Recallium vs Mem0

Feature comparison

Product details checked September 8, 2026; features and availability vary by edition. The benchmark figures measure different things: Recallium’s headline is retrieval recall at depth ten, Mem0’s is answer accuracy at depth 200, so this is not a controlled head-to-head ranking. On the accuracy lane, Mem0’s committed result file records 93.4% — the same as Recallium’s — while retrieving 200 results against Recallium’s 10; the readers differ, so this is a difference in context needed, not a ranking. Sources: Mem0 documentation and the memory-benchmarks repository.

How they differ in practice

Different product categories, benchmarked on common ground. Mem0 is a memory layer for AI applications: it extracts facts and preferences from conversations and serves them back to your app, scoped per user, agent or run. Recallium provides shared team context for coding agents: it captures what agents learn while working in a repository, architecture, decisions and fixes, and gives it to every agent and teammate on that codebase. Both are measured on LongMemEval-S because retrieval quality is measurable and comparable, not because the products are substitutes.

When to choose Recallium

• Your agents work in a shared codebase and need the same context
• You want decisions, patterns and fixes captured from agent sessions, not just facts about users
• Your team uses Claude, Cursor, Codex or VS Code and wants one memory across them
• You want a benchmark whose reader, judge and depth are published

When to choose Mem0

• You are building a custom AI application and need per-user memory
• You want Python or JavaScript SDKs and application integrations
• You want to self-host the open-source library today