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Memory in agents

Intermediate⏱ 10 min read📅 Updated: 2026-02-17

🧠 Introduction – Why Memory Matters

Imagine every time you talk to someone, they forget everything about you. Frustrating right? 😤

Same problem AI agents ku irukku – memory illama every conversation fresh start!

Without Memory:

❌ "Who are you?" – every time kekkum
❌ Past conversations remember pannaadhu
❌ User preferences theriyaadhu
❌ Learned patterns lost

With Memory:

✅ Knows your name, preferences, history
✅ Builds on past conversations
✅ Personalizes responses
✅ Gets smarter over time

Memory = What makes an agent truly intelligent! 🧠💾

📊 Types of Agent Memory

Agent memory 4 main types la varum:

Memory Type	Duration	What It Stores	Human Equivalent
Working Memory	Current task	Active context, variables	RAM
Short-term	Session	Conversation history	Today's events
Long-term	Permanent	Facts, preferences, patterns	Life experiences
Episodic	Permanent	Specific past events	"Remember that time..."

1. Working Memory 🔄

Current task-oda variables and state
LLM context window la irukku
Task complete aanaa clear aagum

2. Short-term Memory 📝

Current conversation history
Last few interactions
Session end aanaa gone (usually)

3. Long-term Memory 💾

User preferences, facts, knowledge
Persists across sessions
Stored in databases

4. Episodic Memory 📸

Specific past interactions/events
"Last week nee flight book panniya"
Rich context with timestamps

🏗️ Agent Memory Architecture

🏗️ Architecture Diagram

```
┌─────────────────────────────────────────────┐
│              AGENT CORE                     │
│                                             │
│  ┌────────────────────────────────────────┐ │
│  │  🔄 WORKING MEMORY                     │ │
│  │  Current task state, active variables  │ │
│  │  LLM Context Window (128K tokens)      │ │
│  └────────────────────────────────────────┘ │
│                                             │
│  ┌────────────────────────────────────────┐ │
│  │  📝 SHORT-TERM MEMORY                  │ │
│  │  Conversation buffer (last N messages) │ │
│  │  Session-scoped                        │ │
│  └──────────────┬─────────────────────────┘ │
│                 │ Save important info       │
│  ┌──────────────▼─────────────────────────┐ │
│  │  💾 LONG-TERM MEMORY                   │ │
│  │  ┌──────────┐  ┌───────────────────┐   │ │
│  │  │ Vector   │  │ Key-Value Store   │   │ │
│  │  │ Database │  │ (Preferences,     │   │ │
│  │  │ (Semantic│  │  Facts, Rules)    │   │ │
│  │  │  Search) │  │                   │   │ │
│  │  └──────────┘  └───────────────────┘   │ │
│  │  ┌──────────────────────────────────┐  │ │
│  │  │ 📸 Episodic Memory              │  │ │
│  │  │ Past interactions with context   │  │ │
│  │  └──────────────────────────────────┘  │ │
│  └────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
```

🔍 Memory Storage Technologies

How to actually store agent memory?

Technology	Type	Best For	Example
In-memory array	Working	Current conversation	JavaScript array
Redis	Short-term	Session data, cache	Redis Cloud
Vector DB	Long-term	Semantic search	Pinecone, ChromaDB
SQL Database	Long-term	Structured data	PostgreSQL
Document DB	Episodic	Event logs	MongoDB
File System	Long-term	Simple persistence	JSON files

Vector Database – The Star ⭐

Vector DB agent memory la game changer:

Text → Embedding (mathematical vector) aa convert
Similar embeddings cluster aagum
Query time la semantically similar content retrieve

code

User says: "I like spicy food"
→ Embedding: [0.23, 0.87, 0.12, ...]
→ Stored in Vector DB

Later, user asks: "Suggest restaurant"
→ Agent retrieves: "User prefers spicy food"
→ Suggests: "Try Saravana Bhavan's spicy specials! 🌶️"

🎬 Memory in Action – Personal Assistant

✅ Example

Day 1:

User: "I'm Ravi, I work at TCS, I like South Indian food"

Agent: *Stores to long-term memory*

- Name: Ravi

- Company: TCS

- Food preference: South Indian

Day 5:

User: "Book lunch for me"

Agent: *Retrieves from memory*

"Hi Ravi! 👋 Since you like South Indian food, I found Murugan Idli Shop near your TCS office. Table for 1 at 12:30 PM?"

Day 15:

User: "Same as last time"

Agent: *Retrieves episodic memory*

"Booking Murugan Idli Shop again? Same time 12:30 PM? Last time you ordered the special thali – want that again? 🍽️"

See how memory makes the agent feel human? 🤝

📝 Memory Management Strategies

Too much memory = noise. Too little = forgetful. Balance venum!

1. Summarization 📋

Long conversations → condensed summaries
Keep key facts, discard filler
Periodically summarize and compress

2. Relevance Scoring ⭐

Each memory item ku relevance score
High relevance → keep, Low relevance → archive/delete
Recency + frequency + importance = score

3. Forgetting Mechanism 🗑️

Old, unused memories slowly decay
Like human forgetting – natural and necessary!
Prevents context window overflow

4. Memory Tiers 📊

Tier	Content	Retention
Hot	Recent, frequently used	Always loaded
Warm	Important but not frequent	Load on demand
Cold	Old, rarely used	Archive, retrieve if needed
Delete	Irrelevant, outdated	Remove permanently

5. Selective Storage 🎯

Store facts and decisions, not raw conversation
"User likes pizza" ✅
"User said 'hmm let me think about pizza'" ❌

🔄 Memory Read/Write Cycle

Agent memory epdi use pannum? Read-Write cycle follow pannum:

WRITE (Storing memories):

code

1. Agent detects important information
2. Classify: fact / preference / event / decision
3. Generate embedding (for semantic search)
4. Store in appropriate memory tier
5. Index for quick retrieval

READ (Retrieving memories):

code

1. New user message arrives
2. Generate query embedding
3. Search relevant memories (semantic search)
4. Rank by relevance + recency
5. Inject top-K memories into LLM context
6. Agent responds with memory context

Key metrics:

Metric	Target
Retrieval accuracy	>90% relevant memories
Retrieval latency	<200ms
Memory freshness	Updated within session
Context utilization	<70% of context window

Don't fill entire context window with memories! Leave room for reasoning! 🧠

🛠️ Implementation Approaches

Approach 1: Conversation Buffer (Simple)

code

memory = []
memory.append({"role": "user", "content": "..."})
memory.append({"role": "assistant", "content": "..."})
# Send last N messages to LLM

✅ Easy | ❌ No long-term persistence

Approach 2: Summary Memory (Medium)

code

# After every 10 messages, summarize
summary = llm.summarize(last_10_messages)
memory.append(summary)
# Keep summaries + recent messages

✅ Compact | ❌ Loses details

Approach 3: Vector Memory (Advanced)

code

# Store each important fact as embedding
embedding = embed("User works at TCS")
vector_db.store(embedding, metadata)
# Retrieve relevant memories per query
results = vector_db.search(query_embedding, top_k=5)

✅ Semantic search | ✅ Scalable | ❌ More complex

Approach 4: Hybrid (Production) ⭐

code

Working: Current conversation buffer
Short-term: Last session summary
Long-term: Vector DB for facts + preferences
Episodic: Document DB for past events

✅ Best of all worlds | ❌ Most complex to build

🧪 Try It – Memory-Aware Agent

📋 Copy-Paste Prompt

```
You are a Personal Assistant with MEMORY capabilities.

MEMORY SYSTEM:
- You can STORE facts using [STORE: key=value]
- You can RECALL facts using [RECALL: key]
- You maintain a memory log below

CURRENT MEMORY:
- name: (empty)
- preferences: (empty)
- past_actions: (empty)

INSTRUCTIONS:
1. When user shares information, STORE it
2. When answering, RECALL relevant memories
3. Show your memory operations explicitly

CONVERSATION:
User: "Hi! I'm Kavitha. I'm a teacher and I love reading 
Tamil novels. I'm vegetarian."

Process this message:
1. What will you STORE?
2. How will you respond using memory?
3. Show your updated memory state.
```

Watch how explicit memory management works! 📚

⚠️ Memory Pitfalls to Avoid

⚠️ Warning

Common memory mistakes:

1. Storing everything 💾

- Not all info important. Be selective!

- "User said hi" – don't store this!

2. No memory expiry ⏰

- Old memories can mislead agent

- "User lived in Chennai" (but moved to Bangalore 6 months ago)

3. Privacy violations 🔒

- Sensitive data (passwords, medical info) needs special handling

- GDPR/privacy compliance essential

4. Memory conflicts ⚔️

- "User likes coffee" vs "User switched to tea"

- Latest info should override old info

5. Context window flooding 🌊

- Too many memories injected = no room for reasoning

- Limit to top 5-10 most relevant memories per query

💡 Memory Best Practices

💡 Tip

Production-ready memory tips:

- 🎯 Store structured data – JSON > free text

- 📅 Timestamp everything – when was this learned?

- 🏷️ Tag memories – category, importance, source

- 🔄 Regular cleanup – archive old, merge duplicates

- 🔒 Encrypt sensitive data – user privacy first

- 📊 Monitor memory usage – track size, retrieval quality

- 🧪 Test retrieval – does the right memory come back?

🌍 Real-World Memory Systems

How popular agents handle memory:

Agent/Product	Memory Approach	Details
ChatGPT	Memory feature	User facts stored, toggleable
Claude	Project knowledge	Files as long-term context
Mem0	AI memory layer	Open-source memory framework
LangChain	Memory modules	Buffer, Summary, Vector options
Notion AI	Workspace context	Uses your Notion pages as memory

Mem0 (formerly OpenMemory) is particularly interesting – it provides a standalone memory layer any agent can use! 🧠

📝 Summary

Key Takeaways:

✅ 4 Memory types: Working, Short-term, Long-term, Episodic

✅ Vector DB enables semantic memory search – game changer!

✅ Memory management: Summarize, Score, Forget, Tier, Select

✅ Read/Write cycle: Detect → Store → Index → Retrieve → Inject

✅ Implementation: Buffer (simple) → Summary → Vector → Hybrid (best)

✅ Avoid pitfalls: Over-storing, no expiry, privacy issues, conflicts

✅ Best practices: Structured data, timestamps, encryption, monitoring

Next article la AI Task Automation deep dive paapom – agents use panni complex tasks automate! ⚙️

🏁 🎮 Mini Challenge

Challenge: Design Memory System for Personal Shopping Agent

Agent-laa memory systems implement panna practical exercise:

Scenario: Customer shopping assistant agent – purchases track, preferences remember panna-onum

Step 1: Working Memory (3 mins)

Current shopping session ma active variables:

Current items browsing
Budget remaining
Session start time

How irukku, when clear aagum? (On session end)

Step 2: Short-term Memory (4 mins)

This session's conversation:

Customer: "Show me blue shirts under ₹1000"
Agent: "Found 5 items..."
Customer: "Add medium size to cart"

How store? (Conversation buffer)

How long keep? (Session duration)

Step 3: Long-term Memory (4 mins)

Persistent user profile:

Favorite brands: Nike, Adidas
Preferred sizes: M, L
Budget range: ₹500-3000
Past purchases: List

How store? (Database, vector DB)

How retrieve? (Semantic search)

Step 4: Episodic Memory (2 mins)

Specific past events:

"Last week nee sports shoes buy pannya"
"2 months ago return request file panniya"

How track? (Timestamped events)

Step 5: Implement (2 mins)

Pseudo-code write pannunga:

code

On new customer:
1. Load long-term memory (preferences)
2. Initialize working memory (session)
3. Start short-term buffer (conversation)
4. Log episodic events (history)
On session end:
1. Save important info to long-term
2. Clear working memory
3. Log summary to episodic

Memory architecture complete! 🧠💾

💼 Interview Questions

Q1: AI Agent memory – human memory maari work pannuma?

A: Inspired by human memory, but implementation different. Humans: biological storage, chemical transfers. AI: database storage, vector embeddings, retrieval algorithms. Same concepts, different substrate!

Q2: Vector database why important agent memory-ku?

A: Text-a mathematical vectors convert panni similar content quickly find panna. Example: "flight booking" + "airline reservation" similar vectors (semantically close). Similarity search fast, relevant memories quickly retrieve, retrieval accurate!

Q3: Memory too much irundha problems enna?

Context window overflow (token limit exceed)
Retrieval slow (too much search)
Irrelevant info mix (noise increase)
Cost increase (more tokens = more API calls)

Solution: Memory management – relevant info keep, obsolete info delete, summarization use!

Q4: Long-term memory ku best storage technology?

Vector DB (semantic search): Pinecone, Weaviate, Chroma
Key-value store (fast retrieval): Redis
SQL DB (structured): PostgreSQL
Graph DB (relationships): Neo4j

Most agents: Vector DB (semantic) + Cache (speed) combination use!

Q5: Memory privacy security – user data protection?

Encrypt data rest-la (at-rest encryption)
Encrypt transmission-la (TLS)
Access control (who read/write panna-onum)
Data retention policy (delete after X days)
Compliance (GDPR, privacy laws)

User data sacred! Proper handling essential! 🔒

❓ Frequently Asked Questions

❓ AI Agent ku memory edhuku?

Memory illama every conversation fresh start. Past interactions, user preferences, learned patterns ellaam lost aagum. Memory irundha personalized, context-aware responses kodukka mudiyum.

❓ Vector database enna?

Text-a mathematical vectors (numbers) aa convert panni store pannum database. Similar content-a quickly find panna helpful. Pinecone, Weaviate, ChromaDB popular options.

❓ Memory too much aanaa enna aagum?

Context window overflow aagum, irrelevant info mix aagum, response quality drop aagum. Memory management – what to remember, what to forget – important skill!

❓ Human memory maari AI memory work pannuma?

Inspired by human memory, but not identical. Short-term (working memory), long-term (stored knowledge), episodic (specific events) – similar concepts but different implementations.

🧠Knowledge Check

Quiz 1 of 1

Test your memory knowledge:

0 of 1 answered

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Exam Prep

Memory in agents

🧠 Introduction – Why Memory Matters

📊 Types of Agent Memory

🏗️ Agent Memory Architecture

🔍 Memory Storage Technologies

🎬 Memory in Action – Personal Assistant

📝 Memory Management Strategies

🔄 Memory Read/Write Cycle

🛠️ Implementation Approaches

🧪 Try It – Memory-Aware Agent

⚠️ Memory Pitfalls to Avoid

💡 Memory Best Practices

🌍 Real-World Memory Systems

📝 Summary

🏁 🎮 Mini Challenge

💼 Interview Questions

❓ Frequently Asked Questions