pip install mem0ai
export MEM0_API_KEY="m0-your-api-key"

from mem0 import MemoryClient

client = MemoryClient(api_key="m0-xxx")

from mem0 import AsyncMemoryClient

client = AsyncMemoryClient(api_key="m0-xxx")

# Or use as context manager
async with AsyncMemoryClient(api_key="m0-xxx") as client:
    results = await client.search("query", filters={"user_id": "alice"})

messages = [
    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
    {"role": "assistant", "content": "Got it! I'll remember that."}
]
client.add(messages, user_id="alice")

results = client.search("dietary preferences", filters={"user_id": "alice"})
for mem in results.get("results", []):
    print(mem["memory"], mem["score"])

memory = client.get(memory_id="ea925981-...")

memories = client.get_all(filters={"user_id": "alice"})
# With compound filters
memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]})

client.update("ea925981-...", text="Updated: vegan since 2024")
client.update("ea925981-...", metadata={"verified": True})

client.delete("ea925981-...")

client.delete_all(user_id="alice")

history = client.history("ea925981-...")
# Returns: [{previous_value, new_value, action, timestamps}]

client.batch_update([
    {"memory_id": "uuid-1", "text": "Updated text"},
    {"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
])

client.batch_delete([
    {"memory_id": "uuid-1"},
    {"memory_id": "uuid-2"},
])

users = client.users()
# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}

client.delete_users(user_id="alice")

client.reset()

import json

schema = json.dumps({
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "preferences": {"type": "array", "items": {"type": "string"}},
    }
})
export = client.create_memory_export(schema=schema, user_id="alice")

result = client.get_memory_export(memory_export_id=export["id"])

summary = client.get_summary(filters={"user_id": "alice"})

client.feedback(
    memory_id="mem-123",
    feedback="POSITIVE",  # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
    feedback_reason="Accurately captured preference"
)

# List
webhooks = client.get_webhooks(project_id="proj_123")

# Create
webhook = client.create_webhook(
    url="https://your-app.com/webhook",
    name="Memory Logger",
    project_id="proj_123",
    event_types=["memory_add", "memory_update"]
)

# Update
client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")

# Delete
client.delete_webhook(webhook_id=123)

# Get project config
config = client.project.get(fields=["custom_categories", "custom_instructions"])

# Update project settings
client.project.update(
    custom_instructions="Extract dietary preferences and health info",
    custom_categories=[{"health": "Medical and dietary info"}],
    multilingual=True,
)

# Create/delete project
client.project.create(name="My Project", description="...")
client.project.delete()

# Member management
members = client.project.get_members()
client.project.add_member(email="[email protected]", role="READER")  # READER or OWNER
client.project.update_member(email="[email protected]", role="OWNER")
client.project.remove_member(email="[email protected]")

pip install mem0ai

from mem0 import Memory

m = Memory()  # Uses default config (OpenAI embedder + in-memory vector store)

config = {
    "llm": {
        "provider": "openai",        # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
        "config": {
            "model": "gpt-5-mini",
            "api_key": "sk-xxx",
        }
    },
    "embedder": {
        "provider": "openai",        # openai, ollama, azure, lmstudio, google, huggingface
        "config": {
            "model": "text-embedding-3-small",
            "api_key": "sk-xxx",
        }
    },
    "vector_store": {
        "provider": "qdrant",        # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
        "config": {
            "collection_name": "my_memories",
            "host": "localhost",
            "port": 6333,
        }
    },
    "history_db_path": "history.db",              # SQLite path for change history
    "custom_instructions": "...",                  # Custom LLM prompt for extraction
}

m = Memory.from_config(config)

with Memory(config) as m:
    m.add("I prefer dark mode", user_id="alice")
    results = m.search("preferences", filters={"user_id": "alice"})
# SQLite connections released automatically

m.add("I'm a vegetarian", user_id="alice")
m.add([
    {"role": "user", "content": "I like hiking"},
    {"role": "assistant", "content": "Great outdoor activity!"}
], user_id="alice")

results = m.search("dietary preferences", filters={"user_id": "alice"}, top_k=5)

m.reset()

from mem0 import AsyncMemory

m = AsyncMemory(config)
await m.add("text", user_id="alice")
results = await m.search("query", filters={"user_id": "alice"})