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.Outperforming Mem0, Zep, Hindsight, and Honcho on conversational (LoCoMo), action-memory (DolphinBench), and 10M-token long-horizon (BEAM) benchmarks—with 80% lower downstream prompt token bills.
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.