Long Term Memory

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

store = InMemoryStore()  # Or any other Store object
agent = create_deep_agent(
    store=store,
    use_longterm_memory=True
)

# Transient file (lost after thread ends)
agent.invoke({
    "messages": [{"role": "user", "content": "Write draft to /draft.txt"}]
})

# Persistent file (survives across threads)
agent.invoke({
    "messages": [{"role": "user", "content": "Save final report to /memories/report.txt"}]
})

import uuid

# Thread 1: Write to long-term memory
config1 = {"configurable": {"thread_id": str(uuid.uuid4())}}
agent.invoke({
    "messages": [{"role": "user", "content": "Save my preferences to /memories/preferences.txt"}]
}, config=config1)

# Thread 2: Read from long-term memory (different conversation!)
config2 = {"configurable": {"thread_id": str(uuid.uuid4())}}
agent.invoke({
    "messages": [{"role": "user", "content": "What are my preferences?"}]
}, config=config2)
# Agent can read /memories/preferences.txt from the first thread

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."""
)

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

# Conversation 2: Use that knowledge
agent.invoke({
    "messages": [{"role": "user", "content": "What framework are we using?"}]
})
# Agent reads /memories/project_notes.txt from previous conversation

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)

# ✅ Good: Organized and descriptive
/memories/user_preferences/language.txt
/memories/projects/project_alpha/status.txt
/memories/research/quantum_computing/sources.txt

# ❌ Bad: Generic and unorganized
/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"  # Namespace isolation
    }
}

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

# ❌ Development only - data lost on restart
store = InMemoryStore()

# ✅ Production - data persists
from langgraph.store.postgres import PostgresStore
store = PostgresStore(connection_string=os.environ["DATABASE_URL"])

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

# Example output:
# Transient files:
# - /draft.txt
# - /temp_notes.txt
#
# Long-term files:
# - /memories/user_preferences.txt
# - /memories/project_status.txt

# ❌ This will error
agent = create_deep_agent(use_longterm_memory=True)  # Missing store!

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