// comparison

AgentRAM vs LangMem

Last updated: July 2026

At a glance

	AgentRAM	LangMem
What it is	A hosted memory API	An open-source SDK you run in your own stack
Core approach	Key-value memory over HTTP	LLM-driven memory manager over a store you provide
Decides what to store	You do, explicitly	An LLM extracts and consolidates it for you
Retrieval	By key, plus literal text search	Semantic similarity search
Embeddings	None	Yes, over your chosen store
Storage	Included and hosted	You bring your own (vector DB, Postgres, key-value)
LLM required	No	Yes, you configure and pay for one
Memory types	Key-value	Semantic, episodic, procedural
Pricing model	Pay-per-operation, 1 credit each	Free and open-source; you pay for the LLM and storage
Infrastructure to run	None	You host the library and its store
Framework fit	Any stack, plain REST	LangChain and LangGraph first; works standalone
API and SDKs	REST, plus official Python and TypeScript SDKs	Python SDK
Multi-agent shared memory	First-class shared namespaces	Namespaces per user or tenant
Best fit	Simple structured memory, no infra	Automatic extraction and semantic recall

LangMem is open-source and evolving. Confirm current details in the LangChain docs before deciding.

When LangMem is the right call

LangMem does real work that AgentRAM deliberately does not. Pick it if any of these match:

• You want memory extracted automatically. LangMem's LLM-driven manager reads a conversation, decides what is worth keeping, and writes it for you, rather than you naming each fact.
• You need semantic recall across accumulated memory, where the query words will not match the stored words and similarity search is what you want.
• You want memory that consolidates over time, merging duplicates, updating facts that changed, and summarizing old material through a background process.
• You are already in LangChain or LangGraph and want memory tools that drop straight into that agent loop.
• You are comfortable running the pieces: providing a storage backend, wiring an LLM provider, and paying for the model calls that extraction and consolidation require.

When AgentRAM is the right call

AgentRAM is the lower-friction choice when your memory is structured and you do not need an extraction engine:

• Your agent already knows what to store. Preferences, state, results, IDs, things you can name and look up by key. You do not need an LLM to decide what to remember, so that machinery is cost and latency you would pay without using.
• You want nothing to run. AgentRAM is a hosted endpoint. There is no library to host, no vector database to operate, and no LLM provider to wire up and pay for.
• You want predictable pay-per-operation pricing. One store or one read is one credit, and read-only checks are free. No per-call model costs stacked underneath.
• You are framework-independent. Plain REST, plus official pip install agentram-sdk and npm install agentram-sdk clients, so it fits any stack rather than assuming LangChain.
• 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.

The core difference

LangMem is built on the idea that deciding what to remember is itself a job for a model. It reads the raw conversation, extracts the durable facts, keeps them tidy over time, and finds them again by meaning. That is genuinely useful when memory is messy and open-ended.

AgentRAM is built on the opposite idea: that a large share of agent memory is not messy at all. When your agent already knows the key it wants, storing and reading a value should not require an LLM, an embedding model, or a database you operate. It should be one request each way, against something you do not have to run.

They sit at different layers. LangMem is the manager, and it still needs a store beneath it. AgentRAM is that store, offered as a managed service. Strip LangMem down to only its storage layer and you are close to what AgentRAM gives you out of the box, without the extraction engine on top.

How to decide

The honest test: do you want something to decide what to remember, or do you already know?

If your agent produces open-ended conversations and you want facts pulled out, consolidated, and recalled by meaning without writing that logic yourself, LangMem is built for exactly that, and its LangChain integration will save you assembly.

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