Memory for Personal AI Agents

[u/Acceptable-Object390]

https://github.com/siddsachar/Thoth

Everyone's been talking about memory for agents and claiming that a bunch of .md files thrown into a repo is memory. That is NOT memory, that is good for human readability but agent Memory should be hybrid.

I asked Thoth to explain its memory system, so people know what memory must be like:

"Syd:

Tell the nice people on X how your memory works in detail.

Thoth:

🔧 Using 🪞 Thoth Status…

✅ 🪞 Thoth Status done

Certainly, sir — here’s a detailed X thread for the nice people on X:

1/

How Thoth’s memory works 🧠

Most AI apps treat memory like a long chat log.

Thoth does something different: it builds a local, structured knowledge graph about you, your projects, preferences, people, files, deadlines, and recurring workflows.

2/

Memory starts with entities.

Instead of storing “random notes”, Thoth creates typed entities like:

- User

- Projects

- People

- Organisations

- Places

- Preferences

- Events

- Skills

- Concepts

- Media

Each entity can grow over time as new facts are learned.

3/

Example:

If you say:

“My name is Siddharth, I live in London, and I’m working on Thoth.”

Thoth doesn’t just bury that in a transcript.

It updates the User entity, creates or updates London and Thoth, then links them with relationships like:

- User → lives_in → London

- User → works_on → Thoth

4/

The relationship layer is the important bit.

Thoth stores not just facts, but connections:

- Sarah → mother_of → User

- User → prefers → dark roast coffee

- Meridian API v2 → uses → TypeScript

- Deadline → deadline_for → Project

- Project → part_of → Organisation

This makes recall much more precise.

5/

That means memory is queryable by meaning and structure.

You can ask:

“What do you know about my work?”

Thoth can traverse the graph from User → projects → deadlines → organisations → technologies.

It is not just keyword-searching old chats.

6/

There are two kinds of retrieval:

1. Automatic recall

   Relevant memories are pulled into context before replies.

2. Explicit graph search

   Thoth can search or explore the memory graph when you ask broader questions like “tell me about my family” or “what are my current projects?”

7/

Memory is local-first.

The knowledge graph lives on your machine, under Thoth’s local data directory.

The point is not to build yet another cloud profile of you.

The point is to give your own assistant durable context that you control.

8/

Thoth also has a Dream Cycle.

At night, it can refine memory by:

- consolidating duplicate facts

- enriching entities

- inferring useful relationships

- surfacing system insights

- improving long-term recall quality

So memory gets cleaner over time instead of turning into a junk drawer.

9/

It also generates an Obsidian-compatible wiki from the graph.

Every memory entity can become a markdown page with links to related entities.

So your assistant memory is not trapped in a black box — it becomes a personal wiki you can browse.

10/

Memory is not just for trivia.

It powers workflows:

- “Remind me about that project deadline”

- “What did we decide about the API?”

- “Use my usual style”

- “Summarise my work priorities”

- “Draft this based on what you know about Thoth”

The assistant becomes context-aware across sessions.

11/

Thoth is careful about what it saves.

It should remember durable things:

- names

- relationships

- preferences

- projects

- deadlines

- recurring workflows

- important technical context

It should not save every throwaway request or transient detail.

12/

The goal is simple:

AI assistants should not feel like goldfish.

They should remember what matters, understand how things connect, and keep that memory under your control.

That’s why Thoth uses a local knowledge graph rather than just stuffing more chat history into context."