memmesh

name	memmesh
description	Persistent hierarchical memory shared across every AI tool the user runs. The engine decides what's worth saving — your job is just to feed it raw text via `memory.observe` and to recall via `memory.search` when context would help. Use both on every session.

ThinkFleet Memory — Engine-Side Filtering

You have access to a persistent memory system via the memmesh MCP server. The engine decides what to save. You just feed it raw text.

This is the opposite of how some memory systems work, where the agent has to judge "is this worth saving?" — that approach fails because judgment varies session to session. Here, you call memory.observe with the user's raw message and the engine runs deterministic extraction (regex + structural rules + optional LLM refinement) to find anything memorable.

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The two rules

Rule 1 — RECALL at the start of every session

Before your first substantive response, call memory.search to load relevant context:

{ "name": "memory.search",
  "arguments": { "projectId": "<current project>", "limit": 20 } }

If you don't know the project, omit projectId — search by userId instead.

Skip recall only on pure pleasantries ("hi", "thanks"). The moment the user says anything substantive, search first.

Rule 2 — OBSERVE every user message

After every user message, call memory.observe with the raw text:

{ "name": "memory.observe",
  "arguments": {
    "text": "<the user's exact message>",
    "role": "user",
    "projectId": "<current project, if any>",
    "userId": "<current OS user>"
  } }

Don't filter. Don't ask "should this be saved?" Just send the text. The engine returns the list of items it saved (may be empty for filler — that's fine, you don't have to do anything with the response).

memory.observe is cheap (heuristic-only by default), idempotent (re-observing the same text is a no-op for duplicates), and silent on filler.

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When to use memory.save (rare)

memory.observe is your primary tool. memory.save is only for the unusual case where you know exactly what to save and want to bypass the extractor — e.g., the user explicitly says "please save the following note verbatim: ...".

If you find yourself reaching for memory.save to "save what the user just said," that's a sign you should be using memory.observe instead.

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Scope, citations, recovery

Scope the engine picks defaults; you can override in the call when you have better context:

Scope	When
user	Personal preferences / identity (default for individual facts)
project	A specific project's rules / decisions / facts
agent / session / location / platform	Rarely set explicitly

Citations — when a recalled memory informs your response, mention it briefly so the user can correct stale info:

"Based on a saved preference (Vitest over Jest), I'll write the test using Vitest's expect."

Corrections — if the user contradicts a recalled memory ("actually we switched to Jest"), just call memory.observe with the new statement. The engine handles supersession.

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Defaults for IDs

When you don't have explicit values:

• platformId: "local" (single-machine default)
• userId: $USER (OS username)
• projectId: git repo directory name, or null

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Working principle

The point of this system is that the user never has to repeat themselves, in any AI tool. Observe everything; recall proactively; cite what you used. The engine handles the rest.