장기 메모리

from deepagents import create_deep_agent
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()  # 또는 다른 Store 객체
agent = create_deep_agent(
    store=store,
    use_longterm_memory=True
)

# 일시적 파일 (스레드 종료 후 손실)
agent.invoke({
    "messages": [{"role": "user", "content": "Write draft to /draft.txt"}]
})

# 영구 파일 (스레드 간에 유지)
agent.invoke({
    "messages": [{"role": "user", "content": "Save final report to /memories/report.txt"}]
})

import uuid

# 스레드 1: 장기 메모리에 쓰기
config1 = {"configurable": {"thread_id": str(uuid.uuid4())}}
agent.invoke({
    "messages": [{"role": "user", "content": "Save my preferences to /memories/preferences.txt"}]
}, config=config1)

# 스레드 2: 장기 메모리에서 읽기 (다른 대화!)
config2 = {"configurable": {"thread_id": str(uuid.uuid4())}}
agent.invoke({
    "messages": [{"role": "user", "content": "What are my preferences?"}]
}, config=config2)
# 에이전트가 첫 번째 스레드의 /memories/preferences.txt를 읽을 수 있음

agent = create_deep_agent(
    store=store,
    use_longterm_memory=True,
    system_prompt="""When users tell you their preferences, save them to
    /memories/user_preferences.txt so you remember them in future conversations."""
)

agent = create_deep_agent(
    store=store,
    use_longterm_memory=True,
    system_prompt="""You have a file at /memories/instructions.txt with additional
    instructions and preferences.

    Read this file at the start of conversations to understand user preferences.

    When users provide feedback like "please always do X" or "I prefer Y",
    update /memories/instructions.txt using the edit_file tool."""
)

# 대화 1: 프로젝트에 대해 학습
agent.invoke({
    "messages": [{"role": "user", "content": "We're building a web app with React. Save project notes."}]
})

# 대화 2: 해당 지식 사용
agent.invoke({
    "messages": [{"role": "user", "content": "What framework are we using?"}]
})
# 에이전트가 이전 대화의 /memories/project_notes.txt를 읽음

research_agent = create_deep_agent(
    store=store,
    use_longterm_memory=True,
    system_prompt="""You are a research assistant.

    Save your research progress to /memories/research/:
    - /memories/research/sources.txt - List of sources found
    - /memories/research/notes.txt - Key findings and notes
    - /memories/research/report.md - Final report draft

    This allows research to continue across multiple sessions."""
)

from langgraph.store.memory import InMemoryStore

store = InMemoryStore()
agent = create_deep_agent(store=store, use_longterm_memory=True)

from langgraph.store.postgres import PostgresStore
import os

store = PostgresStore(connection_string=os.environ["DATABASE_URL"])
agent = create_deep_agent(store=store, use_longterm_memory=True)

# ✅ 좋음: 체계적이고 설명적임
/memories/user_preferences/language.txt
/memories/projects/project_alpha/status.txt
/memories/research/quantum_computing/sources.txt

# ❌ 나쁨: 일반적이고 비체계적임
/memories/temp.txt
/memories/data.txt
/memories/file1.txt

system_prompt="""You have access to two types of storage:

SHORT-TERM (paths without /memories/):
- Current conversation notes
- Temporary scratch work
- Draft documents

LONG-TERM (paths starting with /memories/):
- User preferences and settings
- Completed reports and documents
- Knowledge that should persist across conversations
- Project state and progress

Always use /memories/ for information that should survive beyond this conversation."""

config = {
    "configurable": {
        "thread_id": "thread-123",
    },
    "metadata": {
        "assistant_id": "user-456"  # 네임스페이스 격리
    }
}

agent.invoke({"messages": [...]}, config=config)

# ❌ 개발용만 - 재시작 시 데이터 손실
store = InMemoryStore()

# ✅ 프로덕션용 - 데이터 유지
from langgraph.store.postgres import PostgresStore
store = PostgresStore(connection_string=os.environ["DATABASE_URL"])

agent.invoke({
    "messages": [{"role": "user", "content": "List all files"}]
})

# 예시 출력:
# Transient files:
# - /draft.txt
# - /temp_notes.txt
#
# Long-term files:
# - /memories/user_preferences.txt
# - /memories/project_status.txt

# ❌ 이는 오류가 발생함
agent = create_deep_agent(use_longterm_memory=True)  # store가 누락됨!

# ✅ 올바름
agent = create_deep_agent(
    use_longterm_memory=True,
    store=InMemoryStore()
)