Agentic Memory Architect & Episodic Memory Guide

English | Bahasa Indonesia

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<a name="english"></a>

English

Orchestration & Integration

Connects and orchestrates with:

• multi-agent-orchestration — shared memory state across agent swarms
• pydantic-ai-expert — type-safe memory injection into Pydantic AI agents
• session-memory-manager — short-term session checkpoint layer
• vector-db-rag-expert — pgvector/HNSW for long-term episodic retrieval
• database-orm-expert — Prisma/Drizzle schema for memory persistence
• ai-llm-integration-expert — context window injection at inference time
• zero-to-prod-orchestrator — memory layer provisioned in Phase 2 (Foundation)

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Purpose

Design and implement persistent, multi-tier memory systems for autonomous AI agents that transcend simple context windows. Enable agents to remember users across sessions, recall past decisions, maintain knowledge graphs, and operate with human-like episodic continuity — all while remaining within token budget constraints.

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Memory Tier Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    AGENT MEMORY HIERARCHY                        │
├─────────────────────────────────────────────────────────────────┤
│  TIER 0 │ In-Context Working Memory (current conversation)       │
│         │ ≤ 128K tokens │ Lost on session end                   │
│         │ Implementation: Raw messages array in LLM call         │
├─────────────────────────────────────────────────────────────────┤
│  TIER 1 │ Session Memory (within-session graph)                  │
│         │ Hours to days │ Redis / Zep session store              │
│         │ Implementation: Zep v2 session + entity graph          │
├─────────────────────────────────────────────────────────────────┤
│  TIER 2 │ Episodic Memory (cross-session, user-scoped)           │
│         │ Weeks to months │ Mem0 + pgvector HNSW                 │
│         │ Implementation: Mem0 Memory.add() / Memory.search()    │
├─────────────────────────────────────────────────────────────────┤
│  TIER 3 │ Semantic / Procedural Memory (agent knowledge base)    │
│         │ Permanent │ pgvector + structured KB tables            │
│         │ Implementation: RAG pipeline over domain knowledge     │
└─────────────────────────────────────────────────────────────────┘

flowchart TD
    IN["User Input"] --> T0["Tier 0: In-Context\n(current messages)"]
    T0 --> AGENT["Agent LLM Call"]
    AGENT --> OUT["Response"]
    AGENT <-->|"recall + store"| T1["Tier 1: Zep Session\n(entity + temporal graph)"]
    AGENT <-->|"recall + store"| T2["Tier 2: Mem0 Episodic\n(cross-session user memories)"]
    AGENT <-->|"retrieve only"| T3["Tier 3: pgvector RAG\n(domain knowledge base)"]
    T1 -->|"promotes key facts"| T2
    T2 -->|"populates embeddings"| T3

---

1. Mem0 v2 — Cross-Session Episodic Memory

Mem0 provides a managed memory layer that extracts and stores entities, preferences, and facts from conversations, making them searchable across sessions.

pip install mem0ai
# OR for self-hosted:
pip install mem0ai[oss]

Python: Full CRUD Memory Operations

import os
from mem0 import Memory

# Initialize with custom vector store (pgvector)
config = {
    "vector_store": {
        "provider": "pgvector",
        "config": {
            "dbname": "mem0_db",
            "collection_name": "agent_memories",
            "embedding_model_dims": 1536,
            "host": os.environ["PGVECTOR_HOST"],
            "port": 5432,
            "user": os.environ["PGVECTOR_USER"],
            "password": os.environ["PGVECTOR_PASSWORD"],
        },
    },
    "llm": {
        "provider": "anthropic",
        "config": {
            "model": "claude-sonnet-4-5",
            "api_key": os.environ["ANTHROPIC_API_KEY"],
        },
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small",
            "api_key": os.environ["OPENAI_API_KEY"],
        },
    },
}

memory = Memory.from_config(config)

# ADD: Store memories from a conversation turn
def add_conversation_memory(user_id: str, messages: list[dict]) -> list[dict]:
    """
    Extract and store memories from a conversation.
    Mem0 automatically identifies entities, preferences, and facts.
    Returns list of memory entries created.
    """
    result = memory.add(
        messages=messages,
        user_id=user_id,
        metadata={"source": "conversation", "app": "my-agent"},
    )
    return result  # [{"id": "uuid", "memory": "User prefers TypeScript over Python", ...}]


# SEARCH: Retrieve relevant memories for the current query
def recall_memories(user_id: str, query: str, top_k: int = 5) -> list[dict]:
    """
    Vector-similarity search over user's episodic memories.
    Returns ranked list of relevant memories.
    """
    results = memory.search(
        query=query,
        user_id=user_id,
        limit=top_k,
    )
    return results  # [{"id": "...", "memory": "...", "score": 0.92, ...}]


# GET ALL: List all memories for a user
def get_all_memories(user_id: str) -> list[dict]:
    return memory.get_all(user_id=user_id)


# UPDATE: Correct or expand an existing memory
def update_memory(memory_id: str, new_data: str) -> dict:
    return memory.update(memory_id=memory_id, data=new_data)


# DELETE: Remove a specific memory
def delete_memory(memory_id: str) -> None:
    memory.delete(memory_id=memory_id)


# RESET: Clear all memories for a user (use with caution)
def reset_user_memories(user_id: str) -> None:
    memory.delete_all(user_id=user_id)


# --- AGENT LOOP INTEGRATION ---
async def agent_with_memory(user_id: str, user_input: str) -> str:
    import anthropic

    # 1. Recall relevant past memories
    memories = recall_memories(user_id, user_input, top_k=5)
    memory_context = "\n".join(
        f"- {m['memory']}" for m in memories if m.get("score", 0) > 0.7
    )

    system_prompt = f"""You are a helpful AI assistant.
You have the following relevant memories about this user:
{memory_context if memory_context else "No prior memories found."}
Use these memories to personalize your response."""

    client = anthropic.Anthropic()
    response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=2048,
        system=system_prompt,
        messages=[{"role": "user", "content": user_input}],
    )
    assistant_reply = response.content[0].text

    # 2. Store this turn in Mem0 for future sessions
    add_conversation_memory(
        user_id,
        [
            {"role": "user", "content": user_input},
            {"role": "assistant", "content": assistant_reply},
        ],
    )

    return assistant_reply

TypeScript: Mem0 Client

import MemoryClient from "mem0ai";

const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });

export interface Memory {
  id: string;
  memory: string;
  score?: number;
  created_at: string;
  updated_at: string;
}

export async function addMemory(
  userId: string,
  messages: Array<{ role: "user" | "assistant"; content: string }>,
): Promise<Memory[]> {
  const result = await mem0.add(messages, { user_id: userId });
  return result as Memory[];
}

export async function searchMemory(
  userId: string,
  query: string,
  limit = 5,
): Promise<Memory[]> {
  const results = await mem0.search(query, { user_id: userId, limit });
  return results as Memory[];
}

export async function deleteMemory(memoryId: string): Promise<void> {
  await mem0.delete(memoryId);
}

export async function getAllMemories(userId: string): Promise<Memory[]> {
  const result = await mem0.getAll({ user_id: userId });
  return result as Memory[];
}

---

2. Letta (MemGPT) — Unbounded In-Context Memory Management

Letta enables LLMs to manage their own memory by providing explicit memory blocks (core_memory, archival_memory) that the model can read/write through tool calls, effectively giving LLMs unlimited memory.

pip install letta-client
letta server start  # starts local Letta server on :8283

Python: Persisted Agent with Custom Memory

from letta_client import Letta

client = Letta(base_url="http://localhost:8283")

# Create a persisted agent with custom memory blocks
agent = client.agents.create(
    name="research-assistant",
    model="anthropic/claude-sonnet-4-5",
    embedding="openai/text-embedding-3-small",
    memory_blocks=[
        {
            "label": "human",
            "value": "Name: Unknown\nPreferences: Unknown\nProjects: None yet.",
            "limit": 2000,
        },
        {
            "label": "persona",
            "value": (
                "I am a research assistant with persistent memory. "
                "I remember user preferences, past discussions, and project context "
                "across all sessions."
            ),
            "limit": 2000,
        },
    ],
    tools=["core_memory_append", "core_memory_replace", "archival_memory_insert",
           "archival_memory_search"],
)

print(f"Agent ID: {agent.id}")  # Save this for future sessions


def chat_with_persisted_agent(agent_id: str, message: str) -> str:
    """Send a message to a persisted Letta agent and get a response."""
    resp = client.agents.messages.create(
        agent_id=agent_id,
        messages=[{"role": "user", "content": message}],
    )
    # Extract the assistant text from the response
    for msg in resp.messages:
        if msg.message_type == "assistant_message":
            return msg.content
    return ""


def get_agent_memory(agent_id: str) -> dict:
    """Inspect current in-context memory blocks of the agent."""
    memory = client.agents.core_memory.retrieve(agent_id=agent_id)
    return {block.label: block.value for block in memory.memory.values()}


def search_archival_memory(agent_id: str, query: str) -> list[str]:
    """Search the agent's long-term archival memory store."""
    results = client.agents.archival_memory.list(agent_id=agent_id, query=query)
    return [r.text for r in results.archival_memory]

---

3. Zep v2 — Temporal Memory & User Knowledge Graph

Zep provides a fast memory service with temporal awareness, entity extraction, and user knowledge graph construction. It handles the Tier 1 (session) and bridges to Tier 2 (episodic).

pip install zep-cloud
# OR self-hosted:
docker run -p 8000:8000 ghcr.io/getzep/zep:latest

Python: Zep Session + Graph Memory

import os
from zep_cloud.client import AsyncZep
from zep_cloud.types import Message, RoleType

zep = AsyncZep(api_key=os.environ["ZEP_API_KEY"])


async def ensure_user_and_session(user_id: str, session_id: str) -> None:
    """Idempotently create user and session in Zep."""
    try:
        await zep.user.add(user_id=user_id)
    except Exception:
        pass  # User already exists

    try:
        await zep.memory.add_session(
            session_id=session_id,
            user_id=user_id,
            metadata={"app": "my-agent", "version": "1.0"},
        )
    except Exception:
        pass  # Session already exists


async def store_turn(
    session_id: str,
    user_message: str,
    assistant_message: str,
) -> None:
    """Persist a conversation turn to Zep for memory extraction."""
    await zep.memory.add(
        session_id=session_id,
        messages=[
            Message(role_type=RoleType.UserRole, role="user", content=user_message),
            Message(role_type=RoleType.AssistantRole, role="assistant", content=assistant_message),
        ],
    )


async def recall_session_context(session_id: str) -> str:
    """Retrieve Zep's synthesized memory summary + relevant facts."""
    memory = await zep.memory.get(session_id=session_id)
    parts = []
    if memory.summary and memory.summary.content:
        parts.append(f"Session Summary:\n{memory.summary.content}")
    if memory.facts:
        facts_text = "\n".join(f"- {fact}" for fact in memory.facts[:10])
        parts.append(f"Key Facts:\n{facts_text}")
    return "\n\n".join(parts)


async def search_user_graph(user_id: str, query: str) -> list[dict]:
    """
    Search the user's temporal knowledge graph for entities and relations.
    Returns enriched context about what Zep knows about this user.
    """
    results = await zep.graph.search(
        user_id=user_id,
        query=query,
        scope="edges",       # 'nodes' | 'edges' — edges capture relations
        limit=10,
        reranker="rrf",      # Reciprocal Rank Fusion for hybrid search
    )
    return [
        {
            "source": edge.source_node_name,
            "relation": edge.relation,
            "target": edge.target_node_name,
            "fact": edge.fact,
            "created_at": edge.created_at.isoformat() if edge.created_at else None,
        }
        for edge in (results.edges or [])
    ]


async def add_structured_data_to_graph(user_id: str, data: str, data_type: str = "text") -> None:
    """
    Inject structured information (e.g., user profile JSON) into Zep's graph.
    Zep will extract entities and relations automatically.
    """
    await zep.graph.add(user_id=user_id, data=data, type=data_type)

---

4. pgvector + HNSW — Long-Term Episodic Storage

For Tier 2/3 storage, use PostgreSQL with pgvector and HNSW indexes for sub-millisecond similarity search over millions of memory embeddings.

SQL Schema

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Episodic memory table
CREATE TABLE agent_memories (
    id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id      TEXT NOT NULL,
    agent_id     TEXT,
    content      TEXT NOT NULL,
    embedding    VECTOR(1536) NOT NULL,  -- OpenAI text-embedding-3-small
    tier         SMALLINT NOT NULL DEFAULT 2,  -- 1=session, 2=episodic, 3=semantic
    source       TEXT NOT NULL DEFAULT 'conversation',
    metadata     JSONB NOT NULL DEFAULT '{}',
    created_at   TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    expires_at   TIMESTAMPTZ              -- NULL = permanent
);

-- HNSW index for cosine similarity (outperforms IVFFlat at recall@10)
CREATE INDEX idx_memories_embedding_hnsw ON agent_memories
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

-- B-tree for user-scoped filtering
CREATE INDEX idx_memories_user_id ON agent_memories (user_id);

-- Composite for time-scoped recall
CREATE INDEX idx_memories_user_created ON agent_memories (user_id, created_at DESC);

TypeScript: pgvector Memory Repository

import { Pool } from "pg";
import OpenAI from "openai";

const pool = new Pool({ connectionString: process.env.DATABASE_URL });
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

export interface EpisodicMemory {
  id: string;
  userId: string;
  content: string;
  metadata: Record<string, unknown>;
  createdAt: Date;
  similarity?: number;
}

async function embed(text: string): Promise<number[]> {
  const resp = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: text,
  });
  return resp.data[0].embedding;
}

export async function storeMemory(
  userId: string,
  content: string,
  metadata: Record<string, unknown> = {},
  tier: 1 | 2 | 3 = 2,
): Promise<string> {
  const embedding = await embed(content);
  const vectorLiteral = `[${embedding.join(",")}]`;

  const result = await pool.query<{ id: string }>(
    `INSERT INTO agent_memories (user_id, content, embedding, tier, metadata)
     VALUES ($1, $2, $3::vector, $4, $5)
     RETURNING id`,
    [userId, content, vectorLiteral, tier, JSON.stringify(metadata)],
  );

  return result.rows[0].id;
}

export async function recallMemories(
  userId: string,
  query: string,
  topK = 5,
  minSimilarity = 0.70,
): Promise<EpisodicMemory[]> {
  const embedding = await embed(query);
  const vectorLiteral = `[${embedding.join(",")}]`;

  const result = await pool.query<EpisodicMemory & { similarity: number }>(
    `SELECT
       id,
       user_id AS "userId",
       content,
       metadata,
       created_at AS "createdAt",
       1 - (embedding <=> $1::vector) AS similarity
     FROM agent_memories
     WHERE user_id = $2
       AND (expires_at IS NULL OR expires_at > NOW())
       AND 1 - (embedding <=> $1::vector) >= $3
     ORDER BY embedding <=> $1::vector
     LIMIT $4`,
    [vectorLiteral, userId, minSimilarity, topK],
  );

  return result.rows;
}

export async function deleteExpiredMemories(): Promise<number> {
  const result = await pool.query(
    `DELETE FROM agent_memories WHERE expires_at IS NOT NULL AND expires_at <= NOW()`,
  );
  return result.rowCount ?? 0;
}

---

5. Memory Injection Template — RAG Pipeline Integration

import { recallMemories } from "./memory-repository";
import { searchMemory } from "./mem0-client";

export async function buildMemoryAugmentedPrompt(
  userId: string,
  userInput: string,
  systemBase: string,
): Promise<{ system: string; injectedMemoryCount: number }> {
  // Parallel recall from Tier 2 (pgvector) and Mem0
  const [pgMemories, mem0Memories] = await Promise.all([
    recallMemories(userId, userInput, 5, 0.72),
    searchMemory(userId, userInput, 5),
  ]);

  const allMemories = [
    ...pgMemories.map((m) => ({ content: m.content, score: m.similarity ?? 0, source: "episodic" })),
    ...mem0Memories
      .filter((m) => (m.score ?? 0) > 0.7)
      .map((m) => ({ content: m.memory, score: m.score ?? 0, source: "mem0" })),
  ]
    .sort((a, b) => b.score - a.score)
    .slice(0, 8);                         // Top 8 memories across both sources

  const memoryBlock =
    allMemories.length > 0
      ? `\n\n## Relevant Memories About This User\n${allMemories
          .map((m) => `- [${m.source}] ${m.content}`)
          .join("\n")}`
      : "";

  return {
    system: `${systemBase}${memoryBlock}`,
    injectedMemoryCount: allMemories.length,
  };
}

---

6. Memory CRUD — Full TypeScript Interface

// memory-manager.ts — unified interface over Mem0 + pgvector
import { addMemory, searchMemory, getAllMemories, deleteMemory } from "./mem0-client";
import { storeMemory, recallMemories } from "./memory-repository";

export type MemoryBackend = "mem0" | "pgvector" | "both";

export async function createMemory(
  userId: string,
  content: string,
  backend: MemoryBackend = "both",
  metadata: Record<string, unknown> = {},
): Promise<{ mem0Ids?: string[]; pgvectorId?: string }> {
  const results: { mem0Ids?: string[]; pgvectorId?: string } = {};

  if (backend === "mem0" || backend === "both") {
    const mem0Result = await addMemory(userId, [
      { role: "user", content: `Remember this: ${content}` },
    ]);
    results.mem0Ids = mem0Result.map((m) => m.id);
  }

  if (backend === "pgvector" || backend === "both") {
    results.pgvectorId = await storeMemory(userId, content, metadata);
  }

  return results;
}

export async function queryMemory(
  userId: string,
  query: string,
  backend: MemoryBackend = "both",
): Promise<Array<{ content: string; score: number; source: string }>> {
  const results: Array<{ content: string; score: number; source: string }> = [];

  if (backend === "mem0" || backend === "both") {
    const mem0Results = await searchMemory(userId, query, 5);
    results.push(
      ...mem0Results.map((m) => ({
        content: m.memory,
        score: m.score ?? 0.5,
        source: "mem0",
      })),
    );
  }

  if (backend === "pgvector" || backend === "both") {
    const pgResults = await recallMemories(userId, query, 5);
    results.push(
      ...pgResults.map((m) => ({
        content: m.content,
        score: m.similarity ?? 0.5,
        source: "pgvector",
      })),
    );
  }

  return results.sort((a, b) => b.score - a.score).slice(0, 10);
}

export async function purgeUserMemory(
  userId: string,
  backend: MemoryBackend = "both",
): Promise<void> {
  const { Pool } = await import("pg");
  const pool = new Pool({ connectionString: process.env.DATABASE_URL });

  const tasks: Promise<void>[] = [];

  if (backend === "mem0" || backend === "both") {
    const { default: MemoryClient } = await import("mem0ai");
    const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
    tasks.push(mem0.deleteAll({ user_id: userId }).then(() => undefined));
  }

  if (backend === "pgvector" || backend === "both") {
    tasks.push(
      pool
        .query("DELETE FROM agent_memories WHERE user_id = $1", [userId])
        .then(() => undefined),
    );
  }

  await Promise.all(tasks);
}

---

<a name="bahasa-indonesia"></a>

Bahasa Indonesia

Integrasi Orkestrasi

Terhubung dan mengorkestrasi bersama:

• multi-agent-orchestration — state memori bersama di seluruh agent swarm
• pydantic-ai-expert — injeksi memori type-safe ke agen Pydantic AI
• session-memory-manager — lapisan checkpoint sesi jangka pendek
• vector-db-rag-expert — pgvector/HNSW untuk retrieval episodik jangka panjang
• database-orm-expert — skema Prisma/Drizzle untuk persistensi memori
• ai-llm-integration-expert — injeksi context window saat inferensi
• zero-to-prod-orchestrator — lapisan memori diprovisioning di Phase 2 (Foundation)

---

Tujuan

Merancang dan mengimplementasikan sistem memori multi-tier yang persisten untuk agen AI otonom yang melampaui context window sederhana. Memungkinkan agen untuk mengingat pengguna lintas sesi, memanggil keputusan masa lalu, mempertahankan knowledge graph, dan beroperasi dengan kontinuitas episodik menyerupai manusia — sambil tetap dalam batas anggaran token.

---

Arsitektur Tier Memori

Tier	Nama	Durasi	Teknologi	Kapasitas
0	In-Context Working Memory	Sesi saat ini	Array messages LLM	≤ 128K token
1	Session Memory	Jam hingga hari	Zep v2 session + entity graph	Tak terbatas (ringkasan)
2	Episodic Memory	Minggu hingga bulan	Mem0 + pgvector HNSW	Jutaan memori
3	Semantic / Procedural	Permanen	pgvector RAG + tabel KB terstruktur	Basis pengetahuan domain

---

1. Mem0 v2 — Memori Episodik Lintas Sesi

Mem0 menyediakan lapisan memori terkelola yang mengekstrak dan menyimpan entitas, preferensi, dan fakta dari percakapan, sehingga dapat dicari lintas sesi.

Konsep kunci:

• Memory.add(messages, user_id): ekstrak dan simpan memori dari percakapan — Mem0 secara otomatis mengidentifikasi entitas (nama, preferensi, kendala proyek)
• Memory.search(query, user_id, limit): pencarian vektor-kemiripan atas memori episodik pengguna — kembalikan daftar berperingkat fakta yang relevan
• Multi-user isolation: setiap memori terikat pada user_id — tidak ada kebocoran antar pengguna
• Entity extraction: Mem0 menggunakan LLM untuk mengekstrak entitas terstruktur (nama, preferensi, batasan) dari percakapan mentah

Dukungan backend: Mem0 Cloud (API), atau self-hosted dengan pgvector, Qdrant, Pinecone, atau Chroma.

Lihat implementasi lengkap Python dan TypeScript di seksi English.

---

2. Letta/MemGPT — Manajemen Memori In-Context Tak Terbatas

Letta memungkinkan LLM mengelola memori sendiri melalui tool calls eksplisit ke blok memori:

• core_memory_append: tambahkan fakta baru ke blok memori inti (persona, human profile)
• core_memory_replace: perbarui fakta yang sudah ada di memori inti
• archival_memory_insert: simpan informasi ke penyimpanan arsip jangka panjang (vektor)
• archival_memory_search: cari arsip menggunakan pencarian semantik

Pola penggunaan:

1. Buat agen persisten dengan client.agents.create() — simpan agent_id
2. Gunakan agent_id yang sama di semua sesi masa depan — agen ingat konteks sebelumnya
3. Agen secara proaktif mengelola memorinya sendiri melalui tool calls saat konteks penuh

---

3. Zep v2 — Memori Temporal & Knowledge Graph Pengguna

Zep menyediakan layanan memori cepat dengan kesadaran temporal, ekstraksi entitas, dan konstruksi knowledge graph pengguna.

Fitur utama:

• Temporal memory: setiap fakta dicap waktu — agen dapat mempertanyakan "apa yang pengguna katakan minggu lalu tentang X?"
• Entity graph: Zep secara otomatis membangun graf pengetahuan entitas dari percakapan (orang, tempat, preferensi, keputusan)
• graph.add(): injeksikan data terstruktur (JSON profil pengguna) ke grafik — Zep mengekstrak entitas dan relasi
• graph.search(scope="edges"): cari relasi di knowledge graph menggunakan hybrid search (vektor + kata kunci) + RRF reranking
• Session summary: Zep secara otomatis merangkum sesi panjang menjadi ringkasan yang dapat diinjeksikan ke konteks agen

---

4. pgvector + HNSW — Penyimpanan Episodik Jangka Panjang

Untuk penyimpanan Tier 2/3, gunakan PostgreSQL dengan pgvector dan indeks HNSW untuk pencarian kemiripan sub-milidetik atas jutaan embedding memori.

Mengapa HNSW daripada IVFFlat?

• HNSW: recall@10 = 99%+, latensi stabil tanpa perlu VACUUM
• IVFFlat: recall@10 = 95%, memerlukan pemilihan nlist yang tepat dan VACUUM rutin

Parameter HNSW yang direkomendasikan:

• m = 16: koneksi per lapisan — keseimbangan antara performa recall dan penggunaan memori
• ef_construction = 64: kualitas indeks saat build — nilai lebih tinggi = recall lebih baik, build lebih lambat
• ef_search = 40 (opsional via SET hnsw.ef_search = 40): kualitas pencarian saat query

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5. Template Injeksi Memori ke RAG Pipeline

Alur retrieval memori optimal:

Query pengguna
    ↓
[Parallel Recall]
    ├── pgvector HNSW search (Tier 2 episodic)
    └── Mem0 semantic search (cross-session)
    ↓
[Merge & Rerank by similarity score]
    ↓
[Filter: min_similarity = 0.70, top_k = 8]
    ↓
[Inject ke system prompt sebagai "Relevant Memories" block]
    ↓
LLM call → response
    ↓
[Store turn to Mem0 + pgvector async]

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Anti-Slop Checklist Memori

• user_id selalu unik dan konsisten antar sesi — gunakan UUID deterministik (mis. uuid5(NAMESPACE_DNS, email))
• Minimal similarity threshold 0.70 untuk menghindari injeksi memori tidak relevan
• Memori yang disimpan tidak melebihi expires_at — implementasikan TTL untuk memori sementara
• Zep session dibuat sebelum memory.add() dipanggil — validasi idempoten
• Letta agent_id disimpan di database untuk reuse lintas sesi
• Purge memori pengguna tersedia untuk kepatuhan GDPR/PDPA
• Semua operasi memori async — tidak memblokir response agen
• Indeks HNSW di-REINDEX setelah lebih dari 10% baris dihapus