ArgosBrain vs Mem0

What Mem0 is

The baseline, stated fairly.

Mem0 (MIT OSS + Cloud) is the current SOTA for general conversational agent memory. Hybrid vector + optional graph (Mem0g). An LLM extraction pipeline (GPT-4o-mini with function calling) converts conversation history into facts, stored in a vector DB; Mem0g adds a directed labeled knowledge graph with conflict detection.

How it actually works

Technical facts.

ArchitectureHybrid vector; Mem0g adds graph (Pro tier).
Write costOne LLM extraction call per ingest batch; graph variant adds entity + relation + conflict calls.
Read costEmbedding + vector search, no read-time LLM, avg <7k tokens vs 25k+ baselines.
BenchmarksLoCoMo 91.6%, LongMemEval 93.4%, BEAM 64.1/48.6 (1M/10M), +29.6 temporal / +23.1 multi-hop over prior SOTA.
Code-specific featuresNone. No AST, no symbols, no file-hash invalidation.
PricingHobby free (10K / 1K), Starter $19/mo, Pro $249/mo (graph), Enterprise custom.

Sources: Mem0 docs · Paper

Verdict

Where each one wins.

↑ Where ArgosBrain wins

Code-native. Mem0 stores extracted facts as prose. It cannot answer resolve_member("foo.bar") deterministically.
$0 write path — ingest is LLM-free (tree-sitter + graph). Mem0 pays LLM on every write.
File-hash staleness. Mem0's conversational memories have no notion of "the code changed, this fact is stale."
In-process Rust. Mem0 requires a vector DB backend.

↑ Where Mem0 wins

91.6% on LoCoMo is the bar; ArgosBrain's non-negotiable is ≥91.6% on LongMemEval — we match, we don't beat, on general memory.
Managed Cloud with team features.
Larger OSS community and integrations.

When to choose which

Honest recommendation.

Choose Mem0 if

Your agent is conversational (support bot, assistant)
You need team/cloud features

Choose ArgosBrain if

Your agent writes code