Agent Memory
Give agents the ability to remember and learn across conversations.
When to Use This Skill
Invoke this skill when:
- •Adding conversation history
- •Implementing long-term memory
- •Building personalized agents
- •Managing context windows
Parameter Schema
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
task | string | Yes | Memory goal | - |
memory_type | enum | No | buffer, summary, vector, hybrid | hybrid |
persistence | enum | No | session, user, global | session |
Quick Start
python
from langchain.memory import ConversationBufferWindowMemory # Simple buffer (last k messages) memory = ConversationBufferWindowMemory(k=10) # With summarization from langchain.memory import ConversationSummaryBufferMemory memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=2000) # Vector store memory from langchain.memory import VectorStoreRetrieverMemory memory = VectorStoreRetrieverMemory(retriever=vectorstore.as_retriever())
Memory Types
| Type | Use Case | Pros | Cons |
|---|---|---|---|
| Buffer | Short chats | Simple | No compression |
| Summary | Long chats | Compact | Loses detail |
| Vector | Semantic recall | Relevant | Slower |
| Hybrid | Production | Best of all | Complex |
Multi-Layer Architecture
python
class ProductionMemory:
def __init__(self):
self.short_term = BufferMemory(k=10) # Recent
self.summary = SummaryMemory() # Compressed
self.long_term = VectorMemory() # Semantic
Troubleshooting
| Issue | Solution |
|---|---|
| Context overflow | Add summarization |
| Slow retrieval | Cache, reduce k |
| Irrelevant recall | Improve embeddings |
| Memory not persisting | Check storage backend |
Best Practices
- •Use multi-layer memory for production
- •Set token limits to prevent overflow
- •Add metadata (timestamps, importance)
- •Implement TTL for old memories
Related Skills
- •
rag-systems- Vector retrieval - •
llm-integration- Context management - •
ai-agent-basics- Agent architecture