--- title: "Memory Lifecycle" description: "The memory lifecycle manages the end-to-end journey of stored information, orchestrating how conversations are parsed into structured facts, linked across entities, updated in place, or systematica..." last_updated: "2026-10-11T17:37:10.294005+00:00" canonical_url: "https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/python-core/memory-lifecycle" ---
Relevant source files The following files were used as context for generating this wiki page: - [mem0/memory/main.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py) - [mem0-ts/src/oss/src/memory/index.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/memory/index.ts) - [mem0-ts/src/oss/src/prompts/index.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/prompts/index.ts) - [integrations/opencode-plugin/opencode-mem0.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/opencode-plugin/opencode-mem0.ts) - [integrations/claude-code-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/claude-code-plugin/core/memory_core.py) - [mem0/configs/prompts.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/configs/prompts.py) - [integrations/antigravity-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/antigravity-plugin/core/memory_core.py) - [integrations/hermes-plugin-mem0/__init__.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/hermes-plugin-mem0/__init__.py) - [integrations/mem0-agent-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/mem0-agent-plugin/core/memory_core.py) - [integrations/codex-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/codex-plugin/core/memory_core.py) - [integrations/kimi-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/kimi-plugin/core/memory_core.py) - [integrations/agent-plugin-core/python/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/agent-plugin-core/python/memory_core.py) - [integrations/cursor-plugin/core/memory_core.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/cursor-plugin/core/memory_core.py) - [server/main.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/server/main.py) - [integrations/agent-plugin-core/typescript/src/lifecycle.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/agent-plugin-core/typescript/src/lifecycle.ts) - [integrations/hermes-plugin-mem0/_backend.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/hermes-plugin-mem0/_backend.py) - [integrations/vercel-ai-sdk/src/mem0-utils.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/mem0-utils.ts) - [mem0/client/main.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/client/main.py) - [mem0/configs/base.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/configs/base.py) - [mem0/memory/setup.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/setup.py) - [mem0/memory/notices.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py) - [server/server_state.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/server/server_state.py)
## Overview The memory lifecycle manages the end-to-end journey of stored information, orchestrating how conversations are parsed into structured facts, linked across entities, updated in place, or systematically cleared. Operating across synchronous and asynchronous interfaces mem0/memory/main.py, the system standardizes public methods mem0/memory/main.py alongside language model fact extraction mem0-ts/src/oss/src/prompts/index.ts to transform raw interactions into queryable vectors. It governs metadata tracking, temporal expiration filters, and telemetry-driven operational notices mem0/memory/notices.py to maintain high retrieval precision and scale thresholds mem0-ts/src/oss/src/memory/index.ts. Sources: [mem0/memory/main.py:1960-2005](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L1960-L2005), [mem0-ts/src/oss/src/memory/index.ts:1-82](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/memory/index.ts#L1-L82), [mem0-ts/src/oss/src/prompts/index.ts:1-43](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/prompts/index.ts#L1-L43), [mem0/memory/notices.py:99-163](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py#L99-L163) ## Public Memory Engine Interface ### Engine Method Signatures The memory engine exposes core operations for both synchronous and asynchronous execution across management routines mem0/memory/main.py. The public interface handles initialization through `MemoryConfig` mem0/configs/base.py and coordinates internal lifecycle helpers like `_create_memory`, `_update_memory`, `_delete_memory`, and `reset` mem0/memory/main.py. | Method | Parameters | Return Type | Purpose | | :--- | :--- | :--- | :--- | | `history` | `memory_id` | `list` | Get the history of changes for a memory by ID mem0/memory/main.py | | `_create_memory` | `data`, `existing_embeddings`, `metadata=None` | `str` | Internal helper to embed, store, and record audit history for new text mem0/memory/main.py | | `_create_procedural_memory` | `messages`, `metadata=None`, `prompt=None` | `dict` | Generate procedural memory summaries using an LLM prompt workflow mem0/memory/main.py | | `_update_memory` | `memory_id`, `data`, `existing_embeddings`, `metadata=None` | `str` | Modify existing memory text and refresh entity-store links mem0/memory/main.py | | `_delete_memory` | `memory_id`, `existing_memory=None` | `str` | Remove vector entries, log deletion audit history, and clear entity relations mem0/memory/main.py | | `reset` | None | `None` | Reset history database, close connections, and reinitialize vector store collections mem0/memory/main.py | Sources: [mem0/memory/main.py:1960-2162](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L1960-L2162) ### Configuration and Item Schemas The engine initializes using `MemoryConfig` defaults and validates core payloads against `MemoryItem` mem0/configs/base.py. The configuration binds vector store settings, language models, embedding models, and audit database paths mem0/configs/base.py. ```python class MemoryItem(BaseModel): id: str = Field(..., description="The unique identifier for the text data") memory: str = Field( ..., description="The memory deduced from the text data" ) # TODO After prompt changes from platform, update this hash: Optional[str] = Field(None, description="The hash of the memory") # The metadata value can be anything and not just string. Fix it metadata: Optional[Dict[str, Any]] = Field(None, description="Additional metadata for the text data") score: Optional[float] = Field(None, description="The score associated with the text data") created_at: Optional[str] = Field(None, description="The timestamp when the memory was created") updated_at: Optional[str] = Field(None, description="The timestamp when the memory was updated") class MemoryConfig(BaseModel): vector_store: VectorStoreConfig = Field( description="Configuration for the vector store", default_factory=VectorStoreConfig, ) llm: LlmConfig = Field( description="Configuration for the language model", default_factory=LlmConfig, ) embedder: EmbedderConfig = Field( description="Configuration for the embedding model", default_factory=EmbedderConfig, ) history_db_path: str = Field( description="Path to the history database", default=os.path.join(mem0_dir, "history.db"), ) reranker: Optional[RerankerConfig] = Field( description="Configuration for the reranker", default=None, ) version: str = Field( description="The version of the API", default="v1.1", ) custom_instructions: Optional[str] = Field( description="Custom instructions for fact extraction", default=None, ) ``` Sources: [mem0/configs/base.py:16-58](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/configs/base.py#L16-L58) > [!NOTE] > During a database or vector store reset, `reset()` mem0/memory/main.py closes active SQLite connections, creates a fresh `SQLiteManager` instance at `history_db_path` mem0/configs/base.py, and attempts collection recreation via `VectorStoreFactory`. Sources: [mem0/memory/main.py:2144-2162](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2144-L2162), [mem0/configs/base.py:42-45](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/configs/base.py#L42-L45) ## LLM Fact Extraction Workflow ### Fact Distillation Prompts and Schemas The fact extraction workflow transforms raw conversational turns into discrete JSON objects containing structured fact lists mem0/configs/prompts.py. The engine uses targeted templates such as `USER_MEMORY_EXTRACTION_PROMPT` and `AGENT_MEMORY_EXTRACTION_PROMPT` to isolate user preferences, personal details, plans, and professional attributes mem0/configs/prompts.py. > [!WARNING] > Fact generation must isolate target participants strictly based on designated roles; `USER_MEMORY_EXTRACTION_PROMPT` explicitly penalizes the model if information from assistant or system messages is included mem0/configs/prompts.py. Sources: [mem0/configs/prompts.py:63-121](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/configs/prompts.py#L63-L121) ### Procedural Memory Generation Procedural memory creation aggregates entire conversation histories into a structured operational summary using `_create_procedural_memory` mem0/memory/main.py. The system wraps incoming messages with a system prompt and a terminal user turn, queries the language model, strips code block markdown, and stores the resulting summary with a procedural type tag mem0/memory/main.py. ```python def _create_procedural_memory(self, messages, metadata=None, prompt=None): """ Create a procedural memory Args: messages (list): List of messages to create a procedural memory from. metadata (dict): Metadata to create a procedural memory from. prompt (str, optional): Prompt to use for the procedural memory creation. Defaults to None. """ logger.info("Creating procedural memory") parsed_messages = [ {"role": "system", "content": prompt or PROCEDURAL_MEMORY_SYSTEM_PROMPT}, *messages, { "role": "user", "content": "Create procedural memory of the above conversation.", }, ] try: procedural_memory = self.llm.generate_response(messages=parsed_messages) procedural_memory = remove_code_blocks(procedural_memory) except Exception as e: logger.error(f"Error generating procedural memory summary: {e}") raise if not procedural_memory: raise ValueError( "The LLM returned no content for the procedural memory summary. " "The model may have declined the request or returned an empty response." ) if metadata is None: raise ValueError("Metadata cannot be done for procedural memory.") metadata = {**metadata, "memory_type": MemoryType.PROCEDURAL.value} embeddings = self.embedding_model.embed(procedural_memory, memory_action="add") memory_id = self._create_memory(procedural_memory, {procedural_memory: embeddings}, metadata=metadata) capture_event("mem0._create_procedural_memory", self, {"memory_id": memory_id, "sync_type": "sync"}) result = {"results": [{"id": memory_id, "memory": procedural_memory, "event": "ADD"}]} return result ``` Sources: [mem0/memory/main.py:2007-2050](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2007-L2050) > [!CAUTION] > `_create_procedural_memory` raises a `ValueError` if the provided `metadata` dictionary is `None` or if the language model returns an empty string response mem0/memory/main.py. Sources: [mem0/memory/main.py:2034-2042](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2034-L2042) ## Vector Ingestion and Entity Linking ### Metadata Preparation and Embedding Upsert When new items are created via `_create_memory`, the engine checks whether precomputed embeddings exist in `existing_embeddings` mem0/memory/main.py. If missing, text strings are transformed into dense vector representations by invoking `self.embedding_model.embed(data, memory_action="add")` mem0/memory/main.py. A unique `uuid.uuid4()` identifier is assigned, and metadata dictionaries are cloned and populated with essential audit fields mem0/memory/main.py. ```python def _create_memory(self, data, existing_embeddings, metadata=None): logger.debug(f"Creating memory with {data=}") if data in existing_embeddings: embeddings = existing_embeddings[data] else: embeddings = self.embedding_model.embed(data, memory_action="add") memory_id = str(uuid.uuid4()) new_metadata = deepcopy(metadata) if metadata is not None else {} new_metadata["data"] = data new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest() if "created_at" not in new_metadata: new_metadata["created_at"] = datetime.now(timezone.utc).isoformat() new_metadata["updated_at"] = new_metadata["created_at"] new_metadata["text_lemmatized"] = lemmatize_for_bm25(data) self.vector_store.insert( vectors=[embeddings], ids=[memory_id], payloads=[new_metadata], ) ``` Sources: [mem0/memory/main.py:1975-1994](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L1975-L1994) > [!NOTE] > Metadata preparation automatically calculates an MD5 hash of the raw text and generates lemmatized tokens using `lemmatize_for_bm25(data)` to support keyword matching alongside dense vector search mem0/memory/main.py. Sources: [mem0/memory/main.py:1983-1988](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L1983-L1988) ### Audit Recording and Entity Synchronization Once the vector payload is inserted into the vector store, the transaction is recorded in the relational audit trail via `self.db.add_history` mem0/memory/main.py. During memory updates or deletions, `_update_memory` and `_delete_memory` invoke entity-store cleanup handlers (`_remove_memory_from_entity_store` and `_link_entities_for_memory`) to maintain cross-reference integrity across session filters (`user_id`, `agent_id`, `run_id`) mem0/memory/main.py. ```python self.db.add_history( memory_id, None, data, "ADD", created_at=new_metadata.get("created_at"), updated_at=new_metadata.get("updated_at"), actor_id=new_metadata.get("actor_id"), role=new_metadata.get("role"), ) return memory_id ``` Sources: [mem0/memory/main.py:1995-2005](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L1995-L2005) > [!WARNING] > Entity-store synchronization during memory modifications is text-content sensitive: `_update_memory` skips entity removal and relinking if the underlying text value has not changed (`text_changed` is false) mem0/memory/main.py. Sources: [mem0/memory/main.py:2107-2111](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2107-L2111) ## Memory Updates and Deletion Lifecycle ### Memory Modification and Entity Resynchronization The memory engine updates existing entries through `_update_memory`, which retrieves the target vector payload, validates its existence, and recomputes embeddings and hashes when text content changes mem0/memory/main.py. If the incoming `data` parameter is `None`, the engine retains the previous text value (`prev_value`) mem0/memory/main.py. ```python def _update_memory(self, memory_id, data, existing_embeddings, metadata=None): logger.info(f"Updating memory with {data=}") try: existing_memory = self.vector_store.get(vector_id=memory_id) except Exception: # Backing-store failure, not a bad memory_id: re-raise the original so the REST layer maps it to 5xx, not 4xx. logger.error(f"Error getting memory with ID {memory_id} during update.") raise if existing_memory is None: raise ValueError(f"Memory with id {memory_id} not found. Please provide a valid 'memory_id'") prev_value = existing_memory.payload.get("data") if data is None: data = prev_value if not isinstance(data, str): raise ValueError(f"Memory with id {memory_id} does not have text content to update") text_changed = data != prev_value new_metadata = deepcopy(existing_memory.payload) if metadata is not None: new_metadata.update(_strip_identity_keys(metadata, existing_memory.payload)) new_metadata["data"] = data new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest() new_metadata["text_lemmatized"] = lemmatize_for_bm25(data) new_metadata["created_at"] = existing_memory.payload.get("created_at") new_metadata["updated_at"] = datetime.now(timezone.utc).isoformat() if data in existing_embeddings: embeddings = existing_embeddings[data] else: embeddings = self.embedding_model.embed(data, "update") self.vector_store.update( vector_id=memory_id, vector=embeddings, payload=new_metadata, ) logger.info(f"Updating memory with ID {memory_id=} with {data=}") self.db.add_history( memory_id, prev_value, data, "UPDATE", created_at=new_metadata["created_at"], updated_at=new_metadata["updated_at"], actor_id=new_metadata.get("actor_id"), role=new_metadata.get("role"), ) # Entity-store cleanup: strip this memory's id from old-text entities, # then re-extract entities from the new text and link them back. session_filters = {k: new_metadata[k] for k in ("user_id", "agent_id", "run_id") if new_metadata.get(k)} if text_changed: self._remove_memory_from_entity_store(memory_id, session_filters) self._link_entities_for_memory(memory_id, data, session_filters) return memory_id ``` Sources: [mem0/memory/main.py:2052-2112](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2052-L2112) > [!CAUTION] > If `vector_store.get()` fails due to a backing-store error rather than a missing identifier, the error is re-raised directly so that REST wrapper layers map it to a `5xx` server response instead of a `4xx` client error mem0/memory/main.py. Sources: [mem0/memory/main.py:2055-2060](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2055-L2060) ### Single Deletion and Scope Resets Individual memory removal is executed via `_delete_memory`, which extracts existing payload metadata, normalizes creation timestamps to UTC, updates the vector store, records a `DELETE` event in history with `is_deleted=1`, and purges the memory identifier from the entity store mem0/memory/main.py. Bulk resets are handled by `reset()`, which re-initializes the SQLite history database and recreates or resets the vector collection mem0/memory/main.py. ```python def _delete_memory(self, memory_id, existing_memory=None): logger.info(f"Deleting memory with {memory_id=}") if existing_memory is None: existing_memory = self.vector_store.get(vector_id=memory_id) if existing_memory is None: raise ValueError(f"Memory with id {memory_id} not found. Please provide a valid 'memory_id'") prev_value = existing_memory.payload.get("data", "") created_at = _normalize_iso_timestamp_to_utc(existing_memory.payload.get("created_at")) updated_at = datetime.now(timezone.utc).isoformat() payload = existing_memory.payload or {} session_filters = {k: payload[k] for k in ("user_id", "agent_id", "run_id") if payload.get(k)} self.vector_store.delete(vector_id=memory_id) self.db.add_history( memory_id, prev_value, None, "DELETE", created_at=created_at, updated_at=updated_at, actor_id=existing_memory.payload.get("actor_id"), role=existing_memory.payload.get("role"), is_deleted=1, ) # Entity-store cleanup: strip this memory's id from any entity records # that linked to it. Non-fatal — the helper swallows errors. self._remove_memory_from_entity_store(memory_id, session_filters) return memory_id def reset(self): """ Reset the memory store by: Deletes the vector store collection Resets the database Recreates the vector store with a new client """ logger.warning("Resetting all memories") self.db.reset() self.db.close() self.db = SQLiteManager(self.config.history_db_path) if hasattr(self.vector_store, "reset"): self.vector_store = VectorStoreFactory.reset(self.vector_store) else: logger.warning("Vector store does not support reset. Skipping.") self.vector_store.delete_col() self.vector_store = VectorStoreFactory.create( ``` Sources: [mem0/memory/main.py:2114-2162](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2114-L2162) > [!WARNING] > When executing `reset()`, if the vector store implementation lacks a native `reset()` method, the engine falls back to calling `delete_col()` followed by a factory recreation step mem0/memory/main.py. Sources: [mem0/memory/main.py:2157-2162](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/main.py#L2157-L2162) ## Temporal Filtering and Usage Notices ### Telemetry Feature Flags and Usage Notice Constants The open-source notices framework (`mem0/memory/notices.py`) manages telemetry-driven feature disclosures, temporal usage tracking, decay detection, and scale evaluation thresholds mem0/memory/notices.py. Notice evaluation uses remote configurations fetched from GitHub or bundled fallbacks governed by a time-to-live (`_CONFIG_TTL_SECONDS`) of 3600 seconds mem0/memory/notices.py. | Notice ID / Constant | Value / Threshold | Meaning / Purpose | | :--- | :--- | :--- | | `NOTICE_ID` | `"first_run"` | Identifier for first-run telemetry notices mem0/memory/notices.py | | `TEMPORAL_USAGE_NOTICE_ID` | `"temporal_usage"` | Notice identifier for temporal usage tracking mem0/memory/notices.py | | `DECAY_USAGE_NOTICE_ID` | `"decay_usage"` | Notice identifier for decay and deletion usage mem0/memory/notices.py | | `SCALE_THRESHOLD_NOTICE_ID` | `"scale_threshold"` | Notice identifier for vector scale evaluations mem0/memory/notices.py | | `PERFORMANCE_SLOW_QUERY_NOTICE_ID` | `"performance_slow_query"` | Notice identifier for slow query tracking mem0/memory/notices.py | | `TEMPORAL_USAGE_CAP` | `10` | Maximum temporal usage notices per window mem0/memory/notices.py | | `DECAY_USAGE_CAP` | `10` | Maximum decay usage notices per window mem0/memory/notices.py | | `DECAY_USAGE_DELETE_THRESHOLD` | `5` | Successful deletes required to trigger decay notice mem0/memory/notices.py | | `SCALE_MEMORY_COUNT_THRESHOLD` | `2000` | Vector count threshold triggering scale notices mem0/memory/notices.py | | `SCALE_TOP_K_THRESHOLD` | `50` | `top_k` query parameter threshold for scale notices mem0/memory/notices.py | | `PERFORMANCE_SLOW_QUERY_THRESHOLD_SECONDS` | `2.0` | Query execution time limit for slow query warnings mem0/memory/notices.py | ```python NOTICE_ID = "first_run" TEMPORAL_FEATURE_NOTICE_ID = "temporal_stub" TEMPORAL_USAGE_NOTICE_ID = "temporal_usage" DECAY_FEATURE_NOTICE_ID = "decay_stub" DECAY_USAGE_NOTICE_ID = "decay_usage" SCALE_THRESHOLD_NOTICE_ID = "scale_threshold" PERFORMANCE_SLOW_QUERY_NOTICE_ID = "performance_slow_query" NOTICE_EVENT = "mem0.notice_displayed" DISPLAYED_VARIANT = "displayed" HOLDOUT_VARIANT = "holdout" STATE_SECTION = "notice_state" STATE_KEY = "first_run" TEMPORAL_USAGE_STATE_KEY = "temporal_usage" TEMPORAL_USAGE_CAP = 10 TEMPORAL_USAGE_WINDOW = timedelta(days=7) DECAY_USAGE_STATE_KEY = "decay_usage" DECAY_USAGE_CAP = 10 DECAY_USAGE_WINDOW = timedelta(days=7) DECAY_USAGE_DELETE_THRESHOLD = 5 SCALE_THRESHOLD_STATE_KEY = "scale_threshold" SCALE_THRESHOLD_CAP = 10 SCALE_THRESHOLD_WINDOW = timedelta(days=7) SCALE_MEMORY_COUNT_THRESHOLD = 2000 SCALE_MEMORY_COUNT_CHECK_INTERVAL = 100 SCALE_TOP_K_THRESHOLD = 50 PERFORMANCE_SLOW_QUERY_STATE_KEY = "performance_slow_query" PERFORMANCE_SLOW_QUERY_CAP = 10 PERFORMANCE_SLOW_QUERY_WINDOW = timedelta(days=7) PERFORMANCE_SLOW_QUERY_THRESHOLD_SECONDS = 2.0 ``` Sources: [mem0/memory/notices.py:99-127](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py#L99-L127) > [!WARNING] > The OSS Python Memory SDK explicitly does not support advanced query parameters such as `timestamp`, `reference_date`, or `decay`, raising structured error messages when callers attempt to invoke them mem0/memory/notices.py. Sources: [mem0/memory/notices.py:130-134](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py#L130-L134) ### Temporal Extraction and Expiry Regex Patterns Temporal filtering engines employ strict regular expression boundaries to detect ISO dates and relative conversational time anchors such as yesterday, last week, or specific date ranges mem0/memory/notices.py. ```python _ISO_DATE_RE = re.compile( r"\b\d{4}-\d{2}-\d{2}(?:[T\s]\d{2}:\d{2}(?::\d{2})?(?:\.\d+)?(?:Z|[+-]\d{2}:?\d{2})?)?\b" ) _RELATIVE_TIME_RE = re.compile( r"\b(" r"today|yesterday|tomorrow|" r"last\s+(?:night|week|month|year)|" r"this\s+(?:week|month|year)|" r"next\s+(?:week|month|year)|" r"(?:past|last)\s+\d+\s+(?:day|days|week|weeks|month|months|year|years)|" r"(?:since|before|after|until)\s+(?:today|yesterday|tomorrow|\d{4}-\d{2}-\d{2}|last\s+(?:week|month|year))" r")\b", re.IGNORECASE, ) _RANGE_OPERATORS = {"gt", "gte", "lt", "lte"} ``` Sources: [mem0/memory/notices.py:136-150](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py#L136-L150) > [!NOTE] > Usage notice tracking enforces concurrency protection via `_state_lock` and process-level flags to ensure telemetry events do not exceed capacity caps within their defined rolling time windows mem0/memory/notices.py. Sources: [mem0/memory/notices.py:152-163](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/memory/notices.py#L152-L163) ## Related - [Multi-Signal Retrieval](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/python-core/multi-signal-retrieval) - [Storage and History](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/python-core/storage-and-history) - [Typescript Core](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/typescript-core/typescript-core) ## Sitemap See the full [sitemap](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/llms.txt) for all pages in this wiki.