| name | agent-memory |
| description | Implement agent memory - short-term, long-term, semantic storage, and retrieval |
| sasmp_version | 1.3.0 |
| bonded_agent | 06-agent-memory |
| bond_type | PRIMARY_BOND |
| version | 2.0.0 |
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
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
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 retrievalllm-integration - Context managementai-agent-basics - Agent architecture
References