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Zhongwei Li
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skills/langgraph-master/03_memory_management_store.md
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skills/langgraph-master/03_memory_management_store.md
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# Store (Long-term Memory)
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Long-term memory for sharing information across multiple threads.
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## Overview
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Checkpointer only saves state within a single thread. To share information across multiple threads, use **Store**.
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## Checkpointer vs Store
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| Feature | Checkpointer | Store |
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|---------|-------------|-------|
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| Scope | Single thread | All threads |
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| Purpose | Conversation state | User information |
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| Auto-save | Yes | No (manual) |
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| Search | thread_id | Namespace |
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## Basic Usage
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```python
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from langgraph.store.memory import InMemoryStore
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# Create Store
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store = InMemoryStore()
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# Save user information
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store.put(
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namespace=("users", "user-123"),
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key="preferences",
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value={
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"language": "en",
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"theme": "dark",
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"notifications": True
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}
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)
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# Retrieve user information
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user_prefs = store.get(("users", "user-123"), "preferences")
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```
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## Namespace
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Namespaces are grouped by **tuples**:
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```python
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# User information
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("users", user_id)
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# Session information
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("sessions", session_id)
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# Project information
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("projects", project_id, "documents")
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# Hierarchical structure
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("organization", org_id, "department", dept_id)
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```
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## Store Operations
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### Save
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```python
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store.put(
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namespace=("users", "alice"),
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key="profile",
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value={
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"name": "Alice",
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"email": "alice@example.com",
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"joined": "2024-01-01"
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}
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)
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```
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### Retrieve
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```python
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# Single item
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profile = store.get(("users", "alice"), "profile")
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# All items in namespace
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items = store.search(("users", "alice"))
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```
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### Search
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```python
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# Filter by namespace
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all_users = store.search(("users",))
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# Filter by key
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profiles = store.search(("users",), filter={"key": "profile"})
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```
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### Delete
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```python
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# Single item
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store.delete(("users", "alice"), "profile")
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# Entire namespace
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store.delete_namespace(("users", "alice"))
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```
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## Integration with Graph
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```python
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from langgraph.store.memory import InMemoryStore
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store = InMemoryStore()
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# Integrate Store with graph
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graph = builder.compile(
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checkpointer=checkpointer,
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store=store
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)
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# Use Store within nodes
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def personalized_node(state: State, *, store):
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user_id = state["user_id"]
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# Get user preferences
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prefs = store.get(("users", user_id), "preferences")
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# Process based on preferences
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if prefs and prefs.value.get("language") == "en":
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response = generate_english_response(state)
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else:
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response = generate_default_response(state)
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return {"response": response}
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```
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## Semantic Search
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Store implementations with vector search capability:
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```python
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from langgraph.store.memory import InMemoryStore
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store = InMemoryStore(index={"embed": True})
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# Save documents (automatically vectorized)
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store.put(
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("documents", "doc-1"),
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"content",
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{"text": "LangGraph is an agent framework"}
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)
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# Semantic search
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results = store.search(
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("documents",),
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query="agent development"
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)
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```
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## Practical Example: User Profile
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```python
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class ProfileState(TypedDict):
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user_id: str
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messages: Annotated[list, add_messages]
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def save_user_info(state: ProfileState, *, store):
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"""Extract and save user information from conversation"""
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messages = state["messages"]
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user_id = state["user_id"]
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# Extract information with LLM
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info = extract_user_info(messages)
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if info:
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# Save to Store
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current = store.get(("users", user_id), "profile")
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if current:
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# Merge with existing information
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updated = {**current.value, **info}
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else:
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updated = info
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store.put(
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("users", user_id),
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"profile",
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updated
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)
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return {}
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def personalized_response(state: ProfileState, *, store):
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"""Personalize using user information"""
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user_id = state["user_id"]
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# Get user information
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profile = store.get(("users", user_id), "profile")
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if profile:
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context = f"User context: {profile.value}"
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messages = [
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{"role": "system", "content": context},
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*state["messages"]
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]
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else:
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messages = state["messages"]
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response = llm.invoke(messages)
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return {"messages": [response]}
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```
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## Practical Example: Knowledge Base
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```python
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def query_knowledge_base(state: State, *, store):
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"""Search for knowledge related to question"""
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query = state["messages"][-1].content
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# Semantic search
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relevant_docs = store.search(
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("knowledge",),
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query=query,
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limit=3
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)
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# Add relevant information to context
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context = "\n".join([
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doc.value["text"]
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for doc in relevant_docs
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])
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# Pass to LLM
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response = llm.invoke([
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{"role": "system", "content": f"Context:\n{context}"},
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*state["messages"]
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])
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return {"messages": [response]}
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```
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## Store Implementations
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### InMemoryStore
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```python
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from langgraph.store.memory import InMemoryStore
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store = InMemoryStore()
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```
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### Custom Store
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```python
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from langgraph.store.base import BaseStore
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class RedisStore(BaseStore):
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def __init__(self, redis_client):
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self.redis = redis_client
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def put(self, namespace, key, value):
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ns_key = f"{':'.join(namespace)}:{key}"
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self.redis.set(ns_key, json.dumps(value))
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def get(self, namespace, key):
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ns_key = f"{':'.join(namespace)}:{key}"
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data = self.redis.get(ns_key)
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return json.loads(data) if data else None
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def search(self, namespace, filter=None):
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pattern = f"{':'.join(namespace)}:*"
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keys = self.redis.keys(pattern)
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return [self.get_by_key(k) for k in keys]
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```
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## Best Practices
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1. **Namespace Design**: Hierarchical and meaningful structure
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2. **Key Naming**: Clear and consistent naming conventions
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3. **Data Size**: Store references only for large data
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4. **Cleanup**: Periodic deletion of old data
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## Summary
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Store is long-term memory for sharing information across multiple threads. Use it for persisting user profiles, knowledge bases, settings, etc.
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## Related Pages
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- [03_memory_management_checkpointer.md](03_memory_management_checkpointer.md) - Differences from short-term memory
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- [03_memory_management_persistence.md](03_memory_management_persistence.md) - Persistence basics
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