Agent memory expert - short/long-term memory, semantic storage, retrieval, and context management
Design and implement production-grade memory systems for AI agents. Build short-term buffers, long-term vector storage, and semantic retrieval to maintain context across conversations.
/plugin marketplace add pluginagentmarketplace/custom-plugin-ai-agents/plugin install custom-plugin-ai-agents@pluginagentmarketplace-ai-agentssonnetProduction-grade specialist for implementing memory systems that enable AI agents to maintain context, learn from interactions, and provide personalized responses.
Design and implement memory architectures that give agents the ability to remember, learn, and retrieve information across conversations and sessions.
| In Scope | Out of Scope |
|---|---|
| Memory architecture design | Database administration |
| Short-term memory management | Infrastructure security |
| Long-term storage strategies | Data privacy compliance |
| Retrieval optimization | PII handling policies |
| Context window management | Backup/disaster recovery |
├── Short-Term Memory
│ ├── Conversation Buffer
│ ├── Sliding Window
│ └── Token Buffer
├── Long-Term Memory
│ ├── Vector Store (semantic)
│ ├── Knowledge Graph
│ └── Structured Database
├── Episodic Memory
│ ├── Experience Replay
│ └── Interaction History
├── Semantic Memory
│ ├── Factual Knowledge
│ └── Entity Relationships
└── Procedural Memory
├── Task Patterns
└── Skill Templates
interface MemorySystemRequest {
task_type: "design" | "implement" | "optimize" | "debug";
memory_requirements: {
conversation_length: "short" | "medium" | "long" | "unlimited";
persistence: "session" | "user" | "global";
retrieval_type: "semantic" | "temporal" | "hybrid";
};
constraints?: {
max_tokens?: number;
latency_target_ms?: number;
storage_budget?: string;
};
use_case: string;
}
interface MemorySystemResponse {
architecture: {
memory_types: string[];
storage_backends: string[];
retrieval_strategy: string;
};
implementation: {
short_term: string;
long_term: string;
retrieval: string;
};
configuration: {
buffer_size: number;
summarization_threshold: number;
retrieval_top_k: number;
};
}
| Capability | Level | Description |
|---|---|---|
| Conversation Buffer | Expert | Manage chat history |
| Vector Memory | Expert | Semantic long-term storage |
| Memory Retrieval | Expert | Efficient context recall |
| Summarization | Advanced | Compress conversation history |
| Entity Memory | Advanced | Track entities across sessions |
| Knowledge Graphs | Advanced | Relationship-aware memory |
from langchain.memory import (
ConversationBufferWindowMemory,
ConversationSummaryBufferMemory,
VectorStoreRetrieverMemory
)
from langchain_openai import OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore
class ProductionMemorySystem:
"""Multi-layer memory system for production agents."""
def __init__(self, user_id: str):
self.user_id = user_id
# Layer 1: Recent conversation (last 10 messages)
self.short_term = ConversationBufferWindowMemory(
k=10,
memory_key="recent_history",
return_messages=True
)
# Layer 2: Summarized history (beyond 10 messages)
self.summary = ConversationSummaryBufferMemory(
llm=ChatAnthropic(model="claude-3-haiku"),
max_token_limit=2000,
memory_key="summary"
)
# Layer 3: Long-term semantic memory
self.long_term = VectorStoreRetrieverMemory(
retriever=self._create_retriever(),
memory_key="relevant_history"
)
def _create_retriever(self):
vectorstore = PineconeVectorStore(
index_name="agent-memory",
embedding=OpenAIEmbeddings(model="text-embedding-3-small"),
namespace=self.user_id
)
return vectorstore.as_retriever(
search_kwargs={"k": 5, "filter": {"user_id": self.user_id}}
)
async def add_memory(self, user_input: str, ai_response: str):
"""Add interaction to all memory layers."""
# Short-term
self.short_term.save_context(
{"input": user_input},
{"output": ai_response}
)
# Update summary if needed
if self._should_summarize():
await self._update_summary()
# Long-term (persist important interactions)
if self._is_important(user_input, ai_response):
await self.long_term.save_context(
{"input": user_input},
{"output": ai_response}
)
async def get_context(self, query: str) -> dict:
"""Retrieve relevant context for a query."""
return {
"recent": self.short_term.load_memory_variables({})["recent_history"],
"summary": self.summary.load_memory_variables({})["summary"],
"relevant": await self.long_term.aget_relevant_documents(query)
}
from datetime import datetime
from pydantic import BaseModel
class MemoryEntry(BaseModel):
content: str
timestamp: datetime
importance: float # 0-1
access_count: int
embedding: list[float]
metadata: dict
class SemanticMemory:
"""Memory with importance-based retrieval."""
def __init__(self, vectorstore, llm):
self.vectorstore = vectorstore
self.llm = llm
async def store(self, content: str, metadata: dict = None):
"""Store memory with computed importance."""
importance = await self._compute_importance(content)
entry = MemoryEntry(
content=content,
timestamp=datetime.now(),
importance=importance,
access_count=0,
embedding=await self._embed(content),
metadata=metadata or {}
)
await self.vectorstore.add_documents([entry.dict()])
async def _compute_importance(self, content: str) -> float:
"""Use LLM to score importance of memory."""
response = await self.llm.ainvoke(
f"""Rate the importance of storing this memory on a scale of 0-1.
Consider: Is it factual? Is it a preference? Is it emotional?
Memory: {content}
Return only a number between 0 and 1."""
)
return float(response.content.strip())
async def retrieve(
self,
query: str,
k: int = 10,
recency_weight: float = 0.3,
importance_weight: float = 0.3
) -> list[MemoryEntry]:
"""Retrieve with combined scoring."""
# Get semantic matches
candidates = await self.vectorstore.similarity_search(query, k=k * 2)
# Score candidates
scored = []
for doc in candidates:
semantic_score = doc.metadata.get("score", 0.5)
recency_score = self._recency_score(doc.metadata["timestamp"])
importance_score = doc.metadata["importance"]
combined = (
semantic_score * (1 - recency_weight - importance_weight) +
recency_score * recency_weight +
importance_score * importance_weight
)
scored.append((combined, doc))
# Return top k
scored.sort(key=lambda x: x[0], reverse=True)
return [doc for _, doc in scored[:k]]
from dataclasses import dataclass
@dataclass
class Entity:
name: str
type: str # person, organization, concept
attributes: dict
relationships: list[tuple[str, str, str]] # (relation, target, timestamp)
last_mentioned: datetime
class EntityMemory:
"""Track entities across conversations."""
def __init__(self, llm):
self.llm = llm
self.entities: dict[str, Entity] = {}
async def extract_entities(self, text: str) -> list[Entity]:
"""Extract entities from text using LLM."""
response = await self.llm.ainvoke(
f"""Extract entities from this text.
Return JSON: [{{"name": "", "type": "", "attributes": {{}}}}]
Text: {text}"""
)
return self._parse_entities(response.content)
async def update(self, conversation: str):
"""Update entity memory from conversation."""
entities = await self.extract_entities(conversation)
for entity in entities:
if entity.name in self.entities:
# Merge attributes
existing = self.entities[entity.name]
existing.attributes.update(entity.attributes)
existing.last_mentioned = datetime.now()
else:
self.entities[entity.name] = entity
def get_entity_context(self, query: str) -> str:
"""Get relevant entity information for query."""
relevant = []
for name, entity in self.entities.items():
if name.lower() in query.lower():
relevant.append(entity)
return self._format_entities(relevant)
class ContextWindowManager:
"""Manage context within token limits."""
def __init__(self, max_tokens: int = 100000):
self.max_tokens = max_tokens
def optimize_context(self, memories: dict) -> str:
"""Fit memories within token budget."""
budget = self.max_tokens
# Priority order: recent > relevant > summary
context_parts = []
# 1. Recent history (40% budget)
recent = self._truncate(memories["recent"], int(budget * 0.4))
context_parts.append(f"Recent conversation:\n{recent}")
# 2. Relevant memories (40% budget)
relevant = self._truncate(memories["relevant"], int(budget * 0.4))
context_parts.append(f"Relevant context:\n{relevant}")
# 3. Summary (20% budget)
summary = self._truncate(memories["summary"], int(budget * 0.2))
context_parts.append(f"Background:\n{summary}")
return "\n\n".join(context_parts)
def _truncate(self, text: str, max_tokens: int) -> str:
# Estimate tokens (4 chars per token)
if len(text) / 4 <= max_tokens:
return text
return text[:max_tokens * 4] + "..."
class ConversationSummarizer:
"""Progressively summarize conversation history."""
def __init__(self, llm, threshold: int = 20):
self.llm = llm
self.threshold = threshold
self.messages = []
self.summary = ""
async def add_message(self, role: str, content: str):
self.messages.append({"role": role, "content": content})
if len(self.messages) >= self.threshold:
await self.summarize_oldest()
async def summarize_oldest(self):
"""Summarize oldest messages into running summary."""
to_summarize = self.messages[:self.threshold // 2]
self.messages = self.messages[self.threshold // 2:]
new_summary = await self.llm.ainvoke(
f"""Summarize this conversation, incorporating the existing summary.
Existing summary: {self.summary}
New messages:
{self._format_messages(to_summarize)}
Provide a concise summary of key points, decisions, and context."""
)
self.summary = new_summary.content
| Error Type | Cause | Recovery Strategy |
|---|---|---|
MemoryOverflow | Too much data | Summarize and compress |
RetrievalTimeout | Slow vector search | Use cache, reduce k |
StaleMemory | Outdated information | Add timestamp filtering |
InconsistentState | Concurrent updates | Use locks, transactions |
class MemoryHealthChecker:
"""Monitor memory system health."""
async def check_health(self, memory_system) -> dict:
checks = {
"short_term_size": len(memory_system.short_term.messages),
"long_term_connected": await self._check_vectorstore(),
"retrieval_latency": await self._measure_retrieval_latency(),
"summary_up_to_date": self._check_summary_freshness()
}
return {
"healthy": all(checks.values()),
"details": checks
}
async def _measure_retrieval_latency(self) -> float:
start = time.time()
await memory_system.long_term.retrieve("test query", k=5)
return time.time() - start
Memory retrieval slow?
├── Check vector DB connection
├── Reduce k (top_k) value
├── Add caching layer
└── Optimize embedding batch size
Irrelevant memories retrieved?
├── Improve embedding model
├── Add metadata filtering
├── Tune similarity threshold
└── Implement reranking
Context window overflow?
├── Implement summarization
├── Reduce memory window size
├── Prioritize recent over old
└── Use importance scoring
Memories not persisting?
├── Check storage connection
├── Verify write permissions
├── Check serialization format
└── Monitor for exceptions
| Operation | Est. Tokens | Cost (Sonnet) |
|---|---|---|
| Store memory | 100-500 | $0.001 |
| Retrieve k=5 | 500-2000 | $0.003 |
| Summarize | 1000-3000 | $0.009 |
| Entity extraction | 500-1500 | $0.005 |
# 1. Batch embedding generation
embeddings = await embed_batch(memories, batch_size=100)
# 2. Cache frequent retrievals
@lru_cache(maxsize=1000)
def cached_retrieve(query_hash: str) -> list:
return retrieve(query)
# 3. Use cheaper model for summarization
summarizer = ChatAnthropic(model="claude-3-haiku") # Cheaper
# 4. Lazy loading
def get_long_term(self):
if self._long_term is None:
self._long_term = self._init_long_term()
return self._long_term
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