Semantic Memory for LLM Applications: The Complete Developer's Guide to Building Knowledge-Aware AI Systems

Large language models are remarkably good at generating text—until you ask them about something they should remember from your last conversation. Or yesterday's conversation. Or the preferences you've mentioned a dozen times.

The problem isn't that LLMs are forgetful. The problem is they were never designed to remember in the first place.

Every API call to GPT-4, Claude, or Gemini starts with a blank slate. The model processes your prompt, generates a response, and immediately forgets the entire interaction. There's no state. No continuity. No learning from experience.

This is where semantic memory changes the game.

If you're building anything more sophisticated than a basic chatbot—think AI assistants that know your users, enterprise systems that accumulate domain expertise, or agents that improve with every interaction—you need semantic memory.

This guide covers everything you need to implement production-ready semantic memory for your LLM applications: the theory behind it, the architecture patterns that work, and the code to make it real.

What Is Semantic Memory (And Why Should You Care)?

Semantic memory is one of four memory types in the CoALA (Cognitive Architectures for Language Agents) framework that's becoming the standard for designing AI agent systems. Understanding where it fits helps you build better architectures.

The four memory types:

Working memory (short-term): The immediate context—what's happening right now in this conversation. In LLM terms, this is your context window.
Episodic memory: Specific experiences and events. "Last Tuesday, the user asked about Python decorators and seemed confused about the @property syntax."
Semantic memory: Factual knowledge independent of when or how it was learned. "The user is a Python developer who works on data pipelines at a fintech startup."
Procedural memory: How to do things—skills and workflows. "When the user asks for code review, first check for security issues, then style, then performance."

Semantic memory is your AI's knowledge base. It's the accumulated understanding of facts, concepts, relationships, and truths that persist regardless of specific interactions.

Here's the crucial insight: semantic memory isn't about remembering what happened—it's about knowing what's true.

When you tell an assistant your name is Alex and you prefer dark mode, that's not an event to remember—it's a fact about reality. It should be as reliable as the assistant knowing that Python uses indentation for code blocks.

Why LLMs Can't Do This Natively

LLMs have two types of "memory" built in, and neither works for semantic memory:

Parametric knowledge (what's baked into the model weights during training): This is vast but frozen. The model knows facts from its training data, but it can't learn new facts about your users or domain.

Context window (what you inject into each prompt): This is flexible but ephemeral. You can stuff facts into the context, but you're limited by token limits, and everything resets with each API call.

Neither gives you persistent, growing, queryable knowledge that evolves with use.

That's why you need external semantic memory systems—and why getting the architecture right matters more than most developers realize.

The Architecture of Semantic Memory Systems

A production semantic memory system has four stages: encoding, storage, retrieval, and integration. Get any of these wrong and your whole system underperforms.

Stage 1: Encoding—Turning Knowledge Into Searchable Representations

Raw text isn't searchable by meaning. "The user prefers concise answers" and "User likes brief responses" mean the same thing, but string matching won't find the connection.

Vector embeddings solve this. An embedding model converts text into high-dimensional numerical vectors where semantically similar content clusters together. Two sentences that mean similar things will have vectors that point in similar directions.

from openai import OpenAI

client = OpenAI()

def encode_knowledge(text: str) -> list[float]:
    """Convert text to a semantic vector representation."""
    response = client.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return response.data[0].embedding

# These will have similar vectors despite different words
fact_1 = encode_knowledge("User prefers Python for backend development")
fact_2 = encode_knowledge("The customer likes using Python for server-side code")

Embedding model selection matters:

text-embedding-3-small (OpenAI): Good balance of quality and cost for most applications
text-embedding-3-large (OpenAI): Better for nuanced semantic distinctions
Cohere embed-v3: Strong multilingual support
Open source options (e5-large, bge-large): Self-hosted, no API costs, competitive quality

For semantic memory specifically, you want models that handle factual statements well—not just similarity between documents. Test with your actual knowledge types.

Stage 2: Storage—Where Knowledge Lives

Your encoded knowledge needs a home. The storage layer determines query speed, scalability, and what kinds of searches you can perform.

Vector databases are the default choice. They're optimized for approximate nearest neighbor (ANN) search—finding vectors close to a query vector without checking every single one.

import chromadb
from chromadb.config import Settings

# Initialize persistent storage
client = chromadb.PersistentClient(path="./semantic_memory")

# Create a collection for user knowledge
collection = client.get_or_create_collection(
    name="user_facts",
    metadata={"hnsw:space": "cosine"}  # Use cosine similarity
)

def store_fact(fact_id: str, fact_text: str, metadata: dict):
    """Store a semantic fact with its embedding."""
    embedding = encode_knowledge(fact_text)
    collection.add(
        ids=[fact_id],
        embeddings=[embedding],
        documents=[fact_text],
        metadatas=[metadata]
    )

# Store user knowledge
store_fact(
    fact_id="user_123_lang_pref",
    fact_text="User prefers Python for backend, TypeScript for frontend",
    metadata={
        "user_id": "user_123",
        "category": "preferences",
        "confidence": 0.95,
        "source": "explicit_statement",
        "updated_at": "2026-03-25"
    }
)

Database options and tradeoffs:

Database	Best For	Consideration
ChromaDB	Prototyping, small-medium scale	Easy setup, good Python integration
Pinecone	Managed production systems	Fully managed, scales well, costs at volume
Weaviate	Hybrid search (vector + keyword)	Open source, GraphQL API
PostgreSQL + pgvector	Existing Postgres infrastructure	Familiar tooling, ACID compliance
Redis	Low-latency requirements	In-memory speed, good for hot data
Qdrant	Self-hosted production	Rust performance, good filtering

For most applications, start with ChromaDB for development and move to Pinecone, Qdrant, or pgvector for production.

Stage 3: Retrieval—Finding Relevant Knowledge

When your agent needs knowledge, it queries the semantic memory with the current context and retrieves the most relevant facts.

def retrieve_relevant_facts(
    query: str, 
    user_id: str, 
    n_results: int = 5,
    min_similarity: float = 0.7
) -> list[dict]:
    """Retrieve facts relevant to the current query."""
    query_embedding = encode_knowledge(query)
    
    results = collection.query(
        query_embeddings=[query_embedding],
        n_results=n_results,
        where={"user_id": user_id},  # Filter to this user's facts
        include=["documents", "metadatas", "distances"]
    )
    
    # Filter by similarity threshold and format results
    relevant_facts = []
    for doc, meta, distance in zip(
        results["documents"][0],
        results["metadatas"][0],
        results["distances"][0]
    ):
        similarity = 1 - distance  # Convert distance to similarity
        if similarity >= min_similarity:
            relevant_facts.append({
                "fact": doc,
                "metadata": meta,
                "similarity": similarity
            })
    
    return relevant_facts

# Example: User asks about debugging
query = "How should I debug this Python error?"
facts = retrieve_relevant_facts(query, user_id="user_123")
# Returns: [{"fact": "User prefers Python for backend...", ...}]

Retrieval strategies that actually work:

Similarity threshold filtering: Don't return irrelevant facts just because you asked for 5 results. Set a minimum similarity score.
Metadata filtering: Use metadata to scope queries—filter by user, category, recency, confidence level.
Hybrid search: Combine vector similarity with keyword search. Some facts are better found by exact terms ("API key", "project name") than semantic similarity.
Re-ranking: Retrieve more candidates than needed, then re-rank with a cross-encoder model for better precision.

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")

def retrieve_with_reranking(query: str, user_id: str, n_results: int = 5):
    """Retrieve and re-rank for better precision."""
    # Over-fetch candidates
    candidates = retrieve_relevant_facts(query, user_id, n_results=20, min_similarity=0.5)
    
    if not candidates:
        return []
    
    # Re-rank with cross-encoder
    pairs = [(query, c["fact"]) for c in candidates]
    scores = reranker.predict(pairs)
    
    # Sort by re-ranked score and return top results
    for candidate, score in zip(candidates, scores):
        candidate["rerank_score"] = float(score)
    
    candidates.sort(key=lambda x: x["rerank_score"], reverse=True)
    return candidates[:n_results]

Stage 4: Integration—Injecting Knowledge Into the LLM

Retrieved knowledge means nothing if you don't use it well. The integration layer formats semantic memory for the LLM and manages how it influences responses.

def build_context_with_memory(
    user_message: str,
    user_id: str,
    conversation_history: list[dict]
) -> str:
    """Build a prompt that includes relevant semantic memory."""
    
    # Retrieve relevant facts
    relevant_facts = retrieve_with_reranking(user_message, user_id, n_results=5)
    
    # Format facts for the prompt
    if relevant_facts:
        facts_section = "## Known facts about this user:\n"
        for fact in relevant_facts:
            confidence = fact["metadata"].get("confidence", "unknown")
            facts_section += f"- {fact['fact']} (confidence: {confidence})\n"
    else:
        facts_section = "## No relevant stored facts for this query.\n"
    
    # Build the full prompt
    prompt = f"""You are a helpful AI assistant with access to stored knowledge about the user.

{facts_section}

## Conversation history:
{format_history(conversation_history)}

## Current message:
User: {user_message}

Respond naturally, using the known facts when relevant. Don't explicitly mention "according to my records" unless the user asks how you know something."""
    
    return prompt

Integration patterns:

System prompt injection: Put semantic memory in the system prompt so it shapes the entire conversation.
Retrieval-augmented context: Inject relevant facts into each user turn's context.
Structured memory blocks: Format facts as clearly labeled sections the model can reference.
Confidence-weighted inclusion: Only include high-confidence facts, or mark low-confidence ones explicitly.

Building a Complete Semantic Memory System

Let's put it together into a production-ready implementation. This example uses a layered architecture that separates concerns and makes testing easier.

from dataclasses import dataclass
from datetime import datetime
from typing import Optional
import json
import hashlib

@dataclass
class SemanticFact:
    """A single piece of semantic knowledge."""
    id: str
    content: str
    user_id: str
    category: str
    confidence: float
    source: str  # "explicit", "inferred", "extracted"
    created_at: datetime
    updated_at: datetime
    embedding: Optional[list[float]] = None
    
    def to_storage_format(self) -> dict:
        return {
            "user_id": self.user_id,
            "category": self.category,
            "confidence": self.confidence,
            "source": self.source,
            "created_at": self.created_at.isoformat(),
            "updated_at": self.updated_at.isoformat()
        }


class SemanticMemoryManager:
    """Manages semantic memory for LLM applications."""
    
    def __init__(self, collection_name: str = "semantic_facts"):
        self.client = chromadb.PersistentClient(path="./memory_store")
        self.collection = self.client.get_or_create_collection(
            name=collection_name,
            metadata={"hnsw:space": "cosine"}
        )
        self.openai = OpenAI()
    
    def _generate_embedding(self, text: str) -> list[float]:
        response = self.openai.embeddings.create(
            model="text-embedding-3-small",
            input=text
        )
        return response.data[0].embedding
    
    def _generate_fact_id(self, user_id: str, content: str) -> str:
        """Generate a deterministic ID for deduplication."""
        hash_input = f"{user_id}:{content.lower().strip()}"
        return hashlib.sha256(hash_input.encode()).hexdigest()[:16]
    
    def store_fact(
        self,
        user_id: str,
        content: str,
        category: str = "general",
        confidence: float = 0.8,
        source: str = "explicit"
    ) -> SemanticFact:
        """Store a new semantic fact."""
        now = datetime.utcnow()
        fact_id = self._generate_fact_id(user_id, content)
        
        fact = SemanticFact(
            id=fact_id,
            content=content,
            user_id=user_id,
            category=category,
            confidence=confidence,
            source=source,
            created_at=now,
            updated_at=now,
            embedding=self._generate_embedding(content)
        )
        
        # Upsert to handle updates to existing facts
        self.collection.upsert(
            ids=[fact.id],
            embeddings=[fact.embedding],
            documents=[fact.content],
            metadatas=[fact.to_storage_format()]
        )
        
        return fact
    
    def retrieve_facts(
        self,
        query: str,
        user_id: str,
        categories: Optional[list[str]] = None,
        n_results: int = 10,
        min_confidence: float = 0.5,
        min_similarity: float = 0.6
    ) -> list[dict]:
        """Retrieve relevant facts for a query."""
        query_embedding = self._generate_embedding(query)
        
        # Build filter
        where_filter = {"user_id": user_id}
        if min_confidence > 0:
            where_filter["confidence"] = {"$gte": min_confidence}
        if categories:
            where_filter["category"] = {"$in": categories}
        
        results = self.collection.query(
            query_embeddings=[query_embedding],
            n_results=n_results,
            where=where_filter,
            include=["documents", "metadatas", "distances"]
        )
        
        facts = []
        if results["documents"] and results["documents"][0]:
            for doc, meta, dist in zip(
                results["documents"][0],
                results["metadatas"][0],
                results["distances"][0]
            ):
                similarity = 1 - dist
                if similarity >= min_similarity:
                    facts.append({
                        "content": doc,
                        "metadata": meta,
                        "similarity": round(similarity, 3)
                    })
        
        return facts
    
    def extract_facts_from_conversation(
        self,
        user_id: str,
        conversation: list[dict]
    ) -> list[SemanticFact]:
        """Use an LLM to extract semantic facts from conversation."""
        conversation_text = "\n".join([
            f"{msg['role']}: {msg['content']}" 
            for msg in conversation
        ])
        
        extraction_prompt = f"""Analyze this conversation and extract factual information about the user that would be useful to remember for future conversations.

Focus on:
- Preferences (tools, languages, communication style)
- Professional context (role, industry, projects)
- Technical context (stack, constraints, goals)
- Personal context (timezone, work style)

Conversation:
{conversation_text}

Return a JSON array of facts. Each fact should have:
- "content": The fact as a clear statement
- "category": One of "preferences", "professional", "technical", "personal"
- "confidence": 0.0-1.0 based on how explicit the information was

Only include facts you're confident about. Return [] if no clear facts are present."""

        response = self.openai.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": extraction_prompt}],
            response_format={"type": "json_object"}
        )
        
        try:
            extracted = json.loads(response.choices[0].message.content)
            facts_data = extracted.get("facts", extracted) if isinstance(extracted, dict) else extracted
        except json.JSONDecodeError:
            return []
        
        stored_facts = []
        for fact_data in facts_data:
            if isinstance(fact_data, dict) and "content" in fact_data:
                fact = self.store_fact(
                    user_id=user_id,
                    content=fact_data["content"],
                    category=fact_data.get("category", "general"),
                    confidence=fact_data.get("confidence", 0.7),
                    source="extracted"
                )
                stored_facts.append(fact)
        
        return stored_facts
    
    def update_confidence(self, fact_id: str, new_confidence: float):
        """Update confidence when a fact is confirmed or contradicted."""
        # Get existing fact
        result = self.collection.get(ids=[fact_id], include=["metadatas"])
        if result["metadatas"]:
            metadata = result["metadatas"][0]
            metadata["confidence"] = new_confidence
            metadata["updated_at"] = datetime.utcnow().isoformat()
            self.collection.update(ids=[fact_id], metadatas=[metadata])
    
    def delete_fact(self, fact_id: str):
        """Remove a fact (useful for corrections)."""
        self.collection.delete(ids=[fact_id])
    
    def get_user_facts(self, user_id: str) -> list[dict]:
        """Get all stored facts for a user."""
        results = self.collection.get(
            where={"user_id": user_id},
            include=["documents", "metadatas"]
        )
        
        facts = []
        if results["documents"]:
            for doc, meta in zip(results["documents"], results["metadatas"]):
                facts.append({"content": doc, "metadata": meta})
        return facts

Using the System in Your Agent

class MemoryAwareAgent:
    """An LLM agent with semantic memory."""
    
    def __init__(self, user_id: str):
        self.user_id = user_id
        self.memory = SemanticMemoryManager()
        self.openai = OpenAI()
        self.conversation_history = []
    
    def _build_system_prompt(self, relevant_facts: list[dict]) -> str:
        facts_text = ""
        if relevant_facts:
            facts_text = "\n\nKnown facts about this user:\n"
            for fact in relevant_facts:
                facts_text += f"• {fact['content']}\n"
        
        return f"""You are a helpful AI assistant. You have access to stored information about the user that helps you provide personalized assistance.
{facts_text}
Use this information naturally when relevant. Don't explicitly reference "my records" unless asked."""
    
    def respond(self, user_message: str) -> str:
        # Retrieve relevant semantic memory
        relevant_facts = self.memory.retrieve_facts(
            query=user_message,
            user_id=self.user_id,
            n_results=5
        )
        
        # Build messages with memory context
        messages = [
            {"role": "system", "content": self._build_system_prompt(relevant_facts)}
        ]
        messages.extend(self.conversation_history)
        messages.append({"role": "user", "content": user_message})
        
        # Generate response
        response = self.openai.chat.completions.create(
            model="gpt-4o",
            messages=messages
        )
        
        assistant_message = response.choices[0].message.content
        
        # Update conversation history
        self.conversation_history.append({"role": "user", "content": user_message})
        self.conversation_history.append({"role": "assistant", "content": assistant_message})
        
        # Periodically extract new facts (every 5 turns)
        if len(self.conversation_history) % 10 == 0:
            recent = self.conversation_history[-10:]
            self.memory.extract_facts_from_conversation(self.user_id, recent)
        
        return assistant_message
    
    def explicitly_remember(self, fact: str, category: str = "general"):
        """Let users explicitly tell the agent to remember something."""
        self.memory.store_fact(
            user_id=self.user_id,
            content=fact,
            category=category,
            confidence=0.95,  # High confidence for explicit statements
            source="explicit"
        )


# Usage example
agent = MemoryAwareAgent(user_id="user_456")

# User interactions over time
agent.respond("Hi, I'm working on a Django project")
agent.respond("I prefer using PostgreSQL over MySQL")
agent.explicitly_remember("Always format Python code with Black", "preferences")

# Later, in a new session
agent.respond("What database should I use for my project?")
# Agent retrieves: "User prefers PostgreSQL over MySQL" and responds accordingly

Production Considerations

Building semantic memory that works in development is easy. Building it for production requires attention to several additional concerns.

Handling Contradictions and Updates

Users change. Preferences evolve. Facts become outdated. Your system needs strategies for handling this:

def handle_potential_contradiction(
    self, 
    user_id: str, 
    new_fact: str,
    category: str
) -> dict:
    """Check if a new fact contradicts existing knowledge."""
    # Find similar existing facts
    existing = self.retrieve_facts(
        query=new_fact,
        user_id=user_id,
        categories=[category],
        min_similarity=0.8
    )
    
    if not existing:
        # No similar facts, safe to add
        return {"action": "add", "conflicts": []}
    
    # Use LLM to check for contradictions
    check_prompt = f"""Do these statements contradict each other?

Existing fact: {existing[0]['content']}
New fact: {new_fact}

Respond with JSON: {{"contradicts": true/false, "explanation": "..."}}"""
    
    response = self.openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": check_prompt}],
        response_format={"type": "json_object"}
    )
    
    result = json.loads(response.choices[0].message.content)
    
    if result.get("contradicts"):
        return {
            "action": "update",
            "conflicts": existing,
            "explanation": result.get("explanation")
        }
    return {"action": "add", "conflicts": []}

Privacy and Data Governance

Semantic memory stores personal information. Handle it responsibly:

Data minimization: Only store facts that provide value
Retention policies: Implement automatic expiration for certain fact types
User control: Let users view, edit, and delete their stored facts
Encryption: Encrypt sensitive facts at rest
Access logging: Track what facts are retrieved and when

def get_user_data_export(self, user_id: str) -> dict:
    """GDPR-compliant data export."""
    facts = self.get_user_facts(user_id)
    return {
        "user_id": user_id,
        "exported_at": datetime.utcnow().isoformat(),
        "facts": facts,
        "fact_count": len(facts)
    }

def delete_user_data(self, user_id: str):
    """GDPR-compliant data deletion."""
    # Get all fact IDs for user
    results = self.collection.get(
        where={"user_id": user_id},
        include=[]
    )
    if results["ids"]:
        self.collection.delete(ids=results["ids"])

Scaling Semantic Memory

As your user base grows, semantic memory needs to scale:

Partitioning by user: Each user's facts are naturally isolated. This maps well to sharding strategies.

Index optimization: HNSW indexes need tuning. Higher ef_construction gives better recall but slower builds. Higher ef_search gives better query accuracy but slower searches.

Caching hot facts: Frequently accessed facts (high-confidence, often-retrieved) benefit from caching layers.

Batch operations: Extract and store facts in batches rather than one at a time.

# Example: Batch fact extraction from multiple conversations
def batch_extract_facts(self, extractions: list[dict]):
    """Process multiple extraction jobs efficiently."""
    all_facts = []
    
    for job in extractions:
        facts = self.extract_facts_from_conversation(
            user_id=job["user_id"],
            conversation=job["conversation"]
        )
        all_facts.extend(facts)
    
    # Batch embed and store
    if all_facts:
        texts = [f.content for f in all_facts]
        embeddings = self._batch_embed(texts)
        
        self.collection.upsert(
            ids=[f.id for f in all_facts],
            embeddings=embeddings,
            documents=texts,
            metadatas=[f.to_storage_format() for f in all_facts]
        )
    
    return all_facts

Beyond Basic Semantic Memory: Advanced Patterns

Once you have basic semantic memory working, several advanced patterns can make your system significantly more capable.

Hierarchical Knowledge Organization

Not all facts are equal. Some are broad ("User is a software developer") while others are specific ("User prefers Pydantic for data validation in Python projects").

Organize facts hierarchically to improve retrieval:

FACT_HIERARCHY = {
    "professional": {
        "role": [],
        "industry": [],
        "company": []
    },
    "technical": {
        "languages": [],
        "frameworks": [],
        "tools": [],
        "patterns": []
    },
    "preferences": {
        "communication": [],
        "coding_style": [],
        "workflow": []
    }
}

def categorize_fact(self, fact_content: str) -> tuple[str, str]:
    """Auto-categorize facts into the hierarchy."""
    prompt = f"""Categorize this fact into our knowledge hierarchy.

Fact: {fact_content}

Categories:
- professional.role, professional.industry, professional.company
- technical.languages, technical.frameworks, technical.tools, technical.patterns
- preferences.communication, preferences.coding_style, preferences.workflow

Return JSON: {{"category": "top.sub"}}"""
    
    # ... LLM call to categorize

Vector search finds semantically similar facts. But sometimes you need structurally related facts—facts about the same project, or preferences that apply to the same context.

Knowledge graphs complement vector stores:

# Neo4j example for fact relationships
from neo4j import GraphDatabase

class KnowledgeGraphMemory:
    def __init__(self, uri, user, password):
        self.driver = GraphDatabase.driver(uri, auth=(user, password))
    
    def store_fact_with_relations(
        self, 
        fact: SemanticFact, 
        relates_to: list[str]
    ):
        """Store a fact and its relationships to other facts."""
        with self.driver.session() as session:
            # Create fact node
            session.run("""
                MERGE (f:Fact {id: $id})
                SET f.content = $content,
                    f.user_id = $user_id,
                    f.category = $category
            """, id=fact.id, content=fact.content, 
                user_id=fact.user_id, category=fact.category)
            
            # Create relationships
            for related_id in relates_to:
                session.run("""
                    MATCH (f1:Fact {id: $id1}), (f2:Fact {id: $id2})
                    MERGE (f1)-[:RELATES_TO]->(f2)
                """, id1=fact.id, id2=related_id)

Temporal Awareness

Facts have temporal dimensions. A user's preferences six months ago might not reflect current preferences.

def retrieve_with_recency_boost(
    self,
    query: str,
    user_id: str,
    recency_weight: float = 0.2
) -> list[dict]:
    """Retrieve facts with recency-weighted scoring."""
    facts = self.retrieve_facts(query, user_id, n_results=20)
    
    now = datetime.utcnow()
    for fact in facts:
        updated = datetime.fromisoformat(fact["metadata"]["updated_at"])
        age_days = (now - updated).days
        
        # Decay factor: newer facts get boosted
        recency_score = 1.0 / (1.0 + age_days / 30)  # 30-day half-life
        
        # Combine semantic similarity with recency
        combined_score = (
            (1 - recency_weight) * fact["similarity"] +
            recency_weight * recency_score
        )
        fact["combined_score"] = combined_score
    
    facts.sort(key=lambda x: x["combined_score"], reverse=True)
    return facts[:10]

Dytto: Semantic Memory as a Service

Building all of this from scratch is substantial work. If you're looking for a production-ready semantic memory layer, Dytto provides the infrastructure so you can focus on your application logic.

What Dytto handles:

Automatic fact extraction from conversations—no manual tagging required
Contradiction detection and fact updates across sessions
Privacy-first architecture with user data controls built in
Scalable vector storage without managing infrastructure
Context API that retrieves relevant user knowledge with a single call

import dytto

# Initialize with your API key
client = dytto.Client(api_key="your_api_key")

# Store a fact
client.context.store(
    user_id="user_123",
    fact="User prefers detailed technical explanations",
    category="preferences"
)

# Retrieve relevant context for a query
context = client.context.retrieve(
    user_id="user_123",
    query="Explain how async/await works",
    max_results=5
)

# Auto-extract facts from conversation
client.context.extract_from_conversation(
    user_id="user_123",
    messages=[
        {"role": "user", "content": "I've been using FastAPI for my new project"},
        {"role": "assistant", "content": "Great choice! FastAPI is excellent for..."}
    ]
)

The goal is simple: give your LLM applications memory that works—without the months of infrastructure work to build it yourself.

Key Takeaways

Semantic memory transforms stateless LLMs into systems that accumulate and apply knowledge over time. Here's what matters:

Semantic memory stores facts, not events. It's about what's true, not what happened. Design your fact extraction and storage around this principle.
The four-stage architecture (encode → store → retrieve → integrate) is foundational. Each stage has its own failure modes. Test them independently.
Vector databases are the default choice for semantic memory storage. Start with ChromaDB for development, scale to Pinecone/Qdrant/pgvector for production.
Retrieval quality matters more than storage. Fancy storage doesn't help if you retrieve the wrong facts. Invest in hybrid search, re-ranking, and relevance tuning.
Production requires handling contradictions, privacy, and scale. Basic semantic memory is a weekend project. Production-ready semantic memory is significantly more complex.
Consider managed solutions for faster time-to-value. Services like Dytto handle the infrastructure so you can focus on building your AI application.

The LLM applications that feel magical—the ones that know you, remember your preferences, and improve over time—all have semantic memory underneath. Now you know how to build it.

Ready to add semantic memory to your LLM application? Get started with Dytto's context API or explore our developer documentation for detailed implementation guides.