So it instantly remembers every detail you’ve built and decided with 80% fewer tokens. See performance
Read https://slashml.ai/llms.txt and set up / ml as our AI memory system.Existing memory tools are wrappers that chain LLM prompt calls over external vector and graph databases—adding seconds of inference lag and stuffing thousands of noisy tokens into every prompt. / ml is a purpose-built native memory engine with zero external LLM calls in the memory loop, recalling exact context in 0.86 ms with 80% lower downstream prompt token bills.
Top-10 Retrieval scores 66.9% (68.4% with Mem0g Graph Memory, locked behind the $249/mo Pro paywall). Their blog’s 92.5% claim uses Top-200 Retrieval (~6,956 tokens/query = half of the entire 16K LoCoMo transcript stuffed into every prompt, multiplying downstream LLM token bills ~5× and pushing 26K-token P50 latency to 3,550 ms). On 10M-token BEAM (12.3M tokens, 20,561 chunks), Mem0 drops to 48.6% with recall latency exceeding >10,000 ms and 16–40+ hours of LLM ingestion calls (vs. / ml: 77.2% in 39.8 ms, 3.7× fewer tokens/query, and 2.4 min zero-LLM ingestion).89.0% LoCoMo score invokes a query-time /chat Dialectic LLM reasoning endpoint on top of background “dreaming” LLM synthesis workers—adding 1–5s of LLM inference latency (~2,500 ms P50) and secondary token billing on every recall. On 600-task DolphinBench, Honcho scores 68.5% at $142.85 (3.1× higher run cost than / ml).89.6% only by swapping the answering backbone to Gemini-3 Pro while running multi-pass agentic reflect loops. On DolphinBench it scores 69.5% ($84.65) with severe tail latency: 158.9s–602.9s (10 min) P95.81.6%–85.2% on LoCoMo/LongMemEval subsets. On the independent 600-task DolphinBench coding & tool-use suite, Supermemory scores 59.2%—which is 6.5 percentage points worse than the Built-in no-memory baseline (65.7%)—while inflating total benchmark run cost to $345.64 (5.6× baseline cost).
Verbatim outputs from open-source DolphinBench
(Task #157 · recalled across 5,011 sessions).
“Historical correction: Lantern v0 was not solely Alex’s project, and Alex did not own UI interpretation. Alex owned the data and failure-mode shape; Iris owned UI interpretation and presentation.”
Recalled Session #404 and corrected the false PR description.
Mem0
✗ FAILED
“...The responsibility split was bounded: Alex owned technical monitoring, failure modes, and the shared-registration safety gate; ... Iris owned customer interpretation and feedback.”
Missed Lantern v0 (0 results) and hallucinated roles from an unrelated project.
Honcho ·
Hindsight ·
Supermemory
✗ FAILED
“Historical Lantern v0 responsibility split: Lantern v0 was solely Alex’s project. Alex owned both the data model and the UI interpretation.”
Forgot Session #404 completely and blindly repeated the false PR text.
For chat assistants, coding agents & hobby projects.
The best memory for production agents & startups.
High-throughput multi-tenant memory infrastructure.
Dedicated appliance inside your own VPC or on-prem.
Everything you need to know about giving your AI agents persistent, real-time memory with / ml.
/ ml is a frontier AI memory system. While legacy tools queue background extraction jobs across vector and graph databases (causing 15–30 seconds of write lag), / ml indexes memory synchronously in milliseconds and recalls exact context in 0.86 ms.
Graph Memory connects entities, user preferences, and code relationships across months of sessions. Your agents gain multi-hop reasoning, cross-session continuity, and 77.2% recall even at 10M+ tokens—included on every tier with no $249/mo paywall.
Every retrieved memory is billed as input tokens by your LLM provider (OpenAI, Anthropic, Google). Instead of dumping 6,956 tokens of raw transcript chunks into your prompt like Mem0, / ml retrieves only ~1,400 tokens of high-precision context—delivering higher accuracy (94.5% vs 92.5% on LoCoMo) with an 80% reduction in your downstream token bill.
Yes, / ml provides drop-in compatibility with standard .add() and .search() calls. You can replace your existing memory import in one line and keep your entire LangGraph, CrewAI, or agent architecture intact.
Every / ml workspace (/ ml free, pro, and platform) runs in an isolated, encrypted tenant state with instant cryptographic GDPR deletion. For strict data sovereignty, / ml sovereign deploys as a dedicated VPC appliance directly inside your private cloud or air-gapped environment with zero outbound network calls.
On / ml free, usage stops safely at the free limit with $0 risk. On / ml pro and platform, your agents never experience downtime—usage beyond your monthly pool continues seamlessly via pay-as-you-go overages ($0.50/1k ops on Pro, $0.20/1k ops on Platform) with configurable spend caps.
Start free with / ml free (10k ingests + 1k retrievals/mo, no feature lock), scale on / ml pro ($19/mo), or deploy / ml sovereign on your VPC.
Read https://slashml.ai/llms.txt and set up / ml as our memory system using SLASHML_API_KEY.