Types of Agentic Memory 🧠

Agentic memory is typically divided into several categories based on what is stored, how long it is retained, and how it is used during reasoning.

Memory Hierarchy in Agentic Systems

graph TD
    A[Agent Memory]

    A --> B["Short-Term Memory 💾"]
    A --> C["Long-Term Memory 🛢"]

    C --> D["Episodic Memory 📅"]
    C --> E["Semantic Memory 📚"]
    C --> F["Procedural Memory 🚗"]

    D --> G["Retrieval Layer 📁"]
    E --> G
    F --> G

    G --> H["Vector Database ↗"️]

Real Agent Memory usage

graph TD
    A["User Query ❓"]

    A --> B["Working Memory 💾"]

    B --> C["Reasoning Engine 🧠"]

    C --> D["Retrieval Layer 📁"]

    D --> E["Vector Database ↗️"]
    D --> F["Knowledge Graph 🔗"]
    D --> G["Document Store 🗃"]

    E --> H["Semantic Memory  📚"]
    F --> H
    G --> H

    C --> I["Episodic Memory 📅"]
    C --> J["Procedural Memory 🚗"]

    H --> C
    I --> C
    J --> C

    C --> K["Tool Calls 🧰"]
    C --> L["Agent Response 💬"]

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1. Short-Term Memory 💾 : Working Memory

Stores information needed for the current task or conversation.

Characteristics

• Session-scoped
• Fast access
• Temporary
• Usually stored in context window

Use Cases

• Chatbots
• Task execution
• Multi-turn conversations

Examples

User: Book a flight to Berlin

Memory:
- Departure: Munich
- Date: 15 July
- Airline preference: Lufthansa

---

2. Long-Term Memory 🛢

An umbrella category that includes:

• Episodic memory
• Semantic memory
• Procedural memory

Typically, stored externally:

• Vector DB
• Knowledge Graph
• SQL Database
• Object Storage

Examples

• User preferences
• Historical interactions
• Enterprise documents
• Learned workflows

2.1 Episodic Memory : Past Experiences 📅

Stores past experiences and interactions.

Inspired by human memory.

Characteristics

• Experience-based
• Time-oriented
• Helps personalization
• Learns from previous interactions

Use Cases

• Personal assistants
• Customer support agents
• Learning agents

Examples

Episode:
User asked about Kubernetes
Agent recommended EKS
User preferred self-hosted cluster

Later:

Agent remembers:
"Last time you preferred self-managed Kubernetes."

---

2.2 Semantic Memory : Knowledge 📚

Stores facts and knowledge.

Characteristics

• Fact-based
• Not tied to specific events
• Usually stored in:
  • Vector databases
  • Knowledge graphs
  • Knowledge bases

Use Cases

• RAG systems
• Enterprise knowledge assistants
• Search agents

Examples

Berlin is the capital of Germany.

Docker is a container platform.

TensorRT optimizes LLM inference.

Episodic vs Semantic Memory

Episodic	Semantic
"User asked about Java yesterday"	"Java was released in 1995"
Experience	Fact
Event-based	Knowledge-based
Personalized	General knowledge

---

2.3 Procedural Memory 🚗 : Process / Skill

Stores how to perform tasks.

Characteristics

• Skill-based
• Process-oriented
• Often encoded as workflows or plans

Use Cases

• Workflow automation
• Multi-agent orchestration
• Business process agents

Examples

How to deploy Kubernetes:

1. Create cluster
2. Configure networking
3. Deploy workloads
4. Monitor health

or

Workflow:
Validate input
→ Query API
→ Format result
→ Return response

---

3. Retrieval Memory 📁 : RAG

Memory retrieved dynamically when needed.

RAG + Agents

RAG systems provide external memory.

Agentic systems add:

• planning
• reasoning
• dynamic retrieval
• adaptive execution

A modern research agent may:

1. Generate search queries
2. Retrieve documents
3. Rank relevance
4. Summarize findings
5. Detect knowledge gaps
6. Retrieve additional context
7. Revise conclusions

This creates recursive information acquisition loops.

Flow

graph TD
    A[User Query]
    B[Generate Embedding]
    C["Vector Database ↗️"]
    D[Similarity Search]
    E[Top-K Retrieved Memories]
    F[Context Assembly]
    G[LLM Agent]
    H[Response]

    A --> B
    B --> D
    D --> C
    C --> E
    E --> F
    A --> F
    F --> G
    G --> H

Example

User:
What's my favorite programming language?

Retrieve:
"User prefers Java and Spring Boot."

This is the foundation of most RAG-based agents.

---

4. Shared Memory 🗂 : Multi-Agent Systems

Used when multiple agents collaborate.

All agents can access and update the same state.

graph TD

SharedMemory["Shared Memory 🗂"]
Research["Research Agent 🤖"]
CodingAgent["Coding Agent 🤖"]
ValidationAgent["Validation Agent 🤖"]

Research --> SharedMemory 
CodingAgent --> SharedMemory
ValidationAgent --> SharedMemory

Example

{
  "customer_id": 123,
  "issue": "payment failure",
  "status": "investigating"
}

---

Exam Tip

Which memory type stores facts and knowledge?

Semantic Memory

Which memory type stores previous interactions?

Episodic Memory

Which memory type stores workflows and skills?

Procedural Memory

Which memory type holds current conversation context?

Working (Short-Term) Memory

Which memory type enables personalization from previous user interactions?

Episodic Memory

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