Memory by File

Lightweight, file-based memory storage for maintaining persistent context across conversations using simple text operations.

Installation Instructions

README: https://github.com/g0t4/mcp-server-memory-file

mcp-server-memory

This is an MCP server to interact with a memory text file to help Claude with inter-chat context.

Each line is a memory.

These tools allow Claude (and other MCP clients) to manage memories mid-chat:

• memory_add(memory: string) - append the memory
• memory_search(query: string) - return matching memories (substring exact match) - later, might allow globs/regex
• memory_delete(query: string) - delete matching memories (substring exact match)
• memory_list() - return all memories
• FYI memory_update == memory_delete + memory_add

For example,

• I mention my name => "talking to Wes"
• metion daughter's age => "Wes's daughter is 8"
• say working on a typescript project => "working on typescript project"
• AND, this is critical, can be based on things Claude (assistant/LLM) says or does...
  • Notably, tool use (i.e. run_command)... say there is a failure on a first attempt to use the tool (i.e. the python command isn't present) and then a subsequent tool use succeeds (i.e. using python3 instead of python) => Claude can record "use python3, python is not present"...
• I ask Claude to get rid of memories about X => memory_delete(query: X)
• I correct my name => memory_search("oldname") + memory_delete(each matching record, or a common subset query) + memory_add("newname")

Then, when a new chat begins, Claude will automatically get recent memories (a subset or all) OR can ask for memories (some/more/all). And then can use those to influence responses/tools/etc.

Design

A simple memory text file, why:

• ChatGPT's memory works well and is essentially a text file
  • Maybe it's structured behind the scenes, however if you review your memory its presented as a text file.
• My testing of a similar reminders feature for mcp-server-commands worked great (when Claude had them).
• Unstructured text simplifies the tooling and parameters to basically managing a list of strings.

Cueing mechanism:

• It's also important to have a cue for the model to know when to store memories. This is a bit more unclear how best to do this but..
• Training: OpenAI acknowledges some training of models to know when to store memories. Just like models are trained for tool use.
• Prompt: A system prompt component likely contains a reminder to trigger storing memories.
• Tool alone: In my testing of Claude, with a tool spec alone, and even with hints/suggestions in tool responses, I couldn't get Claude to store memories. So this alone is not sufficient. Seems like Claude's training with tools is to only use them in pursuit of the prompt/request and thus why I believe adding a reminder/cue in a prompt component will work well.

TODOs/Ideas

I have no idea if these are worth the time, just listing ideas here for the future. Perhaps in part to stop myself from working on them :)

• Recency factor: a way to rearrange memories based on recency?
  • Order then becomes relevant for ambiguous memory queries (i.e. work on typescript project and python project then I ask to start a new project, could suggest the most recently used one?)
• Fade out old memories?