gone.
Measure the context you save
Compare the tokens returned by memory with the transcript you would otherwise send. The useful target is not a longer prompt; it is a smaller prompt with a better answer.
Test correction and deletion
Change the fact, supersede it, then delete the user. A memory layer is production-ready only when the wrong thing can be corrected and the departing customer can disappear.
Put one real memory through the whole loop
Add an event, retrieve it after the transcript is gone, correct it, and export the graph. AI Memory is designed so every one of those steps stays visible and reversible.