/ ml vs Mem0

A frontier AI memory system vs. an async LLM wrapper around flat vector search and Neo4j.

/ ml Frontier AI Memory System

✓94.5% LoCoMo at strict Top-20 (~1,400 prompt tokens/query) & 77.2% at 10M tokens
✓0.86 ms P50 recall & synchronous <10 ms writes (immediately searchable)
✓Full Graph Memory included on $0 Free & $19/mo Pro (-80% LLM prompt token bill)

Mem0 / Mem0g Async LLM Wrapper + Vector/Neo4j

✕66.9% LoCoMo at standard Top-10 (stuffs 6,956 tokens @ Top-200 to reach 92.5%) & 48.6% at 10M
✕3,550 ms P50 recall (at Top-200) & 15–30s async write lag (6 tok/s background LLM queue)
✕$249/mo paywall required to unlock Graph Memory (Mem0g); 5× higher LLM prompt bills

LoCoMo Accuracy +27.6 pts

/ ml 94.5%

Mem0 66.9%

Top-20 vs Mem0 Top-10 (92.5% @ Top-200)

Recall P50 Latency 4,127× Faster

/ ml 0.86 ms

Mem0 3,550 ms

Sub-ms state recall vs Top-200 search

Prompt Tokens / Q -80% LLM Bill

/ ml 1,400 tok

Mem0 6,956 tok

Precision Top-20 vs Top-200 context dump

Graph Memory Plan -92% Cost

/ ml $19/mo

Mem0g $249/mo

Full graph included on $0 Free & $19 Pro

How / ml outperforms Mem0 across accuracy, scale, latency, and cost

Evaluated across LoCoMo (ACL 2024; Mem0 arXiv:2504.19413), DolphinBench (600 multi-session agent tasks), and BEAM 100K–10M (ICLR 2026).

LoCoMo · DolphinBench · BEAM 10M · Higher is Better

Retrieval Accuracy Across Scales

+28.6 pts @ 10M Tokens

/ ml LoCoMo Top-20

94.5%

Mem0 LoCoMo Top-200

92.5%

Mem0g Graph Top-10 ($249)

68.4%

Mem0 Paper Top-10

66.9%

/ ml DolphinBench (600)

80.8%

Mem0 DolphinBench (600)

70.7%

/ ml BEAM 10M Scale

77.2%

Mem0 BEAM 10M Scale

48.6%

Recall Latency · Prompt Tokens · Run Cost · Lower is Better

Latency & Token Overhead

0.86 ms · -80% Tokens

/ ml Recall P50

0.86 ms

Mem0 Recall P50 (Top-200)

3,550 ms

/ ml Prompt Tokens / Q

1,400 tok

Mem0 Prompt Tokens / Q

6,956 tok

/ ml Dolphin 600 Cost

$45.90

Mem0 Dolphin 600 Cost

$96.21

/ ml Graph Memory Plan

$19/mo

Mem0 Graph Memory Plan

$249/mo

Three structural bottlenecks every Mem0 developer runs into

Mem0 orchestrates external LLM prompts on top of a vector database. Here is why that architecture breaks down on accuracy, latency, and cost—and why / ml's frontier AI memory system wins.

Mem0 stuffs 6,956 tokens into every query to hide 66.9% precision

/ ml 94.5% @ 1,400 tok (0.86 ms)

Mem0 66.9% @ Top-10 · 6,956 tok @ Top-200

In Mem0's own paper (arXiv:2504.19413), standard Top-10 Retrieval scores only 66.9% on LoCoMo (68.4% with Mem0g graph). To reach their blog's 92.5% headline, Mem0 retrieves Top-200 memories—stuffing 6,956 tokens per query into your LLM prompt, multiplying your OpenAI/Anthropic bill by 5× and pushing recall latency to 3,550 ms.

/ ml Advantage: Scores 94.5% SOTA at strict Top-20 Retrieval (~1,400 tokens/query) in 0.86 ms—cutting your downstream LLM prompt token bill by 80%.

Turn-2 amnesia from background LLM extraction queues

/ ml Synchronous <10 ms · Immediately Searchable

Mem0 6 tok/s · 15–30s Async LLM Queue

Every time you call m.add() in Mem0, it queues 2–4 background LLM calls to extract facts, generate embeddings, and reconcile conflicts at roughly 6 tokens/second. During rapid multi-turn agent loops or tool calls, facts written on Turn 1 are still sitting in Mem0's queue for 15–30 seconds and cannot be recalled on Turn 2.

/ ml Advantage: Updates state synchronously in <10 ms with zero external API fees. Writes are searchable on the very next millisecond.

Graph Memory costs $249/mo—and still drops to 48.6% at 10M tokens

/ ml 77.2% @ 10M · $19/mo ($0 Free)

Mem0g 48.6% @ 10M · $249/mo Paywall

Mem0's $19 Starter tier gives you flat vector search only—locking relational Graph Memory (Mem0g) behind a $249/mo Pro paywall. Worse, as user histories grow to 10M tokens on BEAM, Mem0's vector + graph hybrid saturates and drops to 48.6% accuracy.

/ ml Advantage: Full multi-hop relational & code memory is included on $0 Free and $19/mo Pro, sustaining 77.2% accuracy at 10M tokens (+28.6 pts).

Complete Head-to-Head Breakdown: / ml vs. Mem0

Every benchmark, latency percentile, and billing line-item side by side.

Dimension	Mem0 / Mem0g	/ ml	Your Advantage with / ml
Core Architecture	LLM Extraction Wrapper + Vector DB + Neo4j	Frontier AI Memory System	Single self-contained state machine; zero external DBs
LoCoMo Accuracy	66.9% (Top-10) · 68.4% (Mem0g) · 92.5% (Top-200)	94.5% SOTA (Top-20 Retrieval)	+27.6 pts over Top-10; beats Top-200 with 5× fewer tokens
DolphinBench (600 Tasks)	70.7% accuracy · $96.21 run cost	80.8% accuracy · $45.90 run cost	+10.1 pts higher accuracy at 52% lower total cost
BEAM Scale (100K → 1M → 10M)	70.9% → 64.1% → 48.6%	88.9% → 83.5% → 77.2%	+28.6 pts at 10M tokens (no long-horizon collapse)
Recall Latency (P50)	3,550 ms (Top-200 Retrieval)	0.86 ms	4,127× faster (99.98% latency reduction)
Write Speed & Availability	6 tok/s (15–30s async LLM queue lag)	Synchronous (<10 ms)	245× faster ingestion; zero Turn-2 amnesia
Downstream LLM Prompt Tokens	6,956 tokens / query (at Top-200)	~1,400 precision tokens / query	80% reduction in your OpenAI / Anthropic input bill
Graph Memory Pricing	Locked behind $249/mo Pro paywall	$0 Free (10k+1k) · $19/mo Pro (100k ops)	92% lower monthly plan ($2,760/yr saved)
Self-Hosted VPC Write Compute	2–4 LLM calls/write (~$30,000 per 1M saves)	$0 external LLM API fees (self-contained)	100% elimination of internal LLM extraction bills

Switch from Mem0 to / ml in 1 line of code

100% drop-in API compatibility with Mem0's .add(), .search(), .get_all(), and .delete() methods.

Get Your API Key & Install the / ml SDK

Sign up free at app.slashml.ai to get your <SLASHML_API_KEY> (10,000 ingests + 1,000 retrievals/mo free, zero feature locks), then install the SDK or MCP server. Unlike Mem0, / ml is a frontier AI memory system—requiring zero Docker containers, Qdrant, Neo4j, or external vector databases.

Terminal Installation & API Key Setup Bash / Zsh

$# Export your / ml API key from https://app.slashml.ai+export SLASHML_API_KEY="<SLASHML_API_KEY>" $# Python (uv / pip) or TypeScript / Node.js+uv add slashml   # or: npm install @slashml/memory $# Or MCP & Skill (Claude Code / Cursor / Windsurf → registers ml-memory)+npx -y @slashml/memory --init --skill ml-memory --api-key <SLASHML_API_KEY>

1-Line Drop-In Code Replacement

Replace your import statement and pass api_key="<SLASHML_API_KEY>". / ml provides the exact same .add(), .search(), and .get_all() signatures. All your existing user IDs, session metadata, and prompt injection loops continue working seamlessly.

agent.py — Code Migration Diff Drop-in Compatible

-from mem0 import Memory+from slashml import Memory -# Mem0 dumps 6,956 tokens into your prompt & queues writes for 15-30s-m = Memory()+# / ml is a frontier AI memory system (~1,400 prompt tok, 0.86ms recall)+m = Memory(api_key="<SLASHML_API_KEY>")  # 100% IDENTICAL API CALLS — ZERO REFACTORING: m.add("User prefers dark mode and uses uv for python dependencies", user_id="agent_01")  # Multi-hop graph recall in 0.86 ms: memories = m.search("What packaging tool does user prefer?", user_id="agent_01") print(memories)

Automated Data Importer (1-Command CLI)

Migrate your existing stored memories, user profiles, and graph relationships from Mem0 Cloud or local SQLite/Qdrant into / ml with a single automated CLI command.

Automatic Memory Importer CLI Migration

$# Option A: Import directly from Mem0 Cloud API into / ml+npx -y @slashml/memory migrate --from mem0 --mem0-key $MEM0_API_KEY --api-key <SLASHML_API_KEY> $# Option B: Import from local Mem0 SQLite / JSON backup into / ml+python -m slashml.migrate --from mem0 --input ./mem0_export.json --api-key <SLASHML_API_KEY>

Ready to upgrade from Mem0 in 60 seconds?

Start free with 10,000 ingests + 1,000 retrievals/month and zero feature locks, or get in touch for custom VPC migration support for high-volume enterprise clusters.

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