ConversationEntityMemory

1. What is ConversationEntityMemory?

ConversationEntityMemory extracts and remembers entities (people, places, organizations, objects) from a conversation and stores facts about them.

Instead of remembering full chat text, it remembers structured facts.

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2. Why does it exist?

Buffer-based memories:

• Store raw text

• Forget important facts

• Waste tokens

Entity memory solves:

• “Who is John?”

• “Where does he live?”

• “What company does she work for?”

In short:

Remember important facts, not full conversations.

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3. Real-world analogy

Talking to someone who:

• Doesn’t remember everything you said

• But remembers who people are and key facts about them

That’s entity memory.

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4. Minimal working example (Gemini)

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.memory import ConversationEntityMemory
from langchain.chains import ConversationChain
import os

llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-flash",
    api_key=os.getenv("GEMINI_API_KEY")
)

memory = ConversationEntityMemory(
    llm=llm
)

conversation = ConversationChain(
    llm=llm,
    memory=memory,
    verbose=True
)

conversation.invoke("My name is John and I live in Toronto")
conversation.invoke("I work at Google as a software engineer")
conversation.invoke("Where do I live and where do I work?")

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5. What does it store internally?

Instead of text, it stores entity → facts:

print(memory.entity_store.store)

Example:

{
  "John": "John lives in Toronto and works at Google as a software engineer."
}

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6. How does it work internally? (Important)

Each turn:

1. LLM extracts entities

2. LLM updates facts about entities

3. Stored facts are injected into the next prompt

So memory = LLM-maintained knowledge base

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7. Key characteristics

Feature	Entity Memory
Stores	Facts
Format	Text summaries
Token usage	Low
Long-term facts	✅
Full conversation recall	❌

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8. Comparison with other memories

Memory Type	Remembers
Buffer	Full chat
Window	Recent chat
Token buffer	Token-limited chat
Entity	Facts about entities

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9. Common mistakes

❌ Expecting it to remember conversation flow ❌ Using it for emotional context ❌ Using it without an LLM

Entity memory requires an LLM to extract facts.

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10. When should you use it?

Use ConversationEntityMemory when:

• You care about names, places, roles

• You need long-term factual memory

• You want low token usage

Avoid when:

• You need verbatim chat history

• You need short-term conversational nuance