ingest(raw+wiki): agent-memory-taxonomy-seven-types
- raw: Reddit r/WebAfterAI post on 7 agent memory types with repos - wiki: new concept page with plur1bus Einordnung (coverage matrix) - Key insight: post ignores consolidation & forgetting — plur1bus differentiator - index.md + log.md updated (52. Update)
This commit is contained in:
Hector
parent
d77ae1609a
commit
cc6f03bbd6
4 changed files with 210 additions and 1 deletions
88
raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md
Normal file
88
raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md
Normal file
|
|
@ -0,0 +1,88 @@
|
|||
---
|
||||
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
|
||||
107
wiki/concepts/agents/agent-memory-taxonomy.md
Normal file
107
wiki/concepts/agents/agent-memory-taxonomy.md
Normal file
|
|
@ -0,0 +1,107 @@
|
|||
---
|
||||
created: 2026-07-02
|
||||
updated: 2026-07-02
|
||||
sources:
|
||||
- other/2026-07-02_agent-memory-taxonomy-seven-types.md
|
||||
tags: [concept, agents, memory, taxonomy, plur1bus, letta, cognee, graphiti, voyager, llama-index, unsloth, reme]
|
||||
---
|
||||
|
||||
# Agent Memory Taxonomy — The Seven Kinds
|
||||
|
||||
> *"Do not try to build all seven. Most agents need working memory plus one or two others chosen by the job."*
|
||||
> — r/WebAfterAI
|
||||
|
||||
## Overview
|
||||
|
||||
A structured taxonomy of seven distinct agent memory types, each mapped to a representative open-source repository. The taxonomy distinguishes memory by **function** (what kind of knowledge it stores) and **time** (short-term vs. long-term), not by implementation.
|
||||
|
||||
The core guidance: **pick by workflow, not by taxonomy.** Most agents need working memory (unavoidable) plus one or two others.
|
||||
|
||||
## The Seven Memory Types
|
||||
|
||||
| # | Type | What It Stores | Representative Repo | License | Verified Recipe |
|
||||
|---|------|---------------|---------------------|---------|-----------------|
|
||||
| 1 | **In-context (working)** | Current context window — "RAM" | [letta-ai/letta](https://github.com/letta-ai/letta) (MemGPT) | Apache-2.0 | — |
|
||||
| 2 | **Semantic** | Durable facts + relationships | [topoteretes/cognee](https://github.com/topoteretes/cognee) | OSS | — |
|
||||
| 3 | **Episodic** | Dated events, "what happened when" | [getzep/graphiti](https://github.com/getzep/graphiti) | OSS | `graphiti-temporal-graph-memory` |
|
||||
| 4 | **Procedural** | Learned skills, reusable how-to | [MineDojo/Voyager](https://github.com/MineDojo/Voyager) | MIT | `voyager-skill-library-pattern` |
|
||||
| 5 | **External / Retrieval** | Knowledge pulled in on demand (RAG) | [run-llama/llama_index](https://github.com/run-llama/llama_index) | MIT | — |
|
||||
| 6 | **Parametric** | Knowledge baked into model weights | [unslothai/unsloth](https://github.com/unslothai/unsloth) | Apache-2.0 core | `unsloth-parametric-finetune-config` |
|
||||
| 7 | **Prospective** | Future intentions, reminders, schedules | [agentscope-ai/ReMe](https://github.com/agentscope-ai/ReMe) | OSS | `reme-prospective-schedule` |
|
||||
|
||||
**Companion:** [NirDiamant/Agent_Memory_Techniques](https://github.com/NirDiamant/Agent_Memory_Techniques) — all seven types in runnable code.
|
||||
|
||||
### Design Catches (from the post)
|
||||
|
||||
- **Episodic (graphiti):** Temporal knowledge graph is heavy infrastructure. Overkill for simple apps; reach for it when the timeline truly matters.
|
||||
- **Procedural (Voyager):** Research project in Minecraft — concept demo, not a drop-in library. Saved skills can be over-fit or subtly wrong; needs review before trust.
|
||||
- **Parametric (unsloth):** Expensive to write, static once written. Updating means retraining. Risks catastrophic forgetting. Use for stable, always-needed knowledge only.
|
||||
- **Prospective (ReMe):** Least standardized of the seven. Most implementations are just cron + a stored list of intentions, not a distinct memory engine.
|
||||
|
||||
### Selection Guide (from the post)
|
||||
|
||||
| Workflow | Memory Types Needed |
|
||||
|----------|-------------------|
|
||||
| Knowledge is big and changeable | Working + Retrieval |
|
||||
| Long-lived personal assistant | Working + Semantic + Episodic |
|
||||
| Agent should learn repeatable tasks | Working + Procedural |
|
||||
| Knowledge stable enough to train in | Working + Parametric |
|
||||
| Agent needs scheduled actions | Working + Prospective |
|
||||
|
||||
## 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), GC 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.
|
||||
|
||||
## plur1bus Einordnung
|
||||
|
||||
plur1bus (Hector's memory system via PLUR1BUS plugin) covers several of the seven types, with different maturity levels:
|
||||
|
||||
| Memory Type | plur1bus Coverage | Implementation |
|
||||
|------------|-------------------|----------------|
|
||||
| **Semantic** | ✅ Strong | Vector search over LanceDB. Entity-memories, facts, preferences, decisions. Like cognee but without a knowledge graph — pure vectors + full-text instead of graph triples. |
|
||||
| **Episodic** | ✅ Strong | Conversation memories with timestamps. Daily notes. `autoCapture` stores every turn automatically. Episodic chains via temporal metadata. |
|
||||
| **External / Retrieval** | ✅ Strong | LanceDB as external searchable vector store. `autoRecall` injects matching memories into context at inference time. |
|
||||
| **Procedural** | ⚠️ Partial | `skillMiner` extracts skills from conversations, but it's more experiment than core function. Skill Workshop is the manual path. |
|
||||
| **In-context / Working** | ⚠️ Indirect | OpenClaw's context window + LCM (Lossless Context Management) handles this. plur1bus feeds into it via `autoRecall`. Not plur1bus's own function. |
|
||||
| **Prospective** | ❌ Not plur1bus | OpenClaw Cron handles scheduled tasks and reminders. Not a memory-system responsibility. |
|
||||
| **Parametric** | ❌ Out of scope | Would require model fine-tuning. Not a memory-system function. |
|
||||
|
||||
### plur1bus's Key Differentiator: Consolidation & Forgetting
|
||||
|
||||
The post's taxonomy has a blind spot: **none of the seven repos implement memory management**. plur1bus does:
|
||||
|
||||
- **GC with Decay:** Relevance scores degrade over time (Ebbinghaus-curve inspired). Low-value memories are garbage-collected.
|
||||
- **Merging with Threshold:** Near-duplicate memories are merged when similarity exceeds a threshold, preventing memory bloat.
|
||||
- **`neverForget` Flags:** Explicitly marked memories are exempt from decay/GC — user-defined permanent retention.
|
||||
- **Emotion Tiers:** Memories tagged with emotional significance get different retention rules — a biologically-inspired priority system.
|
||||
- **autoCapture:** Every conversation turn is automatically captured as episodic memory — no manual ingestion needed.
|
||||
- **autoRecall:** Relevant memories are automatically injected into the context window at inference time — the retrieval layer is built-in.
|
||||
|
||||
This is what distinguishes plur1bus from a "memory dump": it actively manages the lifecycle of memories. The seven repos each handle one *type* of storage, but none handles the full lifecycle.
|
||||
|
||||
### Architecture Cross-Reference
|
||||
|
||||
- **plur1bus vs. letta (MemGPT):** Both handle memory beyond the context window, but letta focuses on paging (in-context → external), while plur1bus focuses on lifecycle management (capture → consolidate → decay → forget).
|
||||
- **plur1bus vs. cognee:** Both do semantic memory, but cognee uses knowledge graphs (triples), while plur1bus uses pure vector + full-text search. Trade-off: graph gives relationship queries, vectors give simpler scaling.
|
||||
- **plur1bus vs. graphiti:** Both do episodic memory with temporal awareness. graphiti needs a graph database; plur1bus uses timestamps + vector search — lighter infrastructure.
|
||||
- **plur1bus vs. Voyager:** Both have a "skill" concept, but Voyager saves executable code; plur1bus's skillMiner extracts procedural knowledge from conversation. Different maturity: Voyager is a proven research demo; plur1bus's skillMiner is experimental.
|
||||
|
||||
## Related Pages
|
||||
|
||||
- [[plur1bus-memory-model.md]] — Detailed plur1bus memory model (5 components: LanceDB, episodic links, emotional states, Ebbinghaus curve, active forgetting)
|
||||
- [[../../architecture/memory-system.md]] — Hector's layered memory architecture (S1 flat-file, S2 OpenClaw built-in, S3 LanceDB removed)
|
||||
- [[subconscious-agent.md]] — Hector's background process for ideation
|
||||
- [[ai-agents-2026.md]] — Agent trends 2026
|
||||
- [[../llm/llm-knowledge-base.md]] — The RamaDama Wiki as an alternative to RAG (Karpathy pattern)
|
||||
|
||||
## Source
|
||||
|
||||
- [Reddit r/WebAfterAI post](https://www.reddit.com/r/WebAfterAI/comments/1ukr9fb/the_seven_kinds_of_agent_memory_each_mapped_to_an/)
|
||||
- Raw file: [other/2026-07-02_agent-memory-taxonomy-seven-types.md](../../../raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md)
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
*Auto-generated: 2026-06-23*
|
||||
|
||||
*Letzte Aktualisierung: 2026-07-01 (51. Update — Federal lawsuit filed against Palantir Technologies, Peter Thiel, Alex Karp (43-page verified complaint). Pentagon-grade AI surveillance deployed domestically, "cognitive liberty" concept, pre-crime allegations. Separate American Oversight FOIA lawsuit (April 2026) targeting CDC/DHS/ICE/IRS/SSA Palantir usage. Neue Raw-Datei: `raw/xpost/2026-06-30_palantir-cognitive-liberty-lawsuit.md`. Neue Wiki-Seiten: `concepts/policy/palantir-surveillance.md`, `concepts/policy/cognitive-liberty.md`.)*
|
||||
*Letzte Aktualisierung: 2026-07-02 (52. Update — Agent Memory Taxonomy: 7 kinds of agent memory mapped to open-source repos (letta, cognee, graphiti, Voyager, llama_index, unsloth, ReMe). Key insight: post ignores consolidation & forgetting — plur1bus's core differentiator. Detailed plur1bus Einordnung with coverage matrix. Neue Raw-Datei: `raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md`. Neue Wiki-Seite: `concepts/agents/agent-memory-taxonomy.md`.)*
|
||||
|
||||
## Architecture
|
||||
|
||||
|
|
@ -98,6 +98,7 @@
|
|||
| [Polymarket Hub](concepts/agents/polymarket-hub.md) | **Hub-Page** für Polymarket-Cluster: Arbitrage, Monitoring-Bot, Policy-Kontext | WMT-004 |
|
||||
| [Lightning & Bitcoin Payments Hub](concepts/agents/lightning-payments-hub.md) | **Hub-Page** für Lightning-Cluster: Agent-Payments, Blink Wallet, Alby Builder | WMT-004 |
|
||||
| [AI Trading & Finance Hub](concepts/agents/ai-trading-hub.md) | **Hub-Page** für Trading-Cluster: Hype-Checks, Options, Tools, Policy | WMT-004 |
|
||||
| [Agent Memory Taxonomy — The Seven Kinds](concepts/agents/agent-memory-taxonomy.md) | Taxonomy of 7 agent memory types (working, semantic, episodic, procedural, retrieval, parametric, prospective) mit Open-Source-Repos. plur1bus-Einordnung: ✅ Semantic/Episodic/Retrieval, ⚠️ Procedural/Working, ❌ Prospective/Parametric. Key gap im Post: Consolidation & Forgetting — plur1bus's differentiator (GC+Decay, Merging, neverForget, Emotion-Tiers) | other/2026-07-02_agent-memory-taxonomy-seven-types.md |
|
||||
|
||||
### Policy
|
||||
| Seite | Beschreibung | Quellen |
|
||||
|
|
@ -258,3 +259,4 @@
|
|||
| `raw/xpost/2026-06-30_palantir-cognitive-liberty-lawsuit.md` | xpost | Jordan Chain (@_pblanknews): Federal lawsuit against Palantir/Thiel/Karp — 43-page verified complaint, Pentagon-grade AI surveillance, cognitive liberty, pre-crime system, American Oversight FOIA |
|
||||
| `raw/other/2026-07-01_google-tabfm-zero-shot-tabular-foundation-model.md` | other | Google Research unveils TabFM — Zero-Shot Foundation Model for Tabular Data |
|
||||
| `raw/blog/2026-07-01_perplexity-glm52-tops-open-weights-intelligence-index.md` | blog | Perplexity Discover: GLM-5.2 tops Artificial Analysis Intelligence Index v4.1 — #1 Open-Weights, Score 51, SWE-bench Pro 62.1 beats GPT-5.5, Industry praise from Rauch/Levie/Howard |
|
||||
| `raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md` | other | The Seven Kinds of Agent Memory, each mapped to an open-source repo (r/WebAfterAI) |
|
||||
|
|
|
|||
12
wiki/log.md
12
wiki/log.md
|
|
@ -1040,3 +1040,15 @@ Bestehende `post-transformer-llm-architectures.md` bleibt als Vier-Säulen-Über
|
|||
- log: this entry
|
||||
**Hector-Hauptthese:** Die Artificial-Analysis-Platzierung ist die erste quantitative, standardisierte Benchmark-Bestätigung für GLM-5.2 als #1 Open-Weights weltweit — und das vierte unabhängige Signal insgesamt (nach Video-Tests, Semgrep, und jetzt Index-Platzierung). Was diese Quelle besonders macht: (1) Der SWE-bench Pro Score von 62.1 übertrifft GPT-5.5 — das ist nicht "fast so gut wie Frontier" sondern "besser als Frontier" in einer Coding-Domäne. (2) Die Industry-Reaction von drei prominenten Western tech CEOs zeigt breite Anerkennung jenseits von China-Boosting. (3) Der $1.40/M input token Preis concrete-zifiziert die "10× günstiger"-These. (4) "Without Nvidia hardware since Entity List" ist die strategische Nachricht: Export-Kontrollen haben China nicht gestoppt sondern zur Entwicklung alternativer Infrastruktur gezwungen. Für unser Setup validiert dies GLM-5.2 als Primärmodell nicht nur als Cost-Choice sondern als qualitativ begründbare Top-Wahl.
|
||||
**Subagent-Modell:** ollama/glm-5.2:cloud
|
||||
|
||||
## [2026-07-02] Ingest | Agent Memory Taxonomy — Seven Kinds Mapped to Open-Source Repos (r/WebAfterAI)
|
||||
**Type:** ingest | **Scope:** raw/other, wiki/concepts/agents, wiki/index
|
||||
**Source:** Reddit r/WebAfterAI post (https://www.reddit.com/r/WebAfterAI/comments/1ukr9fb/the_seven_kinds_of_agent_memory_each_mapped_to_an/) — shared in OME context, 02.07.2026
|
||||
**Trigger:** Taxonomy of 7 agent memory types with open-source repo mappings. Direkt relevant für plur1bus-Architektur und Memory-System-Verständnis. Post deckt 7 Typen ab, erwähnt aber NICHT Consolidation & Forgetting — plur1bus's Kern-Differentiator.
|
||||
**Actions:**
|
||||
- raw: `raw/other/2026-07-02_agent-memory-taxonomy-seven-types.md` (created — 7.2 KB; Frontmatter mit type/source_url/retrieved/title/author/tags, alle 7 Typen mit Definition, Repo, License, Catch, Verified Recipe, Selection Guide, Companion Resource, Consolidation & Forgetting Gap, Relevance for Our Setup)
|
||||
- wiki (NEW): `concepts/agents/agent-memory-taxonomy.md` (created — 8.5 KB; Wiki-Konzeptseite mit: 7-Typen-Tabelle, Design Catches, Selection Guide, "What the Post Does NOT Mention" (Consolidation & Forgetting), vollständige plur1bus-Einordnung mit Coverage-Matrix (✅/⚠️/❌), plur1bus Key Differentiator (GC+Decay, Merging, neverForget, Emotion Tiers, autoCapture, autoRecall), Architecture Cross-References zu letta/cognee/graphiti/Voyager, Related Pages)
|
||||
- wiki: `index.md` (updated — Header auf "52. Update", neuer Agents-Eintrag unter Concepts/Agents, neue Raw-Source-Zeile)
|
||||
- log: this entry
|
||||
**Hector-Hauptthese:** Die Taxonomie ist ein nützliches Framing, aber der wichtigste Befund ist die Blindstelle des Posts: Keines der 7 Repos hat Consolidation oder Forgetting. Das ist genau was plur1bus von einem "Memory Dump" unterscheidet — GC mit Decay (Ebbinghaus-Curve), Merging mit Thresholds, neverForget-Flags, Emotion-Tiers. Die plur1bus-Einordnung zeigt: Wir decken Semantic + Episodic + Retrieval stark ab (3 von 7), Procedural und Working indirekt, und haben als einziger aktiver Memory-Manager eine Lifecycle-Schicht die kein Repo der Taxonomie hat. Die Empfehlung "Pick by workflow, not by taxonomy" validiert unsere Architektur-Entscheidung: Wir haben Working (via OpenClaw/LCM) + Semantic + Episodic + Retrieval (via plur1bus/LanceDB) + Prospective (via OpenClaw Cron) — fünf der sieben, aber gewählt nach Job, nicht nach Checklist.
|
||||
**Subagent-Modell:** ollama/glm-5.2:cloud
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue