Memory Write: a local memory server running a 3B-active writer model
An open-source MCP memory server that uses Hy-Memory-30B-A3B for the write path. Developers pay for sync.
Crowded · 3B-active model specialised for memory writes
Written for: Solo builder
Next step
This week, run Hy-Memory quantised under llama.cpp or MLX behind a minimal MCP server with add and update tools, benchmark it against a GPT-based writer on your own agent logs, and post the numbers in the r/LocalLLaMA Hy-Memory thread.
Why now
Memory writes happen on every turn, so running them through a frontier model is the hidden cost in agent memory. This is the first open model built for that job, and it's small enough to run locally. Existing memory frameworks put a general LLM behind their own prompts.
What became possible
Tencent's Hy-Memory-30B-A3B is trained with attributed RL to decide what to keep, how precisely to store it and how to update stored facts, and it works across memory protocols. With 3B active parameters, the write path can run on a laptop instead of a frontier API.
Two ways in
- Memory Write MCP
A local MCP server for coding agents and chat clients. Hy-Memory writes and updates entries in a SQLite store, and plain retrieval serves reads.
- Customer
- Developers using Claude Code, Cursor or local agents who want memory to persist across sessions
- Model
- Open source, with $8/month encrypted sync across machines
- Write-path bench
Replays a week of agent transcripts through different memory writers and scores recall, contradiction handling and cost.
- Customer
- Teams choosing a memory framework
- Model
- Free tool that builds credibility for the paid sync
Competition
This space is crowded. There are dozens of open-source MCP memory servers, many built on SQLite.
Full competition notes (9 competitors)
This space is crowded. There are dozens of open-source MCP memory servers, many built on SQLite. claude-mem already has a large following among Claude Code users. Claude Code ships its own auto memory, and Mem0 has OpenMemory MCP plus several self-hosted forks that run local models through Ollama. The only new piece is Hy-Memory-30B-A3B, which came out October 15, 2026. I found no MCP server built on it yet, but that lead will be short. Tencent trained it specifically to replace Mem0's writer, so connecting it to existing Mem0 MCP servers is mostly a config change, and community GGUF builds already exist. Selling sync is a weak model: Mem0, Supermemory and others already sell hosted sync, and developers who choose a local-first tool tend not to pay for cloud sync. A new entrant could still win on speed and focus. That means shipping the first one-command install that bundles a quantized Hy-Memory writer, using its no-thinking mode for structured updates. It should handle contradictions properly with update and delete operations, not just append, which is a known weakness of Mem0's add-only extraction. It needs to run comfortably on a laptop, which is hard because the model is 30B total parameters and about 60 GB in BF16, so quantization quality matters. Of the two ideas, the write-path bench is the more defensible. Supermemory's MemoryBench compares whole providers on LoCoMo, LongMemEval and ConvoMem, but sources note that today's benchmarks grade retrieval far more than writing and forgetting. Tencent's own scores are vendor-reported and not yet reproduced. A tool that replays a team's real agent transcripts, holds retrieval fixed while swapping writer models (Hy-Memory, GPT-class, Qwen), and scores contradiction handling and cost per write fills a real gap. It could become the credible independent check on Hy-Memory's claims, though it is more of a consulting or open-source credibility play than a large paid product.
- Tencent Hy-Memory-30B-A3B (model)
Released October 15, 2026 under Apache 2.0, about two days ago. Tencent built it as a drop-in writer for Mem0, MemOS and its own Hy-Memory framework. Community GGUF and MLX quants already exist, for example https://huggingface.co/rapatsky/Hy-Memory-30B-A3B-GGUF. The weights are free to everyone, so they are not a moat. I found no MCP server built on it yet.
- claude-mem
A Claude Code plugin with roughly 65K GitHub stars. It records session observations, compresses them with an LLM, stores them in local SQLite plus Chroma, and injects them into later sessions. This is the closest established product to Memory Write MCP's write path for Claude Code users.
- Claude Code auto memory
A built-in feature, on by default since early 2026. Claude writes its own notes to ~/.claude/projects/<project>/memory/ and loads them through a MEMORY.md index. It is free and needs no setup, which raises the bar for any third-party memory server aimed at Claude Code.
- Mem0 OpenMemory MCP
Mem0's official local MCP memory server for Cursor, Claude Desktop, Windsurf and Cline, with a hosted version too. Its writer is an LLM extraction step, which by default calls OpenAI. Because Hy-Memory was trained specifically to replace Mem0's writer, Mem0 or its community could swap the model in with little effort.
- mem0-mcp-selfhosted (community)
A fully local Mem0 MCP server for Claude Code that runs on Ollama, Qdrant and optionally Neo4j. Pointing its Ollama LLM at a Hy-Memory GGUF would roughly reproduce the proposed product today.
- mem0-local-server
A self-hosted Mem0 server with an MCP interface, a dashboard and CPU embeddings, needing no external API keys. It is another local Mem0 wrapper that could take a local writer model.
- claude-memory-mcp (WhenMoon-afk)
An MIT-licensed SQLite plus FTS5 MCP memory server for Claude Desktop, Cursor and Windsurf. It is one of many SQLite MCP memory servers listed on mcp.so, glama and others, including j0hanz memory-mcp, CPersona and Memory Arbiter.
- Supermemory MemoryBench
An open-source MIT harness that benchmarks memory providers (Supermemory, Mem0, Zep) on LoCoMo, LongMemEval and ConvoMem. It has pluggable providers and judges, plus a MemScore that combines quality, latency and token cost. It overlaps heavily with the proposed write-path bench, though it scores whole providers on academic datasets rather than swapping writer models on your own transcripts.
- agentmemory benchmark comparison
A community comparison of memory frameworks across LoCoMo and LongMemEval, with a feature matrix covering capture and write behavior. It is further evidence that framework comparison is already being done in the open.
Checked 2026-10-17
Risks
- The 'beats Opus 5 at memory' claim is unverified and the community is sceptical
- Memory frameworks are crowded and may simply adopt the model
- Runs on 30B total weights, so it needs about 18GB or more of memory to run locally
Strong team fit; open space is the weak point (4).
tech 5 · pull 6 · space 4 · fit 8 · build 7
Exposed
Companies that could benefit or be threatened. News and context, not investment advice.
Momentum of its main topic
Sources
Status history
- 2026-10-17 Flagged as buildable