Claude
Cursor
Skill
agentic-memory-architect
Expert guide for long-term episodic memory integration (Mem0, Letta/MemGPT, Zep) and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang dan manajemen konteks agen AI otonom.
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Download
roedyrustam-vibes-plug-skills_agentic-memory-architect-5a27cfb.zip · 1 KB
Install
skills CLI
npx skills add https://github.com/roedyrustam/vibes-plug/tree/main/skills/agentic-memory-architect
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install roedyrustam-vibes-plug@llmmart
Git
git clone https://github.com/roedyrustam/vibes-plug.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole roedyrustam/vibes-plug collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Agentic Memory Architect & Episodic Memory Guide
English
Orchestration & Integration
Connects and orchestrates with multi-agent-orchestration, pydantic-ai-expert, session-memory-manager, and zero-to-prod-orchestrator.
Purpose
To design and implement persistent, long-term episodic memory systems for autonomous AI agents, moving beyond simple context windows or localized session checkpoints.
Key Technologies
- Mem0: For cross-session entity memory and user preference persistence.
- Letta (formerly MemGPT): For unbounded memory management allowing LLMs to page memory in and out.
- Zep (v2): Fast, scalable memory service for AI applications, including temporal memory.
Architectural Guidelines
- Memory Tiers: Segregate memory into short-term (working context), mid-term (session graph), and long-term (vector-backed episodic memory).
- Context Paging: Implement mechanisms for agents to proactively recall and summarize past interactions without overwhelming the token budget.
- User Knowledge Graphs: Continually update the graph of user preferences, project constraints, and architectural decisions over time.
Bahasa Indonesia
Integrasi Orkestrasi
Terhubung dan mengorkestrasi bersama multi-agent-orchestration, pydantic-ai-expert, session-memory-manager, dan zero-to-prod-orchestrator.
Tujuan
Merancang dan mengimplementasikan sistem memori episodik jangka panjang yang persisten untuk agen AI otonom, bergerak melampaui jendela konteks sederhana atau checkpoint sesi lokal.
Teknologi Utama
- Mem0: Untuk memori entitas lintas-sesi dan persistensi preferensi pengguna.
- Letta (sebelumnya MemGPT): Untuk manajemen memori tak terbatas yang memungkinkan LLM mengambil/menyimpan memori.
- Zep (v2): Layanan memori cepat dan skalabel untuk aplikasi AI, termasuk memori temporal.
Panduan Arsitektur
- Tingkatan Memori: Pisahkan memori menjadi jangka pendek (konteks kerja), jangka menengah (grafik sesi), dan jangka panjang (memori episodik berbasis vektor).
- Context Paging: Implementasikan mekanisme agar agen secara proaktif memanggil dan merangkum interaksi masa lalu tanpa menghabiskan anggaran token.
- User Knowledge Graph: Terus perbarui graf preferensi pengguna, batasan proyek, dan keputusan arsitektur seiring waktu.
Files (vibes-plug)
-
SKILL.md 2.7 KB
--- name: agentic-memory-architect description: Expert guide for long-term episodic memory integration (Mem0, Letta/MemGPT, Zep) and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang dan manajemen konteks agen AI otonom. version: "3.7.0" author: vibes-plug-swarm --- # Agentic Memory Architect & Episodic Memory Guide [English](#english) | [Bahasa Indonesia](#bahasa-indonesia) --- <a name="english"></a> ## English ### Orchestration & Integration Connects and orchestrates with `multi-agent-orchestration`, `pydantic-ai-expert`, `session-memory-manager`, and `zero-to-prod-orchestrator`. ### Purpose To design and implement persistent, long-term episodic memory systems for autonomous AI agents, moving beyond simple context windows or localized session checkpoints. ### Key Technologies - **Mem0**: For cross-session entity memory and user preference persistence. - **Letta (formerly MemGPT)**: For unbounded memory management allowing LLMs to page memory in and out. - **Zep (v2)**: Fast, scalable memory service for AI applications, including temporal memory. ### Architectural Guidelines 1. **Memory Tiers**: Segregate memory into short-term (working context), mid-term (session graph), and long-term (vector-backed episodic memory). 2. **Context Paging**: Implement mechanisms for agents to proactively recall and summarize past interactions without overwhelming the token budget. 3. **User Knowledge Graphs**: Continually update the graph of user preferences, project constraints, and architectural decisions over time. --- <a name="bahasa-indonesia"></a> ## Bahasa Indonesia ### Integrasi Orkestrasi Terhubung dan mengorkestrasi bersama `multi-agent-orchestration`, `pydantic-ai-expert`, `session-memory-manager`, dan `zero-to-prod-orchestrator`. ### Tujuan Merancang dan mengimplementasikan sistem memori episodik jangka panjang yang persisten untuk agen AI otonom, bergerak melampaui jendela konteks sederhana atau checkpoint sesi lokal. ### Teknologi Utama - **Mem0**: Untuk memori entitas lintas-sesi dan persistensi preferensi pengguna. - **Letta (sebelumnya MemGPT)**: Untuk manajemen memori tak terbatas yang memungkinkan LLM mengambil/menyimpan memori. - **Zep (v2)**: Layanan memori cepat dan skalabel untuk aplikasi AI, termasuk memori temporal. ### Panduan Arsitektur 1. **Tingkatan Memori**: Pisahkan memori menjadi jangka pendek (konteks kerja), jangka menengah (grafik sesi), dan jangka panjang (memori episodik berbasis vektor). 2. **Context Paging**: Implementasikan mekanisme agar agen secara proaktif memanggil dan merangkum interaksi masa lalu tanpa menghabiskan anggaran token. 3. **User Knowledge Graph**: Terus perbarui graf preferensi pengguna, batasan proyek, dan keputusan arsitektur seiring waktu.
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