Best Memory APIs for AI Agents: How to Choose

The best memory API for an AI agent is the one that preserves the context your workflow needs and makes its failures observable. Start with the job: remembering a customer's earlier ticket, maintaining a changing user profile, retrieving team documents, or keeping a long-running agent's state. Those requirements lead to different shortlists.

Supermemory, Mem0, Zep, Letta, and Weaviate Engram offer different approaches. A vector database is another valid foundation when you want to implement the memory behavior yourself. This comparison is published by Supermemory and uses documented capabilities to help you choose a pilot. Test your shortlist on the same task before making a performance or cost decision.

Which memory API should you evaluate first?

Your main requirement	A useful starting point	What to verify in a pilot
Content ingestion, reusable user context, and retrieval in an existing application	Supermemory	Your source formats, profile behavior, processing readiness, and scope enforcement
Adding and searching extracted facts with managed or open-source options	Mem0	The selected product's write, update, deletion, and deployment behavior
Managed graph-based context for changing relationships	Zep	Your relationship queries, user context, ingestion lag, and governance requirements
Stateful agents with persistent blocks in their working context	Letta	Whether its agent and memory model fits the application you intend to operate
Memory extraction and retrieval within the Weaviate ecosystem	Weaviate Engram	Required scopes, processing pipeline, search mode, and plan availability
Custom retrieval and lifecycle policies on infrastructure you already operate	A vector database plus application logic	The behavior you must build beyond storage and search

These are starting points, not mutually exclusive capabilities. A product can fit more than one row. Reduce the shortlist using a real requirement instead of counting checkmarks in a feature table.

What does a memory API add to an agent?

A memory API supplies a path for retaining information and retrieving it in later interactions. The application still needs identity, authorization, a policy for what should be remembered, and a way to distinguish current facts from historical ones.

Persisting an entire conversation is useful for reopening it. It does not by itself tell an agent which detail matters in a different conversation. Conversely, a retrieved preference is not a complete transcript. Keep those responsibilities separate when comparing APIs.

For a returning customer, the useful memory might be: the integration failed with a 401 error, rotating the key did not fix it, and the issue remains unresolved. The current subscription or outage status should come from its authoritative system. The support-agent architecture turns that example into a testable workflow.

Supermemory: ingestion, profiles, and retrieval

Supermemory is worth evaluating when you want to add memory to an application while keeping its agent logic. Its documented user profiles provide reusable context alongside targeted retrieval. Connectors offer source-specific ingestion paths.

Test the exact source you intend to use. A connector's existence does not establish that every source permission is automatically enforced for every application user. Check updates, revocation, and deletion, not only the first successful answer.

The AI SDK walkthrough provides a version-pinned entry point. Evaluate its behavior against your existing application before expanding the integration.

Mem0: identify the product and operation

Mem0 documents both managed Platform and open-source usage. Its add-memory guide describes extracting facts from messages and also distinguishes inference from raw-content storage. The current guide describes add behavior as ADD-only; do not assume a new write performs every correction or deletion your application needs.

Test updates explicitly and keep Platform and open-source configurations separate in the comparison. Operating a self-hosted stack is a different commitment from calling a managed endpoint. Neither should inherit a latency or pricing claim measured on the other.

Use the existing Supermemory versus Mem0 comparison for that specific buying decision and the migration guide for a proposed move.

Zep: managed context versus Graphiti

Zep's managed service and the Graphiti framework are related but distinct choices. Graphiti provides a framework for temporal knowledge graphs. Zep offers a managed context platform and documents automatically maintained user summaries.

Test the required relationship queries and the time between ingestion and usable context. The Supermemory versus Zep guide provides a focused evaluation framework.

Letta: memory within a stateful agent model

Letta documents memory blocks that persist in the agent's context and can be read or updated through memory tools. This is a different integration choice from adding a retrieval call to an otherwise unchanged agent.

Evaluate Letta when its stateful agent model matches what you are building. Test how working context grows, how shared blocks behave, and how you inspect or recover state. The integration should fit how your application creates agents and manages their state.

Weaviate Engram: memory is a separate offering

Weaviate Database and Engram should not be collapsed into one feature row. Engram documents memory extraction, scoped persistence, asynchronous processing, and search.

If your team already uses Weaviate, evaluate whether Engram reduces the additional work. The Weaviate comparison separates the database, memory service, and application responsibilities.

How should you compare memory APIs fairly?

Use the same source histories, questions, answering model, and evaluation rubric. Let each provider use a reasonable supported configuration, and record the differences. A search endpoint's mean latency is not comparable with another product's full answer time or write-processing delay.

A small pilot should include these cases:

1. A returning user refers to an unresolved earlier task.
2. A user corrects a preference, then asks a current and a historical question.
3. Two tenants supply similar content; neither may retrieve the other's data.
4. A document changes or loses permission after it was imported.
5. A question has no supporting evidence.
6. A dependency fails during ingestion or retrieval.

Measure answer support, processing lag, retrieval latency, end-to-end time, and cost per successful task. Treat isolation and deletion requirements as release gates. A better average answer score should not cancel out a forbidden disclosure.

Use MemoryBench for reproducible evaluation and add the cases unique to your product. For commercial comparison, apply the same workload to current rate cards and include integration and operating effort through the build-versus-buy model.

Do you need a memory API if you already have RAG?

Not always. If your application already persists the required history, retrieves it under the correct scope, handles corrections, and removes information as promised, another service may add little. If those behaviors are missing, identify the gap and test whether a memory API closes it. The RAG versus agent memory guide explains that boundary.

Choose two plausible options, run the same workflow, and preserve the results. The useful outcome is an agent that continues a customer's work correctly, with an operating model you understand.

Add Supermemory to your shortlist by starting a small evaluation in the console. Bring the same question set, correction cases, and isolation checks you use for the other candidates, then choose from the results.