---
type: other
source_url: https://www.reddit.com/r/WebAfterAI/comments/1ukr9fb/the_seven_kinds_of_agent_memory_each_mapped_to_an/
retrieved: 2026-07-02
title: "The Seven Kinds of Agent Memory, each mapped to an open-source repo"
author: "r/WebAfterAI"
tags: [agent-memory, taxonomy, letta, cognee, graphiti, voyager, llama-index, unsloth, reme, plur1bus]
---

# The Seven Kinds of Agent Memory — Each Mapped to an Open-Source Repo

**Source:** Reddit r/WebAfterAI post
**URL:** https://www.reddit.com/r/WebAfterAI/comments/1ukr9fb/the_seven_kinds_of_agent_memory_each_mapped_to_an/
**Retrieved:** 2026-07-02

## Overview

A taxonomy of seven distinct agent memory types, each mapped to a representative open-source repository. The post emphasizes that most real agents use only two or three of these, not all seven. "Pick by the workflow, not by the taxonomy."

## The Seven Memory Types

### 1. In-context (Working) Memory
- **What it is:** Whatever is in the model's context window right now — the equivalent of what you're holding in your head this second.
- **Repo:** [letta-ai/letta](https://github.com/letta-ai/letta) (Apache-2.0) — the MemGPT project. Treats the context window like RAM, keeping a small core in-context and paging older material out to searchable storage.
- **Verified recipe:** `reme-prospective-schedule`

### 2. Semantic Memory
- **What it is:** Durable facts and relationships — "the user's name is Kai," "the API endpoint is /v2/users."
- **Repo:** [topoteretes/cognee](https://github.com/topoteretes/cognee) — self-hosted knowledge graph + vector index. Facts are searchable by meaning and connected by relationships.
- **Verified recipe:** (from post)

### 3. Episodic Memory
- **What it is:** Specific past events with a time to them — "last Tuesday you decided X" — rather than timeless facts.
- **Repo:** [getzep/graphiti](https://github.com/getzep/graphiti) (open-source) — a temporally-aware knowledge graph that ingests interactions as dated episodes and supports point-in-time queries.
- **Catch:** A temporal knowledge graph is heavy infrastructure (graph database + ingestion pipeline). For a simple app this is overkill; reach for it when the timeline truly matters.
- **Verified recipe:** `graphiti-temporal-graph-memory`

### 4. Procedural Memory
- **What it is:** Learned how-to, reusable skills the agent builds from doing tasks — not facts it looked up.
- **Repo:** [MineDojo/Voyager](https://github.com/MineDojo/Voyager) (MIT) — the classic example. When a task succeeds, the working code is saved to a skill library indexed by a description, and relevant skills are retrieved and reused later. (Same idea behind Hermes Agent's `/learn` skills.)
- **Catch:** Voyager is a research project (lives in Minecraft). Treat it as a concept demo, not a drop-in production library. A saved skill can be over-fit or subtly wrong, so procedural memory needs review before you trust it.
- **Verified recipe:** `voyager-skill-library-pattern`

### 5. External / Retrieval Memory
- **What it is:** Knowledge kept outside the model and pulled in on demand — the classic RAG pattern.
- **Repo:** [run-llama/llama_index](https://github.com/run-llama/llama_index) (MIT) — the leading framework for indexing documents and retrieving relevant pieces at query time.
- **Verified recipe:** (from post)

### 6. Parametric Memory
- **What it is:** Knowledge baked into model weights — the model itself knows it without looking anything up.
- **Repo:** [unslothai/unsloth](https://github.com/unslothai/unsloth) (Apache-2.0 core; Unsloth Studio UI is AGPL-3.0). One of the most efficient ways to fine-tune a model, which is how you write to parametric memory.
- **Workflow:** Baking your domain, jargon, or house style into a small model so it is always on without spending prompt tokens or a retrieval call. Fine-tune once, and the knowledge is native.
- **Catch:** Parametric memory is expensive to write and static once written. Updating means retraining. Risks overfitting and catastrophic forgetting. Use for stable, always-needed knowledge, not for facts that change weekly.
- **Verified recipe:** `unsloth-parametric-finetune-config`

### 7. Prospective Memory
- **What it is:** Remembering to do something in the future — follow-ups, reminders, scheduled steps — rather than recalling the past.
- **Repo:** [agentscope-ai/ReMe](https://github.com/agentscope-ai/ReMe) (open-source) — consolidates conversations into memories and surfaces proactive reminders on a schedule.
- **Workflow:** An agent that says "I will check the deploy in an hour" or "send the weekly digest every Monday" and actually does.
- **Catch:** Least standardized of the seven. Most implementations are really a task queue or cron plus a stored list of intentions, not a distinct memory engine. Treat as a pattern to copy more than a proven dependency.
- **Verified recipe:** `reme-prospective-schedule`

## Guidance: Which Ones You Actually Need

> "Do not try to build all seven. Most agents need working memory (unavoidable) plus one or two others chosen by the job: retrieval memory if the knowledge is big and changeable, semantic or episodic if it is a long-lived personal assistant, procedural if the agent should learn repeatable tasks, parametric only when the knowledge is stable enough to be worth training in, and prospective the moment your agent needs to act on a schedule. Pick by the workflow, not by the taxonomy."

## Companion Resource

- [NirDiamant/Agent_Memory_Techniques](https://github.com/NirDiamant/Agent_Memory_Techniques) — all seven memory types implemented in runnable code. A solid hands-on reference.

## What the Post Does NOT Mention: Consolidation & Forgetting

The taxonomy covers seven *types* of memory but is silent on **memory management** — the processes that distinguish a memory system from a dump:

- **Consolidation:** Merging related memories, promoting important ones, compressing episodic sequences into semantic facts.
- **Forgetting / Decay:** Weighted relevance scores that degrade over time (Ebbinghaus curve), garbage collection of stale or low-value memories.
- **Emotional Tiers / Priority Flags:** Different retention rules for emotionally significant or explicitly flagged ("neverForget") memories.
- **Merging with Thresholds:** Combining near-duplicate memories when similarity exceeds a threshold.

These are not an 8th memory *type* — they are the management layer that makes any of the seven types useful over time. Without consolidation and forgetting, memory grows unbounded and signal-to-noise degrades.

## Relevance for Our Setup

This taxonomy is directly relevant to our plur1bus memory system. See the wiki page [[../../concepts/agents/agent-memory-taxonomy.md]] for a detailed plur1bus mapping.

- **plur1bus** covers semantic, episodic, and external/retrieval strongly, with partial procedural and in-context coverage.
- **Consolidation & Forgetting** is plur1bus's key differentiator — GC with decay, merging with thresholds, neverForget flags, emotion tiers — none of the seven repos have this.
- Cross-ref: [[../../concepts/agents/plur1bus-memory-model.md]] — our existing plur1bus memory model page