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cmoraes10/loci

Typed long-term memory for AI agents. One core, an MCP server, and a Hermes plugin.

View on GitHub ↗https://pypi.org/project/mowave-loci/ ↗
agent-memoryai-agentshermesllmlong-term-memorymcpmcp-servermemorymodelmodel-context-protocolopen-sourcepythonsqlite
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Created Oct 1, 2026Updated Oct 1, 2026

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README

loci

Typed long-term memory for AI agents. One core, two adapters.

The name comes from the method of loci, the ancient memory palace technique where every recollection gets a place and a structure. That is the thesis of this project.

An agent that forgets you every session is a search box with manners. This is the layer that fixes that: durable facts about a person, typed and ranked, extracted from the conversation rather than typed into a settings page, consolidated daily so the list does not rot.

Ported from the memory system running in production in Lima, rewritten host-agnostic.

What it does

Eight categories (routine, study, preferences, finance, goals, relationships, constraints, ephemeral), four importance tiers, a status lifecycle. A preference and a deadline are not the same kind of thing and do not age the same way.

Extraction runs on two paths. A model reads the exchange for meaning; regex heuristics catch the phrasings you wrote them for. Merged with model precedence. When the provider is down the regex path still runs.

A write filter keeps the store from becoming a mood diary. Passing mood is the case it exists for. "Tired today" is true for hours and wrong for months.

The injection cap keeps context from growing without bound. Twelve facts reach the model, ranked by importance and then by deadline proximity and recency.

Daily consolidation and decay keep the list honest. Exact duplicates merge; guesses nobody confirmed expire on their own; ephemeral facts carry a 24-hour TTL; completed goals expire after 90 days.

The two adapters

MCP server Hermes plugin
Runs in Any MCP client Hermes only
Tools remember, recall, forget the same three
Automatic extraction no yes
Context injection on request before every call

MCP is request and response. Nothing calls a server when a turn ends, so there is no moment for it to read the exchange on its own. Through MCP the agent has to call remember on purpose. Automatic extraction needs a hook in the host, and Hermes has one (post_llm_call). Same core underneath; the Hermes adapter just has somewhere to stand.

Install

pip install mowave-loci
pip install mowave-loci[mcp]

MCP client config (after pip install mowave-loci[mcp]):

{
  "mcpServers": {
    "loci": {
      "command": "python",
      "args": ["-m", "loci.adapters.mcp_server.server"]
    }
  }
}

Hermes:

hermes plugins install <you>/loci
hermes plugins enable loci

The store is a SQLite file at ~/.loci/memory.db, overridable with LOCI_DB. No server to run, because a memory layer that needs one is not installable.

Use it directly

from loci import Store, extract, should_persist, context_block

store = Store()
for memory in extract("eu prefiro respostas curtas", assistant_text=reply):
    if should_persist(memory):
        store.upsert(memory)

system_prompt += "\n\n" + context_block(store.active())

Where changes go

This repo owns the shape of a memory and its lifecycle. It does not own:

Thing Owner
Model calls, API keys, provider SDKs your app (inject a ModelExtractor)
The agent loop, tool dispatch, sessions the host (Hermes, OpenClaw, Claude Code)
Scheduling the daily jobs the host's cron, s6 or systemd
Transport and auth the adapter

The core imports nothing but the standard library. If a change here would make that untrue, it belongs in an adapter.

Tests

pytest -q

92 tests against a real SQLite store on a temp file. Nothing of ours is mocked: a memory layer whose tests pass against a fake store tells you nothing about the one people run.

License

MIT.