docs: expand short frontmatter descriptions for SEO

Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>

docs/api-reference/memory/add-memories.mdx

@@ -50,6 +50,7 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 | `app_id` | string | No* | Associates the memory with an app. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
+| `expiration_date` | string | Optional | Date in `YYYY-MM-DD` format. The memory is visible through this date and hidden by default after it passes. |
 
 > \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
 
@@ -83,3 +84,11 @@ The request is queued for background processing. The response contains an `event
 <Info>
 Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
 </Info>
+
+<Info>
+Memories with `expiration_date` remain stored after they expire. Search and get-all hide them by default; pass `show_expired: true` to include them.
+</Info>
+
+<Info>
+Python uses `expiration_date`; TypeScript uses `expirationDate`.
+</Info>

docs/api-reference/memory/get-memories.mdx

@@ -6,6 +6,10 @@ openapi: post /v3/memories/
 
 List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
 
+Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
+
+Python uses `show_expired`; TypeScript uses `showExpired`.
+
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 
 - `in`: Matches any of the values specified
@@ -32,6 +36,7 @@ memories = client.get_all(
             }
         ]
     },
+    show_expired=False,
     page=1,
     page_size=50
 )
@@ -46,12 +51,14 @@ memories = client.get_all(
         {
             "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
             "memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
+            "expiration_date": null,
             "created_at": "2024-07-01T12:00:00Z",
             "updated_at": "2024-07-01T12:00:00Z"
         },
         {
             "id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
             "memory": "Alex prefers vegetarian restaurants",
+            "expiration_date": null,
             "created_at": "2024-07-05T15:30:00Z",
             "updated_at": "2024-07-05T15:30:00Z"
         }

docs/api-reference/memory/search-memories.mdx

@@ -8,6 +8,10 @@ Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retr
 
 Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
 
+Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
+
+Python uses `show_expired`; TypeScript uses `showExpired`.
+
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -30,6 +34,7 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 ```python Platform API Example
 related_memories = client.search(
     query="What are Alice's hobbies?",
+    show_expired=False,
     filters={
         "OR": [
             {
@@ -54,6 +59,7 @@ related_memories = client.search(
         "category": "hobbies"
       },
       "score": 0.82,
+      "expiration_date": null,
       "created_at": "2024-07-26T10:29:36.630547-07:00",
       "updated_at": null,
       "categories": ["hobbies"]

docs/api-reference/memory/update-memory.mdx

@@ -1,5 +1,14 @@
 ---
 title: 'Update Memory'
-description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
+description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
 ---
+
+Use this endpoint to update mutable memory fields. To make a memory expire, set `expiration_date` to a `YYYY-MM-DD` date. To make it permanent again, send `expiration_date: null`.
+
+```python
+client.update("mem_123", expiration_date="2030-01-31")
+client.update("mem_123", expiration_date=None)
+```
+
+TypeScript uses `expirationDate`.

docs/api-reference/organization/remove-org-member.mdx

@@ -0,0 +1,5 @@
+---
+title: "Remove Organization Member"
+description: "Remove a member from a Mem0 organization by revoking their access and clearing their assigned role."
+openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
+---

docs/api-reference/organization/update-org-member.mdx

@@ -0,0 +1,5 @@
+---
+title: "Update Organization Member"
+description: "Update the role assigned to an existing member of a Mem0 organization to change their permissions."
+openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
+---

docs/api-reference/project/remove-project-member.mdx

@@ -0,0 +1,5 @@
+---
+title: "Remove Project Member"
+description: "Remove a member from a Mem0 project to revoke their access and unassign their project-level role."
+openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
+---

docs/api-reference/project/update-project-member.mdx

@@ -0,0 +1,5 @@
+---
+title: "Update Project Member"
+description: "Update the role assigned to an existing member of a Mem0 project to change their project-level permissions."
+openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
+---

docs/api-reference/project/update-project.mdx

@@ -0,0 +1,5 @@
+---
+title: "Update Project"
+description: "Update settings and configuration for an existing Mem0 project, including name and project-level options."
+openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
+---

docs/changelog/highlights.mdx

@@ -113,7 +113,7 @@ Launched a unified Mem0 plugin across three major AI development environments
 
 Major expansion of the provider ecosystem:
 
-- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
+- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE)
 - **Turbopuffer** — New vector database provider for Python SDK
 - **MiniMax** — New LLM provider with dedicated AWS Bedrock support
 - **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK

docs/changelog/sdk.mdx

@@ -121,7 +121,7 @@ mode: "wide"
 
 **New Features:**
 - **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
-- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_details` dict with `semantic_score`, `bm25_score`, `entity_boost`, `raw_score`, `max_possible_score`, `final_score`, and `threshold` so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
+- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_breakdown` dict with `semantic`, `keyword` (normalized BM25), `entity_boost`, and `temporal_boost` signals so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
 
 **Bug Fixes:**
 - **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) consistently across all backends. 11 adapters previously returned raw distance metrics (lower = better) — FAISS, Chroma, Milvus, Redis, Cassandra, PGVector, S3 Vectors, Supabase, Valkey, Azure MySQL, and Vertex AI Vector Search — causing incorrect ranking in multi-store setups ([#5391](https://github.com/mem0ai/mem0/pull/5391))
@@ -229,7 +229,7 @@ mode: "wide"
 **Improvements:**
 - **Telemetry:** Sample OSS hot-path events at 10% via PostHog `before_send` hook to reduce event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))
 
-See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
+See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-v2) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
 
 </Update>
 

docs/components/embedders/models/fastembed.mdx

@@ -0,0 +1,50 @@
+---
+title: "FastEmbed"
+description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
+---
+
+You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
+
+### Installation
+
+```bash
+pip install fastembed
+```
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "fastembed",
+        "config": {
+            "model": "thenlper/gte-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring FastEmbed embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
+| `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |

docs/components/llms/models/litellm.mdx

@@ -4,9 +4,12 @@ description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 
+In the TypeScript SDK, run LiteLLM as a [proxy server](https://docs.litellm.ai/docs/simple_proxy) (an OpenAI-compatible endpoint) and point Mem0 at it via `LITELLM_API_BASE` (defaults to `http://localhost:4000`).
+
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -33,6 +36,33 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Point Mem0 at your LiteLLM proxy. apiKey defaults to "sk-anything"
+// (the proxy handles real auth); baseURL defaults to http://localhost:4000.
+const config = {
+  llm: {
+    provider: 'litellm',
+    config: {
+      apiKey: process.env.LITELLM_API_KEY || 'sk-anything',
+      baseURL: process.env.LITELLM_API_BASE || 'http://localhost:4000',
+      model: 'gpt-5-mini',
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+];
+await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/minimax.mdx

@@ -7,7 +7,8 @@ To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment var
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,9 +37,37 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'minimax',
+    config: {
+      apiKey: process.env.MINIMAX_API_KEY || '',
+      model: 'MiniMax-M2.7',
+      temperature: 0.2,
+      maxTokens: 2000,
+      topP: 1.0,
+    },
+  },
+};
+const memory = new Memory(config);
+const messages = [
+  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
+  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
+];
+await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
+```
+
+</CodeGroup>
+
 You can also configure the API base URL in the config:
 
-```python
+<CodeGroup>
+```python Python
 config = {
     "llm": {
         "provider": "minimax",
@@ -51,6 +80,20 @@ config = {
 }
 ```
 
+```typescript TypeScript
+const config = {
+  llm: {
+    provider: 'minimax',
+    config: {
+      model: 'MiniMax-M2.7',
+      baseURL: 'https://your-custom-endpoint.com',
+      apiKey: 'your-api-key', // alternatively to using the environment variable
+    },
+  },
+};
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).

docs/components/rerankers/models/llm.mdx

@@ -1,226 +0,0 @@
----
-title: LLM as Reranker
-description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
----
-
-<Warning>
-**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
-</Warning>
-
-LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
-
-## Supported LLM Providers
-
-Any LLM provider supported by Mem0 can be used for reranking:
-
-- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
-- **Anthropic**: Claude models
-- **Together**: Open-source models
-- **Groq**: Fast inference
-- **Ollama**: Local models
-- And more...
-
-## Configuration
-
-```python Python
-from mem0 import Memory
-
-config = {
-    "vector_store": {
-        "provider": "chroma",
-        "config": {
-            "collection_name": "my_memories",
-            "path": "./chroma_db"
-        }
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4o-mini"
-        }
-    },
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "api_key": "your-openai-api-key",  # or set OPENAI_API_KEY
-            "top_k": 5,
-            "temperature": 0.0
-        }
-    }
-}
-
-memory = Memory.from_config(config)
-```
-
-## Custom Scoring Prompt
-
-You can provide a custom prompt for relevance scoring:
-
-```python Python
-custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
-
-Query: "{query}"
-Document: "{document}"
-
-Score from 0.0 to 1.0 where:
-- 1.0: Perfect match, directly answers the query
-- 0.8-0.9: Highly relevant, good match  
-- 0.6-0.7: Moderately relevant, partial match
-- 0.4-0.5: Slightly relevant, limited useful information
-- 0.0-0.3: Not relevant or no useful information
-
-Provide only a single numerical score between 0.0 and 1.0."""
-
-config["reranker"]["config"]["scoring_prompt"] = custom_prompt
-```
-
-## Usage Example
-
-```python Python
-import os
-from mem0 import Memory
-
-# Set API key
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-
-# Initialize memory with LLM reranker
-config = {
-    "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "temperature": 0.0
-        }
-    }
-}
-
-memory = Memory.from_config(config)
-
-# Add memories
-messages = [
-    {"role": "user", "content": "I'm learning Python programming"},
-    {"role": "user", "content": "I find object-oriented programming challenging"}, 
-    {"role": "user", "content": "I love hiking in national parks"}
-]
-
-memory.add(messages, user_id="david")
-
-# Search with LLM reranking
-results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
-
-for result in results['results']:
-    print(f"Memory: {result['memory']}")
-    print(f"Vector Score: {result['score']:.3f}")
-    print(f"Rerank Score: {result['rerank_score']:.3f}")
-    print()
-```
-
-```text Output
-Memory: I'm learning Python programming
-Vector Score: 0.856
-Rerank Score: 0.920
-
-Memory: I find object-oriented programming challenging
-Vector Score: 0.782
-Rerank Score: 0.850
-```
-
-## Domain-Specific Scoring
-
-Create specialized scoring for your domain:
-
-```python Python
-medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
-
-Clinical Query: "{query}"
-Medical Record: "{document}"
-
-Consider:
-- Clinical relevance and accuracy
-- Patient safety implications
-- Diagnostic value
-- Treatment relevance
-
-Score from 0.0 to 1.0. Provide only the numerical score."""
-
-config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "scoring_prompt": medical_prompt,
-            "temperature": 0.0
-        }
-    }
-}
-```
-
-## Multiple LLM Providers
-
-Use different LLM providers for reranking:
-
-```python Python
-# Using Anthropic Claude
-anthropic_config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "claude-3-haiku-20240307",
-            "provider": "anthropic",
-            "temperature": 0.0
-        }
-    }
-}
-
-# Using local Ollama model
-ollama_config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "llama2:7b",
-            "provider": "ollama",
-            "temperature": 0.0
-        }
-    }
-}
-```
-
-## Configuration Parameters
-
-| Parameter | Description | Type | Default |
-|-----------|-------------|------|---------|
-| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
-| `provider` | LLM provider name | `str` | `"openai"` |
-| `api_key` | API key for the LLM provider | `str` | `None` |
-| `top_k` | Maximum documents to return | `int` | `None` |
-| `temperature` | Temperature for LLM generation | `float` | `0.0` |
-| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
-| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
-
-## Advantages
-
-- **Maximum Flexibility**: Custom prompts for any use case
-- **Domain Expertise**: Leverage LLM knowledge for specialized domains
-- **Interpretability**: Understand scoring through prompt engineering
-- **Multi-criteria**: Score based on multiple relevance factors
-
-## Considerations
-
-- **Latency**: Higher latency than specialized rerankers
-- **Cost**: LLM API costs per reranking operation
-- **Consistency**: May have slight variations in scoring
-- **Prompt Engineering**: Requires careful prompt design
-
-## Best Practices
-
-1. **Temperature**: Use 0.0 for consistent scoring
-2. **Prompt Design**: Be specific about scoring criteria
-3. **Token Efficiency**: Keep prompts concise to reduce costs
-4. **Caching**: Cache results for repeated queries when possible
-5. **Fallback**: Handle API errors gracefully

docs/components/vectordbs/dbs/neon.mdx

@@ -21,49 +21,47 @@ from mem0 import Memory
 load_dotenv()
 
 config = {
-    "vector_store": {
+"vector_store": {
-        "provider": "pgvector",
+"provider": "pgvector",
-        "config": {
+"config": {
-            "connection_string": os.environ["DATABASE_URL"],
+"connection_string": os.environ["DATABASE_URL"],
-            "collection_name": "memories",
+"collection_name": "memories",
-            "embedding_model_dims": 1536,
+"embedding_model_dims": 1536,
-            "hnsw": True,
+"hnsw": True,
-        },
+},
-    },
+},
 }
 
 m = Memory.from_config(config)
 messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
+{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 
 results = m.search(
-    "What movies should I recommend?",
+"What movies should I recommend?",
-    filters={"user_id": "alice"},
+filters={"user_id": "alice"},
 )
 
 print(results)
-```
+
+````
 
 ```typescript TypeScript
 import "dotenv/config";
 import { Memory } from "mem0ai/oss";
 
-const databaseUrl = new URL(process.env.DATABASE_URL!);
-
 const m = new Memory({
   vectorStore: {
     provider: "pgvector",
     config: {
-      user: decodeURIComponent(databaseUrl.username),
+      connectionString: process.env.DATABASE_URL!,
-      password: decodeURIComponent(databaseUrl.password),
+      ssl: {
-      host: databaseUrl.hostname,
+        rejectUnauthorized: false,
-      port: Number(databaseUrl.port || 5432),
+      },
-      dbname: databaseUrl.pathname.slice(1) || "neondb",
       collectionName: "memories",
       dimension: 1536,
       embeddingModelDims: 1536,
@@ -89,7 +87,8 @@ const results = await m.search("What movies should I recommend?", {
 });
 
 console.log(results);
-```
+````
+
 </CodeGroup>
 
 ## SQL Migration
@@ -116,20 +115,19 @@ DATABASE_URL=postgresql://user:password@ep-example.us-east-2.aws.neon.tech/neond
 | `sslmode` | PostgreSQL SSL mode. Use `require` for Neon. | Driver default |
 </Tab>
 <Tab title="TypeScript">
-The current Mem0 TypeScript `pgvector` adapter takes individual Postgres fields,
+Use the Neon `DATABASE_URL` directly with `connectionString`. Set `ssl` if your runtime needs an explicit TLS config object.
-so parse `DATABASE_URL` before creating `Memory`.
+
+| Parameter            | Description                                    | Default        |
+| -------------------- | ---------------------------------------------- | -------------- |
+| `connectionString`   | Neon Postgres connection string.               | Required       |
+| `ssl`                | Optional TLS settings passed directly to `pg`. | Driver default |
+| `collectionName`     | Name for the vector collection.                | `memories`     |
+| `dimension`          | Vector dimension for Mem0 config.              | Auto-detected  |
+| `embeddingModelDims` | Embedding model dimensions for table creation. | Required       |
+| `hnsw`               | Enables HNSW indexing.                         | `false`        |
+
+**TLS note:** `ssl: true` is sufficient for most Neon connections since Neon uses valid certificates. Use `ssl: { rejectUnauthorized: false }` only when connecting through Neon's connection pooler on certain edge runtimes (e.g. Cloudflare Workers) that require it, or when your environment does not trust the Neon CA chain.
 
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `user` | Database user. | Required |
-| `password` | Database password. | Required |
-| `host` | Database host. | Required |
-| `port` | Database port. | `5432` |
-| `dbname` | Database name. | `vector_store` |
-| `collectionName` | Name for the vector collection. | `memories` |
-| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
-| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
-| `hnsw` | Enables HNSW indexing. | `false` |
 </Tab>
 </Tabs>
 

docs/components/vectordbs/dbs/pgvector.mdx

@@ -2,6 +2,7 @@
 title: "pgvector"
 description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
 ---
+
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
 
 ### Usage
@@ -14,41 +15,38 @@ from mem0 import Memory
 os.environ["OPENAI_API_KEY"] = "sk-xx"
 
 config = {
-    "vector_store": {
+"vector_store": {
-        "provider": "pgvector",
+"provider": "pgvector",
-        "config": {
+"config": {
-            "user": "test",
+"user": "test",
-            "password": "123",
+"password": "123",
-            "host": "127.0.0.1",
+"host": "127.0.0.1",
-            "port": "5432",
+"port": "5432",
-        }
+},
-    }
+}
 }
 
 m = Memory.from_config(config)
 messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
-```
+
+````
 
 ```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
+import { Memory } from "mem0ai/oss";
 
 const config = {
   vectorStore: {
-    provider: 'pgvector',
+    provider: "pgvector",
     config: {
-      collectionName: 'memories',
+      collectionName: "memories",
       embeddingModelDims: 1536,
-      user: 'test',
+      connectionString: "postgresql://test:123@localhost:5432/vector_store",
-      password: '123',
-      host: '127.0.0.1',
-      port: 5432,
-      dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
       diskann: false, // Optional, requires pgvectorscale extension
       hnsw: false, // Optional, for HNSW indexing
     },
@@ -57,37 +55,44 @@ const config = {
 
 const memory = new Memory(config);
 const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-]
+];
+
 await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
+````
+
 </CodeGroup>
 
 ### Config
 
 Here are the parameters available for configuring pgvector:
 
-| Parameter | Description | Default Value |
+| Parameter            | SDK                     | Description                                                                                                                                                    | Default Value                           |
-| --- | --- | --- |
+| -------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| `dbname` | The name of the database | `postgres` |
+| `connectionString`   | TypeScript OSS          | PostgreSQL connection string for direct connections. When set, Mem0 connects to the target database directly and skips the bootstrap `postgres` database flow. | `None`                                  |
-| `collection_name` | The name of the collection | `mem0` |
+| `ssl`                | TypeScript OSS          | SSL option passed directly to `pg`, either `true` or an SSL config object, for both `connectionString` and split-field connections.                            | `None`                                  |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `dbname`             | TypeScript OSS          | Split-field database name. This is only used when `connectionString` is absent.                                                                                | `vector_store`                          |
-| `user` | User name to connect to the database | `None` |
+| `collectionName`     | TypeScript OSS          | Collection name.                                                                                                                                               | `memories`                              |
-| `password` | Password to connect to the database | `None` |
+| `embeddingModelDims` | TypeScript OSS          | Dimensions of the embedding model.                                                                                                                             | Required                                |
-| `host` | The host where the Postgres server is running | `None` |
+| `user`               | TypeScript OSS + Python | Database user for split-field connections.                                                                                                                     | `None`                                  |
-| `port` | The port where the Postgres server is running | `None` |
+| `password`           | TypeScript OSS + Python | Database password for split-field connections.                                                                                                                 | `None`                                  |
-| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
+| `host`               | TypeScript OSS + Python | Database host for split-field connections.                                                                                                                     | `None`                                  |
-| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `port`               | TypeScript OSS + Python | Database port for split-field connections.                                                                                                                     | `None`                                  |
-| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
+| `diskann`            | TypeScript OSS + Python | Whether to use DiskANN for vector similarity search, requires pgvectorscale.                                                                                   | `False`                                 |
-| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
+| `hnsw`               | TypeScript OSS + Python | Whether to use HNSW for vector similarity search.                                                                                                              | TypeScript OSS: `False`, Python: `True` |
-| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
+| `connection_string`  | Python only             | PostgreSQL connection string, overrides individual connection parameters.                                                                                      | `None`                                  |
+| `sslmode`            | Python only             | SSL mode for PostgreSQL connections, such as `require`, `prefer`, or `disable`.                                                                                | `None`                                  |
+| `connection_pool`    | Python only             | psycopg connection pool object, overrides connection string and individual connection parameters.                                                              | `None`                                  |
 
-**Note (TypeScript OSS):** If you omit `dbname`, the TypeScript client uses the database name `vector_store`. Python defaults to `postgres` for `dbname`, as in the table above.
+**TypeScript OSS:** Use `connectionString` plus optional `ssl` for managed Postgres setups. If you omit `connectionString`, Mem0 falls back to split fields and uses `dbname`, `user`, `password`, `host`, `port`, and optional `ssl`.
+
+**Python:** The Python SDK uses snake_case keys such as `connection_string`, `sslmode`, `collection_name`, and `embedding_model_dims`.
+
+**Python connection priority**:
 
-**Note**: The connection parameters have the following priority:
 1. `connection_pool` (highest priority)
 2. `connection_string`
 3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)

docs/docs.json

@@ -273,7 +273,8 @@
                               "components/embedders/models/lmstudio",
                               "components/embedders/models/together",
                               "components/embedders/models/langchain",
-                              "components/embedders/models/aws_bedrock"
+                              "components/embedders/models/aws_bedrock",
+                              "components/embedders/models/fastembed"
                             ]
                           }
                         ]
@@ -533,6 +534,8 @@
                       "api-reference/organization/get-org",
                       "api-reference/organization/get-org-members",
                       "api-reference/organization/add-org-member",
+                      "api-reference/organization/update-org-member",
+                      "api-reference/organization/remove-org-member",
                       "api-reference/organization/delete-org"
                     ]
                   },
@@ -545,6 +548,9 @@
                       "api-reference/project/get-project",
                       "api-reference/project/get-project-members",
                       "api-reference/project/add-project-member",
+                      "api-reference/project/update-project",
+                      "api-reference/project/update-project-member",
+                      "api-reference/project/remove-project-member",
                       "api-reference/project/delete-project"
                     ]
                   },
@@ -624,6 +630,10 @@
     ]
   },
   "redirects": [
+    {
+      "source": "/components/rerankers/models/llm",
+      "destination": "/components/rerankers/models/llm_reranker"
+    },
     {
       "source": "/migration/breaking-changes",
       "destination": "/"

docs/integrations/agno.mdx

@@ -73,7 +73,7 @@ client = MemoryClient()
 # Define the agent
 agent = Agent(
     name="Personal Agent",
-    model=OpenAIChat(id="gpt-4"),
+    model=OpenAIChat(id="gpt-5-mini"),
     description="You are a helpful personal agent that helps me with day to day activities."
                 "You can process both text and images.",
     markdown=True

docs/integrations/autogen.mdx

@@ -43,7 +43,7 @@ OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
 memory_client = MemoryClient()
 agent = ConversableAgent(
     "chatbot",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
     code_execution_config=False,
     human_input_mode="NEVER",
 )
@@ -99,7 +99,7 @@ For more complex scenarios, you can create multiple agents:
 manager = ConversableAgent(
     "manager",
     system_message="You are a manager who helps in resolving complex customer issues.",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
     human_input_mode="NEVER"
 )
 

docs/integrations/claude-code.mdx

@@ -138,9 +138,10 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 
 | Hook | Event | What it does |
 |------|-------|-------------|
+| **Setup** | `Setup` | Installs the mem0 SDK and dependencies (runs on init and maintenance) |
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

docs/integrations/codex.mdx

@@ -123,7 +123,7 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 |------|-------|-------------|
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
-| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

docs/integrations/cursor.mdx

@@ -108,7 +108,7 @@ When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
 |------|-------|-------------|
 | **Session start** | `sessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `preToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |

docs/integrations/langchain-tools.mdx

@@ -98,20 +98,9 @@ add_result = add_tool.invoke(add_input)
 
 ```json Output
 {
-  "results": [
+  "message": "Memory processing has been queued for background execution",
-    {
+  "status": "PENDING",
-      "memory": "Name is Alex",
+  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
-      "event": "ADD"
-    },
-    {
-      "memory": "Is a vegetarian", 
-      "event": "ADD"
-    },
-    {
-      "memory": "Is allergic to nuts",
-      "event": "ADD"
-    }
-  ]
 }
 ```
 </CodeGroup>
@@ -173,23 +162,25 @@ result = search_tool.invoke(search_input)
 ```
 
 ```json Output
-[
+{
-  {
+  "results": [
-    "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
+    {
-    "memory": "Name is Alex",
+      "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
-    "user_id": "alex", 
+      "memory": "Name is Alex",
-    "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+      "user_id": "alex",
-    "metadata": {
+      "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
-      "food": "vegan"
+      "metadata": {
-    },
+        "food": "vegan"
-    "categories": [
+      },
-      "personal_details"
+      "categories": [
-    ],
+        "personal_details"
-    "created_at": "2024-11-27T16:53:43.276872-08:00",
+      ],
-    "updated_at": "2024-11-27T16:53:43.276885-08:00",
+      "created_at": "2024-11-27T16:53:43.276872-08:00",
-    "score": 0.3810526501504994
+      "updated_at": "2024-11-27T16:53:43.276885-08:00",
-  }
+      "score": 0.3810526501504994
-]
+    }
+  ]
+}
 ```
 </CodeGroup>
 

docs/integrations/langgraph.mdx

@@ -41,7 +41,7 @@ load_dotenv()
 # MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key
 
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-4")
+llm = ChatOpenAI(model="gpt-5-mini")
 mem0 = MemoryClient()
 ```
 

docs/integrations/pipecat.mdx

@@ -121,7 +121,7 @@ async def websocket_endpoint(websocket: WebSocket):
     # LLM for response generation
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-3.5-turbo",
+        model="gpt-5-mini",
         system_prompt="You are a helpful assistant that remembers past conversations."
     )
     

docs/integrations/vercel-ai-sdk.mdx

@@ -26,12 +26,12 @@ Install the SDK provider and AI SDK:
 npm install @mem0/vercel-ai-provider ai@^6
 ```
 
-### Peer Dependencies
+### Dependencies
 
-`@mem0/vercel-ai-provider` v3.0.0 requires:
+`@mem0/vercel-ai-provider` bundles `ai`, all `@ai-sdk/*` provider packages, and `@ai-sdk/provider` as regular dependencies — you do **not** need to install them separately. The install command above (`npm install @mem0/vercel-ai-provider ai@^6`) is sufficient.
-- `ai` v6+ (`^6.0.199`)
+
-- `@ai-sdk/provider` v3+ (`^3.0.10`)
+The only true peer dependency is `zod` (optional):
-- Provider packages at v3+: `@ai-sdk/openai@^3`, `@ai-sdk/anthropic@^3`, `@ai-sdk/google@^3`, `@ai-sdk/groq@^3`, `@ai-sdk/cohere@^3`
+- `zod` v3+ (`^3.0.0`) — required only if you use Zod schemas in tool definitions
 
 ## Getting Started
 
@@ -305,6 +305,8 @@ These options can be passed per-request when creating a model instance:
 | `rerank` | `boolean` | Enable reranking of results |
 | `page` | `number` | Page number for pagination |
 | `page_size` | `number` | Results per page |
+| `mem0ApiKey` | `string` | Mem0 API key; overrides the `MEM0_API_KEY` env var |
+| `host` | `string` | Custom Mem0 API base URL for self-hosted deployments |
 
 ## Key Features
 
@@ -312,6 +314,7 @@ These options can be passed per-request when creating a model instance:
 - `retrieveMemories()`: Retrieves memory context for prompts as a formatted system prompt string.
 - `getMemories()`: Get memories from your profile in array format.
 - `addMemories()`: Adds user memories to enhance contextual responses.
+- `searchMemories()`: Searches memories and returns the raw results array (semantic search rather than the full retrieval pipeline).
 
 ## Migrating from v2.x
 

docs/llms.txt

@@ -367,6 +367,8 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
 - [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
 - [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
+- [Update Organization Member](https://docs.mem0.ai/api-reference/organization/update-org-member) [Platform]: Use when updating an org member's role.
+- [Remove Organization Member](https://docs.mem0.ai/api-reference/organization/remove-org-member) [Platform]: Use when removing a member from an organization.
 - [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
 
 ### Projects
@@ -375,6 +377,9 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
 - [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
 - [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
+- [Update Project](https://docs.mem0.ai/api-reference/project/update-project) [Platform]: Use when updating project settings.
+- [Update Project Member](https://docs.mem0.ai/api-reference/project/update-project-member) [Platform]: Use when updating a project member's role.
+- [Remove Project Member](https://docs.mem0.ai/api-reference/project/remove-project-member) [Platform]: Use when removing a member from a project.
 - [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
 
 ### Webhooks
@@ -460,6 +465,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
 - [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
 - [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
+- [FastEmbed](https://docs.mem0.ai/components/embedders/models/fastembed) [OSS]: Use when embeddings run locally via FastEmbed (ONNX).
 
 ### Vector Databases [OSS]
 - [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
@@ -498,7 +504,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
 - [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
 - [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
-- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
-- [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
-- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
 - [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.

docs/migration/oss-v2-to-v3.mdx

@@ -62,7 +62,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `MemoryClient(api_key, org_id, project_id)` | `MemoryClient(api_key)` | Remove `org_id`, `project_id` from constructor |
 | Method options | `client.add(messages, **kwargs)` | `client.add(messages, options=AddMemoryOptions(...))` | Use typed option classes (or `**kwargs` still works) |
-| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `expiration_date`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
 
 ### TypeScript Client SDK
 
@@ -70,7 +70,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `new MemoryClient({ apiKey, organizationId, projectId })` | `new MemoryClient({ apiKey })` | Remove `organizationId`, `projectId`, `organizationName`, `projectName` |
 | All params | snake_case: `user_id`, `agent_id`, `top_k` | camelCase: `userId`, `agentId`, `topK` | Rename all params to camelCase |
-| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `expiration_date`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
 | Output format enum | `OutputFormat.V1`, `OutputFormat.V1_1` | Removed | v1.1 is now always used |
 | API version enum | `API_VERSION.V1`, `API_VERSION.V2` | Removed | Handled internally |
 
@@ -423,7 +423,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 
 **All methods:** `api_version`, `output_format`, `async_mode`, `org_name`, `project_name`, `org_id`, `project_id`
 
-**add():** `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
+**add():** `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
 
 **search():** `enable_graph`
 
@@ -437,7 +437,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 
 **All methods:** `OutputFormat` enum, `API_VERSION` enum
 
-**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `expiration_date` / `expirationDate`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
+**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
 
 **search():** `enable_graph` / `enableGraph`
 

docs/migration/platform-v2-to-v3.mdx

@@ -215,7 +215,7 @@ client.add(messages, user_id="alice")
 # async_mode and output_format removed (async by default, v1.1 always)
 ```
 
-**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
+**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
 
 ### TypeScript Client SDK
 
@@ -242,7 +242,7 @@ await client.search("query", {
 });
 ```
 
-**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
+**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
 
 <Info>
 For the full list of parameter changes across all SDKs, see the [OSS migration guide](/migration/oss-v2-to-v3#removed-parameters-reference).

docs/open-source/configuration.mdx

@@ -130,7 +130,7 @@ memory = Memory.from_config_file("config.yaml")
 </Tabs>
 
 <Info icon="check">
-  Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
+  Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
 </Info>
 
 ## Tune component settings

docs/open-source/features/async-memory.mdx

@@ -18,7 +18,7 @@ icon: "bolt"
 </Warning>
 
 <Note>
-  Working in TypeScript? The Node SDK still uses synchronous calls—use `Memory` there and rely on Python’s `AsyncMemory` when you need awaited operations.
+  Working in TypeScript? The OSS `Memory` class in the Node SDK (`mem0ai/oss`) is also fully async — every method returns a `Promise` and must be `await`ed. Python’s `AsyncMemory` serves the same purpose within Python async frameworks like FastAPI. Both runtimes support awaited memory operations; choose the SDK that matches your language.
 </Note>
 
 ## Feature anatomy

docs/open-source/features/custom-instructions.mdx

@@ -165,8 +165,7 @@ await memory.add("Yesterday, I ordered a laptop, the order id is 12345", { userI
     {"memory": "Ordered a laptop", "event": "ADD"},
     {"memory": "Order ID: 12345", "event": "ADD"},
     {"memory": "Order placed yesterday", "event": "ADD"}
-  ],
+  ]
-  "relations": []
 }
 ```
 </CodeGroup>
@@ -188,8 +187,7 @@ await memory.add("I like going to hikes", { userId: "user123" });
 
 ```json Output
 {
-  "results": [],
+  "results": []
-  "relations": []
 }
 ```
 </CodeGroup>

docs/open-source/features/multimodal-support.mdx

@@ -41,6 +41,14 @@ Multimodal support lets Mem0 extract facts from images alongside regular text. A
 
 ## Configure it
 
+<Warning>
+  You must set `enable_vision: True` in your LLM config for image content to be processed. Without it, image turns are silently dropped and no vision memories are created. Example:
+  ```python
+  config = {"llm": {"provider": "openai", "config": {"enable_vision": True, "vision_details": "auto"}}}
+  client = Memory.from_config(config)
+  ```
+</Warning>
+
 ### Add image messages from URLs
 
 <CodeGroup>
@@ -66,7 +74,7 @@ client.add(messages, user_id="alice")
 ```
 
 ```ts TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 const client = new Memory();
 
@@ -123,7 +131,7 @@ client.add(messages, user_id="alice")
 
 ```ts TypeScript
 import fs from "fs";
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 function encodeImage(imagePath: string) {
   const buffer = fs.readFileSync(imagePath);
@@ -226,7 +234,7 @@ client.add(messages, user_id="user123")
 <CodeGroup>
 ```python Python
 from mem0 import Memory
-from mem0.exceptions import InvalidImageError, FileSizeError
+from mem0.exceptions import ValidationError
 
 client = Memory()
 
@@ -242,16 +250,14 @@ try:
     client.add(messages, user_id="user123")
     print("Image processed successfully")
 
-except InvalidImageError:
+except ValidationError as exc:
-    print("Invalid image format or corrupted file")
+    print(f"Image validation error: {exc}")
-except FileSizeError:
-    print("Image file too large")
 except Exception as exc:
     print(f"Unexpected error: {exc}")
 ```
 
 ```ts TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 const client = new Memory();
 

docs/open-source/features/reranker-search.mdx

@@ -124,7 +124,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key",
             "top_k": 5
         }
@@ -150,7 +150,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key"
         }
     },

docs/openapi.json

@@ -1779,6 +1779,11 @@
 										"type": "object",
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`).",
 										"additionalProperties": true
+									},
+									"show_expired": {
+										"type": "boolean",
+										"default": false,
+										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
 									}
 								}
 							},
@@ -1977,6 +1982,12 @@
 										"additionalProperties": true,
 										"description": "User-supplied metadata to attach to each extracted memory."
 									},
+									"expiration_date": {
+										"type": "string",
+										"format": "date",
+										"nullable": true,
+										"description": "Optional expiration date in YYYY-MM-DD format. After this date, memories are hidden from search and get-all unless `show_expired` is true."
+									},
 									"custom_instructions": {
 										"type": "string",
 										"description": "Project-level instructions that guide extraction for this call."
@@ -2094,6 +2105,11 @@
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`). Supports `AND`, `OR`, `NOT`, and comparison operators (`in`, `gte`, `lte`, `gt`, `lt`, `contains`, `icontains`, `ne`).",
 										"additionalProperties": true
 									},
+									"show_expired": {
+										"type": "boolean",
+										"default": false,
+										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
+									},
 									"top_k": {
 										"type": "integer",
 										"minimum": 1,
@@ -2432,6 +2448,12 @@
 									"metadata": {
 										"type": "object",
 										"description": "Additional metadata associated with the memory"
+									},
+									"expiration_date": {
+										"type": "string",
+										"format": "date",
+										"nullable": true,
+										"description": "Expiration date in YYYY-MM-DD format, or null to clear the expiration date."
 									}
 								}
 							}

integrations/openclaw/package.json

@@ -71,7 +71,8 @@
       "picomatch@<2.3.2": "^2.3.2",
       "@qdrant/js-client-rest": "^1.18.0",
       "uuid@<11.1.1": ">=11.1.1",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0"
     }
   }
 }

integrations/openclaw/pnpm-lock.yaml

@@ -13,6 +13,7 @@ overrides:
   '@qdrant/js-client-rest': ^1.18.0
   uuid@<11.1.1: '>=11.1.1'
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
 
 importers:
 
@@ -1996,9 +1997,9 @@ packages:
   undici-types@6.21.0:
     resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
 
-  undici@6.26.0:
+  undici@7.28.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
-    engines: {node: '>=18.17'}
+    engines: {node: '>=20.18.1'}
 
   util-deprecate@1.0.2:
     resolution: {integrity: sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==}
@@ -2509,7 +2510,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 5.9.3
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -4063,7 +4064,7 @@ snapshots:
 
   undici-types@6.21.0: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   util-deprecate@1.0.2: {}
 

integrations/openclaw/pnpm-workspace.yaml

@@ -21,3 +21,4 @@ overrides:
   "@qdrant/js-client-rest": "^1.18.0"
   "uuid@<11.1.1": ">=11.1.1"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"

integrations/openclaw/skill-loader.test.ts

@@ -4,10 +4,12 @@
 import { describe, it, expect } from "vitest";
 import {
   safePath,
+  normalizeModuleUrlToPath,
   loadSkill,
   loadTriagePrompt,
   loadCompactTriagePrompt,
 } from "./skill-loader.ts";
+import { fileURLToPath, pathToFileURL } from "node:url";
 
 // ---------------------------------------------------------------------------
 // safePath — path containment
@@ -74,6 +76,31 @@ describe("loadSkill path traversal", () => {
   });
 });
 
+describe("normalizeModuleUrlToPath", () => {
+  it("normalizes raw Windows paths before fileURLToPath conversion", () => {
+    const rawWindowsMetaUrl = "C:\\Users\\example\\openclaw\\index.ts";
+    const result = normalizeModuleUrlToPath(rawWindowsMetaUrl);
+    // Assert the decoded property directly rather than reconstructing via the function body
+    expect(typeof result).toBe("string");
+    expect(result).not.toContain("%5C");
+  });
+
+  it("leaves already-correct file URLs unchanged", () => {
+    const fileMetaUrl = "file:///C:/Users/example/openclaw/index.ts";
+    const expected = fileURLToPath(fileMetaUrl);
+
+    expect(normalizeModuleUrlToPath(fileMetaUrl)).toBe(expected);
+  });
+
+  it.skipIf(process.platform === "win32")(
+    "passes POSIX absolute paths through unchanged",
+    () => {
+      const posixPath = "/home/user/openclaw/index.ts";
+      expect(normalizeModuleUrlToPath(posixPath)).toBe(posixPath);
+    },
+  );
+});
+
 describe("loadCompactTriagePrompt", () => {
   it("keeps the core triage instructions without inlining the full skill body", () => {
     const prompt = loadCompactTriagePrompt();

integrations/openclaw/skill-loader.ts

@@ -4,7 +4,7 @@
  */
 
 import * as path from "node:path";
-import { fileURLToPath } from "node:url";
+import { fileURLToPath, pathToFileURL } from "node:url";
 import type { SkillsConfig, CategoryConfig } from "./types.ts";
 import { readText, exists } from "./fs-safe.ts";
 
@@ -84,6 +84,14 @@ function parseSkillFile(content: string): ParsedSkill {
   };
 }
 
+/** @internal — exported for testing only */
+export function normalizeModuleUrlToPath(moduleUrl: string): string {
+  const normalizedUrl = moduleUrl.startsWith("file:")
+    ? moduleUrl
+    : pathToFileURL(moduleUrl).toString();
+  return fileURLToPath(normalizedUrl);
+}
+
 // ============================================================================
 // Skill Loader
 // ============================================================================
@@ -96,7 +104,7 @@ function resolveSkillsDir(): string {
 
   // Strategy 1: import.meta.url (works in native ESM)
   try {
-    const metaDir = path.dirname(fileURLToPath(import.meta.url));
+    const metaDir = path.dirname(normalizeModuleUrlToPath(import.meta.url));
     candidates.push(path.join(metaDir, "skills"));
     candidates.push(path.join(metaDir, "..", "skills"));
   } catch {

integrations/pi-agent-plugin/package.json

@@ -74,6 +74,7 @@
       "form-data@<4.0.6": ">=4.0.6",
       "uuid@<11.1.1": ">=11.1.1",
       "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0",
       "undici@>=8.0.0 <8.5.0": ">=8.5.0"
     }
   }

integrations/pi-agent-plugin/pnpm-lock.yaml

@@ -8,6 +8,7 @@ overrides:
   form-data@<4.0.6: '>=4.0.6'
   uuid@<11.1.1: '>=11.1.1'
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
   undici@>=8.0.0 <8.5.0: '>=8.5.0'
 
 importers:
@@ -2351,9 +2352,9 @@ packages:
   undici-types@7.24.6:
     resolution: {integrity: sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg==}
 
-  undici@6.26.0:
+  undici@7.28.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
-    engines: {node: '>=18.17'}
+    engines: {node: '>=20.18.1'}
 
   undici@8.5.0:
     resolution: {integrity: sha512-xamtWoB1EshgjpmlXd7GGm2VfdDtw1+rD8uhry8pSNW3If6S8E0m2T2+orSKeZXEn/aPJMviCpDBA65WJt8zhg==}
@@ -3202,7 +3203,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 6.0.3
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -4894,7 +4895,7 @@ snapshots:
 
   undici-types@7.24.6: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   undici@8.5.0: {}
 

integrations/pi-agent-plugin/pnpm-workspace.yaml

@@ -5,4 +5,5 @@ overrides:
   "form-data@<4.0.6": ">=4.0.6"
   "uuid@<11.1.1": ">=11.1.1"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"
   "undici@>=8.0.0 <8.5.0": ">=8.5.0"

mem0-ts/package.json

@@ -156,7 +156,8 @@
       "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
       "glob@>=10.2.0 <10.5.0": "^10.5.0",
       "@modelcontextprotocol/sdk": "^1.25.4",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0"
     }
   }
 }

mem0-ts/pnpm-lock.yaml

@@ -23,6 +23,7 @@ overrides:
   glob@>=10.2.0 <10.5.0: ^10.5.0
   '@modelcontextprotocol/sdk': ^1.25.4
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
 
 importers:
 
@@ -2925,9 +2926,9 @@ packages:
   undici-types@6.21.0:
     resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
 
-  undici@6.26.0:
+  undici@7.28.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
-    engines: {node: '>=18.17'}
+    engines: {node: '>=20.18.1'}
 
   update-browserslist-db@1.2.3:
     resolution: {integrity: sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==}
@@ -3747,7 +3748,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 5.5.4
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -6113,7 +6114,7 @@ snapshots:
 
   undici-types@6.21.0: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   update-browserslist-db@1.2.3(browserslist@4.28.2):
     dependencies:

mem0-ts/pnpm-workspace.yaml

@@ -24,3 +24,4 @@ overrides:
   "glob@>=10.2.0 <10.5.0": "^10.5.0"
   "@modelcontextprotocol/sdk": "^1.25.4"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"

mem0-ts/src/client/mem0.ts

@@ -281,19 +281,22 @@ export default class MemoryClient {
       text,
       metadata,
       timestamp,
+      expirationDate,
     }: {
       text?: string;
       metadata?: Record<string, any>;
       timestamp?: number | string;
+      expirationDate?: string | null;
     },
   ): Promise<Array<Memory>> {
     if (
       text === undefined &&
       metadata === undefined &&
-      timestamp === undefined
+      timestamp === undefined &&
+      expirationDate === undefined
     ) {
       throw new Error(
-        "At least one of text, metadata, or timestamp must be provided for update.",
+        "At least one of text, metadata, timestamp, or expirationDate must be provided for update.",
       );
     }
 
@@ -302,6 +305,7 @@ export default class MemoryClient {
     if (text !== undefined) payload.text = text;
     if (metadata !== undefined) payload.metadata = metadata;
     if (timestamp !== undefined) payload.timestamp = timestamp;
+    if (expirationDate !== undefined) payload.expiration_date = expirationDate;
 
     const payloadKeys = Object.keys(payload);
     this._captureEvent("update", [payloadKeys]);

mem0-ts/src/client/mem0.types.ts

@@ -13,6 +13,7 @@ export interface AddMemoryOptions extends EntityOptions {
   customCategories?: custom_categories[];
   customInstructions?: string;
   timestamp?: number;
+  expirationDate?: string;
   structuredDataSchema?: Record<string, any>;
 }
 
@@ -25,6 +26,7 @@ export interface SearchMemoryOptions {
   latestOnly?: boolean;
   fields?: string[];
   categories?: string[];
+  showExpired?: boolean;
 }
 
 export interface GetAllMemoryOptions {
@@ -35,6 +37,7 @@ export interface GetAllMemoryOptions {
   endDate?: string;
   latestOnly?: boolean;
   categories?: string[];
+  showExpired?: boolean;
 }
 
 export interface DeleteAllMemoryOptions extends EntityOptions {}
@@ -119,6 +122,7 @@ export interface Memory {
   memoryType?: string;
   score?: number;
   metadata?: any | null;
+  expirationDate?: string | null;
   owner?: string | null;
   agentId?: string | null;
   appId?: string | null;

mem0-ts/src/client/tests/memoryClient.crud.test.ts

@@ -59,6 +59,21 @@ describe("MemoryClient - add()", () => {
     expect(getFetchBody(call!).user_id).toBe("user_1");
   });
 
+  test("serializes expirationDate as expiration_date", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/add/", { status: 200, body: [createMockMemory()] });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.add([{ role: "user", content: "test" }], {
+      userId: "u1",
+      expirationDate: "2030-01-31",
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
+    expect(getFetchBody(call!).expiration_date).toBe("2030-01-31");
+  });
+
   test("throws an error when given an empty messages array", async () => {
     setupMockFetch();
 
@@ -176,11 +191,26 @@ describe("MemoryClient - update()", () => {
     expect(body.timestamp).toBe(1710600000);
   });
 
+  test("sends expirationDate as expiration_date, including null", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v1/memories/mem_123/", {
+      status: 200,
+      body: createMockMemory(),
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.update("mem_123", { expirationDate: null });
+
+    const call = findFetchCall(mock, "/v1/memories/mem_123/", "PUT");
+    expect(getFetchBody(call!).expiration_date).toBeNull();
+  });
+
   test("throws when no fields provided", async () => {
     setupMockFetch();
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await expect(client.update("mem_123", {})).rejects.toThrow(
-      "At least one of text, metadata, or timestamp must be provided",
+      "At least one of text, metadata, timestamp, or expirationDate must be provided",
     );
   });
 });

mem0-ts/src/client/tests/memoryClient.search.test.ts

@@ -81,6 +81,24 @@ describe("MemoryClient - search()", () => {
     expect(getFetchBody(call!).latest_only).toBe(true);
   });
 
+  test("serializes showExpired as show_expired", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/search/", {
+      status: 200,
+      body: { results: [] },
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.search("test", {
+      filters: { user_id: "u1" },
+      showExpired: true,
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/search/", "POST");
+    expect(getFetchBody(call!).show_expired).toBe(true);
+  });
+
   test("passes complex OR filters through to the API body", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
     extra.set("/v3/memories/search/", {
@@ -300,4 +318,22 @@ describe("MemoryClient - getAll() entity param rejection", () => {
     const call = findFetchCall(mock, "/v3/memories/", "POST");
     expect(getFetchBody(call!).latest_only).toBe(true);
   });
+
+  test("serializes showExpired as show_expired", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/", {
+      status: 200,
+      body: { results: [] },
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.getAll({
+      filters: { user_id: "u1" },
+      showExpired: true,
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/", "POST");
+    expect(getFetchBody(call!).show_expired).toBe(true);
+  });
 });

mem0-ts/src/oss/src/index.ts

@@ -18,6 +18,7 @@ export * from "./llms/ollama";
 export * from "./llms/lmstudio";
 export * from "./llms/mistral";
 export * from "./llms/langchain";
+export * from "./llms/litellm";
 export * from "./vector_stores/base";
 export * from "./vector_stores/memory";
 export * from "./vector_stores/qdrant";

mem0-ts/src/oss/src/llms/litellm.ts

@@ -0,0 +1,39 @@
+import { OpenAILLM } from "./openai";
+import { LLMConfig, Message } from "../types";
+import { LLMResponse } from "./base";
+
+export class LiteLLM extends OpenAILLM {
+  constructor(config: LLMConfig) {
+    super({
+      ...config,
+      apiKey: config.apiKey || process.env.LITELLM_API_KEY || "sk-anything",
+      baseURL:
+        config.baseURL ||
+        process.env.LITELLM_API_BASE ||
+        "http://localhost:4000",
+      model: config.model || "gpt-5-mini",
+    });
+  }
+
+  async generateResponse(
+    messages: Message[],
+    responseFormat?: { type: string },
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
+    try {
+      return await super.generateResponse(messages, responseFormat, tools);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`LiteLLM failed: ${message}`);
+    }
+  }
+
+  async generateChat(messages: Message[]): Promise<LLMResponse> {
+    try {
+      return await super.generateChat(messages);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`LiteLLM failed: ${message}`);
+    }
+  }
+}

mem0-ts/src/oss/src/llms/minimax.ts

@@ -0,0 +1,43 @@
+import { OpenAILLM } from "./openai";
+import { LLMConfig, Message } from "../types";
+import { LLMResponse } from "./base";
+
+export class MiniMaxLLM extends OpenAILLM {
+  constructor(config: LLMConfig) {
+    const apiKey = config.apiKey || process.env.MINIMAX_API_KEY;
+    if (!apiKey) {
+      throw new Error("MiniMax API key is required");
+    }
+    super({
+      ...config,
+      apiKey,
+      baseURL:
+        config.baseURL ||
+        process.env.MINIMAX_API_BASE ||
+        "https://api.minimax.io/v1",
+      model: config.model || "MiniMax-M2.7",
+    });
+  }
+
+  async generateResponse(
+    messages: Message[],
+    responseFormat?: { type: string },
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
+    try {
+      return await super.generateResponse(messages, responseFormat, tools);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`MiniMax LLM failed: ${message}`);
+    }
+  }
+
+  async generateChat(messages: Message[]): Promise<LLMResponse> {
+    try {
+      return await super.generateChat(messages);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`MiniMax LLM failed: ${message}`);
+    }
+  }
+}

mem0-ts/src/oss/src/memory/index.ts

@@ -1601,7 +1601,9 @@ export class Memory {
       has_agent_id: !!config.agentId,
       has_run_id: !!config.runId,
     });
-    const { userId, agentId, runId } = config;
+    const userId = validateAndTrimEntityId(config.userId, "userId");
+    const agentId = validateAndTrimEntityId(config.agentId, "agentId");
+    const runId = validateAndTrimEntityId(config.runId, "runId");
 
     // Convert camelCase entity params to snake_case for filters (matches storage and search/getAll)
     const filters: SearchFilters = {};

mem0-ts/src/oss/src/utils/factory.ts

@@ -22,6 +22,8 @@ import { RedisDB } from "../vector_stores/redis";
 import { OllamaLLM } from "../llms/ollama";
 import { LMStudioLLM } from "../llms/lmstudio";
 import { DeepSeekLLM } from "../llms/deepseek";
+import { LiteLLM } from "../llms/litellm";
+import { MiniMaxLLM } from "../llms/minimax";
 import { SupabaseDB } from "../vector_stores/supabase";
 import { SQLiteManager } from "../storage/SQLiteManager";
 import { MemoryHistoryManager } from "../storage/MemoryHistoryManager";
@@ -85,6 +87,10 @@ export class LLMFactory {
         return new LangchainLLM(config);
       case "deepseek":
         return new DeepSeekLLM(config);
+      case "litellm":
+        return new LiteLLM(config);
+      case "minimax":
+        return new MiniMaxLLM(config);
       default:
         throw new Error(`Unsupported LLM provider: ${provider}`);
     }

mem0-ts/src/oss/src/vector_stores/pgvector.ts

@@ -1,4 +1,4 @@
-import type { Client as ClientType } from "pg";
+import type { Client as ClientType, ClientConfig } from "pg";
 import pkg from "pg";
 const { Client, escapeIdentifier } = pkg;
 import { VectorStore } from "./base";
@@ -157,41 +157,90 @@ export function buildFilterConditions(
 
 interface PGVectorConfig extends VectorStoreConfig {
   dbname?: string;
-  user: string;
+  user?: string;
-  password: string;
+  password?: string;
-  host: string;
+  host?: string;
-  port: number;
+  port?: number;
+  connectionString?: string;
+  ssl?: ClientConfig["ssl"];
   embeddingModelDims: number;
   diskann?: boolean;
   hnsw?: boolean;
 }
 
+function getConnectionString(config: PGVectorConfig): string | undefined {
+  return config.connectionString?.trim() || undefined;
+}
+
+function validateConnectionConfig(config: PGVectorConfig): void {
+  if (getConnectionString(config)) {
+    return;
+  }
+
+  const missingFields = ["user", "password", "host", "port"].filter((field) => {
+    const v = config[field as keyof PGVectorConfig];
+    return v === undefined || v === null || v === "";
+  });
+
+  if (missingFields.length > 0) {
+    throw new Error(
+      `PGVector requires either connectionString or ${missingFields.join(", ")}`,
+    );
+  }
+}
+
+function buildClientConfig(
+  config: PGVectorConfig,
+  database?: string,
+): ClientConfig {
+  const connectionString = getConnectionString(config);
+  if (connectionString) {
+    return {
+      connectionString,
+      ...(config.ssl !== undefined ? { ssl: config.ssl } : {}),
+    };
+  }
+
+  return {
+    database,
+    user: config.user,
+    password: config.password,
+    host: config.host,
+    port: config.port,
+    ...(config.ssl !== undefined ? { ssl: config.ssl } : {}),
+  };
+}
+
 export class PGVector implements VectorStore {
   private client: ClientType;
   private collectionName: string;
   private useDiskann: boolean;
   private useHnsw: boolean;
   private readonly dbName: string;
+  private readonly useDirectConnection: boolean;
   private config: PGVectorConfig;
   private _initPromise?: Promise<void>;
 
   constructor(config: PGVectorConfig) {
+    validateConnectionConfig(config);
     this.collectionName = validateIdentifier(
       config.collectionName || "memories",
       "collectionName",
     );
     this.useDiskann = config.diskann || false;
     this.useHnsw = config.hnsw || false;
-    this.dbName = validateIdentifier(config.dbname || "vector_store", "dbname");
+    this.useDirectConnection = !!getConnectionString(config);
+    this.dbName = this.useDirectConnection
+      ? ""
+      : validateIdentifier(config.dbname || "vector_store", "dbname");
     this.config = config;
 
-    this.client = new Client({
+    this.client = new Client(
-      database: "postgres", // Initially connect to default postgres database
+      buildClientConfig(
-      user: config.user,
+        config,
-      password: config.password,
+        this.useDirectConnection ? undefined : "postgres",
-      host: config.host,
+      ),
-      port: config.port,
+    );
-    });
     this.initialize().catch(console.error);
   }
 
@@ -210,29 +259,20 @@ export class PGVector implements VectorStore {
     try {
       await this.client.connect();
 
-      // Check if database exists
+      if (!this.useDirectConnection) {
-      const dbExists = await this.checkDatabaseExists(this.dbName);
+        const dbExists = await this.checkDatabaseExists(this.dbName);
-      if (!dbExists) {
+        if (!dbExists) {
-        await this.createDatabase(this.dbName);
+          await this.createDatabase(this.dbName);
+        }
+
+        await this.client.end();
+
+        this.client = new Client(buildClientConfig(this.config, this.dbName));
+        await this.client.connect();
       }
 
-      // Disconnect from postgres database
-      await this.client.end();
-
-      // Connect to the target database
-      this.client = new Client({
-        database: this.dbName,
-        user: this.config.user,
-        password: this.config.password,
-        host: this.config.host,
-        port: this.config.port,
-      });
-      await this.client.connect();
-
-      // Create vector extension
       await this.client.query("CREATE EXTENSION IF NOT EXISTS vector");
 
-      // Create memory_migrations table
       await this.client.query(`
         CREATE TABLE IF NOT EXISTS memory_migrations (
           id SERIAL PRIMARY KEY,
@@ -240,7 +280,6 @@ export class PGVector implements VectorStore {
         )
       `);
 
-      // Check if the collection exists
       const collections = await this.listCols();
       if (!collections.includes(this.collectionName)) {
         await this.createCol(this.config.embeddingModelDims);

mem0-ts/src/oss/tests/factory.unit.test.ts

@@ -92,6 +92,16 @@ jest.mock("../src/llms/deepseek", () => ({
     .fn()
     .mockImplementation((config) => ({ type: "deepseek-llm", config })),
 }));
+jest.mock("../src/llms/litellm", () => ({
+  LiteLLM: jest
+    .fn()
+    .mockImplementation((config) => ({ type: "litellm-llm", config })),
+}));
+jest.mock("../src/llms/minimax", () => ({
+  MiniMaxLLM: jest
+    .fn()
+    .mockImplementation((config) => ({ type: "minimax-llm", config })),
+}));
 
 jest.mock("../src/vector_stores/qdrant", () => ({
   Qdrant: jest
@@ -206,6 +216,8 @@ describe("LLMFactory", () => {
     ["langchain"],
     ["lmstudio"],
     ["deepseek"],
+    ["litellm"],
+    ["minimax"],
   ])("creates LLM for provider '%s'", (provider) => {
     expect(() => LLMFactory.create(provider, dummyLLMConfig)).not.toThrow();
   });

mem0-ts/src/oss/tests/litellm.test.ts

@@ -0,0 +1,131 @@
+/// <reference types="jest" />
+/**
+ * LiteLLM — unit tests (mocked OpenAI).
+ */
+
+import { LiteLLM } from "../src/llms/litellm";
+
+const mockCreate = jest.fn();
+
+jest.mock("openai", () => {
+  return jest.fn().mockImplementation(() => ({
+    chat: { completions: { create: mockCreate } },
+  }));
+});
+
+describe("LiteLLM (unit)", () => {
+  beforeEach(() => mockCreate.mockClear());
+
+  it("uses default baseURL when none is provided", () => {
+    const llm = new LiteLLM({});
+    expect(llm).toBeDefined();
+  });
+
+  it("generateResponse() returns a text response", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "Hello, world!",
+            role: "assistant",
+            tool_calls: null,
+          },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({ baseURL: "http://localhost:4000" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hi" },
+    ]);
+
+    expect(mockCreate).toHaveBeenCalledTimes(1);
+    expect(result).toBe("Hello, world!");
+  });
+
+  it("generateResponse() handles tool calls", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "",
+            role: "assistant",
+            tool_calls: [
+              {
+                function: {
+                  name: "get_weather",
+                  arguments: '{"city": "London"}',
+                },
+              },
+            ],
+          },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({});
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "What is the weather?" }],
+      undefined,
+      [{ type: "function", function: { name: "get_weather" } }],
+    );
+
+    expect(result).toEqual({
+      content: "",
+      role: "assistant",
+      toolCalls: [{ name: "get_weather", arguments: '{"city": "London"}' }],
+    });
+  });
+
+  it("generateResponse() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Connection refused"));
+
+    const llm = new LiteLLM({});
+
+    await expect(
+      llm.generateResponse([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("LiteLLM failed: Connection refused");
+  });
+
+  it("generateChat() returns LLMResponse shape", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: { content: "I can help with that.", role: "assistant" },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({});
+    const result = await llm.generateChat([
+      { role: "user", content: "Help me" },
+    ]);
+
+    expect(result).toEqual({
+      content: "I can help with that.",
+      role: "assistant",
+    });
+  });
+
+  it("generateChat() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Timeout"));
+
+    const llm = new LiteLLM({});
+
+    await expect(
+      llm.generateChat([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("LiteLLM failed: Timeout");
+  });
+
+  it("respects LITELLM_API_BASE env var", () => {
+    const original = process.env.LITELLM_API_BASE;
+    process.env.LITELLM_API_BASE = "http://custom-proxy:8080";
+    try {
+      const llm = new LiteLLM({});
+      expect(llm).toBeDefined();
+    } finally {
+      if (original !== undefined) process.env.LITELLM_API_BASE = original;
+      else delete process.env.LITELLM_API_BASE;
+    }
+  });
+});

mem0-ts/src/oss/tests/memory.validation.test.ts

@@ -314,4 +314,27 @@ describe("Memory Input Validation", () => {
       expect(result.results).toBeDefined();
     });
   });
+
+  describe("deleteAll() entity ID validation", () => {
+    it("should throw error when userId is whitespace-only", async () => {
+      await expect(memory.deleteAll({ userId: "   " })).rejects.toThrow(
+        "Invalid userId",
+      );
+    });
+
+    it("should throw error when userId contains internal whitespace", async () => {
+      await expect(memory.deleteAll({ userId: "user 123" })).rejects.toThrow(
+        "Invalid userId: cannot contain whitespace",
+      );
+    });
+
+    it("should trim userId before listing memories", async () => {
+      const listSpy = jest.spyOn(memory["vectorStore"], "list");
+      listSpy.mockResolvedValue([[], null]);
+
+      await memory.deleteAll({ userId: "  alice  " });
+
+      expect(listSpy).toHaveBeenCalledWith({ user_id: "alice" });
+    });
+  });
 });

mem0-ts/src/oss/tests/minimax.test.ts

@@ -0,0 +1,166 @@
+/// <reference types="jest" />
+/**
+ * MiniMax LLM - unit tests (mocked OpenAI).
+ */
+
+let capturedConstructorArgs: any;
+const mockCreate = jest.fn();
+
+jest.mock("openai", () => {
+  return jest.fn().mockImplementation((args: any) => {
+    capturedConstructorArgs = args;
+    return {
+      chat: { completions: { create: mockCreate } },
+    };
+  });
+});
+
+import { MiniMaxLLM } from "../src/llms/minimax";
+
+describe("MiniMaxLLM (unit)", () => {
+  beforeEach(() => {
+    capturedConstructorArgs = undefined;
+    mockCreate.mockClear();
+    delete process.env.MINIMAX_API_KEY;
+    delete process.env.MINIMAX_API_BASE;
+  });
+
+  it("throws when no API key is provided", () => {
+    expect(() => new MiniMaxLLM({})).toThrow("MiniMax API key is required");
+  });
+
+  it("uses MiniMax defaults with an explicit API key", () => {
+    new MiniMaxLLM({ apiKey: "test-key" });
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "test-key",
+      baseURL: "https://api.minimax.io/v1",
+    });
+  });
+
+  it("uses environment variables when config does not provide credentials", () => {
+    process.env.MINIMAX_API_KEY = "env-key";
+    process.env.MINIMAX_API_BASE = "https://example.minimax.test/v1";
+
+    new MiniMaxLLM({});
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "env-key",
+      baseURL: "https://example.minimax.test/v1",
+    });
+  });
+
+  it("config values take precedence over environment variables", () => {
+    process.env.MINIMAX_API_KEY = "env-key";
+    process.env.MINIMAX_API_BASE = "https://env.minimax.test/v1";
+
+    new MiniMaxLLM({
+      apiKey: "config-key",
+      baseURL: "https://config.minimax.test/v1",
+      model: "MiniMax-M1",
+    });
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "config-key",
+      baseURL: "https://config.minimax.test/v1",
+    });
+  });
+
+  it("generateResponse() returns a text response", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "Hello from MiniMax",
+            role: "assistant",
+            tool_calls: null,
+          },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hi" },
+    ]);
+
+    expect(mockCreate).toHaveBeenCalledWith(
+      expect.objectContaining({ model: "MiniMax-M2.7" }),
+    );
+    expect(result).toBe("Hello from MiniMax");
+  });
+
+  it("generateResponse() handles tool calls", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "",
+            role: "assistant",
+            tool_calls: [
+              {
+                function: {
+                  name: "search_memory",
+                  arguments: '{"query": "alice"}',
+                },
+              },
+            ],
+          },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "Find Alice" }],
+      undefined,
+      [{ type: "function", function: { name: "search_memory" } }],
+    );
+
+    expect(result).toEqual({
+      content: "",
+      role: "assistant",
+      toolCalls: [{ name: "search_memory", arguments: '{"query": "alice"}' }],
+    });
+  });
+
+  it("generateResponse() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Connection refused"));
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+
+    await expect(
+      llm.generateResponse([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("MiniMax LLM failed: Connection refused");
+  });
+
+  it("generateChat() returns LLMResponse shape", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: { content: "I can help with that.", role: "assistant" },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateChat([
+      { role: "user", content: "Help me" },
+    ]);
+
+    expect(result).toEqual({
+      content: "I can help with that.",
+      role: "assistant",
+    });
+  });
+
+  it("generateChat() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Timeout"));
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+
+    await expect(
+      llm.generateChat([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("MiniMax LLM failed: Timeout");
+  });
+});

mem0-ts/src/oss/tests/pgvector.filters.test.ts

@@ -1,5 +1,3 @@
-/// <reference types="jest" />
-
 jest.mock("pg", () => {
   const Client = jest.fn().mockImplementation(() => ({
     connect: jest.fn().mockResolvedValue(undefined),

mem0-ts/src/oss/tests/pgvector.unit.test.ts

@@ -1,5 +1,3 @@
-/// <reference types="jest" />
-
 const searchRows = [
   {
     id: "a",
@@ -23,9 +21,13 @@ const searchRows = [
   },
 ];
 
+const mockState = {
+  databaseExists: true,
+};
+
 function mockPgQuery(sql: string) {
   if (sql.includes("SELECT 1 FROM pg_database")) {
-    return { rows: [{ "?column?": 1 }] };
+    return { rows: mockState.databaseExists ? [{ "?column?": 1 }] : [] };
   }
 
   if (sql.includes("FROM information_schema.tables")) {
@@ -69,13 +71,129 @@ jest.mock("pg", () => {
 
 import { PGVector } from "../src/vector_stores/pgvector";
 
-describe("PGVector - search()", () => {
+function getClientQueries(client: { query: jest.Mock }) {
+  return client.query.mock.calls.map(([sql]) => sql as string);
+}
+
+describe("PGVector", () => {
   beforeEach(() => {
     const pg = require("pg");
+    mockState.databaseExists = true;
     pg.__mock.Client.mockClear();
     pg.__mock.clients.length = 0;
   });
 
+  test("uses one direct client for connectionString mode and skips bootstrap database creation", async () => {
+    mockState.databaseExists = false;
+
+    const ssl = { rejectUnauthorized: false };
+    const store = new PGVector({
+      collectionName: "memories",
+      connectionString:
+        "postgresql://postgres:postgres@db.example.com:5432/neondb",
+      ssl,
+      embeddingModelDims: 3,
+      dimension: 3,
+    } as any);
+
+    await store.initialize();
+
+    const pg = require("pg");
+    expect(pg.__mock.Client).toHaveBeenCalledTimes(1);
+    expect(pg.__mock.Client).toHaveBeenCalledWith({
+      connectionString:
+        "postgresql://postgres:postgres@db.example.com:5432/neondb",
+      ssl,
+    });
+
+    const directClient = pg.__mock.clients[0];
+    const queries = getClientQueries(directClient);
+
+    expect(queries).not.toEqual(
+      expect.arrayContaining([
+        expect.stringContaining("SELECT 1 FROM pg_database"),
+      ]),
+    );
+    expect(queries).not.toEqual(
+      expect.arrayContaining([expect.stringContaining("CREATE DATABASE")]),
+    );
+    expect(queries).toEqual(
+      expect.arrayContaining([
+        "CREATE EXTENSION IF NOT EXISTS vector",
+        expect.stringContaining("FROM information_schema.tables"),
+      ]),
+    );
+  });
+
+  test("keeps the split-field bootstrap flow when connectionString is absent", async () => {
+    mockState.databaseExists = false;
+    const ssl = { rejectUnauthorized: false };
+
+    const store = new PGVector({
+      collectionName: "memories",
+      user: "postgres",
+      password: "postgres",
+      host: "localhost",
+      port: 5432,
+      dbname: "vector_store",
+      ssl,
+      embeddingModelDims: 3,
+      dimension: 3,
+    } as any);
+
+    await store.initialize();
+
+    const pg = require("pg");
+    expect(pg.__mock.Client).toHaveBeenCalledTimes(2);
+    expect(pg.__mock.Client).toHaveBeenNthCalledWith(1, {
+      database: "postgres",
+      user: "postgres",
+      password: "postgres",
+      host: "localhost",
+      port: 5432,
+      ssl,
+    });
+    expect(pg.__mock.Client).toHaveBeenNthCalledWith(2, {
+      database: "vector_store",
+      user: "postgres",
+      password: "postgres",
+      host: "localhost",
+      port: 5432,
+      ssl,
+    });
+
+    const bootstrapClient = pg.__mock.clients[0];
+    const activeClient = pg.__mock.clients[1];
+    const bootstrapQueries = getClientQueries(bootstrapClient);
+
+    expect(bootstrapQueries).toEqual(
+      expect.arrayContaining([
+        "SELECT 1 FROM pg_database WHERE datname = $1",
+        'CREATE DATABASE "vector_store"',
+      ]),
+    );
+    expect(bootstrapClient.end).toHaveBeenCalledTimes(1);
+    expect(getClientQueries(activeClient)).toEqual(
+      expect.arrayContaining([
+        "CREATE EXTENSION IF NOT EXISTS vector",
+        expect.stringContaining("FROM information_schema.tables"),
+      ]),
+    );
+  });
+
+  test("throws when connectionString is absent and split-field params are missing", () => {
+    expect(
+      () =>
+        new PGVector({
+          collectionName: "memories",
+          embeddingModelDims: 3,
+          dimension: 3,
+        } as any),
+    ).toThrow(
+      "PGVector requires either connectionString or user, password, host, port",
+    );
+  });
+
   test("returns similarity score (1 - distance) clamped to [0, 1]", async () => {
     const store = new PGVector({
       collectionName: "memories",

mem0/client/main.py

@@ -344,24 +344,26 @@ class MemoryClient:
         Args:
             memory_id: The ID of the memory to update.
             options: Typed options (UpdateMemoryOptions) with text, metadata,
-                     and/or timestamp fields.
+                     timestamp, and/or expiration_date fields.
-            **kwargs: Alternatively pass text, metadata, timestamp as keyword args.
+            **kwargs: Alternatively pass text, metadata, timestamp, or
+                      expiration_date as keyword args.
 
         Returns:
             Dict[str, Any]: The response from the server.
 
         Raises:
-            ValueError: If none of text, metadata, or timestamp are provided.
+            ValueError: If none of text, metadata, timestamp, or expiration_date are provided.
 
         Example:
             >>> client.update("mem_123", UpdateMemoryOptions(text="Updated text"))
             >>> client.update("mem_123", text="Updated text")
+            >>> client.update("mem_123", expiration_date=None)
         """
         payload = {**(options.model_dump(exclude_unset=True) if options else {}), **kwargs}
-        payload = {k: v for k, v in payload.items() if v is not None}
+        payload = {k: v for k, v in payload.items() if v is not None or k == "expiration_date"}
 
         if not payload:
-            raise ValueError("At least one of text, metadata, or timestamp must be provided for update.")
+            raise ValueError("At least one of text, metadata, timestamp, or expiration_date must be provided for update.")
 
         capture_client_event("client.update", self, {"memory_id": memory_id, "sync_type": "sync"})
         params = self._prepare_params()
@@ -1260,24 +1262,26 @@ class AsyncMemoryClient:
         Args:
             memory_id: The ID of the memory to update.
             options: Typed options (UpdateMemoryOptions) with text, metadata,
-                     and/or timestamp fields.
+                     timestamp, and/or expiration_date fields.
-            **kwargs: Alternatively pass text, metadata, timestamp as keyword args.
+            **kwargs: Alternatively pass text, metadata, timestamp, or
+                      expiration_date as keyword args.
 
         Returns:
             Dict[str, Any]: The response from the server.
 
         Raises:
-            ValueError: If none of text, metadata, or timestamp are provided.
+            ValueError: If none of text, metadata, timestamp, or expiration_date are provided.
 
         Example:
             >>> await client.update("mem_123", UpdateMemoryOptions(text="Updated text"))
             >>> await client.update("mem_123", text="Updated text")
+            >>> await client.update("mem_123", expiration_date=None)
         """
         payload = {**(options.model_dump(exclude_unset=True) if options else {}), **kwargs}
-        payload = {k: v for k, v in payload.items() if v is not None}
+        payload = {k: v for k, v in payload.items() if v is not None or k == "expiration_date"}
 
         if not payload:
-            raise ValueError("At least one of text, metadata, or timestamp must be provided for update.")
+            raise ValueError("At least one of text, metadata, timestamp, or expiration_date must be provided for update.")
 
         capture_client_event("client.update", self, {"memory_id": memory_id, "sync_type": "async"})
         params = self._prepare_params()

mem0/client/types.py

@@ -29,6 +29,7 @@ class AddMemoryOptions(BaseModel):
     )
     custom_instructions: Optional[str] = Field(default=None, description="Custom instructions for fact extraction")
     timestamp: Optional[int] = Field(default=None, description="Unix timestamp for the memory")
+    expiration_date: Optional[str] = Field(default=None, description="Expiration date in YYYY-MM-DD format")
     structured_data_schema: Optional[Dict[str, Any]] = Field(
         default=None, description="Schema for structured data extraction"
     )
@@ -50,6 +51,7 @@ class SearchMemoryOptions(BaseModel):
     threshold: Optional[float] = Field(default=None, description="Minimum similarity score threshold")
     fields: Optional[List[str]] = Field(default=None, description="Fields to include in the response")
     categories: Optional[List[str]] = Field(default=None, description="Categories to filter by")
+    show_expired: Optional[bool] = Field(default=None, description="Whether to include expired memories")
 
 
 class GetAllMemoryOptions(BaseModel):
@@ -71,6 +73,7 @@ class GetAllMemoryOptions(BaseModel):
         default=None, description="Filter memories created on or before this date (ISO 8601)"
     )
     categories: Optional[List[str]] = Field(default=None, description="Categories to filter by")
+    show_expired: Optional[bool] = Field(default=None, description="Whether to include expired memories")
 
 
 class DeleteAllMemoryOptions(BaseModel):
@@ -91,6 +94,7 @@ class UpdateMemoryOptions(BaseModel):
     text: Optional[str] = Field(default=None, description="New text content for the memory")
     metadata: Optional[Dict[str, Any]] = Field(default=None, description="Updated metadata")
     timestamp: Optional[Union[int, float, str]] = Field(default=None, description="Updated timestamp")
+    expiration_date: Optional[str] = Field(default=None, description="Expiration date in YYYY-MM-DD format, or None to clear")
 
 
 class ProjectUpdateOptions(BaseModel):

mem0/memory/main.py

@@ -9,7 +9,7 @@ import time
 import uuid
 import warnings
 from copy import deepcopy
-from datetime import datetime, timezone
+from datetime import date, datetime, timezone
 from typing import Any, Dict, Optional
 
 from pydantic import ValidationError
@@ -378,9 +378,37 @@ def _entity_collection_name(provider: str, collection_name: str) -> str:
     return f"{collection_name}{separator}entities"
 
 
+def _normalize_expiration_date(value: Any) -> Optional[str]:
+    if value is None:
+        return None
+    if isinstance(value, datetime):
+        return value.date().isoformat()
+    if isinstance(value, date):
+        return value.isoformat()
+    if isinstance(value, str):
+        try:
+            return date.fromisoformat(value).isoformat()
+        except ValueError as exc:
+            raise ValueError("expiration_date must be a valid date in YYYY-MM-DD format.") from exc
+    raise ValueError("expiration_date must be a date string in YYYY-MM-DD format.")
+
+
+def _payload_is_expired(payload: Optional[Dict[str, Any]]) -> bool:
+    if not payload:
+        return False
+    expiration_date = payload.get("expiration_date")
+    if not expiration_date:
+        return False
+    try:
+        return date.fromisoformat(str(expiration_date)) < datetime.now(timezone.utc).date()
+    except ValueError:
+        return False
+
+
 setup_config()
 logger = logging.getLogger(__name__)
 
+_UNSET = object()
 _PROJECT_UPDATE_UNSUPPORTED_ERROR = "Project updates are not supported by the OSS Memory SDK."
 
 
@@ -694,6 +722,7 @@ class Memory(MemoryBase):
         run_id: Optional[str] = None,
         metadata: Optional[Dict[str, Any]] = None,
         timestamp: Optional[Any] = None,
+        expiration_date: Optional[Any] = None,
         infer: bool = True,
         memory_type: Optional[str] = None,
         prompt: Optional[str] = None,
@@ -712,6 +741,8 @@ class Memory(MemoryBase):
             run_id (str, optional): ID of the run creating the memory. Defaults to None.
             metadata (dict, optional): Metadata to store with the memory. Defaults to None.
             timestamp (Any, optional): Platform-only temporal parameter. Not supported in OSS.
+            expiration_date (Any, optional): Date in YYYY-MM-DD format. Expired memories are hidden
+                from search and get_all unless show_expired is True.
             infer (bool, optional): If True (default), an LLM is used to extract key facts from
                 'messages' and decide whether to add, update, or delete related memories.
                 If False, 'messages' are added as raw memories directly.
@@ -737,6 +768,7 @@ class Memory(MemoryBase):
         if timestamp is not None:
             raise ValueError(get_temporal_feature_error_message("sync", "add", "timestamp"))
 
+        normalized_expiration_date = _normalize_expiration_date(expiration_date)
         temporal_usage_notice = detect_temporal_usage_from_metadata(metadata)
         processed_metadata, effective_filters = _build_filters_and_metadata(
             user_id=user_id,
@@ -744,6 +776,8 @@ class Memory(MemoryBase):
             run_id=run_id,
             input_metadata=metadata,
         )
+        if normalized_expiration_date is not None:
+            processed_metadata["expiration_date"] = normalized_expiration_date
 
         if memory_type is not None and memory_type != MemoryType.PROCEDURAL.value:
             raise Mem0ValidationError(
@@ -1141,6 +1175,7 @@ class Memory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
 
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
@@ -1169,6 +1204,7 @@ class Memory(MemoryBase):
         *,
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 20,
+        show_expired: bool = False,
         **kwargs,
     ):
         """
@@ -1179,6 +1215,7 @@ class Memory(MemoryBase):
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
             top_k (int, optional): The maximum number of memories to return. Defaults to 20.
+            show_expired (bool, optional): Include expired memories. Defaults to False.
 
         Returns:
             dict: A dictionary containing a list of memories under the "results" key.
@@ -1217,6 +1254,7 @@ class Memory(MemoryBase):
             )
 
         limit = top_k
+        fetch_limit = limit if show_expired else max(limit * 4, 60)
         scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
 
         keys, encoded_ids = process_telemetry_filters(effective_filters)
@@ -1224,7 +1262,7 @@ class Memory(MemoryBase):
             "mem0.get_all", self, {"limit": limit, "keys": keys, "encoded_ids": encoded_ids, "sync_type": "sync"}
         )
 
-        all_memories_result = self._get_all_from_vector_store(effective_filters, limit)
+        all_memories_result = self._get_all_from_vector_store(effective_filters, fetch_limit, show_expired, limit)
 
         if scale_threshold_notice:
             display_scale_threshold_notice(self, "sync", "get_all", *scale_threshold_notice)
@@ -1232,7 +1270,7 @@ class Memory(MemoryBase):
             display_first_run_notice(self, "sync", "get_all")
         return {"results": all_memories_result}
 
-    def _get_all_from_vector_store(self, filters, limit):
+    def _get_all_from_vector_store(self, filters, limit, show_expired=False, output_limit=None):
         memories_result = self.vector_store.list(filters=filters, top_k=limit)
 
         # Handle different vector store return formats by inspecting first element
@@ -1255,11 +1293,14 @@ class Memory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
         formatted_memories = []
         for mem in actual_memories:
+            if not show_expired and _payload_is_expired(mem.payload):
+                continue
             memory_item_dict = MemoryItem(
                 id=mem.id,
                 memory=mem.payload.get("data", ""),
@@ -1277,6 +1318,8 @@ class Memory(MemoryBase):
                 memory_item_dict["metadata"] = additional_metadata
 
             formatted_memories.append(memory_item_dict)
+            if output_limit is not None and len(formatted_memories) >= output_limit:
+                break
 
         return formatted_memories
 
@@ -1290,6 +1333,7 @@ class Memory(MemoryBase):
         rerank: bool = False,
         explain: bool = False,
         reference_date: Optional[Any] = None,
+        show_expired: bool = False,
         **kwargs,
     ):
         """
@@ -1322,6 +1366,7 @@ class Memory(MemoryBase):
             rerank (bool, optional): Whether to rerank results. Defaults to False.
             explain (bool, optional): Whether to include score_details for each result. Defaults to False.
             reference_date (Any, optional): Platform-only temporal parameter. Not supported in OSS.
+            show_expired (bool, optional): Include expired memories. Defaults to False.
 
         Returns:
             dict: A dictionary containing the search results under a "results" key.
@@ -1393,7 +1438,9 @@ class Memory(MemoryBase):
         )
 
         search_start = time.perf_counter()
-        original_memories = self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
+        original_memories = self._search_vector_store(
+            query, effective_filters, limit, threshold, explain=explain, show_expired=show_expired
+        )
         search_elapsed_seconds = time.perf_counter() - search_start
 
         # Apply reranking if enabled and reranker is available
@@ -1525,7 +1572,7 @@ class Memory(MemoryBase):
                 return True
         return False
 
-    def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False):
+    def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False, show_expired=False):
         # Guard against None threshold (backward compat)
         if threshold is None:
             threshold = 0.1
@@ -1566,11 +1613,14 @@ class Memory(MemoryBase):
         # Step 7: Build candidate set from semantic results
         candidates = []
         for mem in semantic_results:
+            payload = mem.payload if hasattr(mem, 'payload') else {}
+            if not show_expired and _payload_is_expired(payload):
+                continue
             mem_id = str(mem.id)
             candidates.append({
                 "id": mem_id,
                 "score": mem.score,
-                "payload": mem.payload if hasattr(mem, 'payload') else {},
+                "payload": payload,
             })
 
         # Step 8: Score and rank
@@ -1591,6 +1641,7 @@ class Memory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
@@ -1708,14 +1759,21 @@ class Memory(MemoryBase):
 
         return memory_boosts
 
-    def update(self, memory_id, data, metadata: Optional[Dict[str, Any]] = None):
+    def update(
+        self,
+        memory_id,
+        data: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        expiration_date: Any = _UNSET,
+    ):
         """
         Update a memory by ID.
 
         Args:
             memory_id (str): ID of the memory to update.
-            data (str): New content to update the memory with.
+            data (str, optional): New content to update the memory with.
             metadata (dict, optional): Metadata to update with the memory. Defaults to None.
+            expiration_date (Any, optional): Date in YYYY-MM-DD format, or None to clear it.
 
         Returns:
             dict: Success message indicating the memory was updated.
@@ -1726,9 +1784,19 @@ class Memory(MemoryBase):
         """
         capture_event("mem0.update", self, {"memory_id": memory_id, "sync_type": "sync"})
 
-        existing_embeddings = {data: self.embedding_model.embed(data, "update")}
+        if data is None and metadata is None and expiration_date is _UNSET:
+            raise ValueError("At least one of data, metadata, or expiration_date must be provided.")
 
-        self._update_memory(memory_id, data, existing_embeddings, metadata)
+        update_metadata = deepcopy(metadata) if metadata is not None else None
+        if expiration_date is not _UNSET:
+            update_metadata = update_metadata or {}
+            update_metadata["expiration_date"] = _normalize_expiration_date(expiration_date)
+
+        existing_embeddings = {}
+        if data is not None:
+            existing_embeddings[data] = self.embedding_model.embed(data, "update")
+
+        self._update_memory(memory_id, data, existing_embeddings, update_metadata)
         display_first_run_notice(self, "sync", "update")
         return {"message": "Memory updated successfully!"}
 
@@ -1762,6 +1830,10 @@ class Memory(MemoryBase):
             agent_id (str, optional): ID of the agent to delete memories for. Defaults to None.
             run_id (str, optional): ID of the run to delete memories for. Defaults to None.
         """
+        user_id = _validate_and_trim_entity_id(user_id, "user_id")
+        agent_id = _validate_and_trim_entity_id(agent_id, "agent_id")
+        run_id = _validate_and_trim_entity_id(run_id, "run_id")
+
         filters: Dict[str, Any] = {}
         if user_id:
             filters["user_id"] = user_id
@@ -1891,6 +1963,11 @@ class Memory(MemoryBase):
             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:
@@ -1932,8 +2009,9 @@ class Memory(MemoryBase):
         # 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)}
-        self._remove_memory_from_entity_store(memory_id, session_filters)
+        if text_changed:
-        self._link_entities_for_memory(memory_id, data, session_filters)
+            self._remove_memory_from_entity_store(memory_id, session_filters)
+            self._link_entities_for_memory(memory_id, data, session_filters)
 
         return memory_id
 
@@ -2280,6 +2358,7 @@ class AsyncMemory(MemoryBase):
         run_id: Optional[str] = None,
         metadata: Optional[Dict[str, Any]] = None,
         timestamp: Optional[Any] = None,
+        expiration_date: Optional[Any] = None,
         infer: bool = True,
         memory_type: Optional[str] = None,
         prompt: Optional[str] = None,
@@ -2295,6 +2374,8 @@ class AsyncMemory(MemoryBase):
             run_id (str, optional): ID of the run creating the memory. Defaults to None.
             metadata (dict, optional): Metadata to store with the memory. Defaults to None.
             timestamp (Any, optional): Platform-only temporal parameter. Not supported in OSS.
+            expiration_date (Any, optional): Date in YYYY-MM-DD format. Expired memories are hidden
+                from search and get_all unless show_expired is True.
             infer (bool, optional): Whether to infer the memories. Defaults to True.
             memory_type (str, optional): Type of memory to create. Defaults to None.
                                          Pass "procedural_memory" to create procedural memories.
@@ -2306,10 +2387,13 @@ class AsyncMemory(MemoryBase):
         if timestamp is not None:
             raise ValueError(await get_temporal_feature_error_message_async("async", "add", "timestamp"))
 
+        normalized_expiration_date = _normalize_expiration_date(expiration_date)
         temporal_usage_notice = detect_temporal_usage_from_metadata(metadata)
         processed_metadata, effective_filters = _build_filters_and_metadata(
             user_id=user_id, agent_id=agent_id, run_id=run_id, input_metadata=metadata
         )
+        if normalized_expiration_date is not None:
+            processed_metadata["expiration_date"] = normalized_expiration_date
 
         if memory_type is not None and memory_type != MemoryType.PROCEDURAL.value:
             raise ValueError(
@@ -2712,6 +2796,7 @@ class AsyncMemory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
 
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
@@ -2740,6 +2825,7 @@ class AsyncMemory(MemoryBase):
         *,
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 20,
+        show_expired: bool = False,
         **kwargs,
     ):
         """
@@ -2750,6 +2836,7 @@ class AsyncMemory(MemoryBase):
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
             top_k (int, optional): The maximum number of memories to return. Defaults to 20.
+            show_expired (bool, optional): Include expired memories. Defaults to False.
 
         Returns:
             dict: A dictionary containing a list of memories under the "results" key.
@@ -2788,6 +2875,7 @@ class AsyncMemory(MemoryBase):
             )
 
         limit = top_k
+        fetch_limit = limit if show_expired else max(limit * 4, 60)
         scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
 
         keys, encoded_ids = process_telemetry_filters(effective_filters)
@@ -2795,7 +2883,7 @@ class AsyncMemory(MemoryBase):
             "mem0.get_all", self, {"limit": limit, "keys": keys, "encoded_ids": encoded_ids, "sync_type": "async"}
         )
 
-        all_memories_result = await self._get_all_from_vector_store(effective_filters, limit)
+        all_memories_result = await self._get_all_from_vector_store(effective_filters, fetch_limit, show_expired, limit)
 
         if scale_threshold_notice:
             await display_scale_threshold_notice_async(self, "async", "get_all", *scale_threshold_notice)
@@ -2803,7 +2891,7 @@ class AsyncMemory(MemoryBase):
             await display_first_run_notice_async(self, "async", "get_all")
         return {"results": all_memories_result}
 
-    async def _get_all_from_vector_store(self, filters, limit):
+    async def _get_all_from_vector_store(self, filters, limit, show_expired=False, output_limit=None):
         memories_result = await asyncio.to_thread(self.vector_store.list, filters=filters, top_k=limit)
 
         # Handle different vector store return formats by inspecting first element
@@ -2826,11 +2914,14 @@ class AsyncMemory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
         formatted_memories = []
         for mem in actual_memories:
+            if not show_expired and _payload_is_expired(mem.payload):
+                continue
             memory_item_dict = MemoryItem(
                 id=mem.id,
                 memory=mem.payload.get("data", ""),
@@ -2848,6 +2939,8 @@ class AsyncMemory(MemoryBase):
                 memory_item_dict["metadata"] = additional_metadata
 
             formatted_memories.append(memory_item_dict)
+            if output_limit is not None and len(formatted_memories) >= output_limit:
+                break
 
         return formatted_memories
 
@@ -2861,6 +2954,7 @@ class AsyncMemory(MemoryBase):
         rerank: bool = False,
         explain: bool = False,
         reference_date: Optional[Any] = None,
+        show_expired: bool = False,
         **kwargs,
     ):
         """
@@ -2893,6 +2987,7 @@ class AsyncMemory(MemoryBase):
             rerank (bool, optional): Whether to rerank results. Defaults to False.
             explain (bool, optional): Whether to include score_details for each result. Defaults to False.
             reference_date (Any, optional): Platform-only temporal parameter. Not supported in OSS.
+            show_expired (bool, optional): Include expired memories. Defaults to False.
 
         Returns:
             dict: A dictionary containing the search results under a "results" key.
@@ -2968,7 +3063,9 @@ class AsyncMemory(MemoryBase):
         )
 
         search_start = time.perf_counter()
-        original_memories = await self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
+        original_memories = await self._search_vector_store(
+            query, effective_filters, limit, threshold, explain=explain, show_expired=show_expired
+        )
         search_elapsed_seconds = time.perf_counter() - search_start
 
         # Apply reranking if enabled and reranker is available
@@ -3103,7 +3200,7 @@ class AsyncMemory(MemoryBase):
                 return True
         return False
 
-    async def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False):
+    async def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False, show_expired=False):
         if threshold is None:
             threshold = 0.1
 
@@ -3143,11 +3240,14 @@ class AsyncMemory(MemoryBase):
         # Step 7: Build candidate set from semantic results
         candidates = []
         for mem in semantic_results:
+            payload = mem.payload if hasattr(mem, 'payload') else {}
+            if not show_expired and _payload_is_expired(payload):
+                continue
             mem_id = str(mem.id)
             candidates.append({
                 "id": mem_id,
                 "score": mem.score,
-                "payload": mem.payload if hasattr(mem, 'payload') else {},
+                "payload": payload,
             })
 
         # Step 8: Score and rank
@@ -3168,6 +3268,7 @@ class AsyncMemory(MemoryBase):
             "actor_id",
             "role",
             "attributed_to",
+            "expiration_date",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
@@ -3276,14 +3377,21 @@ class AsyncMemory(MemoryBase):
 
         return memory_boosts
 
-    async def update(self, memory_id, data, metadata: Optional[Dict[str, Any]] = None):
+    async def update(
+        self,
+        memory_id,
+        data: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        expiration_date: Any = _UNSET,
+    ):
         """
         Update a memory by ID asynchronously.
 
         Args:
             memory_id (str): ID of the memory to update.
-            data (str): New content to update the memory with.
+            data (str, optional): New content to update the memory with.
             metadata (dict, optional): Metadata to update with the memory. Defaults to None.
+            expiration_date (Any, optional): Date in YYYY-MM-DD format, or None to clear it.
 
         Returns:
             dict: Success message indicating the memory was updated.
@@ -3294,10 +3402,20 @@ class AsyncMemory(MemoryBase):
         """
         capture_event("mem0.update", self, {"memory_id": memory_id, "sync_type": "async"})
 
-        embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
+        if data is None and metadata is None and expiration_date is _UNSET:
-        existing_embeddings = {data: embeddings}
+            raise ValueError("At least one of data, metadata, or expiration_date must be provided.")
 
-        await self._update_memory(memory_id, data, existing_embeddings, metadata)
+        update_metadata = deepcopy(metadata) if metadata is not None else None
+        if expiration_date is not _UNSET:
+            update_metadata = update_metadata or {}
+            update_metadata["expiration_date"] = _normalize_expiration_date(expiration_date)
+
+        existing_embeddings = {}
+        if data is not None:
+            embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
+            existing_embeddings[data] = embeddings
+
+        await self._update_memory(memory_id, data, existing_embeddings, update_metadata)
         await display_first_run_notice_async(self, "async", "update")
         return {"message": "Memory updated successfully!"}
 
@@ -3331,6 +3449,10 @@ class AsyncMemory(MemoryBase):
             agent_id (str, optional): ID of the agent to delete memories for. Defaults to None.
             run_id (str, optional): ID of the run to delete memories for. Defaults to None.
         """
+        user_id = _validate_and_trim_entity_id(user_id, "user_id")
+        agent_id = _validate_and_trim_entity_id(agent_id, "agent_id")
+        run_id = _validate_and_trim_entity_id(run_id, "run_id")
+
         filters = {}
         if user_id:
             filters["user_id"] = user_id
@@ -3456,7 +3578,7 @@ class AsyncMemory(MemoryBase):
             if llm is not None:
                 parsed_messages = convert_to_messages(parsed_messages)
                 response = await asyncio.to_thread(llm.invoke, input=parsed_messages)
-                procedural_memory = response.content
+                procedural_memory = remove_code_blocks(response.content)
             else:
                 procedural_memory = await asyncio.to_thread(self.llm.generate_response, messages=parsed_messages)
                 procedural_memory = remove_code_blocks(procedural_memory)
@@ -3491,6 +3613,11 @@ class AsyncMemory(MemoryBase):
             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:
@@ -3534,8 +3661,9 @@ class AsyncMemory(MemoryBase):
         # 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)}
-        await self._remove_memory_from_entity_store(memory_id, session_filters)
+        if text_changed:
-        await self._link_entities_for_memory(memory_id, data, session_filters)
+            await self._remove_memory_from_entity_store(memory_id, session_filters)
+            await self._link_entities_for_memory(memory_id, data, session_filters)
 
         return memory_id
 

mem0/memory/notices.py

@@ -1418,7 +1418,30 @@ def _get_provider_memory_count(memory_instance) -> Optional[int]:
     try:
         col_info = getattr(vector_store, "col_info", None)
         if callable(col_info):
-            return _extract_count(col_info())
+            collection_name = getattr(vector_store, "collection_name", None)
+            if collection_name is None:
+                schema = getattr(vector_store, "schema", None)
+                if isinstance(schema, dict):
+                    index = schema.get("index")
+                    if isinstance(index, dict):
+                        collection_name = index.get("name")
+            if collection_name is not None:
+                try:
+                    info = col_info(collection_name)
+                except TypeError:
+                    info = col_info()
+            else:
+                info = col_info()
+            value = _extract_count(info)
+            if value is not None:
+                return value
+
+            client = getattr(vector_store, "client", None)
+            client_count = (
+                getattr(client, "count", None) if client is not None and collection_name is not None else None
+            )
+            if callable(client_count):
+                return _extract_count(client_count(index=collection_name))
     except Exception:
         return None
 
@@ -1430,7 +1453,7 @@ def _extract_count(info: Any) -> Optional[int]:
         return None
 
     if isinstance(info, dict):
-        for key in ("count", "points_count", "vectors_count", "indexed_vectors_count"):
+        for key in ("count", "points_count", "vectors_count", "indexed_vectors_count", "num_docs"):
             value = _coerce_nonnegative_int(info.get(key), None)
             if value is not None:
                 return value
@@ -1445,7 +1468,7 @@ def _extract_count(info: Any) -> Optional[int]:
         except Exception:
             return None
 
-    for attr in ("count", "points_count", "vectors_count", "indexed_vectors_count"):
+    for attr in ("count", "points_count", "vectors_count", "indexed_vectors_count", "num_docs"):
         value = _coerce_nonnegative_int(getattr(info, attr, None), None)
         if value is not None:
             return value

mem0/reranker/huggingface_reranker.py

@@ -59,6 +59,26 @@ class HuggingFaceReranker(BaseReranker):
         self.model.to(self.device)
         self.model.eval()
 
+    @staticmethod
+    def _normalize_scores(scores: List[float]) -> List[float]:
+        """Map raw cross-encoder logits into the [0, 1] range via a sigmoid.
+
+        Cross-encoder rerankers (e.g. ``BAAI/bge-reranker-*``) emit unbounded
+        logits; the documented way to obtain an interpretable [0, 1] relevance
+        score is a per-document sigmoid (``1 / (1 + e^-x)``), which preserves the
+        ranking order.
+
+        This replaces the previous min-max scaling, which produced *set-relative*
+        scores: the lowest-ranked document was always forced to 0.0, and a single
+        document (or any set of tied scores) collapsed to 0.0 — wrongly reporting
+        a result as completely irrelevant. Sigmoid scores each document on its own
+        merit, so those cases are handled naturally.
+        """
+        if not scores:
+            return []
+        arr = np.asarray(scores, dtype=float)
+        return (1.0 / (1.0 + np.exp(-arr))).tolist()
+
     def rerank(self, query: str, documents: List[Dict[str, Any]], top_k: int = None) -> List[Dict[str, Any]]:
         """
         Rerank documents using HuggingFace cross-encoder model.
@@ -118,9 +138,7 @@ class HuggingFaceReranker(BaseReranker):
 
             # Normalize scores if requested
             if self.config.normalize:
-                scores = np.array(scores)
+                scores = self._normalize_scores(scores)
-                scores = (scores - scores.min()) / (scores.max() - scores.min() + 1e-8)
-                scores = scores.tolist()
 
             # Combine documents with scores
             doc_score_pairs = list(zip(documents, scores))

mem0/vector_stores/redis.py

@@ -124,11 +124,13 @@ class RedisDB(VectorStoreBase):
         data = []
         for vector, payload, id in zip(vectors, payloads, ids):
             # Start with required fields
+            created_at_str = payload.get("created_at")
+            created_at_ts = int(datetime.fromisoformat(created_at_str).timestamp()) if created_at_str else 0
             entry = {
                 "memory_id": id,
-                "hash": payload["hash"],
+                "hash": payload.get("hash", ""),
-                "memory": payload["data"],
+                "memory": payload.get("data", ""),
-                "created_at": int(datetime.fromisoformat(payload["created_at"]).timestamp()),
+                "created_at": created_at_ts,
                 "embedding": np.array(vector, dtype=np.float32).tobytes(),
             }
 
@@ -244,12 +246,16 @@ class RedisDB(VectorStoreBase):
         self.index.drop_keys(f"{self.schema['index']['prefix']}:{vector_id}")
 
     def update(self, vector_id=None, vector=None, payload=None):
+        created_at_str = payload.get("created_at")
+        created_at_ts = int(datetime.fromisoformat(created_at_str).timestamp()) if created_at_str else 0
+        updated_at_str = payload.get("updated_at")
+        updated_at_ts = int(datetime.fromisoformat(updated_at_str).timestamp()) if updated_at_str else 0
         data = {
             "memory_id": vector_id,
-            "hash": payload["hash"],
+            "hash": payload.get("hash", ""),
-            "memory": payload["data"],
+            "memory": payload.get("data", ""),
-            "created_at": int(datetime.fromisoformat(payload["created_at"]).timestamp()),
+            "created_at": created_at_ts,
-            "updated_at": int(datetime.fromisoformat(payload["updated_at"]).timestamp()),
+            "updated_at": updated_at_ts,
         }
 
         # Only update embedding if vector is provided

mem0/vector_stores/valkey.py

@@ -41,7 +41,20 @@ class OutputData(BaseModel):
     payload: Dict
 
 
+_VALKEY_TAG_SPECIAL = set(r',.<>{}[]"\':;!@#$%^&*()-+=~| ')
+
+
 class ValkeyDB(VectorStoreBase):
+    @staticmethod
+    def _escape_tag_value(value):
+        """Escape special characters in a Valkey FT.SEARCH tag filter value.
+
+        Without escaping, characters like * (wildcard) or | (OR) alter query
+        semantics and can bypass tenant-isolation filters.
+        """
+        s = str(value)
+        return "".join(f"\\{c}" if c in _VALKEY_TAG_SPECIAL else c for c in s)
+
     def __init__(
         self,
         valkey_url: str,
@@ -329,8 +342,8 @@ class ValkeyDB(VectorStoreBase):
             knn_part (str): The KNN part of the query.
             filters (dict, optional): Filters to apply to the search. Each key-value pair
                 becomes a tag filter (@key:{value}). None values are ignored.
-                Values are used as-is (no validation) - wildcards, lists, etc. are
+                Values are escaped via _escape_tag_value() before interpolation
-                passed through literally to Valkey search. Multiple filters are
+                to prevent wildcard/operator injection. Multiple filters are
                 combined with AND logic (space-separated).
 
         Returns:
@@ -345,8 +358,8 @@ class ValkeyDB(VectorStoreBase):
         filter_parts = []
         for key, value in filters.items():
             if value is not None:
-                # Use the correct filter syntax for Valkey
+                escaped = self._escape_tag_value(value)
-                filter_parts.append(f"@{key}:{{{value}}}")
+                filter_parts.append(f"@{key}:{{{escaped}}}")
 
         # No valid filter parts
         if not filter_parts:
@@ -750,34 +763,6 @@ class ValkeyDB(VectorStoreBase):
             logger.exception(f"Error resetting index {self.collection_name}: {e}")
             raise
 
-    def _build_list_query(self, filters=None):
-        """
-        Build a query for listing vectors.
-
-        Args:
-            filters (dict, optional): Filters to apply to the list. Each key-value pair
-                becomes a tag filter (@key:{value}). None values are ignored.
-                Values are used as-is (no validation) - wildcards, lists, etc. are
-                passed through literally to Valkey search.
-
-        Returns:
-            str: The query string. Returns "*" if no valid filters provided.
-        """
-        # Default query
-        q = "*"
-
-        # Add filters if provided
-        if filters and any(value is not None for key, value in filters.items()):
-            filter_conditions = []
-            for key, value in filters.items():
-                if value is not None:
-                    filter_conditions.append(f"@{key}:{{{value}}}")
-
-            if filter_conditions:
-                q = " ".join(filter_conditions)
-
-        return q
-
     def list(self, filters: dict = None, top_k: int = None) -> list:
         """
         List all recent created memories from the vector store.
@@ -785,8 +770,8 @@ class ValkeyDB(VectorStoreBase):
         Args:
             filters (dict, optional): Filters to apply to the list. Each key-value pair
                 becomes a tag filter (@key:{value}). None values are ignored.
-                Values are used as-is without validation - wildcards, special characters,
+                Values are escaped via _escape_tag_value() before interpolation
-                lists, etc. are passed through literally to Valkey search.
+                to prevent wildcard/operator injection.
                 Multiple filters are combined with AND logic.
             top_k (int, optional): Maximum number of results to return. Defaults to 1000
                 if not specified.

server/main.py

@@ -182,14 +182,16 @@ class MemoryCreate(BaseModel):
     agent_id: Optional[str] = None
     run_id: Optional[str] = None
     metadata: Optional[Dict[str, Any]] = None
+    expiration_date: Optional[str] = Field(None, description="Expiration date in YYYY-MM-DD format.")
     infer: Optional[bool] = Field(None, description="Whether to extract facts from messages. Defaults to True.")
     memory_type: Optional[str] = Field(None, description="Type of memory to store (e.g. 'core').")
     prompt: Optional[str] = Field(None, description="Custom prompt to use for fact extraction.")
 
 
 class MemoryUpdate(BaseModel):
-    text: str = Field(..., description="New content to update the memory with.")
+    text: Optional[str] = Field(None, description="New content to update the memory with.")
     metadata: Optional[Dict[str, Any]] = Field(None, description="Metadata to update.")
+    expiration_date: Optional[str] = Field(None, description="Expiration date in YYYY-MM-DD format, or null to clear.")
 
 
 class SearchRequest(BaseModel):
@@ -201,6 +203,7 @@ class SearchRequest(BaseModel):
     top_k: Optional[int] = Field(None, description="Maximum number of results to return.")
     threshold: Optional[float] = Field(None, description="Minimum similarity score for results.")
     explain: Optional[bool] = Field(None, description="Include score details for each search result.")
+    show_expired: Optional[bool] = Field(None, description="Include expired memories.")
 
 
 class GenerateInstructionsRequest(BaseModel):
@@ -379,7 +382,7 @@ def add_memory(memory_create: MemoryCreate, _auth=Depends(verify_auth)):
 
 
 ALL_MEMORIES_LIMIT = 1000
-_RESERVED_PAYLOAD_KEYS = {"data", "user_id", "agent_id", "run_id", "hash", "created_at", "updated_at"}
+_RESERVED_PAYLOAD_KEYS = {"data", "user_id", "agent_id", "run_id", "hash", "created_at", "updated_at", "expiration_date"}
 
 
 def _serialize_memory(row: Any) -> Dict[str, Any]:
@@ -391,6 +394,7 @@ def _serialize_memory(row: Any) -> Dict[str, Any]:
         "agent_id": payload.get("agent_id"),
         "run_id": payload.get("run_id"),
         "hash": payload.get("hash"),
+        "expiration_date": payload.get("expiration_date"),
         "metadata": {k: v for k, v in payload.items() if k not in _RESERVED_PAYLOAD_KEYS},
         "created_at": payload.get("created_at"),
         "updated_at": payload.get("updated_at"),
@@ -410,6 +414,7 @@ def get_all_memories(
     run_id: Optional[str] = None,
     agent_id: Optional[str] = None,
     top_k: Optional[int] = Query(None, ge=0, le=ALL_MEMORIES_LIMIT),
+    show_expired: bool = Query(False),
     _auth=Depends(verify_auth),
 ):
     """Retrieve stored memories. Lists all memories when no identifier is provided (admin only)."""
@@ -418,6 +423,7 @@ def get_all_memories(
             auth_type = getattr(request.state, "auth_type", "none")
             if _auth is not None and _auth.role != "admin" and auth_type not in {"admin_api_key", "disabled"}:
                 raise HTTPException(status_code=403, detail="Admin role required to list all memories.")
+            # Admin all-memory listing is intentionally raw; scoped get_all below applies expiry visibility.
             return _list_all_memories(limit=top_k if top_k is not None else ALL_MEMORIES_LIMIT)
         filters = {
             k: v for k, v in {"user_id": user_id, "run_id": run_id, "agent_id": agent_id}.items() if v is not None
@@ -425,6 +431,7 @@ def get_all_memories(
         params = {"filters": filters}
         if top_k is not None:
             params["top_k"] = top_k
+        params["show_expired"] = show_expired
         return get_memory_instance().get_all(**params)
     except HTTPException:
         raise
@@ -465,6 +472,8 @@ def search_memories(search_req: SearchRequest, _auth=Depends(verify_auth)):
             params["threshold"] = search_req.threshold
         if search_req.explain is not None:
             params["explain"] = search_req.explain
+        if search_req.show_expired is not None:
+            params["show_expired"] = search_req.show_expired
         return get_memory_instance().search(query=search_req.query, filters=filters, **params)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
@@ -478,9 +487,15 @@ def search_memories(search_req: SearchRequest, _auth=Depends(verify_auth)):
 def update_memory(memory_id: str, updated_memory: MemoryUpdate, _auth=Depends(verify_auth)):
     """Update an existing memory."""
     try:
-        return get_memory_instance().update(
+        fields_set = getattr(updated_memory, "model_fields_set", getattr(updated_memory, "__fields_set__", set()))
-            memory_id=memory_id, data=updated_memory.text, metadata=updated_memory.metadata
+        params = {"memory_id": memory_id}
-        )
+        if "text" in fields_set:
+            params["data"] = updated_memory.text
+        if "metadata" in fields_set:
+            params["metadata"] = updated_memory.metadata
+        if "expiration_date" in fields_set:
+            params["expiration_date"] = updated_memory.expiration_date
+        return get_memory_instance().update(**params)
     except (ValueError, Mem0ValidationError) as e:
         raise _client_error(e)
     except Exception:

tests/memory/test_main.py

@@ -167,6 +167,33 @@ class TestAsyncUpdate:
             "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, {}
         )
 
+    @pytest.mark.asyncio
+    async def test_async_update_can_change_expiration_date_without_changing_text(self, mock_async_memory, mocker):
+        mock_async_memory.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+        mock_async_memory.vector_store.get = Mock(
+            return_value=Mock(
+                payload={
+                    "data": "Existing memory",
+                    "user_id": "test_user",
+                    "created_at": "2026-01-01T00:00:00+00:00",
+                    "expiration_date": "2026-12-31",
+                }
+            )
+        )
+        mock_async_memory.vector_store.update = Mock()
+        mock_async_memory.db.add_history = Mock()
+        mock_async_memory._remove_memory_from_entity_store = mocker.AsyncMock()
+        mock_async_memory._link_entities_for_memory = mocker.AsyncMock()
+
+        result = await mock_async_memory.update("test_id", expiration_date="2999-01-01")
+
+        assert result["message"] == "Memory updated successfully!"
+        payload = mock_async_memory.vector_store.update.call_args.kwargs["payload"]
+        assert payload["data"] == "Existing memory"
+        assert payload["expiration_date"] == "2999-01-01"
+        mock_async_memory._remove_memory_from_entity_store.assert_not_called()
+        mock_async_memory._link_entities_for_memory.assert_not_called()
+
 
 @pytest.mark.asyncio
 class TestAsyncAddToVectorStoreErrors:

tests/memory/test_notices.py

@@ -1403,6 +1403,30 @@ def test_scale_threshold_provider_count_helpers_are_safe():
         def col_info(self):
             return {"count": 2300}
 
+    class RedisInfoStore:
+        def __init__(self):
+            self.schema = {"index": {"name": "test_collection"}}
+
+        def count(self):
+            raise RuntimeError("count unavailable")
+
+        def col_info(self, name):
+            assert name == self.schema["index"]["name"]
+            return {"index_name": "test_collection", "num_docs": 2300}
+
+    class SearchMetadataStore:
+        def __init__(self):
+            self.collection_name = "test_collection"
+            self.client = MagicMock()
+            self.client.count.return_value = {"count": 2400}
+
+        def count(self):
+            raise RuntimeError("count unavailable")
+
+        def col_info(self, name):
+            assert name == self.collection_name
+            return {"test_collection": {"settings": {"index": {}}}}
+
     memory = MagicMock()
     memory.vector_store = CountStore()
     assert notices._get_provider_memory_count(memory) == 2100
@@ -1410,7 +1434,15 @@ def test_scale_threshold_provider_count_helpers_are_safe():
     memory.vector_store = FallbackStore()
     assert notices._get_provider_memory_count(memory) == 2300
 
-    assert notices._extract_count({"points_count": 2400}) == 2400
+    memory.vector_store = RedisInfoStore()
+    assert notices._get_provider_memory_count(memory) == 2300
+
+    search_store = SearchMetadataStore()
+    memory.vector_store = search_store
+    assert notices._get_provider_memory_count(memory) == 2400
+    search_store.client.count.assert_called_once_with(index="test_collection")
+
+    assert notices._extract_count({"points_count": 2500}) == 2500
     assert notices._extract_count(Info()) == 2200
     assert notices._extract_count({"count": -1}) is None
 

tests/rerankers/test_huggingface_reranker_normalize.py

@@ -0,0 +1,51 @@
+"""Unit tests for HuggingFaceReranker score normalization.
+
+These exercise the pure ``_normalize_scores`` helper directly, so they do not
+require ``transformers`` / ``torch`` to be installed.
+"""
+
+import math
+
+import pytest
+
+from mem0.reranker.huggingface_reranker import HuggingFaceReranker
+
+
+def _sigmoid(x):
+    return 1.0 / (1.0 + math.exp(-x))
+
+
+class TestHuggingFaceNormalizeScores:
+    def test_logits_mapped_via_sigmoid(self):
+        scores = HuggingFaceReranker._normalize_scores([2.0, 8.0, 5.0])
+        assert scores == pytest.approx([_sigmoid(2.0), _sigmoid(8.0), _sigmoid(5.0)])
+
+    def test_output_bounded_between_zero_and_one(self):
+        for s in HuggingFaceReranker._normalize_scores([-12.0, -1.0, 0.0, 3.0, 15.0]):
+            assert 0.0 <= s <= 1.0
+
+    def test_sigmoid_preserves_ranking_order(self):
+        raw = [1.0, -4.0, 9.0, 2.5]
+        normalized = HuggingFaceReranker._normalize_scores(raw)
+        # argsort of raw and normalized must match — sigmoid is monotonic.
+        assert sorted(range(len(raw)), key=lambda i: raw[i]) == sorted(
+            range(len(normalized)), key=lambda i: normalized[i]
+        )
+
+    def test_single_score_not_collapsed_to_zero(self):
+        # Regression: a lone document used to normalize to ~0.0 under min-max.
+        # A positive logit must now yield a clearly-relevant score (> 0.5).
+        (score,) = HuggingFaceReranker._normalize_scores([4.2])
+        assert score == pytest.approx(_sigmoid(4.2))
+        assert score > 0.5
+
+    def test_tied_scores_not_collapsed_to_zero(self):
+        # Regression: tied candidates all collapsed to ~0.0 under min-max.
+        scores = HuggingFaceReranker._normalize_scores([3.0, 3.0, 3.0])
+        assert scores == pytest.approx([_sigmoid(3.0)] * 3)
+
+    def test_zero_logit_maps_to_half(self):
+        assert HuggingFaceReranker._normalize_scores([0.0]) == pytest.approx([0.5])
+
+    def test_empty_scores(self):
+        assert HuggingFaceReranker._normalize_scores([]) == []

tests/test_client.py

@@ -51,6 +51,20 @@ class TestSearchEntityParamRejection:
             json={"query": "test query", "filters": {"user_id": "u1"}},
         )
 
+    def test_search_passes_show_expired(self, mock_memory_client):
+        """search() should pass show_expired to the API."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {"results": []}
+        mock_response.raise_for_status.return_value = None
+        mock_memory_client.client.post.return_value = mock_response
+
+        mock_memory_client.search("test query", filters={"user_id": "u1"}, show_expired=True)
+
+        mock_memory_client.client.post.assert_called_once_with(
+            "/v3/memories/search/",
+            json={"query": "test query", "filters": {"user_id": "u1"}, "show_expired": True},
+        )
+
     def test_search_rejects_user_id_kwarg(self, mock_memory_client):
         """search() should reject user_id as top-level kwarg."""
         with pytest.raises(ValueError, match=r"user_id"):
@@ -100,6 +114,39 @@ class TestGetAllEntityParamRejection:
         with pytest.raises(ValueError, match=r"run_id"):
             mock_memory_client.get_all(run_id="r1")
 
+    def test_get_all_passes_show_expired(self, mock_memory_client):
+        """get_all() should pass show_expired to the API."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {"results": []}
+        mock_response.raise_for_status.return_value = None
+        mock_memory_client.client.post.return_value = mock_response
+
+        mock_memory_client.get_all(filters={"user_id": "u1"}, show_expired=True)
+
+        mock_memory_client.client.post.assert_called_once_with(
+            "/v3/memories/",
+            json={"filters": {"user_id": "u1"}, "show_expired": True},
+        )
+
+
+class TestUpdateExpirationDate:
+    """Tests for update expiration_date payload handling."""
+
+    def test_update_preserves_null_expiration_date(self, mock_memory_client):
+        """update() should send expiration_date=None so the API can clear it."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {"id": "mem_1", "expiration_date": None}
+        mock_response.raise_for_status.return_value = None
+        mock_memory_client.client.put.return_value = mock_response
+
+        mock_memory_client.update("mem_1", expiration_date=None)
+
+        mock_memory_client.client.put.assert_called_once_with(
+            "/v1/memories/mem_1/",
+            json={"expiration_date": None},
+            params={},
+        )
+
 
 class TestFilterOperatorPassthrough:
     """Tests that AND/OR/NOT filter operators are passed through to the API."""

tests/test_main.py

@@ -65,6 +65,24 @@ def test_add(memory_instance):
     )
 
 
+def test_add_stores_expiration_date(memory_instance):
+    memory_instance._add_to_vector_store = Mock(return_value=[{"memory": "Test memory", "event": "ADD"}])
+
+    memory_instance.add(
+        messages=[{"role": "user", "content": "Test message"}],
+        user_id="test_user",
+        expiration_date="2999-01-01",
+    )
+
+    memory_instance._add_to_vector_store.assert_called_once_with(
+        [{"role": "user", "content": "Test message"}],
+        {"user_id": "test_user", "expiration_date": "2999-01-01"},
+        {"user_id": "test_user"},
+        True,
+        prompt=None,
+    )
+
+
 def test_get(memory_instance):
     mock_memory = Mock(
         id="test_id",
@@ -117,6 +135,39 @@ def test_search(memory_instance):
     )
 
 
+def test_search_hides_expired_memories_by_default(memory_instance):
+    mock_memories = [
+        Mock(id="1", payload={"data": "Expired memory", "user_id": "test_user", "expiration_date": "2000-01-01"}, score=0.9),
+        Mock(id="2", payload={"data": "Active memory", "user_id": "test_user", "expiration_date": "2999-01-01"}, score=0.8),
+    ]
+    memory_instance.vector_store.search = Mock(return_value=mock_memories)
+    memory_instance.vector_store.keyword_search = Mock(return_value=None)
+    memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+
+    with patch("mem0.memory.main.lemmatize_for_bm25", return_value="test query"), \
+         patch("mem0.memory.main.extract_entities", return_value=[]):
+        result = memory_instance.search("test query", filters={"user_id": "test_user"})
+
+    assert [memory["memory"] for memory in result["results"]] == ["Active memory"]
+    assert result["results"][0]["expiration_date"] == "2999-01-01"
+
+
+def test_search_can_show_expired_memories(memory_instance):
+    mock_memories = [
+        Mock(id="1", payload={"data": "Expired memory", "user_id": "test_user", "expiration_date": "2000-01-01"}, score=0.9),
+        Mock(id="2", payload={"data": "Active memory", "user_id": "test_user"}, score=0.8),
+    ]
+    memory_instance.vector_store.search = Mock(return_value=mock_memories)
+    memory_instance.vector_store.keyword_search = Mock(return_value=None)
+    memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+
+    with patch("mem0.memory.main.lemmatize_for_bm25", return_value="test query"), \
+         patch("mem0.memory.main.extract_entities", return_value=[]):
+        result = memory_instance.search("test query", filters={"user_id": "test_user"}, show_expired=True)
+
+    assert [memory["memory"] for memory in result["results"]] == ["Expired memory", "Active memory"]
+
+
 def test_update(memory_instance):
     memory_instance.embedding_model = Mock()
     memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
@@ -161,6 +212,42 @@ def test_update_with_empty_metadata(memory_instance):
     )
 
 
+@pytest.mark.parametrize(
+    ("expiration_date", "expected_expiration_date"),
+    [
+        ("2999-01-01", "2999-01-01"),
+        (None, None),
+    ],
+)
+def test_update_can_change_expiration_date_without_changing_text(
+    memory_instance, expiration_date, expected_expiration_date
+):
+    memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+    memory_instance.vector_store.get = Mock(
+        return_value=Mock(
+            payload={
+                "data": "Existing memory",
+                "user_id": "test_user",
+                "created_at": "2026-01-01T00:00:00+00:00",
+                "expiration_date": "2026-12-31",
+            }
+        )
+    )
+    memory_instance.vector_store.update = Mock()
+    memory_instance.db.add_history = Mock()
+    memory_instance._remove_memory_from_entity_store = Mock()
+    memory_instance._link_entities_for_memory = Mock()
+
+    result = memory_instance.update("test_id", expiration_date=expiration_date)
+
+    assert result["message"] == "Memory updated successfully!"
+    payload = memory_instance.vector_store.update.call_args.kwargs["payload"]
+    assert payload["data"] == "Existing memory"
+    assert payload["expiration_date"] == expected_expiration_date
+    memory_instance._remove_memory_from_entity_store.assert_not_called()
+    memory_instance._link_entities_for_memory.assert_not_called()
+
+
 def test_delete(memory_instance):
     memory_instance._delete_memory = Mock()
 
@@ -200,7 +287,30 @@ def test_get_all(memory_instance):
     assert result["results"][0]["memory"] == "Memory 1"
     assert result["results"][0]["user_id"] == "test_user"
 
-    memory_instance.vector_store.list.assert_called_once_with(filters={"user_id": "test_user"}, top_k=20)
+
+def test_get_all_hides_expired_memories_by_default(memory_instance):
+    mock_memories = [
+        Mock(id="1", payload={"data": "Expired memory", "user_id": "test_user", "expiration_date": "2000-01-01"}),
+        Mock(id="2", payload={"data": "Active memory", "user_id": "test_user", "expiration_date": "2999-01-01"}),
+    ]
+    memory_instance.vector_store.list = Mock(return_value=(mock_memories, None))
+
+    result = memory_instance.get_all(filters={"user_id": "test_user"})
+
+    assert [memory["memory"] for memory in result["results"]] == ["Active memory"]
+    assert result["results"][0]["expiration_date"] == "2999-01-01"
+
+
+def test_get_all_can_show_expired_memories(memory_instance):
+    mock_memories = [
+        Mock(id="1", payload={"data": "Expired memory", "user_id": "test_user", "expiration_date": "2000-01-01"}),
+        Mock(id="2", payload={"data": "Active memory", "user_id": "test_user"}),
+    ]
+    memory_instance.vector_store.list = Mock(return_value=(mock_memories, None))
+
+    result = memory_instance.get_all(filters={"user_id": "test_user"}, show_expired=True)
+
+    assert [memory["memory"] for memory in result["results"]] == ["Expired memory", "Active memory"]
 
 
 def test_no_telemetry_vector_store_when_disabled():
@@ -286,6 +396,24 @@ class TestEntityIdValidation:
         with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
             memory_instance.add("test message", user_id="user 123")
 
+    def test_delete_all_rejects_whitespace_only_user_id(self, memory_instance):
+        """delete_all should reject whitespace-only user_id."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot be empty"):
+            memory_instance.delete_all(user_id="   ")
+
+    def test_delete_all_rejects_internal_whitespace_user_id(self, memory_instance):
+        """delete_all should reject user_id with internal whitespace."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
+            memory_instance.delete_all(user_id="user 123")
+
+    def test_delete_all_trims_user_id_before_list(self, memory_instance):
+        """delete_all should trim leading/trailing whitespace on entity IDs."""
+        memory_instance.vector_store.list = Mock(return_value=([], None))
+
+        memory_instance.delete_all(user_id="  alice  ")
+
+        memory_instance.vector_store.list.assert_called_once_with(filters={"user_id": "alice"})
+
 
 class TestSearchParamValidation:
     """Tests for search parameter validation (threshold and top_k)."""

tests/test_memory.py

@@ -1496,3 +1496,36 @@ class TestAsyncDeleteAllEntityRace:
         mock_entity_store.delete.assert_called_once_with(vector_id="entity-alice")
 
         assert mock_vector_store.delete.call_count == 2
+
+
+@pytest.mark.asyncio
+@patch("mem0.memory.main.VectorStoreFactory")
+@patch("mem0.memory.main.EmbedderFactory")
+@patch("mem0.memory.main.LlmFactory")
+async def test_async_procedural_memory_langchain_strips_code_blocks(mock_llm_factory, mock_emb, mock_vs):
+    """Regression #5710: async LangChain path must call remove_code_blocks()."""
+    mock_vs.return_value = MagicMock()
+    mock_emb.return_value = MagicMock()
+    mock_emb.return_value.embed.return_value = [0.1] * 1536
+    mock_llm_factory.return_value = MagicMock()
+
+    from mem0.memory.main import AsyncMemory
+
+    config = MemoryConfig()
+    memory = AsyncMemory(config)
+    memory.vector_store = MagicMock()
+    memory.vector_store.insert = MagicMock()
+
+    mock_langchain_llm = MagicMock()
+    mock_response = MagicMock()
+    mock_response.content = '```json\n{"key": "value"}\n```'
+    mock_langchain_llm.invoke.return_value = mock_response
+
+    messages = [{"role": "user", "content": "test"}]
+    metadata = {"user_id": "test_user"}
+
+    await memory._create_procedural_memory(messages, metadata=metadata, llm=mock_langchain_llm)
+
+    insert_call = memory.vector_store.insert.call_args
+    stored_data = insert_call[1]["payloads"][0]["data"]
+    assert "```" not in stored_data

tests/test_server_params.py

@@ -564,12 +564,20 @@ class TestUpdateMemory:
         resp = client.put("/memories/mem-1", json={"text": "Likes tennis"})
         assert resp.status_code == 200
         _, kwargs = mock_memory.update.call_args
-        assert kwargs["metadata"] is None
+        assert "metadata" not in kwargs
 
-    def test_missing_text_returns_422(self, client):
+    def test_expiration_date_forwarded_without_text(self, client, mock_memory):
-        """text is required — omitting it should fail validation."""
+        resp = client.put("/memories/mem-1", json={"expiration_date": "2999-01-01"})
-        resp = client.put("/memories/mem-1", json={"metadata": {"k": "v"}})
+        assert resp.status_code == 200
-        assert resp.status_code == 422
+        _, kwargs = mock_memory.update.call_args
+        assert kwargs["expiration_date"] == "2999-01-01"
+        assert "data" not in kwargs
+
+    def test_null_expiration_date_forwarded_for_clear(self, client, mock_memory):
+        resp = client.put("/memories/mem-1", json={"expiration_date": None})
+        assert resp.status_code == 200
+        _, kwargs = mock_memory.update.call_args
+        assert kwargs["expiration_date"] is None
 
     def test_dict_not_passed_as_data(self, client, mock_memory):
         """Regression test for #3933: the entire dict must NOT be passed as data."""

tests/vector_stores/test_redis.py

@@ -180,3 +180,51 @@ def test_get_returns_none_for_missing_id():
 
     assert db.get("missing_id") is None
     mock_index.fetch.assert_called_once_with("missing_id")
+
+
+def test_insert_entity_payload_without_hash_and_created_at():
+    """insert() must not crash on entity payloads that lack hash/created_at."""
+    db, mock_index = _make_redis_db()
+
+    entity_payload = {
+        "data": "OpenAI",
+        "entity_type": "organization",
+        "linked_memory_ids": ["mem-1"],
+        "user_id": "test_user",
+    }
+
+    db.insert(
+        vectors=[[0.1, 0.2, 0.3]],
+        payloads=[entity_payload],
+        ids=["entity-1"],
+    )
+
+    mock_index.load.assert_called_once()
+    data = mock_index.load.call_args[0][0]
+    assert data[0]["memory_id"] == "entity-1"
+    assert data[0]["memory"] == "OpenAI"
+    assert data[0]["hash"] == ""
+    assert data[0]["created_at"] == 0
+
+
+def test_update_entity_payload_without_hash_and_timestamps():
+    """update() must not crash on entity payloads that lack hash/created_at/updated_at."""
+    db, mock_index = _make_redis_db()
+
+    entity_payload = {
+        "data": "OpenAI",
+        "entity_type": "organization",
+        "linked_memory_ids": ["mem-1"],
+        "user_id": "test_user",
+    }
+
+    db.update(vector_id="entity-1", vector=[0.1, 0.2, 0.3], payload=entity_payload)
+
+    mock_index.load.assert_called_once()
+    call_kwargs = mock_index.load.call_args
+    data_dict = call_kwargs[1]["data"][0] if "data" in call_kwargs[1] else call_kwargs[0][0][0]
+    assert data_dict["memory_id"] == "entity-1"
+    assert data_dict["memory"] == "OpenAI"
+    assert data_dict["hash"] == ""
+    assert data_dict["created_at"] == 0
+    assert data_dict["updated_at"] == 0

tests/vector_stores/test_valkey.py

@@ -490,26 +490,6 @@ def test_reset(valkey_db, mock_valkey_client):
         assert result is True
 
 
-def test_build_list_query(valkey_db):
-    """Test building a list query with and without filters."""
-    # Test without filters
-    query = valkey_db._build_list_query(None)
-    assert query == "*"
-
-    # Test with empty filters
-    query = valkey_db._build_list_query({})
-    assert query == "*"
-
-    # Test with filters
-    query = valkey_db._build_list_query({"user_id": "test_user"})
-    assert query == "@user_id:{test_user}"
-
-    # Test with multiple filters
-    query = valkey_db._build_list_query({"user_id": "test_user", "agent_id": "test_agent"})
-    assert "@user_id:{test_user}" in query
-    assert "@agent_id:{test_agent}" in query
-
-
 def test_process_document_fields(valkey_db):
     """Test processing document fields from hash results."""
     # Create a mock result with all fields
@@ -1061,3 +1041,28 @@ def test_build_index_schema_indexes_memory_as_text(valkey_db):
     )
     # And it must not be declared as TAG.
     assert ["memory", "TAG"] != cmd[memory_idx : memory_idx + 2]
+
+
+def test_escape_tag_value_wildcards(valkey_db):
+    """Wildcard characters in filter values must be escaped to prevent query injection."""
+    assert "\\*" in valkey_db._escape_tag_value("*")
+    assert "\\|" in valkey_db._escape_tag_value("a|b")
+
+
+def test_build_search_query_escapes_filter_values(valkey_db):
+    """_build_search_query must escape special chars in filter values."""
+    knn_part = "[KNN 5 @embedding $vec_param AS vector_score]"
+    query = valkey_db._build_search_query(knn_part, {"user_id": "*"})
+    assert "\\*" in query
+    assert "@user_id:{\\*}" in query
+
+
+def test_escape_tag_value_normal_strings(valkey_db):
+    """Normal alphanumeric filter values must pass through unchanged."""
+    assert valkey_db._escape_tag_value("alice") == "alice"
+    assert valkey_db._escape_tag_value("user123") == "user123"
+
+
+def test_escape_tag_value_hyphenated_user_id(valkey_db):
+    """Hyphenated user IDs must have the hyphen escaped for exact-match."""
+    assert valkey_db._escape_tag_value("user-123") == r"user\-123"