Midas · vs mem0
Midas vs mem0
LLM-extracted facts vs. eval-first local recall.
mem0 stores LLM-extracted facts in a managed vector store with a hosted API. Midas inverts that: ingest is LLM-free, persistence is a local SQLite file, and every recall is traceable to the source span that produced it.
Feature by feature
Feature
Midas
mem0
Recall@k (LongMemEval-s)
0.95 (alpha)
~0.68 reported
Ingest cost
LLM-free, deterministic
LLM call per memory
Latency (recall, p50)
~12 ms local
~180 ms hosted
Storage
Local SQLite + embeddings
Hosted vector DB
Source provenance
Span-level, returned by recall
Fact-level, lossy
On-prem / air-gapped
Yes, single file
Hosted by default
MCP server
Built-in
Not native
Eval reproducibility
BENCHMARKS.md + harness
Numbers via blog
When to pick mem0
- You want a hosted SaaS and don't need on-prem.
- You prefer a fact-extraction model and accept LLM cost on every write.
When to pick Midas
- You need eval-first numbers you can re-run on your own corpus.
- You need recall traceable to the original source span.
- You want local-first, single-file persistence and no per-write LLM cost.
Try Midas in 30 seconds.
pip install "midas-memory[all] @ git+https://github.com/vornicx/Midas" View on GitHub Other comparisons