Engram is the better fit if
Choose Engram when one managed MCP memory should work across agents, repositories, and teammates, with explicit recovery after context compaction.
Public benchmark
Engram vs LangMem / LangGraph
Compare Engram and LangMem / LangGraph on public LoCoMo memory tasks. Same-method results are shown where available, with methodology and limitations visible.
Engram92.19%
LangMem / LangGraph83.98%
The short answer
LangMem / LangGraph is the better fit if
Choose LangMem when you are already building a LangGraph application and want memory primitives you can wire into that framework.
What this page does not prove
The benchmark below uses the same public retrieval and answer tasks. It does not measure the wider LangGraph application stack.
What the numbers say
Side by side
+8.21 pp
Engram leads by 8.21 percentage points on R@50.
+19.39 pp
Engram leads by 19.39 percentage points on answer accuracy.
| Decision point | Engram | LangMem / LangGraph |
|---|---|---|
| Primary product shape | Managed memory layer for agent teams | Memory toolkit for LangGraph applications |
| Delivery model | MCP · Built in | Python library plus LangGraph stores |
| Cross-tool agent use | Supported | Possible through your application |
| Context-compaction recovery | Built in | Not measured here |
| Team and cross-repo learning | Built in | Not measured here |
| Same-method answer evidence | Supported | Supported |
Questions buyers ask
Were Engram and LangMem / LangGraph tested the same way?
The values marked as same-method use the same public LoCoMo task, candidate depth, and scoring rule. Product-published numbers from a different protocol are not mixed into this table.
Does the highest retrieval score automatically mean the best product?
No. Retrieval measures whether useful evidence appears in the candidate set. Answer accuracy measures whether that evidence helps produce the right answer. Operations, privacy, integrations, and cost remain separate buying decisions.
Which product should I choose?
Choose Engram for a ready-to-use MCP memory service across agent clients. Choose LangMem when memory is a component inside an application you are building with LangGraph.
Engram is measured two ways: first, whether it retrieves the right evidence; second, whether an LLM can answer correctly from that retrieved context.
We keep retrieval and answer accuracy separate. Both use public LoCoMo, but they measure different parts of the memory loop.