API Reference Changes

from mem0 import Memory

# Basic initialization
m = Memory()

# With configuration
config = {
    "version": "v1.0",  # Supported in v0.x
    "vector_store": {...}
}
m = Memory.from_config(config)

from mem0 import Memory

# Basic initialization (same)
m = Memory()

# With configuration
config = {
    "version": "v1.1",  # v1.1+ only
    "vector_store": {...},
    # New optional features
    "reranker": {
        "provider": "cohere",
        "config": {...}
    }
}
m = Memory.from_config(config)

def add(
    self,
    messages,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    metadata: dict = None,
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
) -> Union[List[dict], dict]

def add(
    self,
    messages,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    metadata: dict = None,
    filters: dict = None,
    infer: bool = True          # ✅ NEW: Control memory inference
) -> dict  # Always returns dict with "results" key

# With output_format="v1.0"
[
    {
        "id": "mem_123",
        "memory": "User loves pizza",
        "event": "ADD"
    }
]

# With output_format="v1.1"
{
    "results": [
        {
            "id": "mem_123",
            "memory": "User loves pizza",
            "event": "ADD"
        }
    ]
}

# Always returns this format
{
    "results": [
        {
            "id": "mem_123",
            "memory": "User loves pizza",
            "metadata": {...},
            "event": "ADD"
        }
    ]
}

def search(
    self,
    query: str,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    limit: int = 100,
    filters: dict = None,       # Basic key-value only
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
) -> Union[List[dict], dict]

def search(
    self,
    query: str,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    limit: int = 100,
    filters: dict = None,       # ✅ ENHANCED: Advanced operators
    rerank: bool = True         # ✅ NEW: Reranking support
) -> dict  # Always returns dict with "results" key

# Simple key-value filtering only
filters = {
    "category": "food",
    "user_id": "alice"
}

# Advanced filtering with operators
filters = {
    "AND": [
        {"category": "food"},
        {"score": {"gte": 0.8}},
        {
            "OR": [
                {"priority": "high"},
                {"urgent": True}
            ]
        }
    ]
}

# Comparison operators
filters = {
    "score": {"gt": 0.5},      # Greater than
    "priority": {"gte": 5},     # Greater than or equal
    "rating": {"lt": 3},        # Less than
    "confidence": {"lte": 0.9}, # Less than or equal
    "status": {"eq": "active"}, # Equal
    "archived": {"ne": True},   # Not equal
    "tags": {"in": ["work", "personal"]},     # In list
    "category": {"nin": ["spam", "deleted"]}  # Not in list
}

def get_all(
    self,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
) -> Union[List[dict], dict]

def get_all(
    self,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    filters: dict = None        # ✅ ENHANCED: Advanced operators
) -> dict  # Always returns dict with "results" key

# Same signature in both versions
def update(
    self,
    memory_id: str,
    data: str
) -> dict

# Same signature in both versions
def delete(
    self,
    memory_id: str
) -> dict

# Same signature in both versions
def delete_all(
    self,
    user_id: str
) -> dict

from mem0 import MemoryClient

client = MemoryClient(api_key="your-key")

# async_mode had to be explicitly set or had different default
result = client.add("content", user_id="alice", async_mode=True)

from mem0 import MemoryClient

client = MemoryClient(api_key="your-key")

# async_mode defaults to True now (better performance)
result = client.add("content", user_id="alice")  # Uses async_mode=True by default

# Can still override if needed
result = client.add("content", user_id="alice", async_mode=False)

config = {
    "vector_store": {...},
    "llm": {...},
    "embedder": {...},
    "graph_store": {...},
    "version": "v1.0",              # ❌ v1.0 no longer supported
    "history_db_path": "...",
    "custom_fact_extraction_prompt": "..."
}

config = {
    "vector_store": {...},
    "llm": {...},
    "embedder": {...},
    "graph_store": {...},
    "reranker": {                   # ✅ NEW: Reranker support
        "provider": "cohere",
        "config": {...}
    },
    "version": "v1.1",              # ✅ v1.1+ only
    "history_db_path": "...",
    "custom_fact_extraction_prompt": "...",
    "custom_update_memory_prompt": "..."  # ✅ NEW: Custom update prompt
}

# Cohere reranker
"reranker": {
    "provider": "cohere",
    "config": {
        "model": "rerank-english-v3.0",
        "api_key": "your-api-key",
        "top_k": 10
    }
}

# Sentence Transformer reranker
"reranker": {
    "provider": "sentence_transformer",
    "config": {
        "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
        "device": "cuda"
    }
}

# Hugging Face reranker
"reranker": {
    "provider": "huggingface",
    "config": {
        "model": "BAAI/bge-reranker-base",
        "device": "cuda"
    }
}

# LLM-based reranker
"reranker": {
    "provider": "llm_reranker",
    "config": {
        "llm": {
            "provider": "openai",
            "config": {
                "model": "gpt-4",
                "api_key": "your-api-key"
            }
        }
    }
}

# Generic exceptions
try:
    result = m.add("content", user_id="alice", version="v1.0")
except Exception as e:
    print(f"Error: {e}")

# More specific error handling
try:
    result = m.add("content", user_id="alice")
except ValueError as e:
    if "v1.0 API format is no longer supported" in str(e):
        # Handle version compatibility error
        pass
    elif "Invalid filter operator" in str(e):
        # Handle filter syntax error
        pass
except TypeError as e:
    # Handle parameter errors
    pass
except Exception as e:
    # Handle unexpected errors
    pass

# Unknown parameters might be ignored
result = m.add("content", user_id="alice", unknown_param="value")

# Unknown parameters raise TypeError
try:
    result = m.add("content", user_id="alice", unknown_param="value")
except TypeError as e:
    print(f"Invalid parameter: {e}")

{
    "id": "mem_123",
    "memory": "User loves pizza",
    "user_id": "alice",
    "metadata": {...},
    "created_at": "2024-01-01T00:00:00Z",
    "updated_at": "2024-01-01T00:00:00Z",
    "score": 0.95  # In search results
}

{
    "id": "mem_123",
    "memory": "User loves pizza",
    "user_id": "alice",
    "agent_id": "assistant",     # ✅ More context
    "run_id": "session_001",     # ✅ More context
    "metadata": {...},
    "categories": ["food"],      # ✅ NEW: Auto-categorization
    "immutable": false,          # ✅ NEW: Immutability flag
    "created_at": "2024-01-01T00:00:00Z",
    "updated_at": "2024-01-01T00:00:00Z",
    "score": 0.95,              # In search results
    "rerank_score": 0.98        # ✅ NEW: If reranking used
}

from mem0 import Memory

m = Memory()

# Add with deprecated parameters
result = m.add(
    "I love pizza",
    user_id="alice",
    output_format="v1.1",
    version="v1.0"
)

# Handle variable response format
if isinstance(result, list):
    memories = result
else:
    memories = result.get("results", [])

for memory in memories:
    print(memory["memory"])

from mem0 import Memory

m = Memory()

# Add without deprecated parameters
result = m.add(
    "I love pizza",
    user_id="alice"
)

# Always dict format with "results" key
for memory in result["results"]:
    print(memory["memory"])

# Basic filtering
results = m.search(
    "food preferences",
    user_id="alice",
    filters={"category": "food"},
    output_format="v1.1"
)

# Enhanced filtering with reranking
results = m.search(
    "food preferences",
    user_id="alice",
    filters={
        "AND": [
            {"category": "food"},
            {"score": {"gte": 0.8}}
        ]
    },
    rerank=True
)