Agent Memory Systems

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.

Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets.

The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge).

Principles

• Memory quality = retrieval quality, not storage quantity
• Chunk for retrieval, not for storage
• Context isolation is the enemy of memory
• Right memory type for right information
• Decay old memories - not everything should be forever
• Test retrieval accuracy before production
• Background memory formation beats real-time

Capabilities

• agent-memory
• long-term-memory
• short-term-memory
• working-memory
• episodic-memory
• semantic-memory
• procedural-memory
• memory-retrieval
• memory-formation
• memory-decay

Scope

• vector-database-operations → data-engineer
• rag-pipeline-architecture → llm-architect
• embedding-model-selection → ml-engineer
• knowledge-graph-design → knowledge-engineer

Tooling

Memory_frameworks

• LangMem (LangChain) - When: LangGraph agents with persistent memory Note: Semantic, episodic, procedural memory types
• MemGPT / Letta - When: Virtual context management, OS-style memory Note: Hierarchical memory tiers, automatic paging
• Mem0 - When: User memory layer for personalization Note: Designed for user preferences and history

Vector_stores

• Pinecone - When: Managed, enterprise-scale (billions of vectors) Note: Best query performance, highest cost
• Qdrant - When: Complex metadata filtering, open-source Note: Rust-based, excellent filtering
• Weaviate - When: Hybrid search, knowledge graph features Note: GraphQL interface, good for relationships
• ChromaDB - When: Prototyping, small/medium apps Note: Developer-friendly, ~20ms p50 at 100K vectors
• pgvector - When: Already using PostgreSQL, simpler setup Note: Good for <1M vectors, familiar tooling

Embedding_models

• OpenAI text-embedding-3-large - When: Best quality, 3072 dimensions Note: $0.13/1M tokens
• OpenAI text-embedding-3-small - When: Good balance, 1536 dimensions Note: $0.02/1M tokens, 5x cheaper
• nomic-embed-text-v1.5 - When: Open-source, local deployment Note: 768 dimensions, good quality
• all-MiniLM-L6-v2 - When: Lightweight, fast local embedding Note: 384 dimensions, lowest latency

Patterns

Memory Type Architecture

Choosing the right memory type for different information

When to use: Designing agent memory system

MEMORY TYPE ARCHITECTURE (CoALA Framework):

""" Three memory types for different purposes:

1. Semantic Memory: Facts and knowledge

   • What you know about the world
   • User preferences, domain knowledge
   • Stored in profiles (structured) or collections (unstructured)

2. Episodic Memory: Experiences and events

   • What happened (timestamped events)
   • Past conversations, task outcomes
   • Used for learning from experience

3. Procedural Memory: How to do things

   • Rules, skills, workflows
   • Often implemented as few-shot examples
   • "How did I solve this before?" """

LangMem Implementation

""" from langmem import MemoryStore from langgraph.graph import StateGraph

Initialize memory store

memory = MemoryStore( connection_string=os.environ["POSTGRES_URL"] )

Semantic memory: user profile

await memory.semantic.upsert( namespace="user_profile", key=user_id, content={ "name": "Alice", "preferences": ["dark mode", "concise responses"], "expertise_level": "developer", } )

Episodic memory: past interaction

await memory.episodic.add( namespace="conversations", content={ "timestamp": datetime.now(), "summary": "Helped debug authentication issue", "outcome": "resolved", "key_insights": ["Token expiry was root cause"], }, metadata={"user_id": user_id, "topic": "debugging"} )

Procedural memory: learned pattern

await memory.procedural.add( namespace="skills", content={ "task_type": "debug_auth", "steps": ["Check token expiry", "Verify refresh flow"], "example_interaction": few_shot_example, } ) """

Memory Retrieval at Runtime

""" async def prepare_context(user_id, query): # Get user profile (semantic) profile = await memory.semantic.get( namespace="user_profile", key=user_id )

# Find relevant past experiences (episodic)
similar_experiences = await memory.episodic.search(
    namespace="conversations",
    query=query,
    filter={"user_id": user_id},
    limit=3
)

# Find relevant skills (procedural)
relevant_skills = await memory.procedural.search(
    namespace="skills",
    query=query,
    limit=2
)

return {
    "profile": profile,
    "past_experiences": similar_experiences,
    "relevant_skills": relevant_skills,
}

"""

Vector Store Selection Pattern

Choosing the right vector database for your use case

When to use: Setting up persistent memory storage

VECTOR STORE SELECTION:

""" Decision matrix:

Pinecone (Enterprise Scale)

""" from pinecone import Pinecone

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"]) index = pc.Index("agent-memory")

Upsert with metadata

index.upsert( vectors=[ { "id": f"memory-{uuid4()}", "values": embedding, "metadata": { "user_id": user_id, "timestamp": datetime.now().isoformat(), "type": "episodic", "content": memory_text, } } ], namespace=namespace )

Query with filter

results = index.query( vector=query_embedding, filter={"user_id": user_id, "type": "episodic"}, top_k=5, include_metadata=True ) """

Qdrant (Complex Filtering)

""" from qdrant_client import QdrantClient from qdrant_client.models import PointStruct, Filter, FieldCondition

client = QdrantClient(url="http://localhost:6333")

Complex filtering with Qdrant

results = client.search( collection_name="agent_memory", query_vector=query_embedding, query_filter=Filter( must=[ FieldCondition(key="user_id", match={"value": user_id}), FieldCondition(key="type", match={"value": "semantic"}), ], should=[ FieldCondition(key="topic", match={"any": ["auth", "security"]}), ] ), limit=5 ) """

ChromaDB (Prototyping)

""" import chromadb

client = chromadb.PersistentClient(path="./memory_db") collection = client.get_or_create_collection("agent_memory")

Simple and fast for prototypes

collection.add( ids=[str(uuid4())], embeddings=[embedding], documents=[memory_text], metadatas=[{"user_id": user_id, "type": "episodic"}] )

results = collection.query( query_embeddings=[query_embedding], n_results=5, where={"user_id": user_id} ) """

Chunking Strategy Pattern

Breaking documents into retrievable chunks

When to use: Processing documents for memory storage

CHUNKING STRATEGIES:

""" The chunking dilemma:

• Too large: Vector loses specificity
• Too small: Loses context

Optimal chunk size depends on:

• Document type (code vs prose vs data)
• Query patterns (factual vs exploratory)
• Embedding model (each has sweet spot)

General guidance: 256-512 tokens for most use cases """

Fixed-Size Chunking (Baseline)

""" from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter( chunk_size=500, # Characters chunk_overlap=50, # Overlap prevents cutting sentences separators=["\n\n", "\n", ". ", " ", ""] # Priority order )

chunks = splitter.split_text(document) """

Semantic Chunking (Better Quality)

""" from langchain_experimental.text_splitter import SemanticChunker from langchain_openai import OpenAIEmbeddings

Splits based on semantic similarity

splitter = SemanticChunker( embeddings=OpenAIEmbeddings(), breakpoint_threshold_type="percentile", breakpoint_threshold_amount=95 )

chunks = splitter.split_text(document) """

Structure-Aware Chunking (Documents with Hierarchy)

""" from langchain.text_splitter import MarkdownHeaderTextSplitter

Respect document structure

splitter = MarkdownHeaderTextSplitter( headers_to_split_on=[ ("#", "Header 1"), ("##", "Header 2"), ("###", "Header 3"), ] )

chunks = splitter.split_text(markdown_doc)

Each chunk has header metadata for context

"""

Contextual Chunking (Anthropic's Approach)

"""

Add context to each chunk before embedding

Reduces retrieval failures by 35%

def add_context_to_chunk(chunk, document_summary): context_prompt = f''' Document summary: {document_summary}

The following is a chunk from this document:
{chunk}
'''
return context_prompt

Embed the contextualized chunk, not raw chunk

for chunk in chunks: contextualized = add_context_to_chunk(chunk, summary) embedding = embed(contextualized) store(chunk, embedding) # Store original, embed contextualized """

Code-Specific Chunking

""" from langchain.text_splitter import Language, RecursiveCharacterTextSplitter

Language-aware splitting

python_splitter = RecursiveCharacterTextSplitter.from_language( language=Language.PYTHON, chunk_size=1000, chunk_overlap=200 )

Respects function/class boundaries

chunks = python_splitter.split_text(python_code) """

Background Memory Formation

Processing memories asynchronously for better quality

When to use: You want higher recall without slowing interactions

BACKGROUND MEMORY FORMATION:

""" Real-time memory extraction slows conversations and adds complexity to agent tool calls. Background processing after conversations yields higher quality memories.

Pattern: Subconscious memory formation """

LangGraph Background Processing

""" from langgraph.graph import StateGraph from langgraph.checkpoint.postgres import PostgresSaver

async def background_memory_processor(thread_id: str): # Run after conversation ends or goes idle conversation = await load_conversation(thread_id)

# Extract insights without time pressure
insights = await llm.invoke('''
    Analyze this conversation and extract:
    1. Key facts learned about the user
    2. User preferences revealed
    3. Tasks completed or pending
    4. Patterns in user behavior

    Be thorough - this runs in background.

    Conversation:
    {conversation}
''')

# Store to long-term memory
for insight in insights:
    await memory.semantic.upsert(
        namespace="user_insights",
        key=generate_key(insight),
        content=insight,
        metadata={"source_thread": thread_id}
    )

Trigger on conversation end or idle timeout

@on_conversation_idle(timeout_minutes=5) async def process_conversation(thread_id): await background_memory_processor(thread_id) """

Memory Consolidation (Like Sleep)

"""

Periodically consolidate and deduplicate memories

async def consolidate_memories(user_id: str): # Get all memories for user memories = await memory.semantic.list( namespace="user_insights", filter={"user_id": user_id} )

# Find similar memories (potential duplicates)
clusters = cluster_by_similarity(memories, threshold=0.9)

# Merge similar memories
for cluster in clusters:
    if len(cluster) > 1:
        merged = await llm.invoke(f'''
            Consolidate these related memories into one:
            {cluster}

            Preserve all important information.
        ''')
        await memory.semantic.upsert(
            namespace="user_insights",
            key=generate_key(merged),
            content=merged
        )
        # Delete originals
        for old in cluster:
            await memory.semantic.delete(old.id)

"""

Memory Decay Pattern

Forgetting old, irrelevant memories

When to use: Memory grows large, retrieval slows down

MEMORY DECAY:

""" Not all memories should live forever:

• Old preferences may be outdated
• Task details lose relevance
• Conflicting memories confuse retrieval

Implement intelligent decay based on:

• Recency (when was it created/accessed?)
• Frequency (how often is it retrieved?)
• Importance (is it a core fact or detail?) """

Time-Based Decay

""" from datetime import datetime, timedelta

async def decay_old_memories(namespace: str, max_age_days: int): cutoff = datetime.now() - timedelta(days=max_age_days)

old_memories = await memory.episodic.list(
    namespace=namespace,
    filter={"last_accessed": {"$lt": cutoff.isoformat()}}
)

for mem in old_memories:
    # Soft delete (mark as archived)
    await memory.episodic.update(
        id=mem.id,
        metadata={"archived": True, "archived_at": datetime.now()}
    )

"""

Utility-Based Decay (MIRIX Approach)

""" def calculate_memory_utility(memory): ''' Composite utility score inspired by cognitive science: - Recency: When was it last accessed? - Frequency: How often is it accessed? - Importance: How critical is this information? ''' now = datetime.now()

# Recency score (exponential decay with 72h half-life)
hours_since_access = (now - memory.last_accessed).total_seconds() / 3600
recency_score = 0.5 ** (hours_since_access / 72)

# Frequency score
frequency_score = min(memory.access_count / 10, 1.0)

# Importance (from metadata or heuristic)
importance = memory.metadata.get("importance", 0.5)

# Weighted combination
utility = (
    0.4 * recency_score +
    0.3 * frequency_score +
    0.3 * importance
)

return utility

async def prune_low_utility_memories(threshold=0.2): all_memories = await memory.list_all() for mem in all_memories: if calculate_memory_utility(mem) < threshold: await memory.archive(mem.id) """

Sharp Edges

Chunking Isolates Information From Its Context

Severity: CRITICAL

Situation: Processing documents for vector storage

Symptoms: Retrieval finds chunks but they don't make sense alone. Agent answers miss the big picture. "The function returns X" retrieved without knowing which function. References to "this" without knowing what "this" refers to.

Why this breaks: When we chunk for AI processing, we're breaking connections, reducing a holistic narrative to isolated fragments that often miss the big picture. A chunk about "the configuration" without context about what system is being configured is nearly useless.

Recommended fix:

Contextual Chunking (Anthropic's approach)

Add document context to each chunk before embedding

Reduces retrieval failures by 35%

def contextualize_chunk(chunk, document): summary = summarize(document)

# LLM generates context for chunk
context = llm.invoke(f'''
    Document summary: {summary}

    Generate a brief context statement for this chunk
    that would help someone understand what it refers to:

    {chunk}
''')

return f"{context}\n\n{chunk}"

Embed the contextualized version

for chunk in chunks: contextualized = contextualize_chunk(chunk, full_doc) embedding = embed(contextualized) # Store original chunk, embed contextualized store(original=chunk, embedding=embedding)

Hierarchical Chunking

Store at multiple granularities

chunks_small = split(doc, size=256) chunks_medium = split(doc, size=512) chunks_large = split(doc, size=1024)

Retrieve at appropriate level based on query

Chunk Size Mismatched to Query Patterns

Severity: HIGH

Situation: Configuring chunking for memory storage

Symptoms: High-quality documents produce low-quality retrievals. Simple questions miss relevant information. Complex questions get fragments instead of complete answers.

Why this breaks: Optimal chunk size depends on query patterns:

• Factual queries need small, specific chunks
• Conceptual queries need larger context
• Code needs function-level boundaries

The sweet spot varies by document type and embedding model. Default 1000 characters works for nothing specific.

Recommended fix:

Test different sizes

from sklearn.metrics import recall_score

def evaluate_chunk_size(documents, test_queries, chunk_size): chunks = split_documents(documents, size=chunk_size) index = build_index(chunks)

correct_retrievals = 0
for query, expected_chunk in test_queries:
    results = index.search(query, k=5)
    if expected_chunk in results:
        correct_retrievals += 1

return correct_retrievals / len(test_queries)

Test multiple sizes

for size in [256, 512, 768, 1024]: recall = evaluate_chunk_size(docs, test_queries, size) print(f"Size {size}: Recall@5 = {recall:.2%}")

Size recommendations by content type

CHUNK_SIZES = { "documentation": 512, # Complete concepts "code": 1000, # Function-level "conversation": 256, # Turn-level "articles": 768, # Paragraph-level }

Use overlap to prevent boundary issues

splitter = RecursiveCharacterTextSplitter( chunk_size=512, chunk_overlap=50, # 10% overlap )

Semantic Search Returns Irrelevant Results

Severity: HIGH

Situation: Querying memory for context

Symptoms: Agent retrieves memories that seem related but aren't useful. "Tell me about the user's preferences" returns conversation about preferences in general, not this user's. High similarity scores for wrong content.

Why this breaks: Semantic similarity isn't the same as relevance. "The user likes Python" and "Python is a programming language" are semantically similar but very different types of information. Without metadata filtering, retrieval is just word matching.

Recommended fix:

Always filter by metadata first

Don't rely on semantic similarity alone

Bad: Only semantic search

results = index.query( vector=query_embedding, top_k=5 )

Good: Filter then search

results = index.query( vector=query_embedding, filter={ "user_id": current_user.id, "type": "preference", "created_after": cutoff_date, }, top_k=5 )

Use hybrid search (semantic + keyword)

from qdrant_client import QdrantClient

client = QdrantClient(...)

Hybrid search with fusion

results = client.search( collection_name="memories", query_vector=semantic_embedding, query_text=query, # Also keyword match fusion={"method": "rrf"}, # Reciprocal Rank Fusion )

Rerank results with cross-encoder

from sentence_transformers import CrossEncoder

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

Initial retrieval (recall-oriented)

candidates = index.query(query_embedding, top_k=20)

Rerank (precision-oriented)

pairs = [(query, c.text) for c in candidates] scores = reranker.predict(pairs) reranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)

Old Memories Override Current Information

Severity: HIGH

Situation: User preferences or facts change over time

Symptoms: Agent uses outdated preferences. "User prefers dark mode" from 6 months ago overrides recent "switch to light mode" request. Agent confidently uses stale data.

Why this breaks: Vector stores don't have temporal awareness by default. A memory from a year ago has the same retrieval weight as one from today. Recent information should generally override old information for preferences and mutable facts.

Recommended fix:

Add temporal scoring

from datetime import datetime, timedelta

def time_decay_score(memory, half_life_days=30): age = (datetime.now() - memory.created_at).days decay = 0.5 ** (age / half_life_days) return decay

def retrieve_with_recency(query, user_id): # Get candidates candidates = index.query( vector=embed(query), filter={"user_id": user_id}, top_k=20 )

# Apply time decay
for candidate in candidates:
    time_score = time_decay_score(candidate)
    candidate.final_score = candidate.similarity * 0.7 + time_score * 0.3

# Re-sort by final score
return sorted(candidates, key=lambda x: x.final_score, reverse=True)[:5]

Update instead of append for preferences

async def update_preference(user_id, category, value): # Delete old preference await memory.delete( filter={"user_id": user_id, "type": "preference", "category": category} )

# Store new preference
await memory.upsert(
    id=f"pref-{user_id}-{category}",
    content={"category": category, "value": value},
    metadata={"updated_at": datetime.now()}
)

Explicit versioning for facts

await memory.upsert( id=f"fact-{fact_id}-v{version}", content=new_fact, metadata={ "version": version, "supersedes": previous_id, "valid_from": datetime.now() } )

Contradictory Memories Retrieved Together

Severity: MEDIUM

Situation: User has changed preferences or provided conflicting info

Symptoms: Agent retrieves "user prefers dark mode" and "user prefers light mode" in same context. Gives inconsistent answers. Seems confused or forgetful to user.

Why this breaks: Without conflict resolution, both old and new information coexist. Semantic search might return both because they're both about the same topic (preferences). Agent has no way to know which is current.

Recommended fix:

Detect conflicts on storage

async def store_with_conflict_check(memory, user_id): # Find potentially conflicting memories similar = await index.query( vector=embed(memory.content), filter={"user_id": user_id, "type": memory.type}, threshold=0.9, # Very similar top_k=5 )

for existing in similar:
    if is_contradictory(memory.content, existing.content):
        # Ask for resolution
        resolution = await resolve_conflict(memory, existing)
        if resolution == "replace":
            await index.delete(existing.id)
        elif resolution == "version":
            await mark_superseded(existing.id, memory.id)

await index.upsert(memory)

Conflict detection heuristic

def is_contradictory(new_content, old_content): # Use LLM to detect contradiction result = llm.invoke(f''' Do these two statements contradict each other?

Statement 1: {old_content}
    Statement 2: {new_content}

    Respond with just YES or NO.
''')
return result.strip().upper() == "YES"

Periodic consolidation

async def consolidate_memories(user_id): all_memories = await index.list(filter={"user_id": user_id}) clusters = cluster_by_topic(all_memories)

for cluster in clusters:
    if has_conflicts(cluster):
        resolved = await llm.invoke(f'''
            These memories may conflict. Create one consolidated
            memory that represents the current truth:
            {cluster}
        ''')
        await replace_cluster(cluster, resolved)

Retrieved Memories Exceed Context Window

Severity: MEDIUM

Situation: Retrieving too many memories at once

Symptoms: Token limit errors. Agent truncates important information. System prompt gets cut off. Retrieved memories compete with user query for space.

Why this breaks: Retrieval typically returns top-k results. If k is too high or chunks are too large, retrieved context overwhelms the window. Critical information (system prompt, recent messages) gets pushed out.

Recommended fix:

Budget tokens for different memory types

TOKEN_BUDGET = { "system_prompt": 500, "user_profile": 200, "recent_messages": 2000, "retrieved_memories": 1000, "current_query": 500, "buffer": 300, # Safety margin }

def budget_aware_retrieval(query, context_limit=4000): remaining = context_limit - TOKEN_BUDGET["system_prompt"] - TOKEN_BUDGET["buffer"]

# Prioritize recent messages
recent = get_recent_messages(limit=TOKEN_BUDGET["recent_messages"])
remaining -= count_tokens(recent)

# Then user profile
profile = get_user_profile(limit=TOKEN_BUDGET["user_profile"])
remaining -= count_tokens(profile)

# Finally retrieved memories with remaining budget
memories = retrieve_memories(query, max_tokens=remaining)

return build_context(profile, recent, memories)

Dynamic k based on chunk size

def retrieve_with_budget(query, max_tokens=1000): avg_chunk_tokens = 150 # From your data max_k = max_tokens // avg_chunk_tokens

results = index.query(query, top_k=max_k)

# Trim if still over budget
total_tokens = 0
filtered = []
for result in results:
    tokens = count_tokens(result.text)
    if total_tokens + tokens <= max_tokens:
        filtered.append(result)
        total_tokens += tokens
    else:
        break

return filtered

Query and Document Embeddings From Different Models

Severity: MEDIUM

Situation: Upgrading embedding model or mixing providers

Symptoms: Retrieval quality suddenly drops. Relevant documents not found. Random results returned. Works for new documents, fails for old.

Why this breaks: Embedding models produce different vector spaces. A query embedded with text-embedding-3 won't match documents embedded with text-ada-002. Mixing models creates garbage similarity scores.

Recommended fix:

Track embedding model in metadata

await index.upsert( id=doc_id, vector=embedding, metadata={ "embedding_model": "text-embedding-3-small", "embedding_version": "2024-01", "content": content } )

Filter by model version on retrieval

results = index.query( vector=query_embedding, filter={"embedding_model": current_model}, top_k=10 )

Migration strategy for model upgrades

async def migrate_embeddings(old_model, new_model): # Get all documents with old model old_docs = await index.list(filter={"embedding_model": old_model})

for doc in old_docs:
    # Re-embed with new model
    new_embedding = await embed(doc.content, model=new_model)

    # Update in place
    await index.update(
        id=doc.id,
        vector=new_embedding,
        metadata={"embedding_model": new_model}
    )

Use separate collections during migration

Old collection: production queries

New collection: re-embedding in progress

Switch over when complete

Validation Checks

In-Memory Store in Production Code

Severity: ERROR

In-memory stores lose data on restart

Message: In-memory store detected. Use persistent storage (Postgres, Qdrant, Pinecone) for production.

Vector Upsert Without Metadata

Severity: WARNING

Vectors should have metadata for filtering

Message: Vector upsert without metadata. Add user_id, type, timestamp for proper filtering.

Query Without User Filtering

Severity: ERROR

Queries should filter by user to prevent data leakage

Message: Vector query without user filtering. Always filter by user_id to prevent data leakage.

Hardcoded Chunk Size Without Justification

Severity: INFO

Chunk size should be tested and justified

Message: Hardcoded chunk size. Test different sizes for your content type and measure retrieval accuracy.

Chunking Without Overlap

Severity: WARNING

Chunk overlap prevents boundary issues

Message: Text splitting without overlap. Add chunk_overlap (10-20%) to prevent boundary issues.

Semantic Search Without Filters

Severity: WARNING

Pure semantic search often returns irrelevant results

Message: Pure semantic search. Add metadata filters (user, type, time) for better relevance.

Retrieval Without Result Limit

Severity: WARNING

Unbounded retrieval can overflow context

Message: Retrieval without limit. Set top_k to prevent context overflow.

Embeddings Without Model Version Tracking

Severity: WARNING

Track embedding model to handle migrations

Message: Store embedding model version in metadata to handle model migrations.

Different Models for Document and Query Embedding

Severity: ERROR

Documents and queries must use same embedding model

Message: Ensure same embedding model for indexing and querying.

Collaboration

Delegation Triggers

• user needs vector database at scale -> data-engineer (Production vector store operations)
• user needs embedding model optimization -> ml-engineer (Custom embeddings, fine-tuning)
• user needs knowledge graph -> knowledge-engineer (Graph-based memory structures)
• user needs RAG pipeline -> llm-architect (End-to-end retrieval augmented generation)
• user needs multi-agent shared memory -> multi-agent-orchestration (Memory sharing between agents)

Related Skills

Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder

When to Use

• User mentions or implies: agent memory
• User mentions or implies: long-term memory
• User mentions or implies: memory systems
• User mentions or implies: remember across sessions
• User mentions or implies: memory retrieval
• User mentions or implies: episodic memory
• User mentions or implies: semantic memory
• User mentions or implies: vector store
• User mentions or implies: rag
• User mentions or implies: langmem
• User mentions or implies: memgpt
• User mentions or implies: conversation history