// comparison

AgentRAM vs Supermemory

Last updated: July 2026

At a glance

	AgentRAM	Supermemory
Core approach	Key-value memory over HTTP	Context platform with multi-source ingestion
Retrieval	By key, plus literal text search	Hybrid semantic plus keyword search
Embeddings	None	Yes, managed for you
Source ingestion	You write memories explicitly	Imports documents, pages, and more
Pricing model	Pay-per-operation, 1 credit each	Usage-based with a free tier
Free tier	100 operations, no card	Generous free allowance
Multi-agent shared memory	First-class shared namespaces	Via containers and tags
API style	REST, no SDK required	REST and SDKs
MCP integration	Official npm package	Yes, MCP supported
Self-hosted	No	Limited, tied to higher plans
Best fit	Simple structured memory	Search across varied sources, coding agents

Supermemory's plans and limits change. Confirm current details on their site before deciding.

When Supermemory is the right call

Supermemory is the broader platform, and it earns that scope for search-heavy work. Pick it if any of these match:

• You need to ingest from many sources, turning documents, web pages, and past conversations into retrievable memory without building the pipeline yourself.
• You need semantic search across a large, varied body of text where the query words will not match the stored words. Supermemory manages the embeddings and hybrid retrieval for you.
• You are building a coding agent or a knowledge-heavy assistant, a use case Supermemory is frequently recommended for.
• You want one managed surface that handles extraction, storage, and retrieval together, rather than assembling those parts.

When AgentRAM is the right call

AgentRAM is the lower-friction choice when your memory is structured and you do not need a search platform:

• Your memory is key-value. Preferences, state, results, things you can name and look up by key. You do not need semantic search over a corpus, so the embedding machinery is overhead you would be paying for without using.
• You want pay-per-operation pricing that is easy to predict. One store or one retrieve is one credit, and read-only checks are free.
• You want a plain HTTP API with no ingestion step. Your agent writes what it wants to remember, directly, in one call. There is no pipeline to configure and nothing to index.
• Multi-agent shared memory is core to your design. Shared namespaces give several agents read and write access to one pool with built-in auth.
• You are building MCP-first and want an official server. AgentRAM ships one on npm (npm install agentram-mcp) mapping directly to the API.

The core difference

Supermemory is built around the idea that you have a lot of unstructured material and want your agent to find the relevant parts by meaning. That is a real and common need, and the ingestion plus hybrid search model fits it well.

AgentRAM is built around the opposite idea: that a large share of agent memory is not a search problem at all. When your agent already knows the key it wants, storing and reading a value should not require an embedding model or an ingestion pipeline. It should be one request each way.

How to decide

The honest test: are you searching across content, or looking up values you named?

If you are searching across documents, pages, and history to surface relevant passages by meaning, Supermemory is built for that and will save you from assembling it yourself.

If your agent already knows what to store and how it will ask for it, AgentRAM is simpler and cheaper, with no embeddings or ingestion to manage. If you are unsure which side of that line you are on, our guide on agent memory without a vector database walks through exactly how to tell.