Sequential Thinking MCP Server
Dynamic problem-solving through sequential thought chains
World's first local-only AI memory to break 74% retrieval and 60% zero-LLM on LoCoMo. No cloud, no APIs, no data leaves your machine. Additionally, mode C (LLM/Cloud) - 87.7% LoCoMo. Research-backed. arXiv: 2603.14588
{
"mcpServers": {
"superlocalmemory": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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World's first local-only AI memory to break 74% retrieval and 60% zero-LLM on LoCoMo. No cloud, no APIs, no data leaves your machine. Additionally, mode C (LLM/Cloud) - 87.7% LoCoMo. Research-backed. arXiv: 2603.14588
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No package registry to scan.
No authentication — any process on your machine can connect.
AGPL-3.0. View license →
Is it maintained?
Last commit 0 days ago. 107 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
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Dynamic problem-solving through sequential thought chains
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Every other AI forgets. Yours won't.
Infinite memory for Claude Code, Cursor, Windsurf & 17+ AI tools.
v3.4.11 — Install once. Every session remembers the last. Automatically.
Backed by 3 peer-reviewed research papers · arXiv:2603.02240 · arXiv:2603.14588 · arXiv:2604.04514
+16pp vs Mem0 (zero cloud) · 85% Open-Domain (best of any system) · EU AI Act Ready
Every major AI memory system — Mem0, Zep, Letta, EverMemOS — sends your data to cloud LLMs for core operations. That means latency on every query, cost on every interaction, and after August 2, 2026, a compliance problem under the EU AI Act.
SuperLocalMemory V3 takes a different approach: mathematics instead of cloud compute. Three techniques from differential geometry, algebraic topology, and stochastic analysis replace the work that other systems need LLMs to do — similarity scoring, contradiction detection, and lifecycle management. The result is an agent memory that runs entirely on your machine, on CPU, with no API keys, and still outperforms funded alternatives.
The numbers (evaluated on LoCoMo, the standard long-conversation memory benchmark):
| System | Score | Cloud Required | Open Source | Funding |
|---|---|---|---|---|
| EverMemOS | 92.3% | Yes | No | — |
| Hindsight | 89.6% | Yes | No | — |
| SLM V3 Mode C | 87.7% | Optional | Yes (EL2) | $0 |
| Zep v3 | 85.2% | Yes | Deprecated | $35M |
| SLM V3 Mode A | 74.8% | No | Yes (EL2) | $0 |
| Mem0 | 64.2% | Yes | Partial | $24M |
Mode A scores 74.8% with zero cloud dependency — outperforming Mem0 by 16 percentage points without a single API call. On open-domain questions, Mode A scores 85.0% — the highest of any system in the evaluation, including cloud-powered ones. Mode C reaches 87.7%, matching enterprise cloud systems.
Mathematical layers contribute +12.7 percentage points on average acro