Skip to main content

acm-memory

Consented, local, auditable autobiographical memory for your AI assistant.

acm-memory is an MCP (Model Context Protocol) server that gives any MCP-capable assistant — Claude Desktop is the primary target — durable memory of the facts you choose to store. Tell your assistant where you keep the spare key today, and it still knows next month, in a new conversation, after a restart.

What makes it different

  • Nothing is stored without your explicit confirmation. Every proposed memory is shown to you verbatim first — in Claude Desktop, as an interactive consent card. A statement that smells like an instruction rather than a fact is refused at commit time even after you say yes.
  • Corrections supersede; history stays visible. When a fact changes, the old value is marked superseded, never silently overwritten — and you can ask to see the full history, or exactly what the model was shown, at any time.
  • Forget means forget. Forgetting a fact tombstones its entire history through every rebuild, with a receipt. It cannot come back.
  • Local by construction. Facts live on your machine. The only model acm-memory itself ever calls is a local judge (gemma4:12b via Ollama on loopback). No telemetry, no cloud calls of its own, no background capture — an MCP server only ever sees explicit tool calls.
  • Auditable. A consent journal records every confirmation, denial, and forget — including how consent arrived (card click vs. model-relayed). Every read is access-logged per client.

Requirements

  • Windows 10/11 (first tester round), 16 GB RAM minimum; a GPU with 8 GB+ VRAM is recommended (CPU-only works, slower).
  • Ollama with the gemma4:12b model (~7.2 GB one-time download — acm-doctor --pull handles it).
  • An MCP host (Claude Desktop recommended; consent cards render there).

Install

uv tool install acm-memory
acm-doctor --pull

Then register the server in your MCP host. For Claude Desktop, add to claude_desktop_config.json (use the absolute path to the installed shim, e.g. %USERPROFILE%\.local\bin\acm-memory-mcp.exe on Windows):

{ "mcpServers": { "acm-memory": { "command": "<absolute path to acm-memory-mcp>", "args": [] } } }

acm-doctor verifies the install end to end (doctor=PASS). Memory lives in ~/.acm/memory (override with ACM_WORKSPACE).

Status

Evaluation release for testers. The memory behavior behind this package was qualified against a measured production baseline (2,209/2,210 across twelve scenario arms) with a pinned local judge. Feedback via the in-chat report_issue tool.

License

Proprietary — evaluation use. See LICENSE.txt.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

acm_memory-0.2.0-py3-none-any.whl (312.0 kB view details)

Uploaded Python 3

File details

Details for the file acm_memory-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: acm_memory-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 312.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.11.9

File hashes

Hashes for acm_memory-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 84c84742081fed12cf1b3f5f0877cde3b17095b7f5570eaa70531c6d9149e028
MD5 7c6d12433d8d1cb9b4d7771b4ba6f956
BLAKE2b-256 706d016b1e4d41a806ddc733625470154d4097a565d247af316f89ecc6ea35de

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page