gh-hiroshi75-ccplugins-langgraph-master-plugin/skills/langgraph-master/03_memory_management_overview.md

03. Memory Management

State management through persistence and checkpoint features.

Overview

LangGraph's built-in persistence layer allows you to save and restore agent state. This enables conversation continuation, error recovery, and time travel.

Memory Types

Short-term Memory: Checkpointer

• Automatically saves state at each superstep
• Thread-based conversation management
• Time travel functionality

Long-term Memory: Store

• Share information across threads
• Persist user information
• Semantic search

Key Features

1. Persistence

Checkpoints: Save state at each superstep

• Snapshot state at each stage of graph execution
• Recoverable from failures
• Track execution history

Threads: Unit of conversation

• Identify conversations by thread_id
• Each thread maintains independent state
• Manage multiple conversations in parallel

StateSnapshot: Representation of checkpoints

• values: State at that point in time
• next: Nodes to execute next
• config: Checkpoint configuration
• metadata: Metadata

2. Human-in-the-Loop

State Inspection: Check state at any point

state = graph.get_state(config)
print(state.values)

Approval Flow: Human approval before critical operations

# Pause graph and wait for approval

3. Memory

Conversation Memory: Memory within a thread

# Conversation continues when called with the same thread_id
config = {"configurable": {"thread_id": "conversation-1"}}
graph.invoke(input, config)

Long-term Memory: Memory across threads

# Save user information in Store
store.put(("user", user_id), "preferences", user_prefs)

4. Time Travel

Replay and fork past executions:

# Resume from specific checkpoint
history = graph.get_state_history(config)
for state in history:
    print(f"Checkpoint: {state.config['configurable']['checkpoint_id']}")

# Re-execute from past checkpoint
graph.invoke(input, past_checkpoint_config)

Checkpointer Implementations

LangGraph provides multiple checkpointer implementations:

InMemorySaver (For Experimentation)

from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

SqliteSaver (For Local Development)

from langgraph.checkpoint.sqlite import SqliteSaver

checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
graph = builder.compile(checkpointer=checkpointer)

PostgresSaver (For Production)

from langgraph.checkpoint.postgres import PostgresSaver

checkpointer = PostgresSaver.from_conn_string(
    "postgresql://user:pass@localhost/db"
)
graph = builder.compile(checkpointer=checkpointer)

Basic Usage Example

from langgraph.checkpoint.memory import MemorySaver

# Compile with checkpointer
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

# Execute with thread_id
config = {"configurable": {"thread_id": "user-123"}}

# First execution
result1 = graph.invoke({"messages": [("user", "Hello")]}, config)

# Continue in same thread
result2 = graph.invoke({"messages": [("user", "How are you?")]}, config)

# Check state
state = graph.get_state(config)
print(state.values)  # All messages so far

# Check history
for state in graph.get_state_history(config):
    print(f"Step: {state.values}")

Key Principles

1. Thread ID Management: Use unique thread_id for each conversation
2. Checkpointer Selection: Choose appropriate implementation for your use case
3. State Minimization: Save only necessary information to keep checkpoint size small
4. Cleanup: Periodically delete old checkpoints

Next Steps

For details on each feature, refer to the following pages:

• 03_memory_management_persistence.md - Persistence details
• 03_memory_management_checkpointer.md - Checkpointer implementation
• 03_memory_management_store.md - Long-term memory management