Learn more · Join Discord · Demo

📄 Benchmarking Mem0's token-efficient memory algorithm →

New Memory Algorithm (April 2026)

Benchmark	Old	New	Tokens	Latency p50
LoCoMo	71.4	91.6	7.0K	0.88s
LongMemEval	67.8	94.8	6.8K	1.09s
BEAM (1M)	—	64.1	6.7K	1.00s
BEAM (10M)	—	48.6	6.9K	1.05s

All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops).

What changed:

• Single-pass ADD-only extraction -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten.
• Agent-generated facts are first-class -- when an agent confirms an action, that information is now stored with equal weight.
• Entity linking -- entities are extracted, embedded, and linked across memories for retrieval boosting.
• Multi-signal retrieval -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
• Temporal Reasoning -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.

See the migration guide for upgrade instructions. The evaluation framework is open-sourced so anyone can reproduce the numbers.

Research Highlights

• 91.6 on LoCoMo -- +20 points over the previous algorithm
• 94.8 on LongMemEval -- +27 points, with +53.6 on assistant memory recall
• 64.1 on BEAM (1M) -- production-scale memory evaluation at 1M tokens
• Read the full paper

Introduction

Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.

Key Features & Use Cases

Core Capabilities:

• Multi-Level Memory: Seamlessly retains User, Session, and Agent state with adaptive personalization
• Developer-Friendly: Intuitive API, cross-platform SDKs, and a fully managed service option

Applications:

• **AI Assi