Xinyu Cai

🛠️ Building (The $1.9/mo Zep-style Mem0 killer natively on DeepSeek). Drop-in MCP layer for Cursor/Claude Code.

[Xinyu Cai @2Hugh3]

Your AI agents need memory to remember. It shouldn't cost enterprise prices.

We built #DeepMem — now open source.
✅Best Mem0 alternative
✅2× faster queries, 2.3× more search hits
✅Self-host free, or cloud from $1.9/mo
✅Quick migration from Mem0 at 1/10th price

Change one import line now to cut 90% cost down.

👉Github:https://t.co/z8VDKTqDTl

@CursorHQ @AIHighlight @ClementDelangue

[DeepMem.dev @DeepMem_]

#DeepMem now open source - gives your AI agents persistent,cross-session memory.

✅Best #Mem0 alternative
✅2× faster queries, 2.3× more search hits
✅Self-host free, or cloud from $1.9/mo
✅Migration from Mem0 by 90% cost down

👉Github:https://t.co/93XJl8tm6H

#AI #agent #LLM

[Xinyu Cai @2Hugh3]

Insane validation for the open-source memory layer space!

Glad to see hybrid vector/graph stores getting natively embedded. We actually hard-forked Mem0 to build DeepMem for devs who want Zep-style temporal graphs and local Ubuntu optimization (~45ms P95, 10x cheaper).

The ecosystem is moving insanely fast. 🔥

[Xinyu Cai @2Hugh3]

Spot on with that final point on 2026 infrastructure maturity. The shift to Zep-style temporal knowledge graphs is the only way to stop agents from bleeding token costs under high-frequency state changes. Flat vector snapshots are dead.

While Anthropic plays its double-bet with hosted lock-ins and Mem0 connectors, independent engineers running real production loops need a bare-metal alternative that doesn't pillage their budget.

We hard-forked the exact cognitive layer you just described into DeepMem—built natively on Ubuntu to commoditize temporal graph traversal.

Raw receipts from our current node clusters:

💰 10x Cheaper: $1.9/mo baseline tier vs the standard VC-wrapper $19/mo marketing hype

⚡️ ~45ms P95 Retrieval: 2x faster than hosted giants trying to resolve graph-states over high-load loops.

🔓 Fully BYOK: Completely platform-agnostic. No vendor lock-in, no enterprise premiums.

You can swap your legacy connector endpoint URI in 5 minutes without changing one line of your agent code. Let the corporate platforms build their walled gardens; we’re giving the temporal graph power back to the individual hackers.

[Xinyu Cai @2Hugh3]

Seeing an agent self-debug a local stack through 40+ messages is wild, but it highlights exactly why legacy vector/SQLite abstraction layers blow up under high-frequency loops.

When your agent squad starts intense multi-session syncs, pushing flat vector snapshots back and forth locally will introduce severe state latency and context drift.

We got fed up with unoptimized memory infrastructure and hard-forked a lightweight, production-ready alternative: DeepMem.

Instead of fighting flat index synchronization, we treat memory natively like a Git history using Zep-style Temporal Graphs. Optimized directly for local/bare-metal nodes:

💰 10x Cheaper: $1.9/mo entry tier vs legacy $19/mo wrapper hype.

⚡️ 2x Faster: ~45ms P95 retrieval latency.

🛠️ 5-Min Setup: A clean endpoint flip that skips the "5 walls and 40 bug-hunting messages" configuration hell.

Next time Monica runs into a context wall or latency bottleneck on that Mac Mini setup, swap the URI and let the raw graph traversal numbers speak.

[Xinyu Cai @2Hugh3]

This is a massive paradigm shift for agent architecture. Moving skill systems from raw file system prompts to structured packages with real-world task signals is exactly how we prevent agents from constantly resetting.

Here is a wild thought for infra engineers: If you combine OpenSpace v2's skill optimization (cutting 46% tokens) with a lightweight, temporal graph-based memory layer like DeepMem (~45ms P95 latency), you essentially eliminate the entire token inflation loop for autonomous agents.

OpenSpace handles the action/skill validation framework, while DeepMem handles high-frequency long-term state retrieval via Zep-style graphs without bloating the context.

Has anyone benchmarked a skill-hub coupled with a dedicated temporal memory layer yet? The combined cost reduction would be insane.

[Xinyu Cai @2Hugh3]

This is a massive paradigm shift for agent architecture. Moving skill systems from raw file system prompts to structured packages with real-world task signals is exactly how we prevent agents from constantly resetting.

Here is a wild thought for infra engineers: If you combine OpenSpace v2's skill optimization (cutting 46% tokens) with a lightweight, temporal graph-based memory layer like DeepMem (~45ms P95 latency), you essentially eliminate the entire token inflation loop for autonomous agents.

OpenSpace handles the action/skill validation framework, while DeepMem handles high-frequency long-term state retrieval via Zep-style graphs without bloating the context.

Has anyone benchmarked a skill-hub coupled with a dedicated temporal memory layer yet? The combined cost reduction would be insane.

[Xinyu Cai @2Hugh3]

The official integration is hyped, but engineers running high-frequency agent loops need a serious cost/latency reality check before binding their API budgets.

Flat vector-hybrid wrappers suffer severe time-sequence drift and massive token bloating when agent states change fast. You end up paying to re-read the exact same data.

We got fed up with the infrastructure extortion and hard-forked the concept into an open alternative: DeepMem.

Ditched flat indexes for Zep-style temporal graphs (structured like a Git history with timestamps). Clocked these raw server metrics on standard Ubuntu nodes:

⚡️ 2x Faster: ~45ms P95 retrieval latency.

💰 10x Cheaper: $1.9/mo entry baseline vs the legacy $19/mo wrapper hype.

🔓 BYOK Ready: Complete "Bring Your Own Key" endpoint freedom.

Swapping the endpoint takes exactly 5 minutes without touching your agent logic. Let the hosted giants play with their partnership decks, let real engineering numbers speak.

[Xinyu Cai @2Hugh3]

@ajs6888 兄弟，加个V Xinyuyi23, 想看看有没有合作机会

[Xinyu Cai @2Hugh3]

Start free at https://t.co/r5v6SDqfoO

[Xinyu Cai @2Hugh3]

Kimi 3 and Claude are engineering marvels. But massive Context Windows are the biggest cost-trap in AI history. 🧵
Everyone is hyped about multi-million context lengths. But here is the dirty infrastructure secret VC-funded marketing departments are hiding from you:
Blindly feeding an entire codebase or chat history into every single request is architectural brute-force. It is a financial extortion trap for your API budget.
Under high-throughput Agent workflows, this legacy approach leads to two fatal execution crises:
1️⃣ The Token Inflation Loop: You aren't just processing new data. Every time your Agent takes a new action, you pay to re-read the exact same historical data millions of times. It’s an exponential wallet drain.
2️⃣ Attention Drift (Lost in the Middle): Stanford researchers already proved it—just because a model can accept millions of tokens doesn't mean it remembers them. Under long contexts, models suffer severe retrieval degradation in the middle sector. Your agent gets blind to its own history.
Vector DB wrappers claim to fix this, but they are completely blind to Time-Sequences, leading to catastrophic memory bloating.
⚡️ Enter the Hard-Fork: DeepMem
We got fed up with paying enterprise premiums for unoptimized cloud infrastructure. We hard-forked the traditional cognitive layer and built DeepMem—natively optimized on Ubuntu 22.04 and dead-locked with DeepSeek to target the exact pain point of context bloating.
We ditched flat vectors for Zep-style Temporal Graphs. We treat your AI’s memory like a Git history with precise timestamps, not a flat vector snapshot.
Real, un-manipulated metrics from our bare-metal servers:
⚡️ 2x Faster: ~45ms P95 retrieval latency. Zero lag.
💰 10x Cheaper: $1.9/mo entry tier vs the legacy $19/mo enterprise standard.
🔓 BYOK Ready: Complete "Bring Your Own Key" freedom. Absolute zero vendor lock-in.
🏁 The Hype is Over. The Receipt is Here.
Stop letting unoptimized memory pipelines pillage your start-up's budget. You can swap your legacy endpoint URL and inject your provider keys in exactly 5 minutes without changing a single line of your agent logic.
Let the VC giants fight over their rigged leaderboard benchmarks. We’re riding off with the real-world efficiency.
👉 DeepMem is completely free to test. Zero credit card required. Claim your baseline quota and view the raw benchmark documentation in the link below 👇

#Kimi3⁠ ⁠#DeepSeek⁠ ⁠#Cursor⁠ ⁠#LLM⁠#Mem0 #memorylayer #cheapmemory

[Xinyu Cai @2Hugh3]

@mem0ai

[Xinyu Cai @2Hugh3]

@bensig

[Xinyu Cai @2Hugh3]

@mem0ai Native Claude memory is great, but legacy pricing and 90ms latency will kill your production budget.

Switch to DeepMem in 5 mins:

10x cheaper ($1.9/mo vs $19/mo)

2x faster (~45ms vs ~90ms)

Built-in temporal sequence to stop memory bloating

Setup link in bio👇

[Xinyu Cai @2Hugh3]

哥们一针见血！普通 RAG 只是死板的“关键词/语义匹配”，高频写入时根本处理不了时序和状态覆盖（State Overwrite）。

DeepMem 没走传统无脑向量的老路，我们原生引入了 Zep 同款时序图谱（Temporal Graphs）。AI 记忆需要像 Git 历史一样按时间戳沉淀，而不是存一张随时被覆盖的旧��照。这也是为什么我们能把 P95 ���迟压到 ~45ms 还省 Token。

[Xinyu Cai @2Hugh3]

Stop Paying $19/mo for Dumb RAG. Most "AI Memory" Startups are Just Overpriced Wrappers.

Everyone is talking about $24M-funded giants fixing AI long-term memory for Cursor and Claude Code. But here is an anti-intuitive truth that VC-backed marketing won't tell you: Traditional vector search is DEAD for complex agent memory.

If you are constantly writing into a basic memory pipeline, you aren't building intelligence—you are just paying for exponential token bloating, legacy retrieval latency, and a ticking vendor lock-in time bomb.

Here is the hard engineering truth:

1. The Rigged Benchmark Deception
The market is flooded with rigged benchmarks touting "99% retrieval accuracy" over toy models. But in the real world, under high-throughput Agent workflows, those unoptimized pipelines lead to massive Memory Bloating. When your Agent writes constantly, a basic vector DB loses the temporal sequence. It is blind to time.

2. The Architectural Reality Check
To build a truly production-ready cognitive layer, you need Temporal Graph Architecture.
Without Zep-style compliant temporal sequence capability, your AI memory cannot resolve state changes over time. It’s like giving an engineer an old snapshot of a codebase instead of the entire Git history.

3. The 10x Extortion Premium
Why pay $19/mo or up to $249/mo for a legacy cloud infrastructure when the underbelly of the network is just a hosted wrapper?

We got fed up with the VC-funded extortion premium, so we hard-forked the concept and built DeepMem—natively optimized on Ubuntu 22.04 and dead-locked with DeepSeek to deliver the world's most aggressive pricing-to-performance ratio:

- P95 Latency: ~45ms (vs ~90ms on bloated enterprise legacy clouds).

-The Price: $1.9/mo for Starter (exactly 1/10 the price of the entry tier elsewhere).

- Zero-Cost Migration: Swap the endpoint URL in 5 mins. Same API, zero code changes, absolute zero vendor lock-in.

-BYOK Ready: Plug in your own LLM provider keys per request.

🏁 The Bottom Line
Stop letting legacy tech debt pillage your API budget. If your Agent needs real, ZEP-compliant temporal memory that doesn't blow up your server or your wallet, you are already here.

👉 Free forever to start. No Credit Card required.
Link to claim your baseline quota is right below in the picture/ bio.

Let the legacy giants joust over their rigged leaderboards. We are riding off with the receipt.
#AI #Cursor #DeepSeek #Coding #OpenSource #Mem0 #memorylayer

[Xinyu Cai @2Hugh3]

@ashwingop

[Xinyu Cai @2Hugh3]

@mem0ai

[Xinyu Cai @2Hugh3]

@mem0ai Spot on. But solving Claude Code’s limits with Mem0’s pricey multi-LLM routing drains tokens fast
So we hard-forked it into DeepMem:
1. Native on DeepSeek ($1.9/mo)
2. Zep-style Temporal Graph to fix memory bloating
Drop-in sync for Cursor. Test free via GitHub OAuth. Link in bio

[Xinyu Cai @2Hugh3]

@mem0ai Great work on token efficiency! For teams looking for a drop-in alternative, we built DeepMem — same API, 10x cheaper, ~45ms p95. Swap the endpoint URL and you're done.

👇 Quick comparison

[Xinyu Cai @2Hugh3]

@mem0ai @NVIDIAAI Nemotron-3 improves retrieval, but high throughput writes on unoptimized pipeline still lead to massive token bloating & latency.

We hard-forked this into DeepMem:

1.Native DeepSeek ($1.9/mo)

2.Zep-style Temporal Graphs to stop bloating

3.~45ms P95 latency

Link in bio