deploy minor version -> 0.1.107rc2 (#2953)

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

.github/workflows/cd.yml

@@ -18,20 +18,17 @@ jobs:
         with:
           python-version: '3.11'
 
-      - name: Install Poetry
+      - name: Install Hatch
         run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
+          pip install hatch
 
       - name: Install dependencies
         run: |
-          cd mem0
-          poetry install
+          hatch env create
 
       - name: Build a binary wheel and a source tarball
         run: |
-          cd mem0
-          poetry build
+          hatch build --clean
 
       # TODO: Needs to setup mem0 repo on Test PyPI
       # - name: Publish distribution 📦 to Test PyPI

.github/workflows/ci.yml

@@ -44,25 +44,24 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: 1.4.2
-          virtualenvs-create: true
-          virtualenvs-in-project: true
+      - name: Install Hatch
+        run: pip install hatch
       - name: Load cached venv
-        id: cached-poetry-dependencies
+        id: cached-hatch-dependencies
         uses: actions/cache@v3
         with:
           path: .venv
-          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
       - name: Install dependencies
-        run: make install_all
-        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        run: |
+          make install_all
+          pip install -e ".[test]"
+          pip install pinecone pinecone-text
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
       - name: Run Formatting
         run: |
-          mkdir -p mem0/.ruff_cache && chmod -R 777 mem0/.ruff_cache
-          cd mem0 && poetry run ruff check . --select F
+          mkdir -p .ruff_cache && chmod -R 777 .ruff_cache
+          hatch run format
       - name: Run tests and generate coverage report
         run: make test
 
@@ -79,25 +78,21 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: 1.4.2
-          virtualenvs-create: true
-          virtualenvs-in-project: true
+      - name: Install Hatch
+        run: pip install hatch
       - name: Load cached venv
-        id: cached-poetry-dependencies
+        id: cached-hatch-dependencies
         uses: actions/cache@v3
         with:
           path: .venv
-          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
       - name: Install dependencies
         run: cd embedchain && make install_all
-        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
       - name: Run Formatting
         run: |
           mkdir -p embedchain/.ruff_cache && chmod -R 777 embedchain/.ruff_cache
-          cd embedchain && poetry run ruff check . --select F
+          cd embedchain && hatch run format
       - name: Lint with ruff
         run: cd embedchain && make lint
       - name: Run tests and generate coverage report

Makefile

@@ -8,37 +8,36 @@ PROJECT_NAME := mem0ai
 all: format sort lint
 
 install:
-	poetry install
+	hatch env create
 
 install_all:
-	poetry install
-	poetry run pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
+	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
 	                        google-generativeai elasticsearch opensearch-py vecs pinecone pinecone-text faiss-cpu langchain-community \
-							upstash-vector azure-search-documents langchain-memgraph
+							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25
 
 # Format code with ruff
 format:
-	poetry run ruff format mem0/
+	hatch run format
 
 # Sort imports with isort
 sort:
-	poetry run isort mem0/
+	hatch run isort mem0/
 
 # Lint code with ruff
 lint:
-	poetry run ruff check mem0/
+	hatch run lint
 
 docs:
 	cd docs && mintlify dev
 
 build:
-	poetry build
+	hatch build
 
 publish:
-	poetry publish
+	hatch publish
 
 clean:
-	poetry run rm -rf dist
+	rm -rf dist
 
 test:
-	poetry run pytest tests
+	hatch run test

cookbooks/customer-support-chatbot.ipynb

@@ -13,7 +13,7 @@
     "import anthropic\n",
     "\n",
     "# Set up environment variables\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\" # needed for embedding model\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\"  # needed for embedding model\n",
     "os.environ[\"ANTHROPIC_API_KEY\"] = \"your_anthropic_api_key\""
    ]
   },
@@ -33,7 +33,7 @@
     "                    \"model\": \"claude-3-5-sonnet-latest\",\n",
     "                    \"temperature\": 0.1,\n",
     "                    \"max_tokens\": 2000,\n",
-    "                }\n",
+    "                },\n",
     "            }\n",
     "        }\n",
     "        self.client = anthropic.Client(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n",
@@ -50,11 +50,7 @@
     "        - Keep track of open issues and follow-ups\n",
     "        \"\"\"\n",
     "\n",
-    "    def store_customer_interaction(self,\n",
-    "                                 user_id: str,\n",
-    "                                 message: str,\n",
-    "                                 response: str,\n",
-    "                                 metadata: Dict = None):\n",
+    "    def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):\n",
     "        \"\"\"Store customer interaction in memory.\"\"\"\n",
     "        if metadata is None:\n",
     "            metadata = {}\n",
@@ -63,24 +59,17 @@
     "        metadata[\"timestamp\"] = datetime.now().isoformat()\n",
     "\n",
     "        # Format conversation for storage\n",
-    "        conversation = [\n",
-    "            {\"role\": \"user\", \"content\": message},\n",
-    "            {\"role\": \"assistant\", \"content\": response}\n",
-    "        ]\n",
+    "        conversation = [{\"role\": \"user\", \"content\": message}, {\"role\": \"assistant\", \"content\": response}]\n",
     "\n",
     "        # Store in Mem0\n",
-    "        self.memory.add(\n",
-    "            conversation,\n",
-    "            user_id=user_id,\n",
-    "            metadata=metadata\n",
-    "        )\n",
+    "        self.memory.add(conversation, user_id=user_id, metadata=metadata)\n",
     "\n",
     "    def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:\n",
     "        \"\"\"Retrieve relevant past interactions.\"\"\"\n",
     "        return self.memory.search(\n",
     "            query=query,\n",
     "            user_id=user_id,\n",
-    "            limit=5  # Adjust based on needs\n",
+    "            limit=5,  # Adjust based on needs\n",
     "        )\n",
     "\n",
     "    def handle_customer_query(self, user_id: str, query: str) -> str:\n",
@@ -112,15 +101,12 @@
     "            model=\"claude-3-5-sonnet-latest\",\n",
     "            messages=[{\"role\": \"user\", \"content\": prompt}],\n",
     "            max_tokens=2000,\n",
-    "            temperature=0.1\n",
+    "            temperature=0.1,\n",
     "        )\n",
     "\n",
     "        # Store interaction\n",
     "        self.store_customer_interaction(\n",
-    "            user_id=user_id,\n",
-    "            message=query,\n",
-    "            response=response,\n",
-    "            metadata={\"type\": \"support_query\"}\n",
+    "            user_id=user_id, message=query, response=response, metadata={\"type\": \"support_query\"}\n",
     "        )\n",
     "\n",
     "        return response.content[0].text"
@@ -203,12 +189,12 @@
     "    # Get user input\n",
     "    query = input()\n",
     "    print(\"Customer:\", query)\n",
-    "    \n",
+    "\n",
     "    # Check if user wants to exit\n",
-    "    if query.lower() == 'exit':\n",
+    "    if query.lower() == \"exit\":\n",
     "        print(\"Thank you for using our support service. Goodbye!\")\n",
     "        break\n",
-    "    \n",
+    "\n",
     "    # Handle the query and print the response\n",
     "    response = chatbot.handle_customer_query(user_id, query)\n",
     "    print(\"Support:\", response, \"\\n\\n\")"

cookbooks/mem0-autogen.ipynb

@@ -25,7 +25,8 @@
    "source": [
     "# Set up ENV Vars\n",
     "import os\n",
-    "os.environ['OPENAI_API_KEY'] = \"sk-xxx\"\n"
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
    ]
   },
   {
@@ -133,11 +134,9 @@
     "assistant_id = os.environ.get(\"ASSISTANT_ID\", None)\n",
     "\n",
     "# LLM Configuration\n",
-    "CACHE_SEED = 42 # choose your poison\n",
+    "CACHE_SEED = 42  # choose your poison\n",
     "llm_config = {\n",
-    "    \"config_list\": [\n",
-    "        {\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}\n",
-    "    ],\n",
+    "    \"config_list\": [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}],\n",
     "    \"cache_seed\": CACHE_SEED,\n",
     "    \"timeout\": 120,\n",
     "    \"temperature\": 0.0,\n",
@@ -348,7 +347,7 @@
    "source": [
     "# Retrieve the memory\n",
     "relevant_memories = MEM0_MEMORY_CLIENT.search(user_query, user_id=USER_ID, limit=3)\n",
-    "relevant_memories_text = '\\n'.join(mem['memory'] for mem in relevant_memories)\n",
+    "relevant_memories_text = \"\\n\".join(mem[\"memory\"] for mem in relevant_memories)\n",
     "print(\"Relevant memories:\")\n",
     "print(relevant_memories_text)\n",
     "\n",
@@ -389,8 +388,8 @@
     "# - Enables more context-aware and personalized agent responses.\n",
     "# - Bridges the gap between human input and AI processing in complex workflows.\n",
     "\n",
-    "class Mem0ProxyCoderAgent(UserProxyAgent):\n",
     "\n",
+    "class Mem0ProxyCoderAgent(UserProxyAgent):\n",
     "    def __init__(self, *args, **kwargs):\n",
     "        super().__init__(*args, **kwargs)\n",
     "        self.memory = MEM0_MEMORY_CLIENT\n",
@@ -399,15 +398,14 @@
     "    def initiate_chat(self, assistant, message):\n",
     "        # Retrieve memory for the agent\n",
     "        agent_memories = self.memory.search(message, agent_id=self.agent_id, limit=3)\n",
-    "        agent_memories_txt = '\\n'.join(mem['memory'] for mem in agent_memories)\n",
+    "        agent_memories_txt = \"\\n\".join(mem[\"memory\"] for mem in agent_memories)\n",
     "        prompt = f\"{message}\\n Coding Preferences: \\n{str(agent_memories_txt)}\"\n",
     "        response = super().initiate_chat(assistant, message=prompt)\n",
     "        # Add new memory after processing the message\n",
     "        response_dist = response.__dict__ if not isinstance(response, dict) else response\n",
     "        MEMORY_DATA = [{\"role\": \"user\", \"content\": message}, {\"role\": \"assistant\", \"content\": response_dist}]\n",
     "        self.memory.add(MEMORY_DATA, agent_id=self.agent_id)\n",
-    "        return response\n",
-    "    "
+    "        return response"
    ]
   },
   {
@@ -560,12 +558,12 @@
     "from cookbooks.helper.mem0_teachability import Mem0Teachability\n",
     "\n",
     "teachability = Mem0Teachability(\n",
-    "                verbosity=2,  # for visibility of what's happening\n",
-    "                recall_threshold=0.5,\n",
-    "                reset_db=False,  # Use True to force-reset the memo DB, and False to use an existing DB.\n",
-    "                agent_id=AGENT_ID,\n",
-    "                memory_client = MEM0_MEMORY_CLIENT,\n",
-    "            )\n",
+    "    verbosity=2,  # for visibility of what's happening\n",
+    "    recall_threshold=0.5,\n",
+    "    reset_db=False,  # Use True to force-reset the memo DB, and False to use an existing DB.\n",
+    "    agent_id=AGENT_ID,\n",
+    "    memory_client=MEM0_MEMORY_CLIENT,\n",
+    ")\n",
     "teachability.add_to_agent(user_proxy)"
    ]
   },

docs/api-reference/memory/v2-get-memories.mdx

@@ -3,12 +3,15 @@ title: 'Get Memories (v2)'
 openapi: post /v2/memories/
 ---
 
-The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
 - `gt`: Greater than
 - `lt`: Less than
+- `ne`: Not equal to
+- `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
 
 <CodeGroup>
 ```python Code
@@ -41,3 +44,22 @@ memories = m.get_all(
 ]
 ```
 </CodeGroup>
+
+<CodeGroup>
+```python Wildcard Example
+# Using wildcard to get all memories for a specific user across all run_ids
+memories = m.get_all(
+    filters={
+        "AND": [
+            {
+                "user_id": "alex"
+            },
+            {
+                "run_id": "*"
+            }
+        ]
+    },
+    version="v2"
+)
+```
+</CodeGroup>

docs/api-reference/memory/v2-search-memories.mdx

@@ -3,7 +3,7 @@ title: 'Search Memories (v2)'
 openapi: post /v2/memories/search/
 ---
 
-The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
@@ -11,6 +11,7 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
 - `lt`: Less than
 - `ne`: Not equal to
 - `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
 
   <CodeGroup>
   ```python Code
@@ -49,3 +50,23 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
   }
   ```
   </CodeGroup>
+
+  <CodeGroup>
+  ```python Wildcard Example
+  # Using wildcard to match all run_ids for a specific user
+  all_memories = m.search(
+      query="What are Alice's hobbies?",
+      version="v2",
+      filters={
+          "AND": [
+              {
+                  "user_id": "alice"
+              },
+              {
+                  "run_id": "*"
+              }
+          ]
+      },
+  )
+  ```
+  </CodeGroup>

docs/changelog.mdx

@@ -8,6 +8,85 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2025-05-10" description="v0.1.100">
+
+**New Features:**
+- **Memory:** Added Group Chat Memory Feature support
+- **Examples:** Added Healthcare assistant using Mem0 and Google ADK
+
+**Bug Fixes:**
+- **SSE:** Fixed SSE connection issues
+- **MCP:** Fixed memories not appearing in MCP clients added from Dashboard
+
+</Update>
+
+<Update label="2025-05-07" description="v0.1.99">
+
+**New Features:**
+- **OpenMemory:** Added OpenMemory support
+- **Neo4j:** Added weights to Neo4j model
+- **AWS:** Added support for Opsearch Serverless
+- **Examples:** Added ElizaOS Example
+
+**Improvements:**
+- **Documentation:** Updated Azure AI documentation
+- **AI SDK:** Added missing parameters and updated demo application
+- **OSS:** Fixed AOSS and AWS BedRock LLM
+
+</Update>
+
+<Update label="2025-04-30" description="v0.1.98">
+
+**New Features:**
+- **Neo4j:** Added support for Neo4j database
+- **AWS:** Added support for AWS Bedrock Embeddings
+
+**Improvements:**
+- **Client:** Updated delete_users() to use V2 API endpoints
+- **Documentation:** Updated timestamp and dual-identity memory management docs
+- **Neo4j:** Improved Neo4j queries and removed warnings
+- **AI SDK:** Added support for graceful failure when services are down
+
+**Bug Fixes:**
+- Fixed AI SDK filters
+- Fixed new memories wrong type
+- Fixed duplicated metadata issue while adding/updating memories
+
+</Update>
+
+<Update label="2025-04-23" description="v0.1.97">
+
+**New Features:**
+- **HuggingFace:** Added support for HF Inference
+
+**Bug Fixes:**
+- Fixed proxy for Mem0
+
+</Update>
+
+<Update label="2025-04-16" description="v0.1.96">
+
+**New Features:**
+- **Vercel AI SDK:** Added Graph Memory support
+
+**Improvements:**
+- **Documentation:** Fixed timestamp and README links
+- **Client:** Updated TS client to use proper types for deleteUsers
+- **Dependencies:** Removed unnecessary dependencies from base package
+
+</Update>
+
+<Update label="2025-04-09" description="v0.1.95">
+
+**Improvements:**
+- **Client:** Fixed Ping Method for using default org_id and project_id
+- **Documentation:** Updated documentation
+
+**Bug Fixes:**
+- Fixed mem0-migrations issue
+
+</Update>
+
 <Update label="2025-04-26" description="v0.1.94">
 
 **New Features:**
@@ -209,6 +288,34 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
+<Update label="2025-06-06" description="v2.1.30">
+**New Features:**
+- **OSS:** Added Cloudflare support
+
+**Improvements:**
+- **OSS:** Fixed baseURL param in LLM Config.
+</Update>
+
+<Update label="2025-05-30" description="v2.1.29">
+**Improvements:**
+- **Client:** Added Async Mode Param for `add` method.
+</Update>
+
+<Update label="2025-05-30" description="v2.1.28">
+**Improvements:**
+- **SDK:** Update Google SDK Peer Dependency Version.
+</Update>
+
+<Update label="2025-05-27" description="v2.1.27">
+**Improvements:**
+- **OSS:** Added baseURL param in LLM Config.
+</Update>
+  
+<Update label="2025-05-23" description="v2.1.26">
+**Improvements:**
+- **Client:** Removed type `string` from `messages` interface
+</Update>
+
 <Update label="2025-05-08" description="v2.1.25">
 **Improvements:**
 - **Client:** Improved error handling in client.
@@ -330,6 +437,104 @@ mode: "wide"
 
 <Tab title="Platform">
 
+<Update label="2025-05-19" description="">
+
+**Bug Fixes:**
+- **Core:** Fixed unicode error in user_id, agent_id, run_id and app_id
+
+</Update>
+
+<Update label="2025-05-17" description="">
+
+**New Features:**
+- **Graph:** Added Neo4J Graph Migration
+- **API:** Added API to set custom instructions
+
+</Update>
+
+<Update label="2025-05-16" description="">
+
+**New Features:**
+- **API:** Added Org-wide API Limit and Usage
+
+**Improvements:**
+- **Database:** Added migration for "is_deleted" column
+- **Graph:** Improved graph queries
+
+</Update>
+
+<Update label="2025-05-15" description="">
+
+**New Features:**
+- **Lambda:** Added actions to lambda
+- **Core:** Added background runs support
+- **Models:** Added o4-mini for pro users
+
+</Update>
+
+<Update label="2025-05-10" description="">
+
+**New Features:**
+- **Integrations:** Added Intercom Events integration
+- **Billing:** Added prefilled email for payments
+- **Organizations:** Added Pro organization marking
+
+**Improvements:**
+- **UI:** Fixed loading jitter for organization selection
+- **Infrastructure:** Improved production scaling
+
+</Update>
+
+<Update label="2025-05-09" description="">
+
+**Improvements:**
+- **Memory:** Fixed filters in Memory Page
+- **Deployment:** Added custom categories for on-premise
+
+</Update>
+
+<Update label="2025-05-08" description="">
+
+**Improvements:**
+- **Backend:** Updated Django settings for metrics
+- **Memory:** Added retries to memory filtering
+- **Search:** Added scoring mechanism
+
+</Update>
+
+<Update label="2025-05-07" description="">
+
+**Improvements:**
+- **Deployment:** Updated deployment scripts
+- **Testing:** Added code coverage tracking
+- **Memory:** Added background cron job for memory quality
+
+</Update>
+
+<Update label="2025-05-06" description="">
+
+**New Features:**
+- **Models:** Added support for 4.1-mini model
+
+**Improvements:**
+- **Infrastructure:** Increased instance count
+- **API:** Added V2 for Manage Entities
+
+</Update>
+
+<Update label="2025-05-04" description="">
+
+**New Features:**
+- **Testing:** Added code coverage tracking
+- **AI:** Added Keywords AI integration
+
+**Improvements:**
+- **UI:** Updated UI with tabs
+- **Database:** Added migrations for custom instructions
+- **Search:** Added criteria filtering
+
+</Update>
+
 <Update label="2025-04-26" description="">
 
 **Improvements:**
@@ -487,6 +692,11 @@ mode: "wide"
 
 <Tab title="Vercel AI SDK">
 
+<Update label="2025-05-23" description="v1.0.5">
+**New Features:**
+- **Vercel AI SDK:** Added support for Google provider.
+</Update>
+
 <Update label="2025-05-10" description="v1.0.4">
 **New Features:**
 - **Vercel AI SDK:** Added support for new param `output_format`.

docs/components/llms/config.mdx

@@ -110,6 +110,12 @@ Here's a comprehensive list of all parameters that can be used across different
     | `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
+    | `sarvam_base_url`    | Base URL for Sarvam API                       | Sarvam            |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | Sarvam            |
+    | `frequency_penalty`  | Penalize frequent tokens (-2.0 to 2.0)        | Sarvam            |
+    | `presence_penalty`   | Penalize existing tokens (-2.0 to 2.0)        | Sarvam            |
+    | `seed`               | Seed for deterministic sampling               | Sarvam            |
+    | `stop`               | Stop sequences (max 4)                        | Sarvam            |
     | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
   </Tab>
   <Tab title="TypeScript">

docs/components/llms/models/anthropic.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-3-7-sonnet-latest",
+            "model": "claude-sonnet-4-20250514",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -45,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-3-7-sonnet-latest',
+      model: 'claude-sonnet-4-20250514',
       temperature: 0.1,
       maxTokens: 2000,
     },

docs/components/llms/models/sarvam.mdx

@@ -0,0 +1,75 @@
+---
+title: Sarvam AI
+---
+
+<Snippet file="paper-release.mdx" />
+
+**Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.
+
+To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get from their [platform](https://dashboard.sarvam.ai/).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["SARVAM_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "sarvam",
+        "config": {
+            "model": "sarvam-m",
+            "temperature": 0.7,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a 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="alex")
+```
+
+## Advanced Usage with Sarvam-Specific Features
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "llm": {
+        "provider": "sarvam",
+        "config": {
+            "model": {
+                "name": "sarvam-m",
+                "reasoning_effort": "high",  # Enable advanced reasoning
+                "frequency_penalty": 0.1,    # Reduce repetition
+                "seed": 42                   # For deterministic outputs
+            },
+            "temperature": 0.3,
+            "max_tokens": 2000,
+            "api_key": "your-sarvam-api-key"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+
+# Example with Hindi conversation
+messages = [
+    {"role": "user", "content": "मैं SBI में joint account खोलना चाहता हूँ।"},
+    {"role": "assistant", "content": "SBI में joint account खोलने के लिए आपको कुछ documents की जरूरत होगी। क्या आप जानना चाहते हैं कि कौन से documents चाहिए?"}
+]
+m.add(messages, user_id="rajesh", metadata={"language": "hindi", "topic": "banking"})
+```
+
+## Config
+
+All available parameters for the `sarvam` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -34,6 +34,7 @@ To view all supported llms, visit the [Supported LLMs](./models).
   <Card title="Gemini" href="/components/llms/models/gemini" />
   <Card title="DeepSeek" href="/components/llms/models/deepseek" />
   <Card title="xAI" href="/components/llms/models/xAI" />
+  <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
   <Card title="LM Studio" href="/components/llms/models/lmstudio" />
   <Card title="Langchain" href="/components/llms/models/langchain" />
 </CardGroup>

docs/components/vectordbs/dbs/vectorize.mdx

@@ -0,0 +1,45 @@
+[Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) is a vector database offering from Cloudflare, allowing you to build AI-powered applications with vector embeddings.
+
+### Usage
+
+<CodeGroup>
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'vectorize',
+    config: {
+      indexName: 'my-memory-index',
+      accountId: 'your-cloudflare-account-id',
+      apiKey: 'your-cloudflare-api-key',
+      dimension: 1536, // Optional: defaults to 1536
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm looking for a good book to read."},
+    {"role": "assistant", "content": "Sure, what genre are you interested in?"},
+    {"role": "user", "content": "I enjoy fantasy novels with strong world-building."},
+    {"role": "assistant", "content": "Great! I'll keep that in mind for future recommendations."}
+]
+await memory.add(messages, { userId: "bob", metadata: { interest: "books" } });
+```
+</CodeGroup>
+
+### Config
+
+Let's see the available parameters for the `vectorize` config:
+
+<Tabs>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `indexName` | The name of the Vectorize index | `None` (Required) |
+| `accountId` | Your Cloudflare account ID | `None` (Required) |
+| `apiKey` | Your Cloudflare API token | `None` (Required) |
+| `dimension` | Dimensions of the embedding model | `1536` |
+</Tab>
+</Tabs>

docs/components/vectordbs/overview.mdx

@@ -13,7 +13,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis and in-memory vector database.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis,Vectorize and in-memory vector database.
 </Note>
 
 <CardGroup cols={3}>

docs/contributing/development.mdx

@@ -29,7 +29,7 @@ For detailed guidance on pull requests, refer to [GitHub's documentation](https:
 
 ## 📦 Dependency Management
 
-We use `poetry` as our package manager. Install it by following the [official instructions](https://python-poetry.org/docs/#installation).
+We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
 ⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, run:
 
@@ -37,7 +37,7 @@ We use `poetry` as our package manager. Install it by following the [official in
 make install_all
 
 # Activate virtual environment
-poetry shell
+hatch shell
 ```
 
 ---
@@ -60,9 +60,9 @@ Run the linter and fix any reported issues before submitting your PR:
 make lint
 ```
 
-### 🎨 Code Formatting with `black`
+### 🎨 Code Formatting
 
-To maintain a consistent code style, format your code using `black`:
+To maintain a consistent code style, format your code:
 
 ```bash
 make format
@@ -76,7 +76,7 @@ Run tests to verify functionality before submitting your PR:
 make test
 ```
 
-💡 **Note:** Some dependencies have been removed from Poetry to reduce package size. Run `make install_all` to install necessary dependencies before running tests.
+💡 **Note:** Some dependencies have been removed from the main dependencies to reduce package size. Run `make install_all` to install necessary dependencies before running tests.
 
 ---
 

docs/docs.json

@@ -45,21 +45,22 @@
                     "group": "Features",
                     "icon": "star",
                     "pages": [
-                      "features/platform-overview",
-                      "features/advanced-retrieval",
-                      "features/contextual-add",
-                      "features/multimodal-support",
-                      "features/timestamp",
-                      "features/selective-memory",
-                      "features/custom-categories",
-                      "features/custom-instructions",
-                      "features/direct-import",
-                      "features/async-client",
-                      "features/memory-export",
-                      "features/webhooks",
-                      "features/graph-memory",
-                      "features/feedback-mechanism",
-                      "features/expiration-date"
+                      "platform/features/platform-overview",
+                      "platform/features/advanced-retrieval",
+                      "platform/features/criteria-retrieval",
+                      "platform/features/contextual-add",
+                      "platform/features/multimodal-support",
+                      "platform/features/timestamp",
+                      "platform/features/selective-memory",
+                      "platform/features/custom-categories",
+                      "platform/features/custom-instructions",
+                      "platform/features/direct-import",
+                      "platform/features/async-client",
+                      "platform/features/memory-export",
+                      "platform/features/webhooks",
+                      "platform/features/graph-memory",
+                      "platform/features/feedback-mechanism",
+                      "platform/features/expiration-date"
                     ]
                   }
                 ]
@@ -76,10 +77,10 @@
                     "icon": "wrench",
                     "pages": [
                       "open-source/features/async-memory",
-                      "features/openai_compatibility",
-                      "features/custom-fact-extraction-prompt",
-                      "features/custom-update-memory-prompt",
-                      "open-source/multimodal-support",
+                      "open-source/features/openai_compatibility",
+                      "open-source/features/custom-fact-extraction-prompt",
+                      "open-source/features/custom-update-memory-prompt",
+                      "open-source/features/multimodal-support",
                       "open-source/features/rest-api"
                     ]
                   },
@@ -114,6 +115,7 @@
                           "components/llms/models/gemini",
                           "components/llms/models/deepseek",
                           "components/llms/models/xAI",
+                          "components/llms/models/sarvam",
                           "components/llms/models/lmstudio",
                           "components/llms/models/langchain"
                         ]
@@ -203,6 +205,7 @@
                   "examples/aws_example",
                   "examples/mem0-demo",
                   "examples/ai_companion_js",
+                  "examples/collaborative-task-agent",
                   "examples/eliza_os",
                   "examples/mem0-mastra",
                   "examples/mem0-with-ollama",
@@ -217,6 +220,7 @@
                   "examples/mem0-agentic-tool",
                   "examples/openai-inbuilt-tools",
                   "examples/mem0-openai-voice-demo",
+                  "examples/mem0-google-adk-healthcare-assistant",
                   "examples/email_processing",
                   "examples/youtube-assistant"
                 ]
@@ -245,7 +249,9 @@
                   "integrations/elevenlabs",
                   "integrations/pipecat",
                   "integrations/agno",
-                  "integrations/keywords"
+                  "integrations/keywords",
+                  "integrations/raycast",
+                  "integrations/mastra"
                 ]
               }
             ]

docs/examples.mdx

@@ -75,7 +75,11 @@ Explore how **Mem0** can power real-world applications and bring personalized, i
 
   <Card title="Mem0 OpenAI Voice Demo" icon="microphone" href="/examples/mem0-openai-voice-demo">  
     Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.  
-  </Card> 
+  </Card>
+
+  <Card title="Healthcare Assistant Google ADK" icon="microphone" href="/examples/mem0-google-adk-healthcare-assistant">
+    Build a personalized healthcare assistant with persistent memory using Google's ADK and Mem0.
+  </Card>
 
   <Card title="Email Processing" icon="envelope" href="/examples/email_processing">  
     Use Mem0's memory capabilities to process emails and create AI agents with persistent memory.  

docs/examples/collaborative-task-agent.mdx

@@ -0,0 +1,125 @@
+---
+title: Multi-User Collaboration with Mem0
+---
+
+<Snippet file="paper-release.mdx" />
+
+## Overview
+
+Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.
+
+## Setup
+
+Install the required packages:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+import os
+from datetime import datetime
+from collections import defaultdict
+
+# Set your OpenAI API key
+os.environ["OPENAI_API_KEY"] = "sk-your-key"
+
+# Shared project context
+RUN_ID = "project-demo"
+
+# Initialize Mem0
+mem = Memory()
+
+class CollaborativeAgent:
+    def __init__(self, run_id):
+        self.run_id = run_id
+        self.mem = mem
+
+    def add_message(self, role, name, content):
+        msg = {"role": role, "name": name, "content": content}
+        self.mem.add([msg], run_id=self.run_id, infer=False)
+
+    def brainstorm(self, prompt):
+        # Get recent messages for context
+        memories = self.mem.search(prompt, run_id=self.run_id, limit=5)["results"]
+        context = "\n".join(f"- {m['memory']} (by {m.get('actor_id', 'Unknown')})" for m in memories)
+        client = OpenAI()
+        messages = [
+            {"role": "system", "content": "You are a helpful project assistant."},
+            {"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}
+        ]
+        reply = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages
+        ).choices[0].message.content.strip()
+        self.add_message("assistant", "assistant", reply)
+        return reply
+
+    def get_all_messages(self):
+        return self.mem.get_all(run_id=self.run_id)["results"]
+
+    def print_sorted_by_time(self):
+        messages = self.get_all_messages()
+        messages.sort(key=lambda m: m.get('created_at', ''))
+        print("\n--- Messages (sorted by time) ---")
+        for m in messages:
+            who = m.get("actor_id") or "Unknown"
+            ts = m.get('created_at', 'Timestamp N/A')
+            try:
+                dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
+                ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
+            except Exception:
+                ts_fmt = ts
+            print(f"[{ts_fmt}] [{who}] {m['memory']}")
+
+    def print_grouped_by_actor(self):
+        messages = self.get_all_messages()
+        grouped = defaultdict(list)
+        for m in messages:
+            grouped[m.get("actor_id") or "Unknown"].append(m)
+        print("\n--- Messages (grouped by actor) ---")
+        for actor, mems in grouped.items():
+            print(f"\n=== {actor} ===")
+            for m in mems:
+                ts = m.get('created_at', 'Timestamp N/A')
+                try:
+                    dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
+                    ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
+                except Exception:
+                    ts_fmt = ts
+                print(f"[{ts_fmt}] {m['memory']}")
+```
+
+## Usage
+
+```python
+# Example usage
+agent = CollaborativeAgent(RUN_ID)
+agent.add_message("user", "alice", "Let's list tasks for the new landing page.")
+agent.add_message("user", "bob", "I'll own the hero section copy.")
+agent.add_message("user", "carol", "I'll choose product screenshots.")
+
+# Brainstorm with context
+print("\nAssistant reply:\n", agent.brainstorm("What are the current open tasks?"))
+
+# Print all messages sorted by time
+agent.print_sorted_by_time()
+
+# Print all messages grouped by actor
+agent.print_grouped_by_actor()
+```
+
+## Key Points
+
+- Each message is attributed to a user or agent (actor)
+- All messages are stored in a shared project space (`run_id`)
+- You can sort messages by time, group by actor, and format timestamps for clarity
+- Mem0 makes it easy to build collaborative, attributed chat/task systems
+
+## Conclusion
+
+Mem0 enables fast, transparent collaboration for teams and agents, with full attribution, flexible memory search, and easy message organization.

docs/examples/mem0-google-adk-healthcare-assistant.mdx

@@ -0,0 +1,291 @@
+---
+title: 'Healthcare Assistant with Mem0 and Google ADK'
+description: 'Build a personalized healthcare agent that remembers patient information across conversations using Mem0 and Google ADK'
+---
+
+<Snippet file="paper-release.mdx" />
+
+# Healthcare Assistant with Memory
+
+This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.
+
+## Overview
+
+The Healthcare Assistant helps patients by:
+- Remembering their medical history and symptoms
+- Providing general health information
+- Scheduling appointment reminders
+- Maintaining a personalized experience across conversations
+
+By integrating Mem0's memory layer with Google ADK, the assistant maintains context about the patient without requiring them to repeat information.
+
+## Setup
+
+Before you begin, make sure you have:
+
+Installed Google ADK and Mem0 SDK:
+```bash
+pip install google-adk
+pip install mem0ai
+```
+
+## Code Breakdown
+
+Let's get started and understand the different components required in building a healthcare assistant powered by memory
+
+```python
+# Import dependencies
+import os
+from google.adk.agents import Agent
+from google.adk.sessions import InMemorySessionService
+from google.adk.runners import Runner
+from google.genai import types
+from mem0 import MemoryClient
+
+# Set up API keys (replace with your actual keys)
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Define a global user ID for simplicity
+USER_ID = "Alex"
+
+# Initialize Mem0 client
+mem0_client = MemoryClient()
+```
+
+## Define Memory Tools
+
+First, we'll create tools that allow our agent to store and retrieve information using Mem0:
+
+```python
+def save_patient_info(information: str) -> dict:
+    """Saves important patient information to memory."""
+
+    # Store in Mem0
+    response = mem0_client.add(
+        [{"role": "user", "content": information}],
+        user_id=USER_ID,
+        run_id="healthcare_session",
+        metadata={"type": "patient_information"}
+    )
+
+
+def retrieve_patient_info(query: str) -> dict:
+    """Retrieves relevant patient information from memory."""
+
+    # Search Mem0
+    results = mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+
+    # Format and return the results
+    if results and len(results) > 0:
+        memories = [memory["memory"] for memory in results.get('results', [])]
+        return {
+            "status": "success",
+            "memories": memories,
+            "count": len(memories)
+        }
+    else:
+        return {
+            "status": "no_results",
+            "memories": [],
+            "count": 0
+        }
+```
+
+## Define Healthcare Tools
+
+Next, we'll add tools specific to healthcare assistance:
+
+```python
+def schedule_appointment(date: str, time: str, reason: str) -> dict:
+    """Schedules a doctor's appointment."""
+    # In a real app, this would connect to a scheduling system
+    appointment_id = f"APT-{hash(date + time) % 10000}"
+
+    return {
+        "status": "success",
+        "appointment_id": appointment_id,
+        "confirmation": f"Appointment scheduled for {date} at {time} for {reason}",
+        "message": "Please arrive 15 minutes early to complete paperwork."
+    }
+```
+
+## Create the Healthcare Assistant Agent
+
+Now we'll create our main agent with all the tools:
+
+```python
+# Create the agent
+healthcare_agent = Agent(
+    name="healthcare_assistant",
+    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
+    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
+    instruction="""You are a helpful Healthcare Assistant with memory capabilities.
+
+Your primary responsibilities are to:
+1. Remember patient information using the 'save_patient_info' tool when they share symptoms, conditions, or preferences.
+2. Retrieve past patient information using the 'retrieve_patient_info' tool when relevant to the current conversation.
+3. Help schedule appointments using the 'schedule_appointment' tool.
+
+IMPORTANT GUIDELINES:
+- Always be empathetic, professional, and helpful.
+- Save important patient information like symptoms, conditions, allergies, and preferences.
+- Check if you have relevant patient information before asking for details they may have shared previously.
+- Make it clear you are not a doctor and cannot provide medical diagnosis or treatment.
+- For serious symptoms, always recommend consulting a healthcare professional.
+- Keep all patient information confidential.
+""",
+    tools=[save_patient_info, retrieve_patient_info, schedule_appointment]
+)
+```
+
+## Set Up Session and Runner
+
+```python
+# Set up Session Service and Runner
+session_service = InMemorySessionService()
+
+# Define constants for the conversation
+APP_NAME = "healthcare_assistant_app"
+USER_ID = "Alex"
+SESSION_ID = "session_001"
+
+# Create a session
+session = session_service.create_session(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+    session_id=SESSION_ID
+)
+
+# Create the runner
+runner = Runner(
+    agent=healthcare_agent,
+    app_name=APP_NAME,
+    session_service=session_service
+)
+```
+
+## Interact with the Healthcare Assistant
+
+```python
+# Function to interact with the agent
+async def call_agent_async(query, runner, user_id, session_id):
+    """Sends a query to the agent and returns the final response."""
+    print(f"\n>>> Patient: {query}")
+
+    # Format the user's message
+    content = types.Content(
+        role='user',
+        parts=[types.Part(text=query)]
+    )
+
+    # Set user_id for tools to access
+    save_patient_info.user_id = user_id
+    retrieve_patient_info.user_id = user_id
+
+    # Run the agent
+    async for event in runner.run_async(
+        user_id=user_id,
+        session_id=session_id,
+        new_message=content
+    ):
+        if event.is_final_response():
+            if event.content and event.content.parts:
+                response = event.content.parts[0].text
+                print(f"<<< Assistant: {response}")
+                return response
+
+    return "No response received."
+
+# Example conversation flow
+async def run_conversation():
+    # First interaction - patient introduces themselves with key information
+    await call_agent_async(
+        "Hi, I'm Alex. I've been having headaches for the past week, and I have a penicillin allergy.",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Request for health information
+    await call_agent_async(
+        "Can you tell me more about what might be causing my headaches?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Schedule an appointment
+    await call_agent_async(
+        "I think I should see a doctor. Can you help me schedule an appointment for next Monday at 2pm?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Test memory - should remember patient name, symptoms, and allergy
+    await call_agent_async(
+        "What medications should I avoid for my headaches?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+# Run the conversation example
+if __name__ == "__main__":
+    asyncio.run(run_conversation())
+```
+
+## How It Works
+
+This healthcare assistant demonstrates several key capabilities:
+
+1. **Memory Storage**: When Alex mentions her headaches and penicillin allergy, the agent stores this information in Mem0 using the `save_patient_info` tool.
+
+2. **Contextual Retrieval**: When Alex asks about headache causes, the agent uses the `retrieve_patient_info` tool to recall her specific situation.
+
+3. **Memory Application**: When discussing medications, the agent remembers Alex's penicillin allergy without her needing to repeat it, providing safer and more personalized advice.
+
+4. **Conversation Continuity**: The agent maintains context across the entire conversation session, creating a more natural and efficient interaction.
+
+## Key Implementation Details
+
+### User ID Management
+
+Instead of passing the user ID as a parameter to the memory tools (which would require modifying the ADK's tool calling system), we attach it directly to the function object:
+
+```python
+# Set user_id for tools to access
+save_patient_info.user_id = user_id
+retrieve_patient_info.user_id = user_id
+```
+
+Inside the tool functions, we retrieve this attribute:
+
+```python
+# Get user_id from session state or use default
+user_id = getattr(save_patient_info, 'user_id', 'default_user')
+```
+
+This approach allows our tools to maintain user context without complicating their parameter signatures.
+
+### Mem0 Integration
+
+The integration with Mem0 happens through two primary functions:
+
+1. `mem0_client.add()` - Stores new information with appropriate metadata
+2. `mem0_client.search()` - Retrieves relevant memories using semantic search
+
+The `threshold` parameter in the search function ensures that only highly relevant memories are returned.
+
+## Conclusion
+
+This example demonstrates how to build a healthcare assistant with persistent memory using Google ADK and Mem0. The integration allows for a more personalized patient experience by maintaining context across conversation turns, which is particularly valuable in healthcare scenarios where continuity of information is crucial.
+
+By storing and retrieving patient information intelligently, the assistant provides more relevant responses without requiring the patient to repeat their medical history, symptoms, or preferences.

docs/integrations.mdx

@@ -322,4 +322,60 @@ Here are the available integrations for Mem0:
   >
     Build AI applications with persistent memory and comprehensive LLM observability.
   </Card>
+  <Card
+    title="Raycast"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+        viewBox="0 0 24 24"
+        fill="none"
+      >
+        <path
+          d="M3 12L21 12M12 3L12 21M7.5 7.5L16.5 16.5M16.5 7.5L7.5 16.5"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinecap="round"
+        />
+      </svg>
+    }
+    href="/integrations/raycast"
+  >
+    Mem0 Raycast extension for intelligent memory management and retrieval.
+  </Card>
+  <Card
+    title="Mastra"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+        viewBox="0 0 24 24"
+        fill="none"
+      >
+        <path
+          d="M12 2L22 7L12 12L2 7L12 2Z"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+        <path
+          d="M2 17L12 22L22 17"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+        <path
+          d="M2 12L12 17L22 12"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+      </svg>
+    }
+    href="/integrations/mastra"
+  >
+    Build AI agents with persistent memory using Mastra's framework and tools.
+  </Card>
 </CardGroup>

docs/integrations/livekit.mdx

@@ -12,8 +12,7 @@ Before you begin, make sure you have:
 
 1. Installed Livekit Agents SDK with voice dependencies of silero and deepgram:
 ```bash
-pip install livekit \
-livekit-agents \
+pip install livekit-agents[voice] \
 livekit-plugins-silero \
 livekit-plugins-deepgram \
 livekit-plugins-openai
@@ -38,7 +37,7 @@ OPENAI_API_KEY=your_openai_api_key
 
 ## Code Breakdown
 
-Let's break down the key components of this implementation:
+Let's break down the key components of this implementation using LiveKit Agents:
 
 ### 1. Setting Up Dependencies and Environment
 
@@ -51,17 +50,19 @@ from typing import List, Dict, Any, Annotated
 import aiohttp
 from dotenv import load_dotenv
 from livekit.agents import (
+    Agent,
+    AgentSession,
     AutoSubscribe,
     JobContext,
-    JobProcess,
-    WorkerOptions,
-    cli,
     llm,
-    metrics,
+    function_tool,
+    RunContext,
+    cli,
+    WorkerOptions,
+    ModelSettings,
 )
-from livekit import rtc, api
-from livekit.agents.pipeline import VoicePipelineAgent
 from livekit.plugins import deepgram, openai, silero
+from livekit.plugins.turn_detector.multilingual import MultilingualModel
 from mem0 import AsyncMemoryClient
 
 # Load environment variables
@@ -88,36 +89,45 @@ This section handles:
 ### 2. Memory Enrichment Function
 
 ```python
-async def _enrich_with_memory(agent: VoicePipelineAgent, chat_ctx: llm.ChatContext):
-    """Add memories and Augment chat context with relevant memories"""
+async def _enrich_with_memory(chat_ctx: llm.ChatContext):
+    """Add memories and augment chat context with relevant memories"""
     if not chat_ctx.messages:
         return
-    
-    # Store user message in Mem0
+
+    # Get the latest user message
     user_msg = chat_ctx.messages[-1]
+    if user_msg.role != "user":
+        return
+
+    user_content = user_msg.text_content()
+    if not user_content:
+        return
+
+    # Store user message in Mem0
     await mem0.add(
-        [{"role": "user", "content": user_msg.content}], 
+        [{"role": "user", "content": user_content}],
         user_id=USER_ID
     )
-    
+
     # Search for relevant memories
     results = await mem0.search(
-        user_msg.content, 
+        user_content,
         user_id=USER_ID,
     )
-    
+
     # Augment context with retrieved memories
     if results:
         memories = ' '.join([result["memory"] for result in results])
         logger.info(f"Enriching with memory: {memories}")
-        
-        rag_msg = llm.ChatMessage.create(
+
+        # Add memory context as a assistant message
+        memory_msg = llm.ChatMessage.create(
             text=f"Relevant Memory: {memories}\n",
             role="assistant",
         )
-        
+
         # Modify chat context with retrieved memories
-        chat_ctx.messages[-1] = rag_msg
+        chat_ctx.messages[-1] = memory_msg
         chat_ctx.messages.append(user_msg)
 ```
 
@@ -130,62 +140,45 @@ This function:
 ### 3. Prewarm and Entrypoint Functions
 
 ```python
-def prewarm_process(proc: JobProcess):
-    # Preload silero VAD in memory to speed up session start
+def prewarm_process(proc):
+    """Preload components to speed up session start"""
     proc.userdata["vad"] = silero.VAD.load()
 
 async def entrypoint(ctx: JobContext):
+    """Main entrypoint for the memory-enabled voice agent"""
+
     # Connect to LiveKit room
     await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
-    
-    # Wait for participant
-    participant = await ctx.wait_for_participant()
-    
-    # Initialize Mem0 client
-    mem0 = AsyncMemoryClient()
 
-    # Define initial system context
-    initial_ctx = llm.ChatContext().append(
-        role="system",
-        text=(
-            """
-            You are a helpful voice assistant.
-            You are a travel guide named George and will help the user to plan a travel trip of their dreams. 
-            You should help the user plan for various adventures like work retreats, family vacations or solo backpacking trips. 
-            You should be careful to not suggest anything that would be dangerous, illegal or inappropriate.
-            You can remember past interactions and use them to inform your answers.
-            Use semantic memory retrieval to provide contextually relevant responses. 
-            """
-        ),
-    )
-
-    # Create VoicePipelineAgent with memory capabilities
-    agent = VoicePipelineAgent(
-        chat_ctx=initial_ctx,
-        vad=silero.VAD.load(),
+    # Create agent session with modern 1.0 architecture
+    session = AgentSession(
         stt=deepgram.STT(),
         llm=openai.LLM(model="gpt-4o-mini"),
         tts=openai.TTS(),
-        before_llm_cb=_enrich_with_memory,
+        vad=silero.VAD.load(),
+        turn_detection=MultilingualModel(),
     )
 
-    # Start agent and initial greeting
-    agent.start(ctx.room, participant)
-    await agent.say(
-        "Hello! I'm George. Can I help you plan an upcoming trip? ",
-        allow_interruptions=True
+    # Create memory-enabled agent
+    agent = MemoryEnabledAgent()
+
+    # Start the session
+    await session.start(
+        room=ctx.room,
+        agent=agent,
     )
 
-# Run the application
-if __name__ == "__main__":
-    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, prewarm_fnc=prewarm_process))
+    # Initial greeting
+    await session.generate_reply(
+        instructions="Greet the user warmly as George the travel guide and ask how you can help them plan their next adventure."
+    )
 ```
 
 The entrypoint function:
 - Connects to LiveKit room
 - Initializes Mem0 memory client
-- Sets up initial system context
-- Creates a VoicePipelineAgent with memory enrichment
+- Create agent session using `AgentSession` orchestrator with memory enrichment
+- Uses modern turn detection with `MultilingualModel()`
 - Starts the agent with an initial greeting
 
 ## Create a Memory-Enabled Voice Agent
@@ -196,22 +189,22 @@ Now that we've explained each component, here's the complete implementation that
 import asyncio
 import logging
 import os
-from typing import List, Dict, Any, Annotated
+from typing import AsyncIterable, Any
 
-import aiohttp
 from dotenv import load_dotenv
 from livekit.agents import (
-    AutoSubscribe,
+    Agent,
+    AgentSession,
     JobContext,
-    JobProcess,
-    WorkerOptions,
-    cli,
     llm,
-    metrics,
+    function_tool,
+    RunContext,
+    cli,
+    WorkerOptions,
+    ModelSettings,
 )
-from livekit import rtc, api
-from livekit.agents.pipeline import VoicePipelineAgent
 from livekit.plugins import deepgram, openai, silero
+from livekit.plugins.turn_detector.multilingual import MultilingualModel
 from mem0 import AsyncMemoryClient
 
 # Load environment variables
@@ -227,92 +220,129 @@ USER_ID = "voice_user"
 # Initialize Mem0 memory client
 mem0 = AsyncMemoryClient()
 
-def prewarm_process(proc: JobProcess):
-    # Preload silero VAD in memory to speed up session start
-    proc.userdata["vad"] = silero.VAD.load()
+class MemoryEnabledAgent(Agent):
+    """Travel guide agent with Mem0 memory integration"""
 
-async def entrypoint(ctx: JobContext):
-    # Connect to LiveKit room
-    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
-    
-    # Wait for participant
-    participant = await ctx.wait_for_participant()
-    
-    async def _enrich_with_memory(agent: VoicePipelineAgent, chat_ctx: llm.ChatContext):
-        """Add memories and Augment chat context with relevant memories"""
+    def __init__(self):
+        super().__init__(
+            instructions="""
+            You are a helpful voice assistant.
+            You are a travel guide named George and will help the user to plan a travel trip of their dreams.
+            You should help the user plan for various adventures like work retreats, family vacations or solo backpacking trips.
+            You should be careful to not suggest anything that would be dangerous, illegal or inappropriate.
+            You can remember past interactions and use them to inform your answers.
+            Use semantic memory retrieval to provide contextually relevant responses.
+            """
+        )
+
+    async def llm_node(
+        self,
+        chat_ctx: llm.ChatContext,
+        tools: list[llm.FunctionTool],
+        model_settings: ModelSettings,
+    ) -> AsyncIterable[llm.ChatChunk]:
+        """Override LLM node to add memory enrichment before inference"""
+
+        # Enrich context with memory before LLM inference
+        await self._enrich_with_memory(chat_ctx)
+
+        # Call default LLM node with enriched context
+        async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
+            yield chunk
+
+    async def _enrich_with_memory(self, chat_ctx: llm.ChatContext):
+        """Add memories and augment chat context with relevant memories"""
         if not chat_ctx.messages:
             return
-        
-        # Store user message in Mem0
+
+        # Get the latest user message
         user_msg = chat_ctx.messages[-1]
+        if user_msg.role != "user":
+            return
+
+        user_content = user_msg.text_content()
+        if not user_content:
+            return
+
+        # Store user message in Mem0
         await mem0.add(
-            [{"role": "user", "content": user_msg.content}], 
+            [{"role": "user", "content": user_content}],
             user_id=USER_ID
         )
-        
+
         # Search for relevant memories
         results = await mem0.search(
-            user_msg.content, 
+            user_content,
             user_id=USER_ID,
         )
-        
+
         # Augment context with retrieved memories
         if results:
             memories = ' '.join([result["memory"] for result in results])
             logger.info(f"Enriching with memory: {memories}")
-            
-            rag_msg = llm.ChatMessage.create(
+
+            # Add memory context as a assistant message
+            memory_msg = llm.ChatMessage.create(
                 text=f"Relevant Memory: {memories}\n",
                 role="assistant",
             )
-            
+
             # Modify chat context with retrieved memories
-            chat_ctx.messages[-1] = rag_msg
+            chat_ctx.messages[-1] = memory_msg
             chat_ctx.messages.append(user_msg)
 
-    # Define initial system context
-    initial_ctx = llm.ChatContext().append(
-        role="system",
-        text=(
-            """
-            You are a helpful voice assistant.
-            You are a travel guide named George and will help the user to plan a travel trip of their dreams. 
-            You should help the user plan for various adventures like work retreats, family vacations or solo backpacking trips. 
-            You should be careful to not suggest anything that would be dangerous, illegal or inappropriate.
-            You can remember past interactions and use them to inform your answers.
-            Use semantic memory retrieval to provide contextually relevant responses. 
-            """
-        ),
-    )
+def prewarm_process(proc):
+    """Preload components to speed up session start"""
+    proc.userdata["vad"] = silero.VAD.load()
 
-    # Create VoicePipelineAgent with memory capabilities
-    agent = VoicePipelineAgent(
-        chat_ctx=initial_ctx,
-        vad=silero.VAD.load(),
+async def entrypoint(ctx: JobContext):
+    """Main entrypoint for the memory-enabled voice agent"""
+
+    # Connect to LiveKit room
+    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
+
+    # Initialize Mem0 client
+    mem0 = AsyncMemoryClient()
+
+    # Create agent session with modern 1.0 architecture
+    session = AgentSession(
         stt=deepgram.STT(),
         llm=openai.LLM(model="gpt-4o-mini"),
         tts=openai.TTS(),
-        before_llm_cb=_enrich_with_memory,
+        vad=silero.VAD.load(),
+        turn_detection=MultilingualModel(),
     )
 
-    # Start agent and initial greeting
-    agent.start(ctx.room, participant)
-    await agent.say(
-        "Hello! I'm George. Can I help you plan an upcoming trip? ",
+    # Create memory-enabled agent
+    agent = MemoryEnabledAgent()
+
+    # Start the session
+    await session.start(
+        room=ctx.room,
+        agent=agent,
+    )
+
+    # Initial greeting
+    await session.generate_reply(
+        instructions="Greet the user warmly as George the travel guide and ask how you can help them plan their next adventure.",
         allow_interruptions=True
     )
 
 # Run the application
 if __name__ == "__main__":
-    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, prewarm_fnc=prewarm_process))
+    cli.run_app(WorkerOptions(
+        entrypoint_fnc=entrypoint,
+        prewarm_fnc=prewarm_process
+    ))
 ```
 
 ## Key Features of This Implementation
 
 1. **Semantic Memory Retrieval**: Uses Mem0 to store and retrieve contextually relevant memories
-2. **Voice Interaction**: Leverages LiveKit for voice communication
+2. **Voice Interaction**: Leverages LiveKit for voice communication with proper turn detection
 3. **Intelligent Context Management**: Augments conversations with past interactions
 4. **Travel Planning Specialization**: Focused on creating a helpful travel guide assistant
+5. **Function Tools**: Modern tool definition for enhanced capabilities
 
 ## Running the Example
 
@@ -325,13 +355,13 @@ To run this example:
 ```sh
 python mem0-livekit-voice-agent.py start
 ```
-5. After the script starts, you can interact with the voice agent using [Livekit's Agent Platform](https://agents-playground.livekit.io/) and Connect to the agent inorder to start conversations. 
+5. After the script starts, you can interact with the voice agent using [Livekit's Agent Platform](https://agents-playground.livekit.io/) and connect to the agent inorder to start conversations.
 
 ## Best Practices for Voice Agents with Memory
 
 1. **Context Preservation**: Store enough context with each memory for effective retrieval
 2. **Privacy Considerations**: Implement secure memory management
-3. **Relevant Memory Filtering**: Use semantic search to retrieve only the most pertinent memories
+3. **Relevant Memory Filtering**: Use semantic search to retrieve only the most relevant memories
 4. **Error Handling**: Implement robust error handling for memory operations
 
 ## Debugging Function Tools

docs/integrations/mastra.mdx

@@ -0,0 +1,136 @@
+---
+title: Mastra
+---
+
+<Snippet file="paper-release.mdx" />
+
+The [**Mastra**](https://mastra.ai/) integration demonstrates how to use Mastra's agent system with Mem0 as the memory backend through custom tools. This enables agents to remember and recall information across conversations.
+
+## Overview
+
+In this guide, we'll create a Mastra agent that:
+1. Uses Mem0 to store information using a memory tool
+2. Retrieves relevant memories using a search tool
+3. Provides personalized responses based on past interactions
+4. Maintains context across conversations and sessions
+
+## Setup and Configuration
+
+Install the required libraries:
+
+```bash
+npm install @mastra/core @mastra/mem0 @ai-sdk/openai zod
+```
+
+Set up your environment variables:
+
+<Note>Remember to get the Mem0 API key from [Mem0 Platform](https://app.mem0.ai).</Note>
+
+```bash
+MEM0_API_KEY=your-mem0-api-key
+OPENAI_API_KEY=your-openai-api-key
+```
+
+## Initialize Mem0 Integration
+
+Import required modules and set up the Mem0 integration:
+
+```typescript
+import { Mem0Integration } from '@mastra/mem0';
+import { createTool } from '@mastra/core/tools';
+import { Agent } from '@mastra/core/agent';
+import { openai } from '@ai-sdk/openai';
+import { z } from 'zod';
+
+// Initialize Mem0 integration
+const mem0 = new Mem0Integration({
+  config: {
+    apiKey: process.env.MEM0_API_KEY || '',
+    user_id: 'alice', // Unique user identifier
+  },
+});
+```
+
+## Create Memory Tools
+
+Set up tools for memorizing and remembering information:
+
+```typescript
+// Tool for remembering saved memories
+const mem0RememberTool = createTool({
+  id: 'Mem0-remember',
+  description: "Remember your agent memories that you've previously saved using the Mem0-memorize tool.",
+  inputSchema: z.object({
+    question: z.string().describe('Question used to look up the answer in saved memories.'),
+  }),
+  outputSchema: z.object({
+    answer: z.string().describe('Remembered answer'),
+  }),
+  execute: async ({ context }) => {
+    console.log(`Searching memory "${context.question}"`);
+    const memory = await mem0.searchMemory(context.question);
+    console.log(`\nFound memory "${memory}"\n`);
+
+    return {
+      answer: memory,
+    };
+  },
+});
+
+// Tool for saving new memories
+const mem0MemorizeTool = createTool({
+  id: 'Mem0-memorize',
+  description: 'Save information to mem0 so you can remember it later using the Mem0-remember tool.',
+  inputSchema: z.object({
+    statement: z.string().describe('A statement to save into memory'),
+  }),
+  execute: async ({ context }) => {
+    console.log(`\nCreating memory "${context.statement}"\n`);
+    // To reduce latency, memories can be saved async without blocking tool execution
+    void mem0.createMemory(context.statement).then(() => {
+      console.log(`\nMemory "${context.statement}" saved.\n`);
+    });
+    return { success: true };
+  },
+});
+```
+
+## Create Mastra Agent
+
+Initialize an agent with memory tools and clear instructions:
+
+```typescript
+// Create an agent with memory tools
+const mem0Agent = new Agent({
+  name: 'Mem0 Agent',
+  instructions: `
+    You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
+    Use the Mem0-memorize tool to save important information that might be useful later.
+    Use the Mem0-remember tool to recall previously saved information when answering questions.
+  `,
+  model: openai('gpt-4o'),
+  tools: { mem0RememberTool, mem0MemorizeTool },
+});
+```
+
+
+## Key Features
+
+1. **Tool-based Memory Control**: The agent decides when to save and retrieve information using specific tools
+2. **Semantic Search**: Mem0 finds relevant memories based on semantic similarity, not just exact matches
+3. **User-specific Memory Spaces**: Each user_id maintains separate memory contexts
+4. **Asynchronous Saving**: Memories are saved in the background to reduce response latency
+5. **Cross-conversation Persistence**: Memories persist across different conversation threads
+6. **Transparent Operations**: Memory operations are visible through tool usage
+
+## Conclusion
+
+By integrating Mastra with Mem0, you can build intelligent agents that learn and remember information across conversations. The tool-based approach provides transparency and control over memory operations, making it easy to create personalized and context-aware AI experiences.
+
+## Help
+
+- For more details on Mastra, visit the [Mastra documentation](https://docs.mastra.ai/).
+- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- If you need further assistance, please feel free to reach out to us through the following methods:
+
+<Snippet file="get-help.mdx" /> 

docs/integrations/raycast.mdx

@@ -0,0 +1,47 @@
+---
+title: "Raycast Extension"
+description: "Mem0 Raycast extension for intelligent memory management"
+---
+
+# Mem0
+
+Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
+
+## 🚀 Getting Started
+
+**Get your API Key**: You'll need a Mem0 API key to use this extension:
+
+a. Sign up at [app.mem0.ai](https://app.mem0.ai)
+
+b. Navigate to your API Keys page
+
+c. Copy your API key
+
+d. Enter this key in the extension preferences
+
+**Basic Usage**:
+
+- Store memories and text snippets
+- Retrieve context-aware information
+- Manage persistent user preferences
+- Search through stored memories
+
+## ✨ Features
+
+**Remember Everything**: Never lose important information - store notes, preferences, and conversations that your AI can recall later
+
+**Smart Connections**: Automatically links related topics, just like your brain does - helping you discover useful connections
+
+**Cost Saver**: Spend less on AI usage by efficiently retrieving relevant information instead of regenerating responses
+
+## 🔑 How This Helps You
+
+**More Personal Experience**: Your AI remembers your preferences and past conversations, making interactions feel more natural
+
+**Learn Your Style**: Adapts to how you work and what you like, becoming more helpful over time
+
+**No More Repetition**: Stop explaining the same things over and over - your AI remembers your context and preferences
+
+---
+
+<Snippet file="get-help.mdx" /> 

docs/llms.txt

@@ -37,13 +37,13 @@
 
 ## Features
 
-[Graph Memory](https://docs.mem0.ai/features/graph-memory): Mem0's graph memory system builds relationships between entities in your data, enabling contextually relevant retrieval by analyzing connections between information points - activate it with `enable_graph=True` to enhance search results beyond direct semantic matches, ideal for applications tracking evolving relationships.
-[Advanced Retrieval](https://docs.mem0.ai/features/advanced-retrieval): Mem0 offers enhanced search capabilities through three advanced retrieval modes: keyword search (improves recall by matching specific terms), reranking (ensures most relevant results appear first using neural networks), and filtering (narrows results by specific criteria) - each can be enabled independently or in combination to optimize search precision and relevance.
-[Multimodal Support](https://docs.mem0.ai/features/multimodal-support): Mem0 extends beyond text by supporting images and documents (JPG, PNG, MDX, TXT, PDF), allowing users to integrate visual and document content through direct URLs or Base64 encoding, enhancing the memory system's ability to understand and recall information from various media types.
-[Memory Customization](https://docs.mem0.ai/features/selective-memory): Mem0 enables selective memory storage through inclusion and exclusion rules, allowing users to focus on relevant information (like specific topics) while omitting irrelevant data (such as food preferences), resulting in more efficient, accurate, and privacy-conscious AI interactions.
-[Custom Categories](https://docs.mem0.ai/features/custom-categories): Mem0 allows setting custom categories at both project level and during individual API calls, overriding default categories (like personal_details, family, sports) with more specific ones to improve memory categorization accuracy - simply provide a list of category dictionaries with descriptive definitions when adding memories.
-[Async Client](https://docs.mem0.ai/features/async-client): Mem0 provides an AsyncMemoryClient for non-blocking operations, offering the same functionality as the synchronous client (add, search, get_all, delete, etc.) but with async/await support, making it ideal for high-concurrency applications that need to perform memory operations without blocking execution.
-[Memory Export](https://docs.mem0.ai/features/memory-export): Mem0 enables exporting memories in structured formats using customizable Pydantic schemas, allowing you to transform stored memories into specific data structures by defining schemas, submitting export jobs with optional processing instructions, and retrieving the formatted data with various filtering options.
+[Graph Memory](https://docs.mem0.ai/platform/features/graph-memory): Mem0's graph memory system builds relationships between entities in your data, enabling contextually relevant retrieval by analyzing connections between information points - activate it with `enable_graph=True` to enhance search results beyond direct semantic matches, ideal for applications tracking evolving relationships.
+[Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval): Mem0 offers enhanced search capabilities through three advanced retrieval modes: keyword search (improves recall by matching specific terms), reranking (ensures most relevant results appear first using neural networks), and filtering (narrows results by specific criteria) - each can be enabled independently or in combination to optimize search precision and relevance.
+[Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support): Mem0 extends beyond text by supporting images and documents (JPG, PNG, MDX, TXT, PDF), allowing users to integrate visual and document content through direct URLs or Base64 encoding, enhancing the memory system's ability to understand and recall information from various media types.
+[Memory Customization](https://docs.mem0.ai/platform/features/selective-memory): Mem0 enables selective memory storage through inclusion and exclusion rules, allowing users to focus on relevant information (like specific topics) while omitting irrelevant data (such as food preferences), resulting in more efficient, accurate, and privacy-conscious AI interactions.
+[Custom Categories](https://docs.mem0.ai/platform/features/custom-categories): Mem0 allows setting custom categories at both project level and during individual API calls, overriding default categories (like personal_details, family, sports) with more specific ones to improve memory categorization accuracy - simply provide a list of category dictionaries with descriptive definitions when adding memories.
+[Async Client](https://docs.mem0.ai/platform/features/async-client): Mem0 provides an AsyncMemoryClient for non-blocking operations, offering the same functionality as the synchronous client (add, search, get_all, delete, etc.) but with async/await support, making it ideal for high-concurrency applications that need to perform memory operations without blocking execution.
+[Memory Export](https://docs.mem0.ai/platform/features/memory-export): Mem0 enables exporting memories in structured formats using customizable Pydantic schemas, allowing you to transform stored memories into specific data structures by defining schemas, submitting export jobs with optional processing instructions, and retrieving the formatted data with various filtering options.
 
 ## OSS
 
@@ -72,9 +72,9 @@
 
 ### Features
 
-[OpenAI Compatibility](https://docs.mem0.ai/features/openai_compatibility): Mem0 offers seamless integration with OpenAI-compatible APIs, allowing developers to enhance conversational agents with structured memory by initializing with a Mem0 API key (or locally without one), supporting various LLM providers, and enabling personalized responses through user context persistence across interactions with parameters like user_id, agent_id, and custom filters.
-[Custom Fact Extraction Prompt](https://docs.mem0.ai/features/custom-fact-extraction-prompt): Mem0 enables custom fact extraction prompts to tailor information extraction for specific use cases by defining domain-specific examples and formats, allowing precise control over what information is extracted from messages - simply provide a custom prompt with few-shot examples in the config when initializing the Memory client.
-[Custom Update Memory Prompt](https://docs.mem0.ai/features/custom-update-memory-prompt): Mem0 enables customizing the update memory prompt to control how memories are modified by comparing newly retrieved facts with existing memories and determining appropriate actions (add, update, delete, or no change) based on custom logic and examples provided in the prompt configuration.
+[OpenAI Compatibility](https://docs.mem0.ai/open-source/features/openai_compatibility): Mem0 offers seamless integration with OpenAI-compatible APIs, allowing developers to enhance conversational agents with structured memory by initializing with a Mem0 API key (or locally without one), supporting various LLM providers, and enabling personalized responses through user context persistence across interactions with parameters like user_id, agent_id, and custom filters.
+[Custom Fact Extraction Prompt](https://docs.mem0.ai/open-source/features/custom-fact-extraction-prompt): Mem0 enables custom fact extraction prompts to tailor information extraction for specific use cases by defining domain-specific examples and formats, allowing precise control over what information is extracted from messages - simply provide a custom prompt with few-shot examples in the config when initializing the Memory client.
+[Custom Update Memory Prompt](https://docs.mem0.ai/open-source/features/custom-update-memory-prompt): Mem0 enables customizing the update memory prompt to control how memories are modified by comparing newly retrieved facts with existing memories and determining appropriate actions (add, update, delete, or no change) based on custom logic and examples provided in the prompt configuration.
 [REST API Server](https://docs.mem0.ai/open-source/features/rest-api): Mem0 provides a FastAPI-based REST API server that supports core operations (create/retrieve/search/update/delete memories) with OpenAPI documentation at /docs, easily deployable via Docker Compose with pre-configured databases (postgres pgvector, neo4j) - just set OPENAI_API_KEY to get started.
 [Graph Memory](https://docs.mem0.ai/open-source/graph_memory/overview): Mem0's open-source graph memory system enables building and querying relationships between entities by installing with `pip install "mem0ai[graph]"` and configuring a graph store provider (like Neo4j) - this allows for more contextual memory retrieval by combining vector and graph-based approaches to track evolving relationships between information points.
 

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

@@ -0,0 +1,69 @@
+---
+title: Multimodal Support
+description: Integrate images into your interactions with Mem0
+icon: "image"
+iconType: "solid"
+---
+
+<Snippet file="paper-release.mdx" />
+
+Mem0 extends its capabilities beyond text by supporting multimodal data. With this feature, users can seamlessly integrate images into their interactions—allowing Mem0 to extract relevant information.
+
+## How It Works
+
+When a user submits an image, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall multimodal inputs.
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+client = Memory()
+
+messages = [
+    {
+        "role": "user",
+        "content": "Hi, my name is Alice."
+    },
+    {
+        "role": "assistant",
+        "content": "Nice to meet you, Alice! What do you like to eat?"
+    },
+    {
+        "role": "user",
+        "content": {
+            "type": "image_url",
+            "image_url": {
+                "url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+            }
+        }
+    },
+]
+
+# Calling the add method to ingest messages into the memory system
+client.add(messages, user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Name is Alice",
+      "event": "ADD",
+      "id": "7ae113a3-3cb5-46e9-b6f7-486c36391847"
+    },
+    {
+      "memory": "Likes large pizza with toppings including cherry tomatoes, black olives, green spinach, yellow bell peppers, diced ham, and sliced mushrooms",
+      "event": "ADD",
+      "id": "56545065-7dee-4acf-8bf2-a5b2535aabb3"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+Using these methods, you can seamlessly incorporate various media types into your interactions, further enhancing Mem0's multimodal capabilities.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/open-source/graph_memory/features.mdx

@@ -10,13 +10,12 @@ iconType: "solid"
 Graph Memory is a powerful feature that allows users to create and utilize complex relationships between pieces of information.
 
 ## Graph Memory supports the following features:
-A list of features provided by Graph Memory.
 
-### Add Customize Prompt
+### Using Custom Prompts
 
-Users can add a customized prompt that will be used to extract specific entities from the given input text. 
-This allows for more targeted and relevant information extraction based on the user's needs. 
-Here's an example of how to add a customized prompt:
+Users can specify a custom prompt that will be used to extract specific entities from the given input text.
+This allows for more targeted and relevant information extraction based on the user's needs.
+Here's an example of how to specify a custom prompt:
 
 <CodeGroup>
     ```python Python

docs/openapi.json

@@ -2316,6 +2316,11 @@
 										"message": {
 											"type": "string",
 											"example": "Organization created successfully."
+										},
+										"org_id": {
+											"type": "string",
+											"format": "uuid",
+											"description": "Unique identifier for the organization"
 										}
 									}
 								}
@@ -2722,13 +2727,13 @@
 							"schema": {
 								"type": "object",
 								"required": [
-									"username",
+									"email",
 									"role"
 								],
 								"properties": {
-									"username": {
+									"email": {
 										"type": "string",
-										"description": "Username of the member whose role is to be updated"
+										"description": "Email of the member whose role is to be updated"
 									},
 									"role": {
 										"type": "string",
@@ -2798,27 +2803,27 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\n    \"username\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"PUT\", url, json=payload, headers=headers)\n\nprint(response.text)"
+				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\n    \"email\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"PUT\", url, json=payload, headers=headers)\n\nprint(response.text)"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'PUT',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"username\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "const options = {\n  method: 'PUT',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"email\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
 				},
 				{
 				"lang": "cURL",
-				"source": "curl --request PUT \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"username\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
+				"source": "curl --request PUT \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"email\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"PUT\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"PUT\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"PUT\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"PUT\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
-				"source": "HttpResponse<String> response = Unirest.put(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
+				"source": "HttpResponse<String> response = Unirest.put(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
 					}
 				]
 			},
@@ -2847,13 +2852,13 @@
 							"schema": {
 								"type": "object",
 								"required": [
-									"username",
+									"email",
 									"role"
 								],
 								"properties": {
-									"username": {
+									"email": {
 										"type": "string",
-										"description": "Username of the member to be added"
+										"description": "Email of the member to be added"
 									},
 									"role": {
 										"type": "string",
@@ -2923,23 +2928,23 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\n    \"username\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"POST\", url, json=payload, headers=headers)\n\nprint(response.text)"
+				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\n    \"email\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"POST\", url, json=payload, headers=headers)\n\nprint(response.text)"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'POST',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"username\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "const options = {\n  method: 'POST',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"email\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
 				},
 				{
 				"lang": "cURL",
-				"source": "curl --request POST \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"username\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
+				"source": "curl --request POST \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"email\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
@@ -2971,12 +2976,12 @@
 							"schema": {
 								"type": "object",
 								"required": [
-									"username"
+									"email"
 								],
 								"properties": {
-									"username": {
+									"email": {
 										"type": "string",
-										"description": "Username of the member to be removed"
+										"description": "Email of the member to be removed"
 									}
 								}
 							}
@@ -3020,27 +3025,27 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\"username\": \"<string>\"}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"DELETE\", url, json=payload, headers=headers)\n\nprint(response.text)"
+				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\npayload = {\"email\": \"<string>\"}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"DELETE\", url, json=payload, headers=headers)\n\nprint(response.text)"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'DELETE',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"username\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "const options = {\n  method: 'DELETE',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"email\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
 				},
 				{
 				"lang": "cURL",
-				"source": "curl --request DELETE \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"username\": \"<string>\"\n}'"
+				"source": "curl --request DELETE \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"email\": \"<string>\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"username\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"DELETE\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"email\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"DELETE\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"DELETE\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"username\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"DELETE\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"email\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
-				"source": "HttpResponse<String> response = Unirest.delete(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"username\\\": \\\"<string>\\\"\n}\")\n  .asString();"
+				"source": "HttpResponse<String> response = Unirest.delete(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"email\\\": \\\"<string>\\\"\n}\")\n  .asString();"
 					}
 				]
 			}
@@ -3199,6 +3204,11 @@
 										"message": {
 											"type": "string",
 											"example": "Project created successfully."
+										},
+										"project_id": {
+											"type": "string",
+											"format": "uuid",
+											"description": "Unique identifier for the project"
 										}
 									}
 								}
@@ -3753,13 +3763,13 @@
 							"schema": {
 								"type": "object",
 								"required": [
-									"username",
+									"email",
 									"role"
 								],
 								"properties": {
-									"username": {
+									"email": {
 										"type": "string",
-										"description": "Username of the member to be added"
+										"description": "Email of the member to be added"
 									},
 									"role": {
 										"type": "string",
@@ -3823,27 +3833,27 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\npayload = {\n    \"username\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"POST\", url, json=payload, headers=headers)\n\nprint(response.text)"
+				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\npayload = {\n    \"email\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"POST\", url, json=payload, headers=headers)\n\nprint(response.text)"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'POST',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"username\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "const options = {\n  method: 'POST',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"email\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
 				},
 				{
 				"lang": "cURL",
-				"source": "curl --request POST \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"username\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
+				"source": "curl --request POST \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"email\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
-				"source": "HttpResponse<String> response = Unirest.post(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
+				"source": "HttpResponse<String> response = Unirest.post(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
 					}
 				]
 			},
@@ -3881,13 +3891,13 @@
 							"schema": {
 								"type": "object",
 								"required": [
-									"username",
+									"email",
 									"role"
 								],
 								"properties": {
-									"username": {
+									"email": {
 										"type": "string",
-										"description": "Username of the member to be updated"
+										"description": "Email of the member to be updated"
 									},
 									"role": {
 										"type": "string",
@@ -3951,27 +3961,27 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\npayload = {\n    \"username\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"PUT\", url, json=payload, headers=headers)\n\nprint(response.text)"
+				"source": "import requests\n\nurl = \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\npayload = {\n    \"email\": \"<string>\",\n    \"role\": \"<string>\"\n}\nheaders = {\n    \"Authorization\": \"<api-key>\",\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.request(\"PUT\", url, json=payload, headers=headers)\n\nprint(response.text)"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'PUT',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"username\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "const options = {\n  method: 'PUT',\n  headers: {Authorization: 'Token <api-key>', 'Content-Type': 'application/json'},\n  body: '{\"email\":\"<string>\",\"role\":\"<string>\"}'\n};\n\nfetch('https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
 				},
 				{
 				"lang": "cURL",
-				"source": "curl --request PUT \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"username\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
+				"source": "curl --request PUT \\\n  --url https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"email\": \"<string>\",\n  \"role\": \"<string>\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"PUT\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n\n\treq, _ := http.NewRequest(\"PUT\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"PUT\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"PUT\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
-				"source": "HttpResponse<String> response = Unirest.put(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"username\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
+				"source": "HttpResponse<String> response = Unirest.put(\"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"email\\\": \\\"<string>\\\",\n  \\\"role\\\": \\\"<string>\\\"\n}\")\n  .asString();"
 					}
 				]
 			},
@@ -3999,10 +4009,10 @@
 					}
 				},
 				{
-					"name": "username",
+					"name": "email",
 					"in": "query",
 					"required": true,
-					"description": "Username of the member to be removed",
+					"description": "Email of the member to be removed",
 					"schema": {
 						"type": "string"
 					}
@@ -4809,7 +4819,7 @@
 				"type": "object",
 				"properties": {
 					"messages": {
-						"description": "An array of message objects representing the content of the memory. Each message object typically contains 'role' and 'content' fields, where 'role' indicates the sender (e.g., 'user', 'assistant', 'system') and 'content' contains the actual message text. This structure allows for the representation of conversations or multi-part memories.",
+						"description": "An array of message objects representing the content of the memory. Each message object typically contains 'role' and 'content' fields, where 'role' indicates the sender either 'user' or 'assistant' and 'content' contains the actual message text. This structure allows for the representation of conversations or multi-part memories.",
 						"type": "array",
 						"items": {
 							"type": "object",

docs/openmemory/overview.mdx

@@ -6,6 +6,24 @@ iconType: "solid"
 
 <Snippet file="paper-release.mdx" />
 
+## 🚀 Hosted OpenMemory MCP Now Available!
+
+#### Sign Up Now - [app.openmemory.dev](https://app.openmemory.dev)
+
+Everything you love about OpenMemory MCP but with zero setup.
+
+✅ Works with all MCP-compatible tools (Claude Desktop, Cursor...)  
+✅ Same standard memory ops: `add_memories`, `search_memory`, etc  
+✅ One-click provisioning, no Docker required  
+✅ Powered by Mem0  
+
+Add shared, persistent, low-friction memory to your MCP-compatible clients in seconds.
+
+### 🌟 Get Started Now
+**Sign up and get your access key at [app.openmemory.dev](https://app.openmemory.dev)**
+
+Example installation: `npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key`
+
 OpenMemory is a local memory infrastructure powered by Mem0 that lets you carry your memory accross any AI app. It provides a unified memory layer that stays with you, enabling agents and assistants to remember what matters across applications.
 
 <img src="https://github.com/user-attachments/assets/3c701757-ad82-4afa-bfbe-e049c2b4320b" alt="OpenMemory UI" />
@@ -16,6 +34,33 @@ The OpenMemory MCP Server is a private, local-first memory server that creates a
 
 The OpenMemory MCP Server ensures all memory stays local, structured, and under your control with no cloud sync or external storage.
 
+## OpenMemory Easy Setup
+
+### Prerequisites
+- Docker
+- OpenAI API Key
+
+You can quickly run OpenMemory by running the following command:
+
+```bash
+curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
+```
+
+You should set the `OPENAI_API_KEY` as a global environment variable:
+
+```bash
+export OPENAI_API_KEY=your_api_key
+```
+
+You can also set the `OPENAI_API_KEY` as a parameter to the script:
+
+```bash
+curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | OPENAI_API_KEY=your_api_key bash
+```
+
+This will start the OpenMemory server and the OpenMemory UI. Deleting the container will lead to the deletion of the memory store.
+We suggest you follow the instructions [here](/openmemory/quickstart#setting-up-openmemory) to set up OpenMemory on your local machine, with more persistant memory store.
+
 ## How the OpenMemory MCP Server Works
 
 Built around the Model Context Protocol (MCP), the OpenMemory MCP Server exposes a standardized set of memory tools:
@@ -68,9 +113,8 @@ This is just the beginning. The MCP server is the first core layer in the OpenMe
 
 ## Getting Started Today
 
-- Github Repository: https://github.com/mem0ai/mem0
-- Read the documentation: [Docs Link]
-- Join our community: [Discord link]
+- Repository: [GitHub](https://github.com/mem0ai/mem0/tree/main/openmemory)
+- Join our community: [Discord](https://discord.gg/6PzXDgEjG5)
 
 With OpenMemory, your AI memories stay private, portable, and under your control, exactly where they belong.
 

docs/openmemory/quickstart.mdx

@@ -6,28 +6,147 @@ iconType: "solid"
 
 <Snippet file="paper-release.mdx" />
 
+## 🚀 Hosted OpenMemory MCP Now Available!
+
+#### Sign Up Now - [app.openmemory.dev](https://app.openmemory.dev)
+
+Everything you love about OpenMemory MCP but with zero setup.
+
+✅ Works with all MCP-compatible tools (Claude Desktop, Cursor...)  
+✅ Same standard memory ops: `add_memories`, `search_memory`, etc  
+✅ One-click provisioning, no Docker required  
+✅ Powered by Mem0  
+
+Add shared, persistent, low-friction memory to your MCP-compatible clients in seconds.
+
+### 🌟 Get Started Now
+**Sign up and get your access key at [app.openmemory.dev](https://app.openmemory.dev)**
+
+Example installation: `npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key`
+
+## Getting Started with Hosted OpenMemory
+
+The fastest way to get started is with our hosted version - no setup required:
+
+### 1. Get your API key
+Visit [app.openmemory.dev](https://app.openmemory.dev) to sign up and get your `OPENMEMORY_API_KEY`.
+
+### 2. Install and connect to your preferred client
+Example commands (replace `your-key` with your actual API key):
+
+For Claude Desktop: `npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key`
+
+For Cursor: `npx @openmemory/install --client cursor --env OPENMEMORY_API_KEY=your-key`
+
+For Windsurf: `npx @openmemory/install --client windsurf --env OPENMEMORY_API_KEY=your-key`
+
+That's it! Your AI client now has persistent memory across sessions.
+
+## Local Setup (Self-Hosted)
+
+Prefer to run OpenMemory locally? Follow the instructions below for a self-hosted setup.
+
+## OpenMemory Easy Setup
+
+### Prerequisites
+- Docker
+- OpenAI API Key
+
+You can quickly run OpenMemory by running the following command:
+
+```bash
+curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
+```
+
+You should set the `OPENAI_API_KEY` as a global environment variable:
+
+```bash
+export OPENAI_API_KEY=your_api_key
+```
+
+You can also set the `OPENAI_API_KEY` as a parameter to the script:
+
+```bash
+curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | OPENAI_API_KEY=your_api_key bash
+```
+
+This will start the OpenMemory server and the OpenMemory UI. Deleting the container will lead to the deletion of the memory store.
+We suggest you follow the instructions below to set up OpenMemory on your local machine, with more persistant memory store.
 
 ## Setting Up OpenMemory
 
 Getting started with OpenMemory is straight forward and takes just a few minutes to set up on your local machine. Follow these steps:
 
 ### Getting started
-First clone the repository and then follow the instructions:
+
+
+### 1. First clone the repository and then follow the instructions:
 ```bash
 # Clone the repository
 git clone https://github.com/mem0ai/mem0.git
 cd mem0/openmemory
-
-# Create the backend .env file with your OpenAI key
-make env
-
-# Build the Docker images
-make build
-
-# Start all services (API server, vector database, and MCP server components)
-make up
 ```
 
+### 2. Set Up Environment Variables
+
+Before running the project, you need to configure environment variables for both the API and the UI.
+
+You can do this in one of the following ways:
+
+- **Manually**:  
+  Create a `.env` file in each of the following directories:
+  - `/api/.env`
+  - `/ui/.env`
+
+- **Using `.env.example` files**:  
+  Copy and rename the example files:
+
+  ```bash
+  cp api/.env.example api/.env
+  cp ui/.env.example ui/.env
+  ```
+
+ - **Using Makefile** (if supported):  
+    Run:
+
+   ```bash
+   make env
+   ```
+- #### Example `/api/.env`
+
+``` bash
+OPENAI_API_KEY=sk-xxx
+USER=<user-id> # The User Id you want to associate the memories with 
+```
+- #### Example `/ui/.env`
+
+```bash
+NEXT_PUBLIC_API_URL=http://localhost:8765
+NEXT_PUBLIC_USER_ID=<user-id> # Same as the user id for environment variable in api
+```
+
+### 3. Build and Run the Project
+You can run the project using the following two commands:
+```bash
+make build # builds the mcp server and ui
+make up  # runs openmemory mcp server and ui
+``` 
+
+After running these commands, you will have:
+- OpenMemory MCP server running at: http://localhost:8765 (API documentation available at http://localhost:8765/docs)
+- OpenMemory UI running at: http://localhost:3000
+
+#### UI not working on http://localhost:3000?
+
+If the UI does not start properly on http://localhost:3000, try running it manually:
+
+```bash
+cd ui
+pnpm install
+pnpm dev
+```
+
+
 You can configure the MCP client using the following command (replace username with your username):
 
 ```bash
@@ -40,4 +159,4 @@ Once set up, OpenMemory runs locally on your machine, ensuring all your AI memor
 
 ### Getting Started Today
 
-- Github Repository: https://github.com/mem0ai/mem0/openmemory
+- Github Repository: https://github.com/mem0ai/mem0/tree/main/openmemory

docs/overview.mdx

@@ -5,6 +5,10 @@ iconType: "solid"
 ---
 
 <Snippet file="paper-release.mdx" />
+<Note type="info">
+🎉 We're excited to announce that Claude 4 is now available with Mem0! Check it out [here](components/llms/models/anthropic).
+</Note>
+
 
 # Introduction
 

docs/platform/features/criteria-retrieval.mdx

@@ -0,0 +1,184 @@
+---
+title: Criteria Retrieval
+icon: "magnifying-glass-plus"
+iconType: "solid"
+---
+
+<Snippet file="paper-release.mdx" />
+
+Mem0's **Criteria Retrieval** feature allows you to retrieve memories based on specific criteria. This is useful when you need to find memories that match certain conditions or criteria, such as emotional content, sentiment, or other custom attributes.
+
+## Setting Up Custom Criteria
+
+You can define custom criteria at the project level, assigning weights to each criterion. These weights will be normalized during memory retrieval.
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(
+    api_key="mem0_api_key",
+    org_id="mem0_organization_id",
+    project_id="mem0_project_id"
+)
+
+# Define custom criteria with weights
+retrieval_criteria = [
+    {
+        "name": "joy",
+        "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the sentence. A higher score reflects greater joy.",
+        "weight": 3
+    },
+    {
+        "name": "curiosity",
+        "description": "Assess the extent to which the sentence reflects inquisitiveness, interest in exploring new information, or asking questions. A higher score reflects stronger curiosity.",
+        "weight": 2
+    },
+    {
+        "name": "emotion",
+        "description": "Evaluate the presence and depth of sadness or negative emotional tone, including expressions of disappointment, frustration, or sorrow. A higher score reflects greater sadness.",
+        "weight": 1
+    }
+]
+
+# Update project with custom criteria
+client.update_project(
+    retrieval_criteria=retrieval_criteria
+)
+```
+
+## Using Criteria Retrieval
+
+After setting up your criteria, you can use them to filter and retrieve memories. Here's an example:
+
+```python
+# Add some example memories
+messages = [
+    {"role": "user", "content": "What a beautiful sunny day! I feel so refreshed and ready to take on anything!"},
+    {"role": "user", "content": "I've always wondered how storms form—what triggers them in the atmosphere?"},
+    {"role": "user", "content": "It's been raining for days, and it just makes everything feel heavier."},
+    {"role": "user", "content": "Finally I get time to draw something today, after a long time!! I am super happy today."}
+]
+
+client.add(messages, user_id="alice")
+
+# Search with criteria-based filtering
+filters = {
+    "AND": [
+        {"user_id": "alice"}
+    ]
+}
+results_with_criteria = client.search(
+    query="Why I am feeling happy today?", 
+    filters=filters, 
+    version="v2"
+)
+
+# Standard search without criteria filtering
+results_without_criteria = client.search(
+    query="Why I am feeling happy today?", 
+    user_id="alice"
+)
+```
+
+## Search Results Comparison
+
+Let's compare the results from criteria-based retrieval versus standard retrieval to see how the emotional criteria affects ranking:
+
+### Search Results (with Criteria)
+```python
+[
+    {
+        "memory": "User feels refreshed and ready to take on anything on a beautiful sunny day",
+        "score": 0.666,
+        ...
+    },
+    {
+        "memory": "User finally has time to draw something after a long time",
+        "score": 0.616,
+        ...
+    },
+    {
+        "memory": "User is happy today",
+        "score": 0.500,
+        ...
+    },
+    {
+        "memory": "User is curious about how storms form and what triggers them in the atmosphere.",
+        "score": 0.400,
+        ...
+    },
+    {
+        "memory": "It has been raining for days, making everything feel heavier.",
+        "score": 0.116,
+        ...
+    }
+]
+```
+
+### Search Results (without Criteria)
+```python
+[
+    {
+        "memory": "User is happy today",
+        "score": 0.607,
+        ...
+    },
+    {
+        "memory": "User feels refreshed and ready to take on anything on a beautiful sunny day",
+        "score": 0.512,
+        ...
+    },
+    {
+        "memory": "It has been raining for days, making everything feel heavier.",
+        "score": 0.4617,
+        ...
+    },
+    {
+        "memory": "User is curious about how storms form and what triggers them in the atmosphere.",
+        "score": 0.340,
+        ...
+    },
+    {
+        "memory": "User finally has time to draw something after a long time",
+        "score": 0.336,
+        ...
+    }
+]
+```
+
+Looking at the example results above, we can see how criteria-based filtering affects the output:
+
+1. **Memory Ordering**: With criteria, memories with high joy scores (like feeling refreshed and drawing) are ranked higher, while without criteria, the most relevant memory ("User is happy today") comes first.
+
+2. **Score Distribution**: With criteria, scores are more spread out (0.116 to 0.666) and reflect the criteria weights, while without criteria, scores are more clustered (0.336 to 0.607) and based purely on relevance.
+
+3. **Negative Content**: With criteria, the negative memory about rain has a much lower score (0.116) due to the emotion criteria, while without criteria it maintains a relatively high score (0.4617) due to its relevance.
+
+4. **Curiosity Content**: The storm-related memory gets a moderate score (0.400) with criteria due to the curiosity weighting, while without criteria it's ranked lower (0.340) as it's less relevant to the happiness query.
+
+## Key Differences
+
+1. **Scoring**: With criteria, normalized scores (0-1) are used based on custom criteria weights, while without criteria, standard relevance scoring is used
+
+2. **Ordering**: With criteria, memories are first retrieved by relevance, then criteria-based filtering and prioritization is applied, while without criteria, ordering is solely by relevance
+
+3. **Filtering**: With criteria, post-retrieval filtering based on custom criteria (joy, curiosity, etc.) is available, which isn't available without criteria
+
+<Note>
+When no custom criteria are specified, the search will default to standard relevance-based retrieval. In this case, results are returned based solely on their relevance to the query, without any additional filtering or prioritization that would normally be applied through criteria.
+</Note>
+
+## How It Works
+
+1. **Criteria Definition**: Define custom criteria with names, descriptions, and weights
+2. **Project Configuration**: Apply these criteria at the project level
+3. **Memory Retrieval**: Use v2 search with filters to retrieve memories based on your criteria
+4. **Weighted Scoring**: Memories are scored based on the defined criteria weights
+
+<Note>
+Criteria retrieval is currently supported only in search v2. Make sure to use `version="v2"` when performing searches with custom criteria.
+</Note>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/platform/features/graph-memory.mdx

@@ -281,6 +281,67 @@ console.log(memories);
 
 </CodeGroup>
 
+### Setting Graph Memory at Project Level
+
+Instead of passing `enable_graph=True` to every add call, you can enable it once at the project level:
+
+<CodeGroup>
+
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(
+    api_key="your-api-key",
+    org_id="your-org-id",
+    project_id="your-project-id"
+)
+
+# Enable graph memory for all operations in this project
+client.update_project(enable_graph=True, version="v1")
+
+# Now all add operations will use graph memory by default
+messages = [
+    {"role": "user", "content": "My name is Joseph"},
+    {"role": "assistant", "content": "Hello Joseph, it's nice to meet you!"},
+    {"role": "user", "content": "I'm from Seattle and I work as a software engineer"}
+]
+
+client.add(
+    messages,
+    user_id="joseph",
+    output_format="v1.1"
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0";
+
+const client = new MemoryClient({
+  apiKey: "your-api-key",
+  org_id: "your-org-id",
+  project_id: "your-project-id"
+});
+
+# Enable graph memory for all operations in this project
+await client.updateProject({ enable_graph: true, version: "v1" });
+
+# Now all add operations will use graph memory by default
+const messages = [
+  { role: "user", content: "My name is Joseph" },
+  { role: "assistant", content: "Hello Joseph, it's nice to meet you!" },
+  { role: "user", content: "I'm from Seattle and I work as a software engineer" }
+];
+
+await client.add({
+  messages,
+  user_id: "joseph",
+  output_format: "v1.1"
+});
+```
+
+</CodeGroup>
+
+
 ## Best Practices
 
 - Enable Graph Memory for applications where understanding context and relationships between memories is important

docs/platform/features/memory-export.mdx

@@ -131,12 +131,14 @@ curl -X POST "https://api.mem0.ai/v1/memories/export/" \
 
 ### Retrieve Export
 
-Once the export job is complete, you can retrieve the structured data:
+Once the export job is complete, you can retrieve the structured data in two ways:
+
+#### Using Filters
 
 <CodeGroup>
 
 ```python Python
-# Corrected date range (assuming you meant July 10 to July 20)
+# Retrieve using filters
 filters = {
     "AND": [
         {"created_at": {"gte": "2024-07-10", "lte": "2024-07-20"}},
@@ -148,9 +150,27 @@ response = client.get_memory_export(filters=filters)
 print(response)
 ```
 
-```bash cURL
-curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=alice" \
-     -H "Authorization: Token your-api-key"
+```json Output
+{
+    "full_name": "John Doe",
+    "current_role": "Senior Software Engineer",
+    "years_experience": 8,
+    "employment_status": "full_time",
+    "education_level": "masters",
+    "skills": ["Python", "AWS", "Machine Learning"]
+}
+```
+
+</CodeGroup>
+
+#### Using Export ID
+
+<CodeGroup>
+
+```python Python
+# Retrieve using export ID
+response = client.get_memory_export(memory_export_id="550e8400-e29b-41d4-a716-446655440000")
+print(response)
 ```
 
 ```json Output

docs/platform/features/timestamp.mdx

@@ -59,7 +59,10 @@ five_days_ago = current_time - timedelta(days=5)
 unix_timestamp = int(five_days_ago.timestamp())
 
 # Add memory with custom timestamp
-client.add("I'm travelling to SF", user_id="user1", timestamp=unix_timestamp)
+messages = [
+    {"role": "user", "content": "I'm travelling to SF"}
+]
+client.add(messages, user_id="user1", timestamp=unix_timestamp)
 ```
 
 ```javascript JavaScript
@@ -77,7 +80,10 @@ fiveDaysAgo.setDate(currentTime.getDate() - 5);
 const unixTimestamp = Math.floor(fiveDaysAgo.getTime() / 1000);
 
 // Add memory with custom timestamp
-client.add("I'm travelling to SF", { user_id: "user1", timestamp: unixTimestamp })
+const messages = [
+    {"role": "user", "content": "I'm travelling to SF"}
+]
+client.add(messages, { user_id: "user1", timestamp: unixTimestamp })
     .then(response => console.log(response))
     .catch(error => console.error(error));
 ```
@@ -119,14 +125,20 @@ For example, to create a memory with a timestamp of January 1, 2023:
 # January 1, 2023 timestamp
 january_2023_timestamp = 1672531200  # Unix timestamp for 2023-01-01 00:00:00 UTC
 
-client.add("Important historical information", user_id="user1", timestamp=january_2023_timestamp)
+messages = [
+    {"role": "user", "content": "I'm travelling to SF"}
+]
+client.add(messages, user_id="user1", timestamp=january_2023_timestamp)
 ```
 
 ```javascript JavaScript
 // January 1, 2023 timestamp
 const january2023Timestamp = 1672531200;  // Unix timestamp for 2023-01-01 00:00:00 UTC
 
-client.add("Important historical information", { user_id: "user1", timestamp: january2023Timestamp })
+const messages = [
+    {"role": "user", "content": "I'm travelling to SF"}
+]
+client.add(messages, { user_id: "user1", timestamp: january2023Timestamp })
     .then(response => console.log(response))
     .catch(error => console.error(error));
 ```

docs/platform/quickstart.mdx

@@ -64,7 +64,10 @@ client = AsyncMemoryClient()
 
 
 async def main():
-    response = await client.add("I'm travelling to SF", user_id="john")
+    messages = [
+        {"role": "user", "content": "I'm travelling to SF"}
+    ]
+    response = await client.add(messages, user_id="john")
     print(response)
 
 await main()
@@ -139,7 +142,20 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
 </CodeGroup>
 
 <Note>
-    Messages passed along with `user_id`, `run_id`, or `app_id` are stored as user memories, while messages from the assistant are excluded from memory. To store messages for the assistant, use `agent_id` exclusively and avoid including other IDs, such as user_id, alongside it. This ensures the memory is properly attributed to the assistant.
+    When passing `user_id`, memories are primarily created based on user messages, but may be influenced by assistant messages for contextual understanding. For example, in a conversation about food preferences, both the user's stated preferences and their responses to the assistant's questions would form user memories. Similarly, when using `agent_id`, assistant messages are prioritized, but user messages might influence the agent's memories based on context. This approach ensures comprehensive memory creation while maintaining appropriate attribution to either users or agents.
+    
+    **Example:**
+    ```
+    User: My favorite cuisine is Italian
+    Assistant: Nice! What about Indian cuisine?
+    User: Don't like it much since I cannot eat spicy food
+    
+    Resulting user memories:
+    memory1 - Likes Italian food
+    memory2 - Doesn't like Indian food since cannot eat spicy 
+    
+    (memory2 comes from user's response about Indian cuisine)
+    ```
 </Note>
 
 <Note>Metadata allows you to store structured information (location, timestamp, user state) with memories. Add it during creation to enable precise filtering and retrieval during searches.</Note>
@@ -442,7 +458,7 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
 
 #### Search using custom filters
 
-Our advanced search allows you to set custom search filters. You can filter by user_id, agent_id, app_id, run_id, created_at, updated_at, categories, and text. The filters support logical operators (AND, OR) and comparison operators (in, gte, lte, gt, lt, ne, contains, icontains). For more details, see [V2 Search Memories](/api-reference/memory/v2-search-memories).
+Our advanced search allows you to set custom search filters. You can filter by user_id, agent_id, app_id, run_id, created_at, updated_at, categories, and text. The filters support logical operators (AND, OR) and comparison operators (in, gte, lte, gt, lt, ne, contains, icontains, *). The wildcard character (*) matches everything for a specific field. For more details, see [V2 Search Memories](/api-reference/memory/v2-search-memories).
 
 Here you need to define `version` as `v2` in the search method.
 
@@ -678,6 +694,152 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
 
 </CodeGroup>
 
+Example 4: Search using NOT filters
+<CodeGroup>
+```python Python
+query = "What do you know about me?"
+filters = {
+    "NOT": [
+        {
+            "categories": {
+                "contains": "food_preferences"
+            }
+        }
+    ]
+}
+client.search(query, version="v2", filters=filters)
+```
+
+```javascript JavaScript
+const query = "What do you know about me?";
+const filters = {
+    "NOT": [
+        {
+            "categories": {
+                "contains": "food_preferences"
+            }
+        }
+    ]
+};
+
+client.search(query, { version: "v2", filters })
+    .then(results => console.log(results))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "query": "What do you know about me?",
+         "filters": {
+            "NOT": [
+                {
+                    "categories": {
+                        "contains": "food_preferences"
+                    }
+                }
+            ]
+        }
+     }'
+```
+
+```json Output
+{
+    "results": [
+        {
+            "id": "123abc-d456-7890-efgh-ijklmnopqrst",
+            "memory": "Lives in San Francisco",
+            "user_id": "alex",
+            "metadata": null,
+            "categories": ["location"],
+            "immutable": false,
+            "expiration_date": null,
+            "created_at": "2024-07-20T01:30:36.275141-07:00",
+            "updated_at": "2024-07-20T01:30:36.275172-07:00"
+        }
+    ]
+}
+```
+
+</CodeGroup>
+
+Example 5: Search using wildcard filters
+<CodeGroup>
+```python Python
+query = "What do you know about me?"
+filters = {
+    "AND": [
+        {
+            "user_id": "alex"
+        },
+        {
+            "run_id": "*"  # Matches all run_ids
+        }
+    ]
+}
+client.search(query, version="v2", filters=filters)
+```
+
+```javascript JavaScript
+const query = "What do you know about me?";
+const filters = {
+    "AND": [
+        {
+            "user_id": "alex"
+        },
+        {
+            "run_id": "*"  // Matches all run_ids
+        }
+    ]
+};
+
+client.search(query, { version: "v2", filters })
+    .then(results => console.log(results))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "query": "What do you know about me?",
+         "filters": {
+             "AND": [
+                 {
+                     "user_id": "alex"
+                 },
+                 {
+                     "run_id": "*"
+                 }
+             ]
+         }
+     }'
+```
+
+```json Output
+{
+    "results": [
+        {
+            "id": "7f165f7e-b411-4afe-b7e5-35789b72c4a5",
+            "memory": "Name: Alex. Vegetarian. Allergic to nuts.",
+            "user_id": "alex",
+            "run_id": "session-1",
+            "metadata": null,
+            "categories": ["food_preferences"],
+            "immutable": false,
+            "expiration_date": null,
+            "created_at": "2024-07-20T01:30:36.275141-07:00",
+            "updated_at": "2024-07-20T01:30:36.275172-07:00"
+        }
+    ]
+}
+```
+
+</CodeGroup>
+
 
 ### 4.3 Get All Users
 
@@ -1051,7 +1213,7 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&keywords=to play&page
 
 #### Get all memories using custom filters
 
-Our advanced retrieval allows you to set custom filters when fetching memories. You can filter by user_id, agent_id, app_id, run_id, created_at, updated_at, categories, and keywords. The filters support logical operators (AND, OR) and comparison operators (in, gte, lte, gt, lt, ne, contains, icontains). For more details, see [v2 Get Memories](/api-reference/memory/v2-get-memories).
+Our advanced retrieval allows you to set custom filters when fetching memories. You can filter by user_id, agent_id, app_id, run_id, created_at, updated_at, categories, and keywords. The filters support logical operators (AND, OR) and comparison operators (in, gte, lte, gt, lt, ne, contains, icontains, *). The wildcard character (*) matches everything for a specific field. For more details, see [v2 Get Memories](/api-reference/memory/v2-get-memories).
 
 Here you need to define `version` as `v2` in the get_all method.
 
@@ -1295,6 +1457,234 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
 
 </CodeGroup>
 
+Example 3: Get all memories using NOT filters
+<CodeGroup>
+```python Python
+filters = {
+    "NOT": [
+        {
+            "categories": {
+                "contains": "food_preferences"
+            }
+        }
+    ]
+}
+
+# Default (No Pagination)
+client.get_all(version="v2", filters=filters)
+
+# Pagination (You can also use the page and page_size parameters)
+client.get_all(version="v2", filters=filters, page=1, page_size=50)
+```
+
+```javascript JavaScript
+const filters = {
+    "NOT": [
+        {
+            "categories": {
+                "contains": "food_preferences"
+            }
+        }
+    ]
+};
+
+// Default (No Pagination)
+client.getAll({ version: "v2", filters })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+
+// Pagination (You can also use the page and page_size parameters)
+client.getAll({ version: "v2", filters, page: 1, page_size: 50 })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+# Default (No Pagination)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+            "NOT": [
+                {
+                    "categories": {
+                        "contains": "food_preferences"
+                    }
+                }
+            ]
+        }
+     }'
+
+# Pagination (You can also use the page and page_size parameters)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+            "NOT": [
+                {
+                    "categories": {
+                        "contains": "food_preferences"
+                    }
+                }
+            ]
+        }
+     }'
+```
+
+```json Output
+[
+    {
+        "id": "789xyz-e012-3456-fghi-jklmnopqrstu",
+        "memory": "Works as a software engineer",
+        "user_id": "alex",
+        "metadata": {"job": "tech"},
+        "categories": ["work"],
+        "immutable": false,
+        "expiration_date": null,
+        "created_at": "2024-07-20T01:30:36.275141-07:00",
+        "updated_at": "2024-07-20T01:30:36.275172-07:00"
+    }
+]
+```
+
+```json Output (Paginated)
+{
+    "count": 1,
+    "next": null,
+    "previous": null,
+    "results": [
+        {
+            "id": "789xyz-e012-3456-fghi-jklmnopqrstu",
+            "memory": "Works as a software engineer",
+            "user_id": "alex",
+            "metadata": {"job": "tech"},
+            "categories": ["work"],
+            "immutable": false,
+            "expiration_date": null,
+            "created_at": "2024-07-20T01:30:36.275141-07:00",
+            "updated_at": "2024-07-20T01:30:36.275172-07:00"
+        }
+    ]
+}
+```
+
+</CodeGroup>
+
+Example 4: Get all memories using wildcard filters
+<CodeGroup>
+```python Python
+filters = {
+    "AND": [
+        {
+            "user_id": "alex"
+        },
+        {
+            "run_id": "*"  # Matches all run_ids
+        }
+    ]
+}
+
+# Default (No Pagination)
+client.get_all(version="v2", filters=filters)
+
+# Pagination (You can also use the page and page_size parameters)
+client.get_all(version="v2", filters=filters, page=1, page_size=50)
+```
+
+```javascript JavaScript
+const filters = {
+    "AND": [
+        {
+            "user_id": "alex"
+        },
+        {
+            "run_id": "*"  // Matches all run_ids
+        }
+    ]
+};
+
+// Default (No Pagination)
+client.getAll({ version: "v2", filters })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+
+// Pagination (You can also use the page and page_size parameters)
+client.getAll({ version: "v2", filters, page: 1, page_size: 50 })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+# Default (No Pagination)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+             "AND": [
+                {"user_id":"alex"},
+                {"run_id": "*"}
+             ]
+         }
+     }'
+
+# Pagination (You can also use the page and page_size parameters)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+             "AND": [
+                {"user_id":"alex"},
+                {"run_id": "*"}
+             ]
+         }
+     }'
+```
+
+```json Output (Default)
+[
+    {
+        "id": "f38b689d-6b24-45b7-bced-17fbb4d8bac7",
+        "memory": "Name: Alex. Vegetarian. Allergic to nuts.",
+        "user_id": "alex",
+        "run_id": "session-1",
+        "metadata": null,
+        "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
+        "created_at": "2024-07-20T01:30:36.275141-07:00",
+        "updated_at": "2024-07-20T01:30:36.275172-07:00"
+    }
+]
+```
+
+```json Output (Paginated)
+{
+    "count": 1,
+    "next": null,
+    "previous": null,
+    "results": [
+        {
+            "id": "f38b689d-6b24-45b7-bced-17fbb4d8bac7",
+            "memory": "Name: Alex. Vegetarian. Allergic to nuts.",
+            "user_id": "alex",
+            "run_id": "session-1",
+            "metadata": null,
+            "categories": ["food_preferences"],
+            "immutable": false,
+            "expiration_date": null,
+            "created_at": "2024-07-20T01:30:36.275141-07:00",
+            "updated_at": "2024-07-20T01:30:36.275172-07:00"
+        }
+    ]
+}
+```
+
+</CodeGroup>
+
 
 ### 4.5 Memory History
 
@@ -1547,11 +1937,17 @@ Fun fact: You can also delete the memory using the `add()` method by passing a n
 <CodeGroup>
 
 ```python Python
-client.add("Delete all of my food preferences", user_id="alex")
+messages = [
+    {"role": "user", "content": "Delete all of my food preferences"}
+]
+client.add(messages, user_id="alex")
 ```
 
 ```javascript JavaScript
-client.add("Delete all of my food preferences", { user_id: "alex" })
+const messages = [
+    {"role": "user", "content": "Delete all of my food preferences"}
+]
+client.add(messages, { user_id: "alex" })
     .then(result => console.log(result))
     .catch(error => console.error(error));
 ```

docs/quickstart.mdx

@@ -5,6 +5,9 @@ iconType: "solid"
 ---
 
 <Snippet file="paper-release.mdx" />
+<Note type="info">
+🎉 We're excited to announce that Claude 4 is now available with Mem0! Check it out [here](components/llms/models/anthropic).
+</Note>
 
 
 Mem0 offers two powerful ways to leverage our technology: [our managed platform](#mem0-platform-managed-solution) and [our open source solution](#mem0-open-source).

evaluation/evals.py

@@ -14,46 +14,47 @@ def process_item(item_data):
     local_results = defaultdict(list)
 
     for item in v:
-        gt_answer = str(item['answer'])
-        pred_answer = str(item['response'])
-        category = str(item['category'])
-        question = str(item['question'])
+        gt_answer = str(item["answer"])
+        pred_answer = str(item["response"])
+        category = str(item["category"])
+        question = str(item["question"])
 
         # Skip category 5
-        if category == '5':
+        if category == "5":
             continue
 
         metrics = calculate_metrics(pred_answer, gt_answer)
         bleu_scores = calculate_bleu_scores(pred_answer, gt_answer)
         llm_score = evaluate_llm_judge(question, gt_answer, pred_answer)
 
-        local_results[k].append({
-            "question": question,
-            "answer": gt_answer,
-            "response": pred_answer,
-            "category": category,
-            "bleu_score": bleu_scores["bleu1"],
-            "f1_score": metrics["f1"],
-            "llm_score": llm_score
-        })
+        local_results[k].append(
+            {
+                "question": question,
+                "answer": gt_answer,
+                "response": pred_answer,
+                "category": category,
+                "bleu_score": bleu_scores["bleu1"],
+                "f1_score": metrics["f1"],
+                "llm_score": llm_score,
+            }
+        )
 
     return local_results
 
 
 def main():
-    parser = argparse.ArgumentParser(description='Evaluate RAG results')
-    parser.add_argument('--input_file', type=str,
-                        default="results/rag_results_500_k1.json",
-                        help='Path to the input dataset file')
-    parser.add_argument('--output_file', type=str,
-                        default="evaluation_metrics.json",
-                        help='Path to save the evaluation results')
-    parser.add_argument('--max_workers', type=int, default=10,
-                        help='Maximum number of worker threads')
+    parser = argparse.ArgumentParser(description="Evaluate RAG results")
+    parser.add_argument(
+        "--input_file", type=str, default="results/rag_results_500_k1.json", help="Path to the input dataset file"
+    )
+    parser.add_argument(
+        "--output_file", type=str, default="evaluation_metrics.json", help="Path to save the evaluation results"
+    )
+    parser.add_argument("--max_workers", type=int, default=10, help="Maximum number of worker threads")
 
     args = parser.parse_args()
 
-    with open(args.input_file, 'r') as f:
+    with open(args.input_file, "r") as f:
         data = json.load(f)
 
     results = defaultdict(list)
@@ -61,18 +62,16 @@ def main():
 
     # Use ThreadPoolExecutor with specified workers
     with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
-        futures = [executor.submit(process_item, item_data) 
-                  for item_data in data.items()]
+        futures = [executor.submit(process_item, item_data) for item_data in data.items()]
 
-        for future in tqdm(concurrent.futures.as_completed(futures), 
-                          total=len(futures)):
+        for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
             local_results = future.result()
             with results_lock:
                 for k, items in local_results.items():
                     results[k].extend(items)
 
     # Save results to JSON file
-    with open(args.output_file, 'w') as f:
+    with open(args.output_file, "w") as f:
         json.dump(results, f, indent=4)
 
     print(f"Results saved to {args.output_file}")

evaluation/generate_scores.py

@@ -3,7 +3,7 @@ import json
 import pandas as pd
 
 # Load the evaluation metrics data
-with open('evaluation_metrics.json', 'r') as f:
+with open("evaluation_metrics.json", "r") as f:
     data = json.load(f)
 
 # Flatten the data into a list of question items
@@ -15,28 +15,20 @@ for key in data:
 df = pd.DataFrame(all_items)
 
 # Convert category to numeric type
-df['category'] = pd.to_numeric(df['category'])
+df["category"] = pd.to_numeric(df["category"])
 
 # Calculate mean scores by category
-result = df.groupby('category').agg({
-    'bleu_score': 'mean',
-    'f1_score': 'mean',
-    'llm_score': 'mean'
-}).round(4)
+result = df.groupby("category").agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
 
 # Add count of questions per category
-result['count'] = df.groupby('category').size()
+result["count"] = df.groupby("category").size()
 
 # Print the results
 print("Mean Scores Per Category:")
 print(result)
 
 # Calculate overall means
-overall_means = df.agg({
-    'bleu_score': 'mean',
-    'f1_score': 'mean',
-    'llm_score': 'mean'
-}).round(4)
+overall_means = df.agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
 
 print("\nOverall Mean Scores:")
-print(overall_means)
+print(overall_means)

evaluation/metrics/llm_judge.py

@@ -33,35 +33,34 @@ Do NOT include both CORRECT and WRONG in your response, or it will break the eva
 Just return the label CORRECT or WRONG in a json format with the key as "label".
 """
 
+
 def evaluate_llm_judge(question, gold_answer, generated_answer):
     """Evaluate the generated answer against the gold answer using an LLM judge."""
     response = client.chat.completions.create(
         model="gpt-4o-mini",
-        messages=[{
-            "role": "user", 
-            "content": ACCURACY_PROMPT.format(
-                question=question, 
-                gold_answer=gold_answer, 
-                generated_answer=generated_answer
-            )
-        }],
+        messages=[
+            {
+                "role": "user",
+                "content": ACCURACY_PROMPT.format(
+                    question=question, gold_answer=gold_answer, generated_answer=generated_answer
+                ),
+            }
+        ],
         response_format={"type": "json_object"},
-        temperature=0.0
+        temperature=0.0,
     )
-    label = json.loads(response.choices[0].message.content)['label']
+    label = json.loads(response.choices[0].message.content)["label"]
     return 1 if label == "CORRECT" else 0
 
 
 def main():
     """Main function to evaluate RAG results using LLM judge."""
-    parser = argparse.ArgumentParser(
-        description='Evaluate RAG results using LLM judge'
-    )
+    parser = argparse.ArgumentParser(description="Evaluate RAG results using LLM judge")
     parser.add_argument(
-        '--input_file',
+        "--input_file",
         type=str,
         default="results/default_run_v4_k30_new_graph.json",
-        help='Path to the input dataset file'
+        help="Path to the input dataset file",
     )
 
     args = parser.parse_args()
@@ -78,10 +77,10 @@ def main():
     index = 0
     for k, v in data.items():
         for x in v:
-            question = x['question']
-            gold_answer = x['answer']
-            generated_answer = x['response']
-            category = x['category']
+            question = x["question"]
+            gold_answer = x["answer"]
+            generated_answer = x["response"]
+            category = x["category"]
 
             # Skip category 5
             if int(category) == 5:
@@ -92,13 +91,15 @@ def main():
             LLM_JUDGE[category].append(label)
 
             # Store the results
-            RESULTS[index].append({
-                "question": question,
-                "gt_answer": gold_answer,
-                "response": generated_answer,
-                "category": category,
-                "llm_label": label
-            })
+            RESULTS[index].append(
+                {
+                    "question": question,
+                    "gt_answer": gold_answer,
+                    "response": generated_answer,
+                    "category": category,
+                    "llm_label": label,
+                }
+            )
 
             # Save intermediate results
             with open(output_path, "w") as f:
@@ -108,8 +109,7 @@ def main():
             print("All categories accuracy:")
             for cat, results in LLM_JUDGE.items():
                 if results:  # Only print if there are results for this category
-                    print(f"  Category {cat}: {np.mean(results):.4f} "
-                          f"({sum(results)}/{len(results)})")
+                    print(f"  Category {cat}: {np.mean(results):.4f} " f"({sum(results)}/{len(results)})")
             print("------------------------------------------")
         index += 1
 

evaluation/metrics/utils.py

@@ -3,7 +3,7 @@ Borrowed from https://github.com/WujiangXu/AgenticMemory/blob/main/utils.py
 
 @article{xu2025mem,
     title={A-mem: Agentic memory for llm agents},
-    author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao 
+    author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao
            and Zhang, Yongfeng},
     journal={arXiv preprint arXiv:2502.12110},
     year={2025}
@@ -26,42 +26,45 @@ from sentence_transformers.util import pytorch_cos_sim
 
 # Download required NLTK data
 try:
-    nltk.download('punkt', quiet=True)
-    nltk.download('wordnet', quiet=True)
+    nltk.download("punkt", quiet=True)
+    nltk.download("wordnet", quiet=True)
 except Exception as e:
     print(f"Error downloading NLTK data: {e}")
 
 # Initialize SentenceTransformer model (this will be reused)
 try:
-    sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
 except Exception as e:
     print(f"Warning: Could not load SentenceTransformer model: {e}")
     sentence_model = None
 
+
 def simple_tokenize(text):
     """Simple tokenization function."""
     # Convert to string if not already
     text = str(text)
-    return text.lower().replace('.', ' ').replace(',', ' ').replace('!', ' ').replace('?', ' ').split()
+    return text.lower().replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
+
 
 def calculate_rouge_scores(prediction: str, reference: str) -> Dict[str, float]:
     """Calculate ROUGE scores for prediction against reference."""
-    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
     scores = scorer.score(reference, prediction)
     return {
-        'rouge1_f': scores['rouge1'].fmeasure,
-        'rouge2_f': scores['rouge2'].fmeasure,
-        'rougeL_f': scores['rougeL'].fmeasure
+        "rouge1_f": scores["rouge1"].fmeasure,
+        "rouge2_f": scores["rouge2"].fmeasure,
+        "rougeL_f": scores["rougeL"].fmeasure,
     }
 
+
 def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
     """Calculate BLEU scores with different n-gram settings."""
     pred_tokens = nltk.word_tokenize(prediction.lower())
     ref_tokens = [nltk.word_tokenize(reference.lower())]
-    
+
     weights_list = [(1, 0, 0, 0), (0.5, 0.5, 0, 0), (0.33, 0.33, 0.33, 0), (0.25, 0.25, 0.25, 0.25)]
     smooth = SmoothingFunction().method1
-    
+
     scores = {}
     for n, weights in enumerate(weights_list, start=1):
         try:
@@ -69,26 +72,20 @@ def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
         except Exception as e:
             print(f"Error calculating BLEU score: {e}")
             score = 0.0
-        scores[f'bleu{n}'] = score
-    
+        scores[f"bleu{n}"] = score
+
     return scores
 
+
 def calculate_bert_scores(prediction: str, reference: str) -> Dict[str, float]:
     """Calculate BERTScore for semantic similarity."""
     try:
-        P, R, F1 = bert_score([prediction], [reference], lang='en', verbose=False)
-        return {
-            'bert_precision': P.item(),
-            'bert_recall': R.item(),
-            'bert_f1': F1.item()
-        }
+        P, R, F1 = bert_score([prediction], [reference], lang="en", verbose=False)
+        return {"bert_precision": P.item(), "bert_recall": R.item(), "bert_f1": F1.item()}
     except Exception as e:
         print(f"Error calculating BERTScore: {e}")
-        return {
-            'bert_precision': 0.0,
-            'bert_recall': 0.0,
-            'bert_f1': 0.0
-        }
+        return {"bert_precision": 0.0, "bert_recall": 0.0, "bert_f1": 0.0}
+
 
 def calculate_meteor_score(prediction: str, reference: str) -> float:
     """Calculate METEOR score for the prediction."""
@@ -98,6 +95,7 @@ def calculate_meteor_score(prediction: str, reference: str) -> float:
         print(f"Error calculating METEOR score: {e}")
         return 0.0
 
+
 def calculate_sentence_similarity(prediction: str, reference: str) -> float:
     """Calculate sentence embedding similarity using SentenceBERT."""
     if sentence_model is None:
@@ -106,7 +104,7 @@ def calculate_sentence_similarity(prediction: str, reference: str) -> float:
         # Encode sentences
         embedding1 = sentence_model.encode([prediction], convert_to_tensor=True)
         embedding2 = sentence_model.encode([reference], convert_to_tensor=True)
-        
+
         # Calculate cosine similarity
         similarity = pytorch_cos_sim(embedding1, embedding2).item()
         return float(similarity)
@@ -114,6 +112,7 @@ def calculate_sentence_similarity(prediction: str, reference: str) -> float:
         print(f"Error calculating sentence similarity: {e}")
         return 0.0
 
+
 def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
     """Calculate comprehensive evaluation metrics for a prediction."""
     # Handle empty or None values
@@ -130,31 +129,31 @@ def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
             "bleu4": 0.0,
             "bert_f1": 0.0,
             "meteor": 0.0,
-            "sbert_similarity": 0.0
+            "sbert_similarity": 0.0,
         }
-    
+
     # Convert to strings if they're not already
     prediction = str(prediction).strip()
     reference = str(reference).strip()
-    
+
     # Calculate exact match
     exact_match = int(prediction.lower() == reference.lower())
-    
+
     # Calculate token-based F1 score
     pred_tokens = set(simple_tokenize(prediction))
     ref_tokens = set(simple_tokenize(reference))
     common_tokens = pred_tokens & ref_tokens
-    
+
     if not pred_tokens or not ref_tokens:
         f1 = 0.0
     else:
         precision = len(common_tokens) / len(pred_tokens)
         recall = len(common_tokens) / len(ref_tokens)
         f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
-    
+
     # Calculate all scores
     bleu_scores = calculate_bleu_scores(prediction, reference)
-    
+
     # Combine all metrics
     metrics = {
         "exact_match": exact_match,
@@ -164,48 +163,49 @@ def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
 
     return metrics
 
-def aggregate_metrics(all_metrics: List[Dict[str, float]], all_categories: List[int]) -> Dict[str, Dict[str, Union[float, Dict[str, float]]]]:
+
+def aggregate_metrics(
+    all_metrics: List[Dict[str, float]], all_categories: List[int]
+) -> Dict[str, Dict[str, Union[float, Dict[str, float]]]]:
     """Calculate aggregate statistics for all metrics, split by category."""
     if not all_metrics:
         return {}
-    
+
     # Initialize aggregates for overall and per-category metrics
     aggregates = defaultdict(list)
     category_aggregates = defaultdict(lambda: defaultdict(list))
-    
+
     # Collect all values for each metric, both overall and per category
     for metrics, category in zip(all_metrics, all_categories):
         for metric_name, value in metrics.items():
             aggregates[metric_name].append(value)
             category_aggregates[category][metric_name].append(value)
-    
+
     # Calculate statistics for overall metrics
-    results = {
-        "overall": {}
-    }
-    
+    results = {"overall": {}}
+
     for metric_name, values in aggregates.items():
         results["overall"][metric_name] = {
-            'mean': statistics.mean(values),
-            'std': statistics.stdev(values) if len(values) > 1 else 0.0,
-            'median': statistics.median(values),
-            'min': min(values),
-            'max': max(values),
-            'count': len(values)
+            "mean": statistics.mean(values),
+            "std": statistics.stdev(values) if len(values) > 1 else 0.0,
+            "median": statistics.median(values),
+            "min": min(values),
+            "max": max(values),
+            "count": len(values),
         }
-    
+
     # Calculate statistics for each category
     for category in sorted(category_aggregates.keys()):
         results[f"category_{category}"] = {}
         for metric_name, values in category_aggregates[category].items():
             if values:  # Only calculate if we have values for this category
                 results[f"category_{category}"][metric_name] = {
-                    'mean': statistics.mean(values),
-                    'std': statistics.stdev(values) if len(values) > 1 else 0.0,
-                    'median': statistics.median(values),
-                    'min': min(values),
-                    'max': max(values),
-                    'count': len(values)
+                    "mean": statistics.mean(values),
+                    "std": statistics.stdev(values) if len(values) > 1 else 0.0,
+                    "median": statistics.median(values),
+                    "min": min(values),
+                    "max": max(values),
+                    "count": len(values),
                 }
-    
+
     return results

evaluation/prompts.py

@@ -144,4 +144,4 @@ ANSWER_PROMPT_ZEP = """
 
     Question: {{question}}
     Answer:
-    """
+    """

evaluation/run_experiments.py

@@ -21,23 +21,15 @@ class Experiment:
 
 
 def main():
-    parser = argparse.ArgumentParser(description='Run memory experiments')
-    parser.add_argument('--technique_type', choices=TECHNIQUES, default='mem0',
-                        help='Memory technique to use')
-    parser.add_argument('--method', choices=METHODS, default='add',
-                        help='Method to use')
-    parser.add_argument('--chunk_size', type=int, default=1000,
-                        help='Chunk size for processing')
-    parser.add_argument('--output_folder', type=str, default='results/',
-                        help='Output path for results')
-    parser.add_argument('--top_k', type=int, default=30,
-                        help='Number of top memories to retrieve')
-    parser.add_argument('--filter_memories', action='store_true', default=False,
-                        help='Whether to filter memories')
-    parser.add_argument('--is_graph', action='store_true', default=False,
-                        help='Whether to use graph-based search')
-    parser.add_argument('--num_chunks', type=int, default=1,
-                        help='Number of chunks to process')
+    parser = argparse.ArgumentParser(description="Run memory experiments")
+    parser.add_argument("--technique_type", choices=TECHNIQUES, default="mem0", help="Memory technique to use")
+    parser.add_argument("--method", choices=METHODS, default="add", help="Method to use")
+    parser.add_argument("--chunk_size", type=int, default=1000, help="Chunk size for processing")
+    parser.add_argument("--output_folder", type=str, default="results/", help="Output path for results")
+    parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve")
+    parser.add_argument("--filter_memories", action="store_true", default=False, help="Whether to filter memories")
+    parser.add_argument("--is_graph", action="store_true", default=False, help="Whether to use graph-based search")
+    parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks to process")
 
     args = parser.parse_args()
 
@@ -46,33 +38,18 @@ def main():
 
     if args.technique_type == "mem0":
         if args.method == "add":
-            memory_manager = MemoryADD(
-                data_path='dataset/locomo10.json',
-                is_graph=args.is_graph
-            )
+            memory_manager = MemoryADD(data_path="dataset/locomo10.json", is_graph=args.is_graph)
             memory_manager.process_all_conversations()
         elif args.method == "search":
             output_file_path = os.path.join(
                 args.output_folder,
-                f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json"
+                f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json",
             )
-            memory_searcher = MemorySearch(
-                output_file_path,
-                args.top_k,
-                args.filter_memories,
-                args.is_graph
-            )
-            memory_searcher.process_data_file('dataset/locomo10.json')
+            memory_searcher = MemorySearch(output_file_path, args.top_k, args.filter_memories, args.is_graph)
+            memory_searcher.process_data_file("dataset/locomo10.json")
     elif args.technique_type == "rag":
-        output_file_path = os.path.join(
-            args.output_folder,
-            f"rag_results_{args.chunk_size}_k{args.num_chunks}.json"
-        )
-        rag_manager = RAGManager(
-            data_path="dataset/locomo10_rag.json",
-            chunk_size=args.chunk_size,
-            k=args.num_chunks
-        )
+        output_file_path = os.path.join(args.output_folder, f"rag_results_{args.chunk_size}_k{args.num_chunks}.json")
+        rag_manager = RAGManager(data_path="dataset/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks)
         rag_manager.process_all_conversations(output_file_path)
     elif args.technique_type == "langmem":
         output_file_path = os.path.join(args.output_folder, "langmem_results.json")
@@ -85,11 +62,7 @@ def main():
         elif args.method == "search":
             output_file_path = os.path.join(args.output_folder, "zep_search_results.json")
             zep_manager = ZepSearch()
-            zep_manager.process_data_file(
-                "dataset/locomo10.json",
-                "1",
-                output_file_path
-            )
+            zep_manager.process_data_file("dataset/locomo10.json", "1", output_file_path)
     elif args.technique_type == "openai":
         output_file_path = os.path.join(args.output_folder, "openai_results.json")
         openai_manager = OpenAIPredict()

evaluation/src/langmem.py

@@ -28,14 +28,12 @@ def get_answer(question, speaker_1_user_id, speaker_1_memories, speaker_2_user_i
         speaker_1_user_id=speaker_1_user_id,
         speaker_1_memories=speaker_1_memories,
         speaker_2_user_id=speaker_2_user_id,
-        speaker_2_memories=speaker_2_memories
+        speaker_2_memories=speaker_2_memories,
     )
 
     t1 = time.time()
     response = client.chat.completions.create(
-        model=os.getenv("MODEL"),
-        messages=[{"role": "system", "content": prompt}],
-        temperature=0.0
+        model=os.getenv("MODEL"), messages=[{"role": "system", "content": prompt}], temperature=0.0
     )
     t2 = time.time()
     return response.choices[0].message.content, t2 - t1
@@ -59,7 +57,9 @@ def prompt(state):
 
 
 class LangMem:
-    def __init__(self,):
+    def __init__(
+        self,
+    ):
         self.store = InMemoryStore(
             index={
                 "dims": 1536,
@@ -80,18 +80,12 @@ class LangMem:
         )
 
     def add_memory(self, message, config):
-        return self.agent.invoke(
-            {"messages": [{"role": "user", "content": message}]},
-            config=config
-        )
+        return self.agent.invoke({"messages": [{"role": "user", "content": message}]}, config=config)
 
     def search_memory(self, query, config):
         try:
             t1 = time.time()
-            response = self.agent.invoke(
-                {"messages": [{"role": "user", "content": query}]},
-                config=config
-            )
+            response = self.agent.invoke({"messages": [{"role": "user", "content": query}]}, config=config)
             t2 = time.time()
             return response["messages"][-1].content, t2 - t1
         except Exception as e:
@@ -102,7 +96,7 @@ class LangMem:
 class LangMemManager:
     def __init__(self, dataset_path):
         self.dataset_path = dataset_path
-        with open(self.dataset_path, 'r') as f:
+        with open(self.dataset_path, "r") as f:
             self.data = json.load(f)
 
     def process_all_conversations(self, output_file_path):
@@ -123,7 +117,7 @@ class LangMemManager:
 
             # Identify speakers
             for conv in chat_history:
-                speakers.add(conv['speaker'])
+                speakers.add(conv["speaker"])
 
             if len(speakers) != 2:
                 raise ValueError(f"Expected 2 speakers, got {len(speakers)}")
@@ -134,50 +128,52 @@ class LangMemManager:
             # Add memories for each message
             for conv in tqdm(chat_history, desc=f"Processing messages {key}", leave=False):
                 message = f"{conv['timestamp']} | {conv['speaker']}: {conv['text']}"
-                if conv['speaker'] == speaker1:
+                if conv["speaker"] == speaker1:
                     agent1.add_memory(message, config)
-                elif conv['speaker'] == speaker2:
+                elif conv["speaker"] == speaker2:
                     agent2.add_memory(message, config)
                 else:
                     raise ValueError(f"Expected speaker1 or speaker2, got {conv['speaker']}")
 
             # Process questions
             for q in tqdm(questions, desc=f"Processing questions {key}", leave=False):
-                category = q['category']
+                category = q["category"]
 
                 if int(category) == 5:
                     continue
 
-                answer = q['answer']
-                question = q['question']
+                answer = q["answer"]
+                question = q["question"]
                 response1, speaker1_memory_time = agent1.search_memory(question, config)
                 response2, speaker2_memory_time = agent2.search_memory(question, config)
 
-                generated_answer, response_time = get_answer(
-                    question, speaker1, response1, speaker2, response2
-                )
+                generated_answer, response_time = get_answer(question, speaker1, response1, speaker2, response2)
 
-                result[key].append({
-                    "question": question,
-                    "answer": answer,
-                    "response1": response1,
-                    "response2": response2,
-                    "category": category,
-                    "speaker1_memory_time": speaker1_memory_time,
-                    "speaker2_memory_time": speaker2_memory_time,
-                    "response_time": response_time,
-                    'response': generated_answer
-                })
+                result[key].append(
+                    {
+                        "question": question,
+                        "answer": answer,
+                        "response1": response1,
+                        "response2": response2,
+                        "category": category,
+                        "speaker1_memory_time": speaker1_memory_time,
+                        "speaker2_memory_time": speaker2_memory_time,
+                        "response_time": response_time,
+                        "response": generated_answer,
+                    }
+                )
 
             return result
 
         # Use multiprocessing to process conversations in parallel
         with mp.Pool(processes=10) as pool:
-            results = list(tqdm(
-                pool.imap(process_conversation, list(self.data.items())),
-                total=len(self.data),
-                desc="Processing conversations"
-            ))
+            results = list(
+                tqdm(
+                    pool.imap(process_conversation, list(self.data.items())),
+                    total=len(self.data),
+                    desc="Processing conversations",
+                )
+            )
 
         # Combine results from all workers
         for result in results:
@@ -185,5 +181,5 @@ class LangMemManager:
                 OUTPUT[key].extend(items)
 
         # Save final results
-        with open(output_file_path, 'w') as f:
+        with open(output_file_path, "w") as f:
             json.dump(OUTPUT, f, indent=4)

evaluation/src/memzero/add.py

@@ -13,7 +13,7 @@ load_dotenv()
 
 
 # Update custom instructions
-custom_instructions ="""
+custom_instructions = """
 Generate personal memories that follow these guidelines:
 
 1. Each memory should be self-contained with complete context, including:
@@ -47,7 +47,7 @@ class MemoryADD:
         self.mem0_client = MemoryClient(
             api_key=os.getenv("MEM0_API_KEY"),
             org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID")
+            project_id=os.getenv("MEM0_PROJECT_ID"),
         )
 
         self.mem0_client.update_project(custom_instructions=custom_instructions)
@@ -59,15 +59,16 @@ class MemoryADD:
             self.load_data()
 
     def load_data(self):
-        with open(self.data_path, 'r') as f:
+        with open(self.data_path, "r") as f:
             self.data = json.load(f)
         return self.data
 
     def add_memory(self, user_id, message, metadata, retries=3):
         for attempt in range(retries):
             try:
-                _ = self.mem0_client.add(message, user_id=user_id, version="v2",
-                                         metadata=metadata, enable_graph=self.is_graph)
+                _ = self.mem0_client.add(
+                    message, user_id=user_id, version="v2", metadata=metadata, enable_graph=self.is_graph
+                )
                 return
             except Exception as e:
                 if attempt < retries - 1:
@@ -78,13 +79,13 @@ class MemoryADD:
 
     def add_memories_for_speaker(self, speaker, messages, timestamp, desc):
         for i in tqdm(range(0, len(messages), self.batch_size), desc=desc):
-            batch_messages = messages[i:i+self.batch_size]
+            batch_messages = messages[i : i + self.batch_size]
             self.add_memory(speaker, batch_messages, metadata={"timestamp": timestamp})
 
     def process_conversation(self, item, idx):
-        conversation = item['conversation']
-        speaker_a = conversation['speaker_a']
-        speaker_b = conversation['speaker_b']
+        conversation = item["conversation"]
+        speaker_a = conversation["speaker_a"]
+        speaker_b = conversation["speaker_b"]
 
         speaker_a_user_id = f"{speaker_a}_{idx}"
         speaker_b_user_id = f"{speaker_b}_{idx}"
@@ -94,7 +95,7 @@ class MemoryADD:
         self.mem0_client.delete_all(user_id=speaker_b_user_id)
 
         for key in conversation.keys():
-            if key in ['speaker_a', 'speaker_b'] or "date" in key or "timestamp" in key:
+            if key in ["speaker_a", "speaker_b"] or "date" in key or "timestamp" in key:
                 continue
 
             date_time_key = key + "_date_time"
@@ -104,10 +105,10 @@ class MemoryADD:
             messages = []
             messages_reverse = []
             for chat in chats:
-                if chat['speaker'] == speaker_a:
+                if chat["speaker"] == speaker_a:
                     messages.append({"role": "user", "content": f"{speaker_a}: {chat['text']}"})
                     messages_reverse.append({"role": "assistant", "content": f"{speaker_a}: {chat['text']}"})
-                elif chat['speaker'] == speaker_b:
+                elif chat["speaker"] == speaker_b:
                     messages.append({"role": "assistant", "content": f"{speaker_b}: {chat['text']}"})
                     messages_reverse.append({"role": "user", "content": f"{speaker_b}: {chat['text']}"})
                 else:
@@ -116,11 +117,11 @@ class MemoryADD:
             # add memories for the two users on different threads
             thread_a = threading.Thread(
                 target=self.add_memories_for_speaker,
-                args=(speaker_a_user_id, messages, timestamp, "Adding Memories for Speaker A")
+                args=(speaker_a_user_id, messages, timestamp, "Adding Memories for Speaker A"),
             )
             thread_b = threading.Thread(
                 target=self.add_memories_for_speaker,
-                args=(speaker_b_user_id, messages_reverse, timestamp, "Adding Memories for Speaker B")
+                args=(speaker_b_user_id, messages_reverse, timestamp, "Adding Memories for Speaker B"),
             )
 
             thread_a.start()
@@ -134,10 +135,7 @@ class MemoryADD:
         if not self.data:
             raise ValueError("No data loaded. Please set data_path and call load_data() first.")
         with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            futures = [
-                executor.submit(self.process_conversation, item, idx)
-                for idx, item in enumerate(self.data)
-            ]
+            futures = [executor.submit(self.process_conversation, item, idx) for idx, item in enumerate(self.data)]
 
             for future in futures:
-                future.result()
+                future.result()

evaluation/src/memzero/search.py

@@ -16,12 +16,11 @@ load_dotenv()
 
 
 class MemorySearch:
-
-    def __init__(self, output_path='results.json', top_k=10, filter_memories=False, is_graph=False):
+    def __init__(self, output_path="results.json", top_k=10, filter_memories=False, is_graph=False):
         self.mem0_client = MemoryClient(
             api_key=os.getenv("MEM0_API_KEY"),
             org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID")
+            project_id=os.getenv("MEM0_PROJECT_ID"),
         )
         self.top_k = top_k
         self.openai_client = OpenAI()
@@ -42,11 +41,18 @@ class MemorySearch:
             try:
                 if self.is_graph:
                     print("Searching with graph")
-                    memories = self.mem0_client.search(query, user_id=user_id, top_k=self.top_k,
-                                                filter_memories=self.filter_memories, enable_graph=True, output_format='v1.1')
+                    memories = self.mem0_client.search(
+                        query,
+                        user_id=user_id,
+                        top_k=self.top_k,
+                        filter_memories=self.filter_memories,
+                        enable_graph=True,
+                        output_format="v1.1",
+                    )
                 else:
-                    memories = self.mem0_client.search(query, user_id=user_id, top_k=self.top_k,
-                                                filter_memories=self.filter_memories)
+                    memories = self.mem0_client.search(
+                        query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories
+                    )
                 break
             except Exception as e:
                 print("Retrying...")
@@ -57,64 +63,86 @@ class MemorySearch:
 
         end_time = time.time()
         if not self.is_graph:
-            semantic_memories = [{'memory': memory['memory'],
-                        'timestamp': memory['metadata']['timestamp'],
-                        'score': round(memory['score'], 2)}
-                    for memory in memories]
+            semantic_memories = [
+                {
+                    "memory": memory["memory"],
+                    "timestamp": memory["metadata"]["timestamp"],
+                    "score": round(memory["score"], 2),
+                }
+                for memory in memories
+            ]
             graph_memories = None
         else:
-            semantic_memories = [{'memory': memory['memory'],
-                        'timestamp': memory['metadata']['timestamp'],
-                        'score': round(memory['score'], 2)} for memory in memories['results']]
-            graph_memories = [{"source": relation['source'], "relationship": relation['relationship'], "target": relation['target']} for relation in memories['relations']]
+            semantic_memories = [
+                {
+                    "memory": memory["memory"],
+                    "timestamp": memory["metadata"]["timestamp"],
+                    "score": round(memory["score"], 2),
+                }
+                for memory in memories["results"]
+            ]
+            graph_memories = [
+                {"source": relation["source"], "relationship": relation["relationship"], "target": relation["target"]}
+                for relation in memories["relations"]
+            ]
         return semantic_memories, graph_memories, end_time - start_time
 
     def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category):
-        speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(speaker_1_user_id, question)
-        speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(speaker_2_user_id, question)
+        speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(
+            speaker_1_user_id, question
+        )
+        speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(
+            speaker_2_user_id, question
+        )
 
-        search_1_memory = [f"{item['timestamp']}: {item['memory']}" 
-                        for item in speaker_1_memories]
-        search_2_memory = [f"{item['timestamp']}: {item['memory']}" 
-                          for item in speaker_2_memories]
+        search_1_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_1_memories]
+        search_2_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_2_memories]
 
         template = Template(self.ANSWER_PROMPT)
         answer_prompt = template.render(
-            speaker_1_user_id=speaker_1_user_id.split('_')[0],
-            speaker_2_user_id=speaker_2_user_id.split('_')[0],
+            speaker_1_user_id=speaker_1_user_id.split("_")[0],
+            speaker_2_user_id=speaker_2_user_id.split("_")[0],
             speaker_1_memories=json.dumps(search_1_memory, indent=4),
             speaker_2_memories=json.dumps(search_2_memory, indent=4),
             speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4),
             speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4),
-            question=question
+            question=question,
         )
 
         t1 = time.time()
         response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"),
-            messages=[
-                {"role": "system", "content": answer_prompt}
-            ],
-            temperature=0.0
+            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
         )
         t2 = time.time()
         response_time = t2 - t1
-        return response.choices[0].message.content, speaker_1_memories, speaker_2_memories, speaker_1_memory_time, speaker_2_memory_time, speaker_1_graph_memories, speaker_2_graph_memories, response_time
+        return (
+            response.choices[0].message.content,
+            speaker_1_memories,
+            speaker_2_memories,
+            speaker_1_memory_time,
+            speaker_2_memory_time,
+            speaker_1_graph_memories,
+            speaker_2_graph_memories,
+            response_time,
+        )
 
     def process_question(self, val, speaker_a_user_id, speaker_b_user_id):
-        question = val.get('question', '')
-        answer = val.get('answer', '')
-        category = val.get('category', -1)
-        evidence = val.get('evidence', [])
-        adversarial_answer = val.get('adversarial_answer', '')
+        question = val.get("question", "")
+        answer = val.get("answer", "")
+        category = val.get("category", -1)
+        evidence = val.get("evidence", [])
+        adversarial_answer = val.get("adversarial_answer", "")
 
-        response, speaker_1_memories, speaker_2_memories, speaker_1_memory_time, speaker_2_memory_time, speaker_1_graph_memories, speaker_2_graph_memories, response_time = self.answer_question(
-            speaker_a_user_id,
-            speaker_b_user_id,
-            question,
-            answer,
-            category
-        )
+        (
+            response,
+            speaker_1_memories,
+            speaker_2_memories,
+            speaker_1_memory_time,
+            speaker_2_memory_time,
+            speaker_1_graph_memories,
+            speaker_2_graph_memories,
+            response_time,
+        ) = self.answer_question(speaker_a_user_id, speaker_b_user_id, question, answer, category)
 
         result = {
             "question": question,
@@ -125,67 +153,63 @@ class MemorySearch:
             "adversarial_answer": adversarial_answer,
             "speaker_1_memories": speaker_1_memories,
             "speaker_2_memories": speaker_2_memories,
-            'num_speaker_1_memories': len(speaker_1_memories),
-            'num_speaker_2_memories': len(speaker_2_memories),
-            'speaker_1_memory_time': speaker_1_memory_time,
-            'speaker_2_memory_time': speaker_2_memory_time,
+            "num_speaker_1_memories": len(speaker_1_memories),
+            "num_speaker_2_memories": len(speaker_2_memories),
+            "speaker_1_memory_time": speaker_1_memory_time,
+            "speaker_2_memory_time": speaker_2_memory_time,
             "speaker_1_graph_memories": speaker_1_graph_memories,
             "speaker_2_graph_memories": speaker_2_graph_memories,
-            "response_time": response_time
+            "response_time": response_time,
         }
 
         # Save results after each question is processed
-        with open(self.output_path, 'w') as f:
+        with open(self.output_path, "w") as f:
             json.dump(self.results, f, indent=4)
 
         return result
 
     def process_data_file(self, file_path):
-        with open(file_path, 'r') as f:
+        with open(file_path, "r") as f:
             data = json.load(f)
 
         for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item['qa']
-            conversation = item['conversation']
-            speaker_a = conversation['speaker_a']
-            speaker_b = conversation['speaker_b']
+            qa = item["qa"]
+            conversation = item["conversation"]
+            speaker_a = conversation["speaker_a"]
+            speaker_b = conversation["speaker_b"]
 
             speaker_a_user_id = f"{speaker_a}_{idx}"
             speaker_b_user_id = f"{speaker_b}_{idx}"
 
-            for question_item in tqdm(qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False):
-                result = self.process_question(
-                    question_item,
-                    speaker_a_user_id,
-                    speaker_b_user_id
-                )
+            for question_item in tqdm(
+                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
+            ):
+                result = self.process_question(question_item, speaker_a_user_id, speaker_b_user_id)
                 self.results[idx].append(result)
 
                 # Save results after each question is processed
-                with open(self.output_path, 'w') as f:
+                with open(self.output_path, "w") as f:
                     json.dump(self.results, f, indent=4)
 
         # Final save at the end
-        with open(self.output_path, 'w') as f:
+        with open(self.output_path, "w") as f:
             json.dump(self.results, f, indent=4)
 
     def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1):
         def process_single_question(val):
             result = self.process_question(val, speaker_a_user_id, speaker_b_user_id)
             # Save results after each question is processed
-            with open(self.output_path, 'w') as f:
+            with open(self.output_path, "w") as f:
                 json.dump(self.results, f, indent=4)
             return result
 
         with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            results = list(tqdm(
-                executor.map(process_single_question, qa_list),
-                total=len(qa_list),
-                desc="Answering Questions"
-            ))
+            results = list(
+                tqdm(executor.map(process_single_question, qa_list), total=len(qa_list), desc="Answering Questions")
+            )
 
         # Final save at the end
-        with open(self.output_path, 'w') as f:
+        with open(self.output_path, "w") as f:
             json.dump(self.results, f, indent=4)
 
         return results

evaluation/src/openai/predict.py

@@ -59,23 +59,19 @@ class OpenAIPredict:
         self.results = defaultdict(list)
 
     def search_memory(self, idx):
-
-        with open(f'memories/{idx}.txt', 'r') as file:
+        with open(f"memories/{idx}.txt", "r") as file:
             memories = file.read()
 
         return memories, 0
 
     def process_question(self, val, idx):
-        question = val.get('question', '')
-        answer = val.get('answer', '')
-        category = val.get('category', -1)
-        evidence = val.get('evidence', [])
-        adversarial_answer = val.get('adversarial_answer', '')
+        question = val.get("question", "")
+        answer = val.get("answer", "")
+        category = val.get("category", -1)
+        evidence = val.get("evidence", [])
+        adversarial_answer = val.get("adversarial_answer", "")
 
-        response, search_memory_time, response_time, context = self.answer_question(
-            idx,
-            question
-        )
+        response, search_memory_time, response_time, context = self.answer_question(idx, question)
 
         result = {
             "question": question,
@@ -86,7 +82,7 @@ class OpenAIPredict:
             "adversarial_answer": adversarial_answer,
             "search_memory_time": search_memory_time,
             "response_time": response_time,
-            "context": context
+            "context": context,
         }
 
         return result
@@ -95,43 +91,35 @@ class OpenAIPredict:
         memories, search_memory_time = self.search_memory(idx)
 
         template = Template(ANSWER_PROMPT)
-        answer_prompt = template.render(
-            memories=memories,
-            question=question
-        )
+        answer_prompt = template.render(memories=memories, question=question)
 
         t1 = time.time()
         response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"),
-            messages=[
-                {"role": "system", "content": answer_prompt}
-            ],
-            temperature=0.0
+            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
         )
         t2 = time.time()
         response_time = t2 - t1
         return response.choices[0].message.content, search_memory_time, response_time, memories
 
     def process_data_file(self, file_path, output_file_path):
-        with open(file_path, 'r') as f:
+        with open(file_path, "r") as f:
             data = json.load(f)
 
         for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item['qa']
+            qa = item["qa"]
 
-            for question_item in tqdm(qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False):
-                result = self.process_question(
-                    question_item,
-                    idx
-                )
+            for question_item in tqdm(
+                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
+            ):
+                result = self.process_question(question_item, idx)
                 self.results[idx].append(result)
 
                 # Save results after each question is processed
-                with open(output_file_path, 'w') as f:
+                with open(output_file_path, "w") as f:
                     json.dump(self.results, f, indent=4)
 
         # Final save at the end
-        with open(output_file_path, 'w') as f:
+        with open(output_file_path, "w") as f:
             json.dump(self.results, f, indent=4)
 
 
@@ -141,4 +129,3 @@ if __name__ == "__main__":
     args = parser.parse_args()
     openai_predict = OpenAIPredict()
     openai_predict.process_data_file("../../dataset/locomo10.json", args.output_file_path)
-

evaluation/src/rag.py

@@ -33,10 +33,7 @@ class RAGManager:
 
     def generate_response(self, question, context):
         template = Template(PROMPT)
-        prompt = template.render(
-            CONTEXT=context,
-            QUESTION=question
-        )
+        prompt = template.render(CONTEXT=context, QUESTION=question)
 
         max_retries = 3
         retries = 0
@@ -47,19 +44,21 @@ class RAGManager:
                 response = self.client.chat.completions.create(
                     model=self.model,
                     messages=[
-                        {"role": "system",
-                         "content": "You are a helpful assistant that can answer "
-                                    "questions based on the provided context."
-                                    "If the question involves timing, use the conversation date for reference."
-                                    "Provide the shortest possible answer."
-                                    "Use words directly from the conversation when possible."
-                                    "Avoid using subjects in your answer."},
-                        {"role": "user", "content": prompt}
+                        {
+                            "role": "system",
+                            "content": "You are a helpful assistant that can answer "
+                            "questions based on the provided context."
+                            "If the question involves timing, use the conversation date for reference."
+                            "Provide the shortest possible answer."
+                            "Use words directly from the conversation when possible."
+                            "Avoid using subjects in your answer.",
+                        },
+                        {"role": "user", "content": prompt},
                     ],
-                    temperature=0
+                    temperature=0,
                 )
                 t2 = time.time()
-                return response.choices[0].message.content.strip(), t2-t1
+                return response.choices[0].message.content.strip(), t2 - t1
             except Exception as e:
                 retries += 1
                 if retries > max_retries:
@@ -69,21 +68,16 @@ class RAGManager:
     def clean_chat_history(self, chat_history):
         cleaned_chat_history = ""
         for c in chat_history:
-            cleaned_chat_history += (f"{c['timestamp']} | {c['speaker']}: "
-                                     f"{c['text']}\n")
+            cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: " f"{c['text']}\n"
 
         return cleaned_chat_history
 
     def calculate_embedding(self, document):
-        response = self.client.embeddings.create(
-            model=os.getenv("EMBEDDING_MODEL"),
-            input=document
-        )
+        response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
         return response.data[0].embedding
 
     def calculate_similarity(self, embedding1, embedding2):
-        return np.dot(embedding1, embedding2) / (
-            np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+        return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
 
     def search(self, query, chunks, embeddings, k=1):
         """
@@ -101,10 +95,7 @@ class RAGManager:
         """
         t1 = time.time()
         query_embedding = self.calculate_embedding(query)
-        similarities = [
-            self.calculate_similarity(query_embedding, embedding) 
-            for embedding in embeddings
-        ]
+        similarities = [self.calculate_similarity(query_embedding, embedding) for embedding in embeddings]
 
         # Get indices of top-k most similar chunks
         if k == 1:
@@ -118,7 +109,7 @@ class RAGManager:
         combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
 
         t2 = time.time()
-        return combined_chunks, t2-t1
+        return combined_chunks, t2 - t1
 
     def create_chunks(self, chat_history, chunk_size=500):
         """
@@ -139,7 +130,7 @@ class RAGManager:
 
         # Split into chunks based on token count
         for i in range(0, len(tokens), chunk_size):
-            chunk_tokens = tokens[i:i+chunk_size]
+            chunk_tokens = tokens[i : i + chunk_size]
             chunk = encoding.decode(chunk_tokens)
             chunks.append(chunk)
 
@@ -159,13 +150,9 @@ class RAGManager:
             chat_history = value["conversation"]
             questions = value["question"]
 
-            chunks, embeddings = self.create_chunks(
-                chat_history, self.chunk_size
-            )
+            chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
 
-            for item in tqdm(
-                questions, desc="Answering questions", leave=False
-            ):
+            for item in tqdm(questions, desc="Answering questions", leave=False):
                 question = item["question"]
                 answer = item.get("answer", "")
                 category = item["category"]
@@ -174,22 +161,20 @@ class RAGManager:
                     context = chunks[0]
                     search_time = 0
                 else:
-                    context, search_time = self.search(
-                        question, chunks, embeddings, k=self.k
-                    )
-                response, response_time = self.generate_response(
-                    question, context
-                )
+                    context, search_time = self.search(question, chunks, embeddings, k=self.k)
+                response, response_time = self.generate_response(question, context)
 
-                FINAL_RESULTS[key].append({
-                    "question": question,
-                    "answer": answer,
-                    "category": category,
-                    "context": context,
-                    "response": response,
-                    "search_time": search_time,
-                    "response_time": response_time,
-                })
+                FINAL_RESULTS[key].append(
+                    {
+                        "question": question,
+                        "answer": answer,
+                        "category": category,
+                        "context": context,
+                        "response": response,
+                        "search_time": search_time,
+                        "response_time": response_time,
+                    }
+                )
                 with open(output_file_path, "w+") as f:
                     json.dump(FINAL_RESULTS, f, indent=4)
 

evaluation/src/utils.py

@@ -1,12 +1,3 @@
-TECHNIQUES = [
-    "mem0",
-    "rag",
-    "langmem",
-    "zep",
-    "openai"
-]
+TECHNIQUES = ["mem0", "rag", "langmem", "zep", "openai"]
 
-METHODS = [
-    "add",
-    "search"
-]
+METHODS = ["add", "search"]

evaluation/src/zep/add.py

@@ -19,12 +19,12 @@ class ZepAdd:
             self.load_data()
 
     def load_data(self):
-        with open(self.data_path, 'r') as f:
+        with open(self.data_path, "r") as f:
             self.data = json.load(f)
         return self.data
 
     def process_conversation(self, run_id, item, idx):
-        conversation = item['conversation']
+        conversation = item["conversation"]
 
         user_id = f"run_id_{run_id}_experiment_user_{idx}"
         session_id = f"run_id_{run_id}_experiment_session_{idx}"
@@ -41,7 +41,7 @@ class ZepAdd:
 
         print("Starting to add memories... for user", user_id)
         for key in tqdm(conversation.keys(), desc=f"Processing user {user_id}"):
-            if key in ['speaker_a', 'speaker_b'] or "date" in key:
+            if key in ["speaker_a", "speaker_b"] or "date" in key:
                 continue
 
             date_time_key = key + "_date_time"
@@ -51,11 +51,13 @@ class ZepAdd:
             for chat in tqdm(chats, desc=f"Adding chats for {key}", leave=False):
                 self.zep_client.memory.add(
                     session_id=session_id,
-                    messages=[Message(
-                        role=chat['speaker'],
-                        role_type="user",
-                        content=f"{timestamp}: {chat['text']}",
-                    )]
+                    messages=[
+                        Message(
+                            role=chat["speaker"],
+                            role_type="user",
+                            content=f"{timestamp}: {chat['text']}",
+                        )
+                    ],
                 )
 
     def process_all_conversations(self, run_id):
@@ -71,4 +73,4 @@ if __name__ == "__main__":
     parser.add_argument("--run_id", type=str, required=True)
     args = parser.parse_args()
     zep_add = ZepAdd(data_path="../../dataset/locomo10.json")
-    zep_add.process_all_conversations(args.run_id)
+    zep_add.process_all_conversations(args.run_id)

evaluation/src/zep/search.py

@@ -42,9 +42,9 @@ class ZepSearch:
         return f"{edge.valid_at if edge.valid_at else 'date unknown'} - {(edge.invalid_at if edge.invalid_at else 'present')}"
 
     def compose_search_context(self, edges: list[EntityEdge], nodes: list[EntityNode]) -> str:
-        facts = [f'  - {edge.fact} ({self.format_edge_date_range(edge)})' for edge in edges]
-        entities = [f'  - {node.name}: {node.summary}' for node in nodes]
-        return TEMPLATE.format(facts='\n'.join(facts), entities='\n'.join(entities))
+        facts = [f"  - {edge.fact} ({self.format_edge_date_range(edge)})" for edge in edges]
+        entities = [f"  - {node.name}: {node.summary}" for node in nodes]
+        return TEMPLATE.format(facts="\n".join(facts), entities="\n".join(entities))
 
     def search_memory(self, run_id, idx, query, max_retries=3, retry_delay=1):
         start_time = time.time()
@@ -52,8 +52,14 @@ class ZepSearch:
         while retries < max_retries:
             try:
                 user_id = f"run_id_{run_id}_experiment_user_{idx}"
-                edges_results = (self.zep_client.graph.search(user_id=user_id, reranker='cross_encoder', query=query, scope='edges', limit=20)).edges
-                node_results = (self.zep_client.graph.search(user_id=user_id, reranker='rrf', query=query, scope='nodes', limit=20)).nodes
+                edges_results = (
+                    self.zep_client.graph.search(
+                        user_id=user_id, reranker="cross_encoder", query=query, scope="edges", limit=20
+                    )
+                ).edges
+                node_results = (
+                    self.zep_client.graph.search(user_id=user_id, reranker="rrf", query=query, scope="nodes", limit=20)
+                ).nodes
                 context = self.compose_search_context(edges_results, node_results)
                 break
             except Exception as e:
@@ -68,17 +74,13 @@ class ZepSearch:
         return context, end_time - start_time
 
     def process_question(self, run_id, val, idx):
-        question = val.get('question', '')
-        answer = val.get('answer', '')
-        category = val.get('category', -1)
-        evidence = val.get('evidence', [])
-        adversarial_answer = val.get('adversarial_answer', '')
+        question = val.get("question", "")
+        answer = val.get("answer", "")
+        category = val.get("category", -1)
+        evidence = val.get("evidence", [])
+        adversarial_answer = val.get("adversarial_answer", "")
 
-        response, search_memory_time, response_time, context = self.answer_question(
-            run_id,
-            idx,
-            question
-        )
+        response, search_memory_time, response_time, context = self.answer_question(run_id, idx, question)
 
         result = {
             "question": question,
@@ -89,7 +91,7 @@ class ZepSearch:
             "adversarial_answer": adversarial_answer,
             "search_memory_time": search_memory_time,
             "response_time": response_time,
-            "context": context
+            "context": context,
         }
 
         return result
@@ -98,44 +100,35 @@ class ZepSearch:
         context, search_memory_time = self.search_memory(run_id, idx, question)
 
         template = Template(ANSWER_PROMPT_ZEP)
-        answer_prompt = template.render(
-            memories=context,
-            question=question
-        )
+        answer_prompt = template.render(memories=context, question=question)
 
         t1 = time.time()
         response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"),
-            messages=[
-                {"role": "system", "content": answer_prompt}
-            ],
-            temperature=0.0
+            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
         )
         t2 = time.time()
         response_time = t2 - t1
         return response.choices[0].message.content, search_memory_time, response_time, context
 
     def process_data_file(self, file_path, run_id, output_file_path):
-        with open(file_path, 'r') as f:
+        with open(file_path, "r") as f:
             data = json.load(f)
 
         for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item['qa']
+            qa = item["qa"]
 
-            for question_item in tqdm(qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False):
-                result = self.process_question(
-                    run_id,
-                    question_item,
-                    idx
-                )
+            for question_item in tqdm(
+                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
+            ):
+                result = self.process_question(run_id, question_item, idx)
                 self.results[idx].append(result)
 
                 # Save results after each question is processed
-                with open(output_file_path, 'w') as f:
+                with open(output_file_path, "w") as f:
                     json.dump(self.results, f, indent=4)
 
         # Final save at the end
-        with open(output_file_path, 'w') as f:
+        with open(output_file_path, "w") as f:
             json.dump(self.results, f, indent=4)
 
 

examples/graph-db-demo/memgraph-example.ipynb

@@ -56,9 +56,7 @@
     "\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = (\n",
-    "    \"\"\n",
-    ")"
+    "os.environ[\"OPENAI_API_KEY\"] = \"\""
    ]
   },
   {
@@ -149,7 +147,7 @@
     "        \"role\": \"assistant\",\n",
     "        \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n",
     "    },\n",
-    "]\n"
+    "]"
    ]
   },
   {
@@ -166,9 +164,7 @@
    "outputs": [],
    "source": [
     "# Store inferred memories (default behavior)\n",
-    "result = m.add(\n",
-    "    messages, user_id=\"alice\", metadata={\"category\": \"movie_recommendations\"}\n",
-    ")"
+    "result = m.add(messages, user_id=\"alice\", metadata={\"category\": \"movie_recommendations\"})"
    ]
   },
   {

examples/graph-db-demo/neo4j-example.ipynb

@@ -0,0 +1,271 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ApdaLD4Qi30H"
+      },
+      "source": [
+        "# Neo4j as Graph Memory"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l7bi3i21i30I"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "### 1. Install Mem0 with Graph Memory support\n",
+        "\n",
+        "To use Mem0 with Graph Memory support, install it using pip:\n",
+        "\n",
+        "```bash\n",
+        "pip install \"mem0ai[graph]\"\n",
+        "```\n",
+        "\n",
+        "This command installs Mem0 along with the necessary dependencies for graph functionality.\n",
+        "\n",
+        "### 2. Install Neo4j\n",
+        "\n",
+        "To utilize Neo4j as Graph Memory, run it with Docker:\n",
+        "\n",
+        "```bash\n",
+        "docker run \\\n",
+        "  -p 7474:7474 -p 7687:7687 \\\n",
+        "  -e NEO4J_AUTH=neo4j/password \\\n",
+        "  neo4j:5\n",
+        "```\n",
+        "\n",
+        "This command starts Neo4j with default credentials (`neo4j` / `password`) and exposes both the HTTP (7474) and Bolt (7687) ports.\n",
+        "\n",
+        "You can access the Neo4j browser at [http://localhost:7474](http://localhost:7474).\n",
+        "\n",
+        "Additional information can be found in the [Neo4j documentation](https://neo4j.com/docs/).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DkeBdFEpi30I"
+      },
+      "source": [
+        "## Configuration\n",
+        "\n",
+        "Do all the imports and configure OpenAI (enter your OpenAI API key):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "d99EfBpii30I"
+      },
+      "outputs": [],
+      "source": [
+        "from mem0 import Memory\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = (\n",
+        "    \"\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QTucZJjIi30J"
+      },
+      "source": [
+        "Set up configuration to use the embedder model and Neo4j as a graph store:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "QSE0RFoSi30J"
+      },
+      "outputs": [],
+      "source": [
+        "config = {\n",
+        "    \"embedder\": {\n",
+        "        \"provider\": \"openai\",\n",
+        "        \"config\": {\"model\": \"text-embedding-3-large\", \"embedding_dims\": 1536},\n",
+        "    },\n",
+        "    \"graph_store\": {\n",
+        "        \"provider\": \"neo4j\",\n",
+        "        \"config\": {\n",
+        "            \"url\": \"bolt://54.87.227.131:7687\",\n",
+        "            \"username\": \"neo4j\",\n",
+        "            \"password\": \"causes-bins-vines\",\n",
+        "        },\n",
+        "    },\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OioTnv6xi30J"
+      },
+      "source": [
+        "## Graph Memory initializiation\n",
+        "\n",
+        "Initialize Neo4j as a Graph Memory store:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "fX-H9vgNi30J"
+      },
+      "outputs": [],
+      "source": [
+        "m = Memory.from_config(config_dict=config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kr1fVMwEi30J"
+      },
+      "source": [
+        "## Store memories\n",
+        "\n",
+        "Create memories:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "sEfogqp_i30J"
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": \"user\",\n",
+        "        \"content\": \"I'm planning to watch a movie tonight. Any recommendations?\",\n",
+        "    },\n",
+        "    {\n",
+        "        \"role\": \"assistant\",\n",
+        "        \"content\": \"How about a thriller movies? They can be quite engaging.\",\n",
+        "    },\n",
+        "    {\n",
+        "        \"role\": \"user\",\n",
+        "        \"content\": \"I'm not a big fan of thriller movies but I love sci-fi movies.\",\n",
+        "    },\n",
+        "    {\n",
+        "        \"role\": \"assistant\",\n",
+        "        \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n",
+        "    },\n",
+        "]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gtBHCyIgi30J"
+      },
+      "source": [
+        "Store memories in Neo4j:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "BMVGgZMFi30K"
+      },
+      "outputs": [],
+      "source": [
+        "# Store inferred memories (default behavior)\n",
+        "result = m.add(\n",
+        "    messages, user_id=\"alice\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lQRptOywi30K"
+      },
+      "source": [
+        "![](https://github.com/tomasonjo/mem0/blob/neo4jexample/examples/graph-db-demo/alice-memories.png?raw=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LBXW7Gv-i30K"
+      },
+      "source": [
+        "## Search memories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UHFDeQBEi30K",
+        "outputId": "2c69de7d-a79a-48f6-e3c4-bd743067857c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loves sci-fi movies 0.3153664287340898\n",
+            "Planning to watch a movie tonight 0.09683349296551162\n",
+            "Not a big fan of thriller movies 0.09468540071789466\n"
+          ]
+        }
+      ],
+      "source": [
+        "for result in m.search(\"what does alice love?\", user_id=\"alice\")[\"results\"]:\n",
+        "    print(result[\"memory\"], result[\"score\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "2jXEIma9kK_Q"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

examples/misc/fitness_checker.py

@@ -20,19 +20,19 @@ agent = Agent(
     name="Fitness Agent",
     model=OpenAIChat(id="gpt-4o"),
     description="You are a helpful fitness assistant who remembers past logs and gives personalized suggestions for Anish's training and diet.",
-    markdown=True
+    markdown=True,
 )
 
 
 # Store user preferences as memory
 def store_user_preferences(conversation: list, user_id: str = USER_ID):
     """Store user preferences from conversation history"""
-    memory_client.add(conversation, user_id=user_id, output_format='v1.1')
+    memory_client.add(conversation, user_id=user_id, output_format="v1.1")
 
 
 # Memory-aware assistant function
 def fitness_coach(user_input: str, user_id: str = USER_ID):
-    memories = memory_client.search(user_input, user_id=user_id)   # Search relevant memories bases on user query
+    memories = memory_client.search(user_input, user_id=user_id)  # Search relevant memories bases on user query
     memory_context = "\n".join(f"- {m['memory']}" for m in memories)
 
     prompt = f"""You are a fitness assistant who helps Anish with his training, recovery, and diet. You have long-term memory of his health, routines, preferences, and past conversations.
@@ -48,113 +48,66 @@ User query:
     memory_client.add(f"User: {user_input}\nAssistant: {response.content}", user_id=user_id)
     return response.content
 
+
 # --------------------------------------------------
 # Store user preferences and memories
 messages = [
     {
         "role": "user",
-        "content": "Hi, I’m Anish. I'm 26 years old, 5'10\", and weigh 72kg. I started working out 6 months ago with the goal of building lean muscle."
+        "content": "Hi, I’m Anish. I'm 26 years old, 5'10\", and weigh 72kg. I started working out 6 months ago with the goal of building lean muscle.",
     },
     {
         "role": "assistant",
-        "content": "Got it — you're 26, 5'10\", 72kg, and on a lean muscle journey. Started gym 6 months ago."
+        "content": "Got it — you're 26, 5'10\", 72kg, and on a lean muscle journey. Started gym 6 months ago.",
     },
     {
         "role": "user",
-        "content": "I follow a push-pull-legs routine and train 5 times a week. My rest days are Wednesday and Sunday."
+        "content": "I follow a push-pull-legs routine and train 5 times a week. My rest days are Wednesday and Sunday.",
     },
     {
         "role": "assistant",
-        "content": "Understood — push-pull-legs split, training 5x/week with rest on Wednesdays and Sundays."
+        "content": "Understood — push-pull-legs split, training 5x/week with rest on Wednesdays and Sundays.",
     },
+    {"role": "user", "content": "After push days, I usually eat high-protein and moderate-carb meals to recover."},
+    {"role": "assistant", "content": "Noted — high-protein, moderate-carb meals after push workouts."},
+    {"role": "user", "content": "For pull days, I take whey protein and eat a banana after training."},
+    {"role": "assistant", "content": "Logged — whey protein and banana post pull workouts."},
+    {"role": "user", "content": "On leg days, I make sure to have complex carbs like rice or oats."},
+    {"role": "assistant", "content": "Noted — complex carbs like rice and oats are part of your leg day meals."},
     {
         "role": "user",
-        "content": "After push days, I usually eat high-protein and moderate-carb meals to recover."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — high-protein, moderate-carb meals after push workouts."
+        "content": "I often feel sore after leg days, so I use turmeric milk and magnesium to help with recovery.",
     },
+    {"role": "assistant", "content": "I'll remember turmeric milk and magnesium as part of your leg day recovery."},
     {
         "role": "user",
-        "content": "For pull days, I take whey protein and eat a banana after training."
+        "content": "Last push day, I did 3x8 bench press at 60kg, 4x12 overhead press, and dips. Felt fatigued after.",
     },
     {
         "role": "assistant",
-        "content": "Logged — whey protein and banana post pull workouts."
+        "content": "Push day logged — 60kg bench, overhead press, dips. You felt fatigued afterward.",
     },
+    {"role": "user", "content": "I prefer light dinners post-workout like tofu, soup, and vegetables."},
+    {"role": "assistant", "content": "Got it — light dinners post-workout: tofu, soup, and veggies."},
     {
         "role": "user",
-        "content": "On leg days, I make sure to have complex carbs like rice or oats."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — complex carbs like rice and oats are part of your leg day meals."
+        "content": "I have mild lactose intolerance, so I avoid dairy. I use almond milk or lactose-free whey.",
     },
+    {"role": "assistant", "content": "Understood — avoiding regular dairy, using almond milk and lactose-free whey."},
     {
         "role": "user",
-        "content": "I often feel sore after leg days, so I use turmeric milk and magnesium to help with recovery."
+        "content": "I get occasional knee pain, so I avoid deep squats and do more hamstring curls and glute bridges on leg days.",
     },
     {
         "role": "assistant",
-        "content": "I'll remember turmeric milk and magnesium as part of your leg day recovery."
-    },
-    {
-        "role": "user",
-        "content": "Last push day, I did 3x8 bench press at 60kg, 4x12 overhead press, and dips. Felt fatigued after."
-    },
-    {
-        "role": "assistant",
-        "content": "Push day logged — 60kg bench, overhead press, dips. You felt fatigued afterward."
-    },
-    {
-        "role": "user",
-        "content": "I prefer light dinners post-workout like tofu, soup, and vegetables."
-    },
-    {
-        "role": "assistant",
-        "content": "Got it — light dinners post-workout: tofu, soup, and veggies."
-    },
-    {
-        "role": "user",
-        "content": "I have mild lactose intolerance, so I avoid dairy. I use almond milk or lactose-free whey."
-    },
-    {
-        "role": "assistant",
-        "content": "Understood — avoiding regular dairy, using almond milk and lactose-free whey."
-    },
-    {
-        "role": "user",
-        "content": "I get occasional knee pain, so I avoid deep squats and do more hamstring curls and glute bridges on leg days."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — due to knee discomfort, you substitute deep squats with curls and glute bridges."
-    },
-    {
-        "role": "user",
-        "content": "I track sleep and notice poor performance when I sleep less than 6 hours."
-    },
-    {
-        "role": "assistant",
-        "content": "Logged — performance drops when you get under 6 hours of sleep."
-    },
-    {
-        "role": "user",
-        "content": "I take magnesium supplements to help with muscle recovery and sleep quality."
-    },
-    {
-        "role": "assistant",
-        "content": "Remembered — magnesium helps you with recovery and sleep."
-    },
-    {
-        "role": "user",
-        "content": "I avoid caffeine after 4 PM because it affects my sleep."
-    },
-    {
-        "role": "assistant",
-        "content": "Got it — you avoid caffeine post-4 PM to protect your sleep."
+        "content": "Noted — due to knee discomfort, you substitute deep squats with curls and glute bridges.",
     },
+    {"role": "user", "content": "I track sleep and notice poor performance when I sleep less than 6 hours."},
+    {"role": "assistant", "content": "Logged — performance drops when you get under 6 hours of sleep."},
+    {"role": "user", "content": "I take magnesium supplements to help with muscle recovery and sleep quality."},
+    {"role": "assistant", "content": "Remembered — magnesium helps you with recovery and sleep."},
+    {"role": "user", "content": "I avoid caffeine after 4 PM because it affects my sleep."},
+    {"role": "assistant", "content": "Got it — you avoid caffeine post-4 PM to protect your sleep."},
 ]
 store_user_preferences(messages)
 

examples/misc/healthcare_assistant_google_adk.py

@@ -0,0 +1,208 @@
+import asyncio
+import warnings
+
+from google.adk.agents import Agent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types
+
+from mem0 import MemoryClient
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+
+# Initialize Mem0 client
+mem0_client = MemoryClient()
+
+
+# Define Memory Tools
+def save_patient_info(information: str) -> dict:
+    """Saves important patient information to memory."""
+    print(f"Storing patient information: {information[:30]}...")
+
+    # Get user_id from session state or use default
+    user_id = getattr(save_patient_info, "user_id", "default_user")
+
+    # Store in Mem0
+    mem0_client.add(
+        [{"role": "user", "content": information}],
+        user_id=user_id,
+        run_id="healthcare_session",
+        metadata={"type": "patient_information"},
+    )
+
+    return {"status": "success", "message": "Information saved"}
+
+
+def retrieve_patient_info(query: str) -> str:
+    """Retrieves relevant patient information from memory."""
+    print(f"Searching for patient information: {query}")
+
+    # Get user_id from session state or use default
+    user_id = getattr(retrieve_patient_info, "user_id", "default_user")
+
+    # Search Mem0
+    results = mem0_client.search(
+        query,
+        user_id=user_id,
+        run_id="healthcare_session",
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+    )
+
+    if not results:
+        return "I don't have any relevant memories about this topic."
+
+    memories = [f"• {result['memory']}" for result in results]
+    return "Here's what I remember that might be relevant:\n" + "\n".join(memories)
+
+
+# Define Healthcare Tools
+def schedule_appointment(date: str, time: str, reason: str) -> dict:
+    """Schedules a doctor's appointment."""
+    # In a real app, this would connect to a scheduling system
+    appointment_id = f"APT-{hash(date + time) % 10000}"
+
+    return {
+        "status": "success",
+        "appointment_id": appointment_id,
+        "confirmation": f"Appointment scheduled for {date} at {time} for {reason}",
+        "message": "Please arrive 15 minutes early to complete paperwork.",
+    }
+
+
+# Create the Healthcare Assistant Agent
+healthcare_agent = Agent(
+    name="healthcare_assistant",
+    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
+    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
+    instruction="""You are a helpful Healthcare Assistant with memory capabilities.
+
+Your primary responsibilities are to:
+1. Remember patient information using the 'save_patient_info' tool when they share symptoms, conditions, or preferences.
+2. Retrieve past patient information using the 'retrieve_patient_info' tool when relevant to the current conversation.
+3. Help schedule appointments using the 'schedule_appointment' tool.
+
+IMPORTANT GUIDELINES:
+- Always be empathetic, professional, and helpful.
+- Save important patient information like symptoms, conditions, allergies, and preferences.
+- Check if you have relevant patient information before asking for details they may have shared previously.
+- Make it clear you are not a doctor and cannot provide medical diagnosis or treatment.
+- For serious symptoms, always recommend consulting a healthcare professional.
+- Keep all patient information confidential.
+""",
+    tools=[save_patient_info, retrieve_patient_info, schedule_appointment],
+)
+
+# Set Up Session and Runner
+session_service = InMemorySessionService()
+
+# Define constants for the conversation
+APP_NAME = "healthcare_assistant_app"
+USER_ID = "Alex"
+SESSION_ID = "session_001"
+
+# Create a session
+session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
+
+# Create the runner
+runner = Runner(agent=healthcare_agent, app_name=APP_NAME, session_service=session_service)
+
+
+# Interact with the Healthcare Assistant
+async def call_agent_async(query, runner, user_id, session_id):
+    """Sends a query to the agent and returns the final response."""
+    print(f"\n>>> Patient: {query}")
+
+    # Format the user's message
+    content = types.Content(role="user", parts=[types.Part(text=query)])
+
+    # Set user_id for tools to access
+    save_patient_info.user_id = user_id
+    retrieve_patient_info.user_id = user_id
+
+    # Run the agent
+    async for event in runner.run_async(user_id=user_id, session_id=session_id, new_message=content):
+        if event.is_final_response():
+            if event.content and event.content.parts:
+                response = event.content.parts[0].text
+                print(f"<<< Assistant: {response}")
+                return response
+
+    return "No response received."
+
+
+# Example conversation flow
+async def run_conversation():
+    # First interaction - patient introduces themselves with key information
+    await call_agent_async(
+        "Hi, I'm Alex. I've been having headaches for the past week, and I have a penicillin allergy.",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID,
+    )
+
+    # Request for health information
+    await call_agent_async(
+        "Can you tell me more about what might be causing my headaches?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID,
+    )
+
+    # Schedule an appointment
+    await call_agent_async(
+        "I think I should see a doctor. Can you help me schedule an appointment for next Monday at 2pm?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID,
+    )
+
+    # Test memory - should remember patient name, symptoms, and allergy
+    await call_agent_async(
+        "What medications should I avoid for my headaches?", runner=runner, user_id=USER_ID, session_id=SESSION_ID
+    )
+
+
+# Interactive mode
+async def interactive_mode():
+    """Run an interactive chat session with the healthcare assistant."""
+    print("=== Healthcare Assistant Interactive Mode ===")
+    print("Enter 'exit' to quit at any time.")
+
+    # Get user information
+    patient_id = input("Enter patient ID (or press Enter for default): ").strip() or USER_ID
+    session_id = f"session_{hash(patient_id) % 1000:03d}"
+
+    # Create session for this user
+    session_service.create_session(app_name=APP_NAME, user_id=patient_id, session_id=session_id)
+
+    print(f"\nStarting conversation with patient ID: {patient_id}")
+    print("Type your message and press Enter.")
+
+    while True:
+        user_input = input("\n>>> Patient: ").strip()
+        if user_input.lower() in ["exit", "quit", "bye"]:
+            print("Ending conversation. Thank you!")
+            break
+
+        await call_agent_async(user_input, runner=runner, user_id=patient_id, session_id=session_id)
+
+
+# Main execution
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Healthcare Assistant with Memory")
+    parser.add_argument("--demo", action="store_true", help="Run the demo conversation")
+    parser.add_argument("--interactive", action="store_true", help="Run in interactive mode")
+    parser.add_argument("--patient-id", type=str, default=USER_ID, help="Patient ID for the conversation")
+    args = parser.parse_args()
+
+    if args.demo:
+        asyncio.run(run_conversation())
+    elif args.interactive:
+        asyncio.run(interactive_mode())
+    else:
+        # Default to demo mode if no arguments provided
+        asyncio.run(run_conversation())

examples/misc/movie_recommendation_grok3.py

@@ -16,26 +16,21 @@ from mem0 import Memory
 
 # Configure Mem0 with Grok 3 and Qdrant
 config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "embedding_model_dims": 384
-        }
-    },
+    "vector_store": {"provider": "qdrant", "config": {"embedding_model_dims": 384}},
     "llm": {
         "provider": "xai",
         "config": {
             "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
-        }
+        },
     },
     "embedder": {
         "provider": "huggingface",
         "config": {
             "model": "all-MiniLM-L6-v2"  # open embedding model
-        }
-    }
+        },
+    },
 }
 
 # Instantiate memory layer
@@ -57,20 +52,14 @@ def recommend_movie_with_memory(user_id: str, user_query: str):
         prompt += f"\nPreviously, the user mentioned: {past_memories}"
 
     # Generate movie recommendation using Grok 3
-    response = grok_client.chat.completions.create(
-        model="grok-3-beta",
-        messages=[
-            {"role": "user", "content": prompt}
-        ]
-    )
+    response = grok_client.chat.completions.create(model="grok-3-beta", messages=[{"role": "user", "content": prompt}])
     recommendation = response.choices[0].message.content
 
     # Store conversation in memory
     memory.add(
-        [{"role": "user", "content": user_query},
-         {"role": "assistant", "content": recommendation}],
+        [{"role": "user", "content": user_query}, {"role": "assistant", "content": recommendation}],
         user_id=user_id,
-        metadata={"category": "movie"}
+        metadata={"category": "movie"},
     )
 
     return recommendation
@@ -81,10 +70,11 @@ if __name__ == "__main__":
     user_id = "arshi"
     recommend_movie_with_memory(user_id, "I'm looking for a movie to watch tonight. Any suggestions?")
     # OUTPUT: You have watched Intersteller last weekend and you don't like horror movies, maybe you can watch "Purple Hearts" today.
-    recommend_movie_with_memory(user_id, "Can we skip the tearjerkers? I really enjoyed Notting Hill and Crazy Rich Asians.")
+    recommend_movie_with_memory(
+        user_id, "Can we skip the tearjerkers? I really enjoyed Notting Hill and Crazy Rich Asians."
+    )
     # OUTPUT: Got it — no sad endings! You might enjoy "The Proposal" or "Love, Rosie". They’re both light-hearted romcoms with happy vibes.
     recommend_movie_with_memory(user_id, "Any light-hearted movie I can watch after work today?")
     # OUTPUT: Since you liked Crazy Rich Asians and The Proposal, how about "The Intern" or "Isn’t It Romantic"? Both are upbeat, funny, and perfect for relaxing.
     recommend_movie_with_memory(user_id, "I’ve already watched The Intern. Something new maybe?")
     # OUTPUT: No problem! Try "Your Place or Mine" - romcoms that match your taste and are tear-free!
-

examples/misc/personal_assistant_agno.py

@@ -23,8 +23,8 @@ agent = Agent(
     name="Personal Agent",
     model=OpenAIChat(id="gpt-4o"),
     description="You are a helpful personal agent that helps me with day to day activities."
-                "You can process both text and images.",
-    markdown=True
+    "You can process both text and images.",
+    markdown=True,
 )
 
 
@@ -35,24 +35,16 @@ def chat_user(user_input: str = None, user_id: str = "user_123", image_path: str
             base64_image = base64.b64encode(image_file.read()).decode("utf-8")
 
         # First: the text message
-        text_msg = {
-            "role": "user",
-            "content": user_input
-        }
+        text_msg = {"role": "user", "content": user_input}
 
         # Second: the image message
         image_msg = {
             "role": "user",
-            "content": {
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{base64_image}"
-                }
-            }
+            "content": {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}},
         }
 
         # Send both as separate message objects
-        client.add([text_msg, image_msg], user_id=user_id, output_format='v1.1')
+        client.add([text_msg, image_msg], user_id=user_id, output_format="v1.1")
         print("✅ Image uploaded and stored in memory.")
 
     if user_input:
@@ -92,10 +84,13 @@ print(chat_user("When is my test?", user_id=user_id))
 # OUTPUT: Your pilot's test is on your birthday, which is in five days. You're turning 25!
 # Good luck with your preparations, and remember to take some time to relax amidst the studying.
 
-print(chat_user("This is the picture of what I brought with me in the trip to Bahamas",
-                image_path="travel_items.jpeg",  # this will be added to Mem0 memory
-                user_id=user_id))
-print(chat_user("hey can you quickly tell me if brought my sunglasses to my trip, not able to find",
-                user_id=user_id))
+print(
+    chat_user(
+        "This is the picture of what I brought with me in the trip to Bahamas",
+        image_path="travel_items.jpeg",  # this will be added to Mem0 memory
+        user_id=user_id,
+    )
+)
+print(chat_user("hey can you quickly tell me if brought my sunglasses to my trip, not able to find", user_id=user_id))
 # OUTPUT: Yes, you did bring your sunglasses on your trip to the Bahamas along with your laptop, face masks and other items..
 # Since you can't find them now, perhaps check the pockets of jackets you wore or in your luggage compartments.

examples/misc/study_buddy.py

@@ -7,6 +7,7 @@ In order to run this file, you need to set up your Mem0 API at Mem0 platform and
 export OPENAI_API_KEY="your_openai_api_key"
 export MEM0_API_KEY="your_mem0_api_key"
 """
+
 import asyncio
 
 from agents import Agent, Runner
@@ -23,25 +24,19 @@ study_agent = Agent(
 - Identify topics the user has struggled with (e.g., "I'm confused", "this is hard")
 - Help with spaced repetition by suggesting topics to revisit based on last review time
 - Personalize answers using stored memories
-- Summarize PDFs or notes the user uploads""")
+- Summarize PDFs or notes the user uploads""",
+)
 
 
 # Upload and store PDF to Mem0
 def upload_pdf(pdf_url: str, user_id: str):
-    pdf_message = {
-        "role": "user",
-        "content": {
-            "type": "pdf_url",
-            "pdf_url": {"url": pdf_url}
-        }
-    }
+    pdf_message = {"role": "user", "content": {"type": "pdf_url", "pdf_url": {"url": pdf_url}}}
     client.add([pdf_message], user_id=user_id)
     print("✅ PDF uploaded and processed into memory.")
 
 
 # Main interaction loop with your personal study buddy
 async def study_buddy(user_id: str, topic: str, user_input: str):
-
     memories = client.search(f"{topic}", user_id=user_id)
     memory_context = "n".join(f"- {m['memory']}" for m in memories)
 
@@ -56,9 +51,11 @@ Now respond to the user's new question or comment:
     result = await Runner.run(study_agent, prompt)
     response = result.final_output
 
-    client.add([
-        {"role": "user", "content": f'''Topic: {topic}nUser: {user_input}nnStudy Assistant: {response}'''}
-    ], user_id=user_id, metadata={"topic": topic})
+    client.add(
+        [{"role": "user", "content": f"""Topic: {topic}nUser: {user_input}nnStudy Assistant: {response}"""}],
+        user_id=user_id,
+        metadata={"topic": topic},
+    )
 
     return response
 
@@ -78,7 +75,12 @@ async def main():
 
     # Demonstrate spaced repetition prompting
     topic = "Momentum Conservation"
-    print(await study_buddy(user_id, topic, "I think we covered this last week. Is it time to review momentum conservation again?"))
+    print(
+        await study_buddy(
+            user_id, topic, "I think we covered this last week. Is it time to review momentum conservation again?"
+        )
+    )
+
 
 if __name__ == "__main__":
     asyncio.run(main())

examples/misc/voice_assistant_elevenlabs.py

@@ -57,7 +57,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I prefer brief and concise responses without unnecessary explanations. I get frustrated when assistants are too wordy or repeat information I already know."
+            "content": "I prefer brief and concise responses without unnecessary explanations. I get frustrated when assistants are too wordy or repeat information I already know.",
         },
         {
             "role": "assistant",
@@ -65,7 +65,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I like to listen to jazz music when I'm working, especially artists like Miles Davis and John Coltrane. I find it helps me focus and be more productive."
+            "content": "I like to listen to jazz music when I'm working, especially artists like Miles Davis and John Coltrane. I find it helps me focus and be more productive.",
         },
         {
             "role": "assistant",
@@ -73,7 +73,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I usually wake up at 7 AM and prefer reminders for meetings 30 minutes in advance. My most productive hours are between 9 AM and noon, so I try to schedule important tasks during that time."
+            "content": "I usually wake up at 7 AM and prefer reminders for meetings 30 minutes in advance. My most productive hours are between 9 AM and noon, so I try to schedule important tasks during that time.",
         },
         {
             "role": "assistant",
@@ -81,7 +81,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "My favorite color is navy blue, and I prefer dark mode in all my apps. I'm allergic to peanuts, so please remind me to check ingredients when I ask about recipes or restaurants."
+            "content": "My favorite color is navy blue, and I prefer dark mode in all my apps. I'm allergic to peanuts, so please remind me to check ingredients when I ask about recipes or restaurants.",
         },
         {
             "role": "assistant",
@@ -89,7 +89,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "My partner's name is Jamie, and we have a golden retriever named Max who is 3 years old. My parents live in Chicago, and I try to visit them once every two months."
+            "content": "My partner's name is Jamie, and we have a golden retriever named Max who is 3 years old. My parents live in Chicago, and I try to visit them once every two months.",
         },
         {
             "role": "assistant",
@@ -135,11 +135,11 @@ def record_audio(filename="input.wav", record_seconds=5):
     stream.close()
     p.terminate()
 
-    with wave.open(filename, 'wb') as wf:
+    with wave.open(filename, "wb") as wf:
         wf.setnchannels(channels)
         wf.setsampwidth(p.get_sample_size(fmt))
         wf.setframerate(rate)
-        wf.writeframes(b''.join(frames))
+        wf.writeframes(b"".join(frames))
 
 
 # ------------------ STT USING WHISPER ------------------
@@ -147,10 +147,7 @@ def transcribe_whisper(audio_path):
     print("🔎 Transcribing with Whisper...")
     try:
         with open(audio_path, "rb") as audio_file:
-            transcript = openai_client.audio.transcriptions.create(
-                model="whisper-1",
-                file=audio_file
-            )
+            transcript = openai_client.audio.transcriptions.create(model="whisper-1", file=audio_file)
         print(f"🗣️ You said: {transcript.text}")
         return transcript.text
     except Exception as e:
@@ -165,9 +162,7 @@ def get_agent_response(user_input):
 
     try:
         task = Task(
-            description=f"Respond to: {user_input}",
-            expected_output="A short and relevant reply.",
-            agent=voice_agent
+            description=f"Respond to: {user_input}", expected_output="A short and relevant reply.", agent=voice_agent
         )
         crew = Crew(
             agents=[voice_agent],
@@ -175,22 +170,19 @@ def get_agent_response(user_input):
             process=Process.sequential,
             verbose=True,
             memory=True,
-            memory_config={
-                "provider": "mem0",
-                "config": {"user_id": USER_ID}
-            }
+            memory_config={"provider": "mem0", "config": {"user_id": USER_ID}},
         )
         result = crew.kickoff()
 
         # Extract the text response from the complex result object
-        if hasattr(result, 'raw'):
+        if hasattr(result, "raw"):
             return result.raw
-        elif isinstance(result, dict) and 'raw' in result:
-            return result['raw']
-        elif isinstance(result, dict) and 'tasks_output' in result:
-            outputs = result['tasks_output']
+        elif isinstance(result, dict) and "raw" in result:
+            return result["raw"]
+        elif isinstance(result, dict) and "tasks_output" in result:
+            outputs = result["tasks_output"]
             if outputs and isinstance(outputs, list) and len(outputs) > 0:
-                return outputs[0].get('raw', str(result))
+                return outputs[0].get("raw", str(result))
 
         # Fallback to string representation if we can't extract the raw response
         return str(result)
@@ -204,10 +196,7 @@ def get_agent_response(user_input):
 def speak_response(text):
     print(f"🤖 Agent: {text}")
     audio = tts_client.text_to_speech.convert(
-        text=text,
-        voice_id="JBFqnCBsd6RMkjVDRZzb",
-        model_id="eleven_multilingual_v2",
-        output_format="mp3_44100_128"
+        text=text, voice_id="JBFqnCBsd6RMkjVDRZzb", model_id="eleven_multilingual_v2", output_format="mp3_44100_128"
     )
     play(audio)
 
@@ -220,7 +209,7 @@ def run_voice_agent():
             record_audio(tmp_audio.name)
             try:
                 user_text = transcribe_whisper(tmp_audio.name)
-                if user_text.lower() in ['exit', 'quit', 'stop']:
+                if user_text.lower() in ["exit", "quit", "stop"]:
                     print("👋 Exiting.")
                     break
                 response = get_agent_response(user_text)

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "2.1.25",
+  "version": "2.1.30",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",
@@ -98,7 +98,7 @@
   },
   "peerDependencies": {
     "@anthropic-ai/sdk": "^0.40.1",
-    "@google/genai": "^0.7.0",
+    "@google/genai": "^1.2.0",
     "@mistralai/mistralai": "^1.5.2",
     "@qdrant/js-client-rest": "1.13.0",
     "@supabase/supabase-js": "^2.49.1",
@@ -107,6 +107,8 @@
     "@types/sqlite3": "3.1.11",
     "groq-sdk": "0.3.0",
     "@langchain/core": "^0.3.44",
+    "cloudflare": "^4.2.0",
+    "@cloudflare/workers-types": "^4.20250504.0",
     "neo4j-driver": "^5.28.1",
     "ollama": "^0.5.14",
     "pg": "8.11.3",

mem0-ts/src/client/mem0.ts

@@ -159,16 +159,9 @@ export default class MemoryClient {
     return jsonResponse;
   }
 
-  _preparePayload(
-    messages: string | Array<Message>,
-    options: MemoryOptions,
-  ): object {
+  _preparePayload(messages: Array<Message>, options: MemoryOptions): object {
     const payload: any = {};
-    if (typeof messages === "string") {
-      payload.messages = [{ role: "user", content: messages }];
-    } else if (Array.isArray(messages)) {
-      payload.messages = messages;
-    }
+    payload.messages = messages;
     return { ...payload, ...options };
   }
 
@@ -217,7 +210,7 @@ export default class MemoryClient {
   }
 
   async add(
-    messages: string | Array<Message>,
+    messages: Array<Message>,
     options: MemoryOptions = {},
   ): Promise<Array<Memory>> {
     if (this.telemetryId === "") await this.ping();

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

@@ -23,6 +23,7 @@ export interface MemoryOptions {
   custom_instructions?: string;
   timestamp?: number;
   output_format?: string | OutputFormat;
+  async_mode?: boolean;
 }
 
 export interface ProjectOptions {
@@ -53,7 +54,7 @@ export interface MultiModalMessages {
 }
 
 export interface Messages {
-  role: string;
+  role: "user" | "assistant";
   content: string | MultiModalMessages;
 }
 

mem0-ts/src/client/telemetry.ts

@@ -1,7 +1,7 @@
 // @ts-nocheck
 import type { TelemetryClient, TelemetryOptions } from "./telemetry.types";
 
-let version = "2.1.16";
+let version = "2.1.26";
 
 // Safely check for process.env in different environments
 let MEM0_TELEMETRY = true;

mem0-ts/src/oss/package.json

@@ -21,6 +21,7 @@
     "@types/redis": "^4.0.10",
     "@types/sqlite3": "^3.1.11",
     "@types/uuid": "^9.0.8",
+    "cloudflare": "^4.2.0",
     "dotenv": "^16.4.4",
     "groq-sdk": "^0.3.0",
     "openai": "^4.28.0",
@@ -31,6 +32,7 @@
     "zod": "^3.22.4"
   },
   "devDependencies": {
+    "@cloudflare/workers-types": "^4.20250504.0",
     "@types/jest": "^29.5.12",
     "jest": "^29.7.0",
     "rimraf": "^5.0.5",

mem0-ts/src/oss/src/config/defaults.ts

@@ -20,6 +20,7 @@ export const DEFAULT_MEMORY_CONFIG: MemoryConfig = {
   llm: {
     provider: "openai",
     config: {
+      baseURL: "https://api.openai.com/v1",
       apiKey: process.env.OPENAI_API_KEY || "",
       model: "gpt-4-turbo-preview",
       modelProperties: undefined,

mem0-ts/src/oss/src/config/manager.ts

@@ -80,6 +80,7 @@ export class ConfigManager {
           }
 
           return {
+            baseURL: userConf?.baseURL || defaultConf.baseURL,
             apiKey:
               userConf?.apiKey !== undefined
                 ? userConf.apiKey

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

@@ -22,4 +22,5 @@ export * from "./vector_stores/qdrant";
 export * from "./vector_stores/redis";
 export * from "./vector_stores/supabase";
 export * from "./vector_stores/langchain";
+export * from "./vector_stores/vectorize";
 export * from "./utils/factory";

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

@@ -7,7 +7,10 @@ export class OpenAILLM implements LLM {
   private model: string;
 
   constructor(config: LLMConfig) {
-    this.openai = new OpenAI({ apiKey: config.apiKey });
+    this.openai = new OpenAI({
+      apiKey: config.apiKey,
+      baseURL: config.baseURL,
+    });
     this.model = config.model || "gpt-4o-mini";
   }
 

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

@@ -39,6 +39,7 @@ export interface HistoryStoreConfig {
 
 export interface LLMConfig {
   provider?: string;
+  baseURL?: string;
   config?: Record<string, any>;
   apiKey?: string;
   model?: string | any;
@@ -116,6 +117,7 @@ export const MemoryConfigSchema = z.object({
       modelProperties: z.record(z.string(), z.any()).optional(),
       apiKey: z.string().optional(),
       model: z.union([z.string(), z.any()]).optional(),
+      baseURL: z.string().optional(),
     }),
   }),
   vectorStore: z.object({
@@ -134,6 +136,7 @@ export const MemoryConfigSchema = z.object({
       apiKey: z.string().optional(),
       model: z.union([z.string(), z.any()]).optional(),
       modelProperties: z.record(z.string(), z.any()).optional(),
+      baseURL: z.string().optional(),
     }),
   }),
   historyDbPath: z.string().optional(),

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

@@ -16,6 +16,7 @@ import { Embedder } from "../embeddings/base";
 import { LLM } from "../llms/base";
 import { VectorStore } from "../vector_stores/base";
 import { Qdrant } from "../vector_stores/qdrant";
+import { VectorizeDB } from "../vector_stores/vectorize";
 import { RedisDB } from "../vector_stores/redis";
 import { OllamaLLM } from "../llms/ollama";
 import { SupabaseDB } from "../vector_stores/supabase";
@@ -90,6 +91,8 @@ export class VectorStoreFactory {
         return new SupabaseDB(config as any);
       case "langchain":
         return new LangchainVectorStore(config as any);
+      case "vectorize":
+        return new VectorizeDB(config as any);
       default:
         throw new Error(`Unsupported vector store provider: ${provider}`);
     }

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

@@ -4,7 +4,7 @@ import type {
   TelemetryEventData,
 } from "./telemetry.types";
 
-let version = "2.1.16";
+let version = "2.1.26";
 
 // Safely check for process.env in different environments
 let MEM0_TELEMETRY = true;

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

@@ -0,0 +1,436 @@
+import Cloudflare from "cloudflare";
+import type { Vectorize, VectorizeVector } from "@cloudflare/workers-types";
+import { VectorStore } from "./base";
+import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
+
+interface VectorizeConfig extends VectorStoreConfig {
+  apiKey?: string;
+  indexName: string;
+  accountId: string;
+}
+
+interface CloudflareVector {
+  id: string;
+  values: number[];
+  metadata?: Record<string, any>;
+}
+
+export class VectorizeDB implements VectorStore {
+  private client: Cloudflare | null = null;
+  private dimensions: number;
+  private indexName: string;
+  private accountId: string;
+
+  constructor(config: VectorizeConfig) {
+    this.client = new Cloudflare({ apiToken: config.apiKey });
+    this.dimensions = config.dimension || 1536;
+    this.indexName = config.indexName;
+    this.accountId = config.accountId;
+    this.initialize().catch(console.error);
+  }
+
+  async insert(
+    vectors: number[][],
+    ids: string[],
+    payloads: Record<string, any>[],
+  ): Promise<void> {
+    try {
+      const vectorObjects: CloudflareVector[] = vectors.map(
+        (vector, index) => ({
+          id: ids[index],
+          values: vector,
+          metadata: payloads[index] || {},
+        }),
+      );
+
+      const ndjsonPayload = vectorObjects
+        .map((v) => JSON.stringify(v))
+        .join("\n");
+
+      const response = await fetch(
+        `https://api.cloudflare.com/client/v4/accounts/${this.accountId}/vectorize/v2/indexes/${this.indexName}/insert`,
+        {
+          method: "POST",
+          headers: {
+            "Content-Type": "application/x-ndjson",
+            Authorization: `Bearer ${this.client?.apiToken}`,
+          },
+          body: ndjsonPayload,
+        },
+      );
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        throw new Error(
+          `Failed to insert vectors: ${response.status} ${errorText}`,
+        );
+      }
+    } catch (error) {
+      console.error("Error inserting vectors:", error);
+      throw new Error(
+        `Failed to insert vectors: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async search(
+    query: number[],
+    limit: number = 5,
+    filters?: SearchFilters,
+  ): Promise<VectorStoreResult[]> {
+    try {
+      const result = await this.client?.vectorize.indexes.query(
+        this.indexName,
+        {
+          account_id: this.accountId,
+          vector: query,
+          filter: filters,
+          returnMetadata: "all",
+          topK: limit,
+        },
+      );
+
+      return (
+        (result?.matches?.map((match) => ({
+          id: match.id,
+          payload: match.metadata,
+          score: match.score,
+        })) as VectorStoreResult[]) || []
+      ); // Return empty array if result or matches is null/undefined
+    } catch (error) {
+      console.error("Error searching vectors:", error);
+      throw new Error(
+        `Failed to search vectors: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async get(vectorId: string): Promise<VectorStoreResult | null> {
+    try {
+      const result = (await this.client?.vectorize.indexes.getByIds(
+        this.indexName,
+        {
+          account_id: this.accountId,
+          ids: [vectorId],
+        },
+      )) as any;
+
+      if (!result?.length) return null;
+
+      return {
+        id: vectorId,
+        payload: result[0].metadata,
+      };
+    } catch (error) {
+      console.error("Error getting vector:", error);
+      throw new Error(
+        `Failed to get vector: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async update(
+    vectorId: string,
+    vector: number[],
+    payload: Record<string, any>,
+  ): Promise<void> {
+    try {
+      const data: VectorizeVector = {
+        id: vectorId,
+        values: vector,
+        metadata: payload,
+      };
+
+      const response = await fetch(
+        `https://api.cloudflare.com/client/v4/accounts/${this.accountId}/vectorize/v2/indexes/${this.indexName}/upsert`,
+        {
+          method: "POST",
+          headers: {
+            "Content-Type": "application/x-ndjson",
+            Authorization: `Bearer ${this.client?.apiToken}`,
+          },
+          body: JSON.stringify(data) + "\n", // ndjson format
+        },
+      );
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        throw new Error(
+          `Failed to update vector: ${response.status} ${errorText}`,
+        );
+      }
+    } catch (error) {
+      console.error("Error updating vector:", error);
+      throw new Error(
+        `Failed to update vector: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async delete(vectorId: string): Promise<void> {
+    try {
+      await this.client?.vectorize.indexes.deleteByIds(this.indexName, {
+        account_id: this.accountId,
+        ids: [vectorId],
+      });
+    } catch (error) {
+      console.error("Error deleting vector:", error);
+      throw new Error(
+        `Failed to delete vector: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async deleteCol(): Promise<void> {
+    try {
+      await this.client?.vectorize.indexes.delete(this.indexName, {
+        account_id: this.accountId,
+      });
+    } catch (error) {
+      console.error("Error deleting collection:", error);
+      throw new Error(
+        `Failed to delete collection: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async list(
+    filters?: SearchFilters,
+    limit: number = 20,
+  ): Promise<[VectorStoreResult[], number]> {
+    try {
+      const result = await this.client?.vectorize.indexes.query(
+        this.indexName,
+        {
+          account_id: this.accountId,
+          vector: Array(this.dimensions).fill(0), // Dummy vector for listing
+          filter: filters,
+          topK: limit,
+          returnMetadata: "all",
+        },
+      );
+
+      const matches =
+        (result?.matches?.map((match) => ({
+          id: match.id,
+          payload: match.metadata,
+          score: match.score,
+        })) as VectorStoreResult[]) || [];
+
+      return [matches, matches.length];
+    } catch (error) {
+      console.error("Error listing vectors:", error);
+      throw new Error(
+        `Failed to list vectors: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  private generateUUID(): string {
+    return "xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx".replace(
+      /[xy]/g,
+      function (c) {
+        const r = (Math.random() * 16) | 0;
+        const v = c === "x" ? r : (r & 0x3) | 0x8;
+        return v.toString(16);
+      },
+    );
+  }
+
+  async getUserId(): Promise<string> {
+    try {
+      let found = false;
+      for await (const index of this.client!.vectorize.indexes.list({
+        account_id: this.accountId,
+      })) {
+        if (index.name === "memory_migrations") {
+          found = true;
+        }
+      }
+
+      if (!found) {
+        await this.client?.vectorize.indexes.create({
+          account_id: this.accountId,
+          name: "memory_migrations",
+          config: {
+            dimensions: 1,
+            metric: "cosine",
+          },
+        });
+      }
+
+      // Now try to get the userId
+      const result: any = await this.client?.vectorize.indexes.query(
+        "memory_migrations",
+        {
+          account_id: this.accountId,
+          vector: [0],
+          topK: 1,
+          returnMetadata: "all",
+        },
+      );
+      if (result.matches.length > 0) {
+        return result.matches[0].metadata.userId as string;
+      }
+
+      // Generate a random userId if none exists
+      const randomUserId =
+        Math.random().toString(36).substring(2, 15) +
+        Math.random().toString(36).substring(2, 15);
+      const data: VectorizeVector = {
+        id: this.generateUUID(),
+        values: [0],
+        metadata: { userId: randomUserId },
+      };
+
+      await fetch(
+        `https://api.cloudflare.com/client/v4/accounts/${this.accountId}/vectorize/v2/indexes/memory_migrations/upsert`,
+        {
+          method: "POST",
+          headers: {
+            "Content-Type": "application/x-ndjson",
+            Authorization: `Bearer ${this.client?.apiToken}`,
+          },
+          body: JSON.stringify(data) + "\n", // ndjson format
+        },
+      );
+      return randomUserId;
+    } catch (error) {
+      console.error("Error getting user ID:", error);
+      throw new Error(
+        `Failed to get user ID: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async setUserId(userId: string): Promise<void> {
+    try {
+      // Get existing point ID
+      const result: any = await this.client?.vectorize.indexes.query(
+        "memory_migrations",
+        {
+          account_id: this.accountId,
+          vector: [0],
+          topK: 1,
+          returnMetadata: "all",
+        },
+      );
+      const pointId =
+        result.matches.length > 0 ? result.matches[0].id : this.generateUUID();
+
+      const data: VectorizeVector = {
+        id: pointId,
+        values: [0],
+        metadata: { userId },
+      };
+      await fetch(
+        `https://api.cloudflare.com/client/v4/accounts/${this.accountId}/vectorize/v2/indexes/memory_migrations/upsert`,
+        {
+          method: "POST",
+          headers: {
+            "Content-Type": "application/x-ndjson",
+            Authorization: `Bearer ${this.client?.apiToken}`,
+          },
+          body: JSON.stringify(data) + "\n", // ndjson format
+        },
+      );
+    } catch (error) {
+      console.error("Error setting user ID:", error);
+      throw new Error(
+        `Failed to set user ID: ${error instanceof Error ? error.message : String(error)}`,
+      );
+    }
+  }
+
+  async initialize(): Promise<void> {
+    try {
+      // Check if the index already exists
+      let indexFound = false;
+      for await (const idx of this.client!.vectorize.indexes.list({
+        account_id: this.accountId,
+      })) {
+        if (idx.name === this.indexName) {
+          indexFound = true;
+          break;
+        }
+      }
+      // If the index doesn't exist, create it
+      if (!indexFound) {
+        try {
+          await this.client?.vectorize.indexes.create({
+            account_id: this.accountId,
+            name: this.indexName,
+            config: {
+              dimensions: this.dimensions,
+              metric: "cosine",
+            },
+          });
+
+          const properties = ["userId", "agentId", "runId"];
+
+          for (const propertyName of properties) {
+            await this.client?.vectorize.indexes.metadataIndex.create(
+              this.indexName,
+              {
+                account_id: this.accountId,
+                indexType: "string",
+                propertyName,
+              },
+            );
+          }
+        } catch (err: any) {
+          throw new Error(err);
+        }
+      }
+
+      // check for metadata index
+      const metadataIndexes =
+        await this.client?.vectorize.indexes.metadataIndex.list(
+          this.indexName,
+          {
+            account_id: this.accountId,
+          },
+        );
+      const existingMetadataIndexes = new Set<string>();
+      for (const metadataIndex of metadataIndexes?.metadataIndexes || []) {
+        existingMetadataIndexes.add(metadataIndex.propertyName!);
+      }
+      const properties = ["userId", "agentId", "runId"];
+      for (const propertyName of properties) {
+        if (!existingMetadataIndexes.has(propertyName)) {
+          await this.client?.vectorize.indexes.metadataIndex.create(
+            this.indexName,
+            {
+              account_id: this.accountId,
+              indexType: "string",
+              propertyName,
+            },
+          );
+        }
+      }
+      // Create memory_migrations collection if it doesn't exist
+      let found = false;
+      for await (const index of this.client!.vectorize.indexes.list({
+        account_id: this.accountId,
+      })) {
+        if (index.name === "memory_migrations") {
+          found = true;
+          break;
+        }
+      }
+
+      if (!found) {
+        await this.client?.vectorize.indexes.create({
+          account_id: this.accountId,
+          name: "memory_migrations",
+          config: {
+            dimensions: 1,
+            metric: "cosine",
+          },
+        });
+      }
+    } catch (err: any) {
+      throw new Error(err);
+    }
+  }
+}

mem0/client/main.py

@@ -3,7 +3,7 @@ import logging
 import os
 import warnings
 from functools import wraps
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional
 
 import httpx
 import requests
@@ -95,10 +95,7 @@ class MemoryClient:
             self.client = client
             # Ensure the client has the correct base_url and headers
             self.client.base_url = httpx.URL(self.host)
-            self.client.headers.update({
-                "Authorization": f"Token {self.api_key}",
-                "Mem0-User-ID": self.user_id
-            })
+            self.client.headers.update({"Authorization": f"Token {self.api_key}", "Mem0-User-ID": self.user_id})
         else:
             self.client = httpx.Client(
                 base_url=self.host,
@@ -132,11 +129,11 @@ class MemoryClient:
             raise ValueError(f"Error: {error_message}")
 
     @api_error_handler
-    def add(self, messages: Union[str, List[Dict[str, str]]], **kwargs) -> Dict[str, Any]:
+    def add(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]:
         """Add a new memory.
 
         Args:
-            messages: Either a string message or a list of message dictionaries.
+            messages: A list of message dictionaries.
             **kwargs: Additional parameters such as user_id, agent_id, app_id, metadata, filters.
 
         Returns:
@@ -237,7 +234,9 @@ class MemoryClient:
         response.raise_for_status()
         if "metadata" in kwargs:
             del kwargs["metadata"]
-        capture_client_event("client.search", self, {"api_version": version, "keys": list(kwargs.keys()), "sync_type": "sync"})
+        capture_client_event(
+            "client.search", self, {"api_version": version, "keys": list(kwargs.keys()), "sync_type": "sync"}
+        )
         return response.json()
 
     @api_error_handler
@@ -357,10 +356,7 @@ class MemoryClient:
         else:
             entities = self.users()
             # Filter entities based on provided IDs using list comprehension
-            to_delete = [
-                {"type": entity["type"], "name": entity["name"]}
-                for entity in entities["results"]
-            ]
+            to_delete = [{"type": entity["type"], "name": entity["name"]} for entity in entities["results"]]
 
         params = self._prepare_params()
 
@@ -373,7 +369,9 @@ class MemoryClient:
             response.raise_for_status()
 
         capture_client_event(
-            "client.delete_users", self, {"user_id": user_id, "agent_id": agent_id, "app_id": app_id, "run_id": run_id, "sync_type": "sync"}
+            "client.delete_users",
+            self,
+            {"user_id": user_id, "agent_id": agent_id, "app_id": app_id, "run_id": run_id, "sync_type": "sync"},
         )
         return {
             "message": "Entity deleted successfully."
@@ -454,7 +452,9 @@ class MemoryClient:
         """
         response = self.client.post("/v1/exports/", json={"schema": schema, **self._prepare_params(kwargs)})
         response.raise_for_status()
-        capture_client_event("client.create_memory_export", self, {"schema": schema, "keys": list(kwargs.keys()), "sync_type": "sync"})
+        capture_client_event(
+            "client.create_memory_export", self, {"schema": schema, "keys": list(kwargs.keys()), "sync_type": "sync"}
+        )
         return response.json()
 
     @api_error_handler
@@ -472,6 +472,22 @@ class MemoryClient:
         capture_client_event("client.get_memory_export", self, {"keys": list(kwargs.keys()), "sync_type": "sync"})
         return response.json()
 
+    @api_error_handler
+    def get_summary(self, filters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Get the summary of a memory export.
+
+        Args:
+            filters: Optional filters to apply to the summary request
+
+        Returns:
+            Dict containing the export status and summary data
+        """
+
+        response = self.client.post("/v1/summary/", json=self._prepare_params({"filters": filters}))
+        response.raise_for_status()
+        capture_client_event("client.get_summary", self, {"sync_type": "sync"})
+        return response.json()
+
     @api_error_handler
     def get_project(self, fields: Optional[List[str]] = None) -> Dict[str, Any]:
         """Get instructions or categories for the current project.
@@ -504,12 +520,17 @@ class MemoryClient:
         custom_instructions: Optional[str] = None,
         custom_categories: Optional[List[str]] = None,
         retrieval_criteria: Optional[List[Dict[str, Any]]] = None,
+        enable_graph: Optional[bool] = None,
+        version: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Update the project settings.
 
         Args:
             custom_instructions: New instructions for the project
             custom_categories: New categories for the project
+            retrieval_criteria: New retrieval criteria for the project
+            enable_graph: Enable or disable the graph for the project
+            version: Version of the project
 
         Returns:
             Dictionary containing the API response.
@@ -521,13 +542,25 @@ class MemoryClient:
         if not (self.org_id and self.project_id):
             raise ValueError("org_id and project_id must be set to update instructions or categories")
 
-        if custom_instructions is None and custom_categories is None and retrieval_criteria is None:
+        if (
+            custom_instructions is None
+            and custom_categories is None
+            and retrieval_criteria is None
+            and enable_graph is None
+            and version is None
+        ):
             raise ValueError(
                 "Currently we only support updating custom_instructions or custom_categories or retrieval_criteria, so you must provide at least one of them"
             )
 
         payload = self._prepare_params(
-            {"custom_instructions": custom_instructions, "custom_categories": custom_categories, "retrieval_criteria": retrieval_criteria}
+            {
+                "custom_instructions": custom_instructions,
+                "custom_categories": custom_categories,
+                "retrieval_criteria": retrieval_criteria,
+                "enable_graph": enable_graph,
+                "version": version,
+            }
         )
         response = self.client.patch(
             f"/api/v1/orgs/organizations/{self.org_id}/projects/{self.project_id}/",
@@ -537,7 +570,14 @@ class MemoryClient:
         capture_client_event(
             "client.update_project",
             self,
-            {"custom_instructions": custom_instructions, "custom_categories": custom_categories, "retrieval_criteria": retrieval_criteria, "sync_type": "sync"},
+            {
+                "custom_instructions": custom_instructions,
+                "custom_categories": custom_categories,
+                "retrieval_criteria": retrieval_criteria,
+                "enable_graph": enable_graph,
+                "version": version,
+                "sync_type": "sync",
+            },
         )
         return response.json()
 
@@ -657,9 +697,7 @@ class MemoryClient:
         capture_client_event("client.feedback", self, data, {"sync_type": "sync"})
         return response.json()
 
-    def _prepare_payload(
-        self, messages: Union[str, List[Dict[str, str]], None], kwargs: Dict[str, Any]
-    ) -> Dict[str, Any]:
+    def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]:
         """Prepare the payload for API requests.
 
         Args:
@@ -670,10 +708,7 @@ class MemoryClient:
             A dictionary containing the prepared payload.
         """
         payload = {}
-        if isinstance(messages, str):
-            payload["messages"] = [{"role": "user", "content": messages}]
-        elif isinstance(messages, list):
-            payload["messages"] = messages
+        payload["messages"] = messages
 
         payload.update({k: v for k, v in kwargs.items() if v is not None})
         return payload
@@ -750,10 +785,7 @@ class AsyncMemoryClient:
             self.async_client = client
             # Ensure the client has the correct base_url and headers
             self.async_client.base_url = httpx.URL(self.host)
-            self.async_client.headers.update({
-                "Authorization": f"Token {self.api_key}",
-                "Mem0-User-ID": self.user_id
-            })
+            self.async_client.headers.update({"Authorization": f"Token {self.api_key}", "Mem0-User-ID": self.user_id})
         else:
             self.async_client = httpx.AsyncClient(
                 base_url=self.host,
@@ -768,7 +800,11 @@ class AsyncMemoryClient:
         """Validate the API key by making a test request."""
         try:
             params = self._prepare_params()
-            response = requests.get(f"{self.host}/v1/ping/", headers={"Authorization": f"Token {self.api_key}", "Mem0-User-ID": self.user_id}, params=params)
+            response = requests.get(
+                f"{self.host}/v1/ping/",
+                headers={"Authorization": f"Token {self.api_key}", "Mem0-User-ID": self.user_id},
+                params=params,
+            )
             data = response.json()
 
             response.raise_for_status()
@@ -779,7 +815,7 @@ class AsyncMemoryClient:
 
             return data.get("user_email")
 
-        except requests.HTTPStatusError as e:
+        except requests.exceptions.HTTPError as e:
             try:
                 error_data = e.response.json()
                 error_message = error_data.get("detail", str(e))
@@ -787,9 +823,7 @@ class AsyncMemoryClient:
                 error_message = str(e)
             raise ValueError(f"Error: {error_message}")
 
-    def _prepare_payload(
-        self, messages: Union[str, List[Dict[str, str]], None], kwargs: Dict[str, Any]
-    ) -> Dict[str, Any]:
+    def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]:
         """Prepare the payload for API requests.
 
         Args:
@@ -800,10 +834,7 @@ class AsyncMemoryClient:
             A dictionary containing the prepared payload.
         """
         payload = {}
-        if isinstance(messages, str):
-            payload["messages"] = [{"role": "user", "content": messages}]
-        elif isinstance(messages, list):
-            payload["messages"] = messages
+        payload["messages"] = messages
 
         payload.update({k: v for k, v in kwargs.items() if v is not None})
         return payload
@@ -840,7 +871,7 @@ class AsyncMemoryClient:
         await self.async_client.aclose()
 
     @api_error_handler
-    async def add(self, messages: Union[str, List[Dict[str, str]]], **kwargs) -> Dict[str, Any]:
+    async def add(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]:
         kwargs = self._prepare_params(kwargs)
         if kwargs.get("output_format") != "v1.1":
             kwargs["output_format"] = "v1.1"
@@ -973,10 +1004,7 @@ class AsyncMemoryClient:
         else:
             entities = await self.users()
             # Filter entities based on provided IDs using list comprehension
-            to_delete = [
-                {"type": entity["type"], "name": entity["name"]}
-                for entity in entities["results"]
-            ]
+            to_delete = [{"type": entity["type"], "name": entity["name"]} for entity in entities["results"]]
 
         params = self._prepare_params()
 
@@ -988,7 +1016,11 @@ class AsyncMemoryClient:
             response = await self.async_client.delete(f"/v2/entities/{entity['type']}/{entity['name']}/", params=params)
             response.raise_for_status()
 
-        capture_client_event("client.delete_users", self, {"user_id": user_id, "agent_id": agent_id, "app_id": app_id, "run_id": run_id, "sync_type": "async"})
+        capture_client_event(
+            "client.delete_users",
+            self,
+            {"user_id": user_id, "agent_id": agent_id, "app_id": app_id, "run_id": run_id, "sync_type": "async"},
+        )
         return {
             "message": "Entity deleted successfully."
             if (user_id or agent_id or app_id or run_id)
@@ -1091,19 +1123,35 @@ class AsyncMemoryClient:
 
     @api_error_handler
     async def update_project(
-        self, custom_instructions: Optional[str] = None, custom_categories: Optional[List[str]] = None,
-        retrieval_criteria: Optional[List[Dict[str, Any]]] = None
+        self,
+        custom_instructions: Optional[str] = None,
+        custom_categories: Optional[List[str]] = None,
+        retrieval_criteria: Optional[List[Dict[str, Any]]] = None,
+        enable_graph: Optional[bool] = None,
+        version: Optional[str] = None,
     ) -> Dict[str, Any]:
         if not (self.org_id and self.project_id):
             raise ValueError("org_id and project_id must be set to update instructions or categories")
 
-        if custom_instructions is None and custom_categories is None and retrieval_criteria is None:
+        if (
+            custom_instructions is None
+            and custom_categories is None
+            and retrieval_criteria is None
+            and enable_graph is None
+            and version is None
+        ):
             raise ValueError(
                 "Currently we only support updating custom_instructions or custom_categories or retrieval_criteria, so you must provide at least one of them"
             )
 
         payload = self._prepare_params(
-            {"custom_instructions": custom_instructions, "custom_categories": custom_categories, "retrieval_criteria": retrieval_criteria}
+            {
+                "custom_instructions": custom_instructions,
+                "custom_categories": custom_categories,
+                "retrieval_criteria": retrieval_criteria,
+                "enable_graph": enable_graph,
+                "version": version,
+            }
         )
         response = await self.async_client.patch(
             f"/api/v1/orgs/organizations/{self.org_id}/projects/{self.project_id}/",
@@ -1113,7 +1161,14 @@ class AsyncMemoryClient:
         capture_client_event(
             "client.update_project",
             self,
-            {"custom_instructions": custom_instructions, "custom_categories": custom_categories, "retrieval_criteria": retrieval_criteria, "sync_type": "async"},
+            {
+                "custom_instructions": custom_instructions,
+                "custom_categories": custom_categories,
+                "retrieval_criteria": retrieval_criteria,
+                "enable_graph": enable_graph,
+                "version": version,
+                "sync_type": "async",
+            },
         )
         return response.json()
 
@@ -1174,4 +1229,3 @@ class AsyncMemoryClient:
         response.raise_for_status()
         capture_client_event("client.feedback", self, data, {"sync_type": "async"})
         return response.json()
-

mem0/configs/llms/base.py

@@ -39,6 +39,8 @@ class BaseLlmConfig(ABC):
         deepseek_base_url: Optional[str] = None,
         # XAI specific
         xai_base_url: Optional[str] = None,
+        # Sarvam specific
+        sarvam_base_url: Optional[str] = "https://api.sarvam.ai/v1",
         # LM Studio specific
         lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
         # AWS Bedrock specific
@@ -89,6 +91,8 @@ class BaseLlmConfig(ABC):
         :type deepseek_base_url: Optional[str], optional
         :param xai_base_url: XAI base URL to be use, defaults to None
         :type xai_base_url: Optional[str], optional
+        :param sarvam_base_url: Sarvam base URL to be use, defaults to "https://api.sarvam.ai/v1"
+        :type sarvam_base_url: Optional[str], optional
         :param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
         :type lmstudio_base_url: Optional[str], optional
         """
@@ -125,6 +129,9 @@ class BaseLlmConfig(ABC):
         # XAI specific
         self.xai_base_url = xai_base_url
 
+        # Sarvam specific
+        self.sarvam_base_url = sarvam_base_url
+
         # LM Studio specific
         self.lmstudio_base_url = lmstudio_base_url
 

mem0/configs/vector_stores/opensearch.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, Union, Type
+from typing import Any, Dict, Optional, Type, Union
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -7,33 +7,17 @@ class OpenSearchConfig(BaseModel):
     collection_name: str = Field("mem0", description="Name of the index")
     host: str = Field("localhost", description="OpenSearch host")
     port: int = Field(9200, description="OpenSearch port")
-    user: Optional[str] = Field(
-        None, description="Username for authentication"
-    )
-    password: Optional[str] = Field(
-        None, description="Password for authentication"
-    )
-    api_key: Optional[str] = Field(
-        None, description="API key for authentication (if applicable)"
-    )
-    embedding_model_dims: int = Field(
-        1536, description="Dimension of the embedding vector"
-    )
-    verify_certs: bool = Field(
-        False, description="Verify SSL certificates (default False for OpenSearch)"
-    )
-    use_ssl: bool = Field(
-        False, description="Use SSL for connection (default False for OpenSearch)"
-    )
-    http_auth: Optional[object] = Field(
-        None, description="HTTP authentication method / AWS SigV4"
-    )
+    user: Optional[str] = Field(None, description="Username for authentication")
+    password: Optional[str] = Field(None, description="Password for authentication")
+    api_key: Optional[str] = Field(None, description="API key for authentication (if applicable)")
+    embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector")
+    verify_certs: bool = Field(False, description="Verify SSL certificates (default False for OpenSearch)")
+    use_ssl: bool = Field(False, description="Use SSL for connection (default False for OpenSearch)")
+    http_auth: Optional[object] = Field(None, description="HTTP authentication method / AWS SigV4")
     connection_class: Optional[Union[str, Type]] = Field(
         "RequestsHttpConnection", description="Connection class for OpenSearch"
     )
-    pool_maxsize: int = Field(
-        20, description="Maximum number of connections in the pool"
-    )
+    pool_maxsize: int = Field(20, description="Maximum number of connections in the pool")
 
     @model_validator(mode="before")
     @classmethod
@@ -41,7 +25,7 @@ class OpenSearchConfig(BaseModel):
         # Check if host is provided
         if not values.get("host"):
             raise ValueError("Host must be provided for OpenSearch")
-        
+
         return values
 
     @model_validator(mode="before")
@@ -52,7 +36,6 @@ class OpenSearchConfig(BaseModel):
         extra_fields = input_fields - allowed_fields
         if extra_fields:
             raise ValueError(
-                f"Extra fields not allowed: {', '.join(extra_fields)}. "
-                f"Allowed fields: {', '.join(allowed_fields)}"
+                f"Extra fields not allowed: {', '.join(extra_fields)}. " f"Allowed fields: {', '.join(allowed_fields)}"
             )
         return values

mem0/embeddings/aws_bedrock.py

@@ -23,12 +23,12 @@ class AWSBedrockEmbedding(EmbeddingBase):
         super().__init__(config)
 
         self.config.model = self.config.model or "amazon.titan-embed-text-v1"
-        
+
         # Get AWS config from environment variables or use defaults
         aws_access_key = os.environ.get("AWS_ACCESS_KEY_ID", "")
         aws_secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY", "")
         aws_region = os.environ.get("AWS_REGION", "us-west-2")
-        
+
         # Check if AWS config is provided in the config
         if hasattr(self.config, "aws_access_key_id"):
             aws_access_key = self.config.aws_access_key_id
@@ -36,7 +36,7 @@ class AWSBedrockEmbedding(EmbeddingBase):
             aws_secret_key = self.config.aws_secret_access_key
         if hasattr(self.config, "aws_region"):
             aws_region = self.config.aws_region
-            
+
         self.client = boto3.client(
             "bedrock-runtime",
             region_name=aws_region,

mem0/embeddings/huggingface.py

@@ -11,6 +11,7 @@ logging.getLogger("transformers").setLevel(logging.WARNING)
 logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
 logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
 
+
 class HuggingFaceEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)

mem0/graphs/configs.py

@@ -10,6 +10,7 @@ class Neo4jConfig(BaseModel):
     username: Optional[str] = Field(None, description="Username for the graph database")
     password: Optional[str] = Field(None, description="Password for the graph database")
     database: Optional[str] = Field(None, description="Database for the graph database")
+    base_label: Optional[bool] = Field(None, description="Whether to use base node label __Entity__ for all entities")
 
     @model_validator(mode="before")
     def check_host_port_or_path(cls, values):
@@ -21,7 +22,8 @@ class Neo4jConfig(BaseModel):
         if not url or not username or not password:
             raise ValueError("Please provide 'url', 'username' and 'password'.")
         return values
-    
+
+
 class MemgraphConfig(BaseModel):
     url: Optional[str] = Field(None, description="Host address for the graph database")
     username: Optional[str] = Field(None, description="Username for the graph database")

mem0/llms/aws_bedrock.py

@@ -20,18 +20,19 @@ def extract_provider(model: str) -> str:
             return provider
     raise ValueError(f"Unknown provider in model: {model}")
 
+
 class AWSBedrockLLM(LLMBase):
     def __init__(self, config: Optional[BaseLlmConfig] = None):
         super().__init__(config)
 
         if not self.config.model:
             self.config.model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
-            
+
         # Get AWS config from environment variables or use defaults
         aws_access_key = os.environ.get("AWS_ACCESS_KEY_ID", "")
         aws_secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY", "")
         aws_region = os.environ.get("AWS_REGION", "us-west-2")
-        
+
         # Check if AWS config is provided in the config
         if hasattr(self.config, "aws_access_key_id"):
             aws_access_key = self.config.aws_access_key_id
@@ -39,14 +40,14 @@ class AWSBedrockLLM(LLMBase):
             aws_secret_key = self.config.aws_secret_access_key
         if hasattr(self.config, "aws_region"):
             aws_region = self.config.aws_region
-            
+
         self.client = boto3.client(
             "bedrock-runtime",
             region_name=aws_region,
             aws_access_key_id=aws_access_key if aws_access_key else None,
             aws_secret_access_key=aws_secret_key if aws_secret_key else None,
         )
-        
+
         self.model_kwargs = {
             "temperature": self.config.temperature,
             "max_tokens_to_sample": self.config.max_tokens,
@@ -145,7 +146,9 @@ class AWSBedrockLLM(LLMBase):
             input_body = {
                 "inputText": prompt,
                 "textGenerationConfig": {
-                    "maxTokenCount": self.model_kwargs["max_tokens_to_sample"] or self.model_kwargs["max_tokens"] or 5000,
+                    "maxTokenCount": self.model_kwargs["max_tokens_to_sample"]
+                    or self.model_kwargs["max_tokens"]
+                    or 5000,
                     "topP": self.model_kwargs["top_p"] or 0.9,
                     "temperature": self.model_kwargs["temperature"] or 0.1,
                 },
@@ -243,22 +246,15 @@ class AWSBedrockLLM(LLMBase):
             body = json.dumps(input_body)
 
             if provider == "anthropic" or provider == "deepseek":
-
                 input_body = {
-                    "messages": [
-                        {
-                            "role": "user",
-                            "content": [{"type": "text", "text": prompt}]
-                        }
-                    ],
+                    "messages": [{"role": "user", "content": [{"type": "text", "text": prompt}]}],
                     "max_tokens": self.model_kwargs["max_tokens_to_sample"] or self.model_kwargs["max_tokens"] or 5000,
                     "temperature": self.model_kwargs["temperature"] or 0.1,
                     "top_p": self.model_kwargs["top_p"] or 0.9,
                     "anthropic_version": "bedrock-2023-05-31",
                 }
-                
+
                 body = json.dumps(input_body)
-                
 
                 response = self.client.invoke_model(
                     body=body,
@@ -272,6 +268,6 @@ class AWSBedrockLLM(LLMBase):
                     modelId=self.config.model,
                     accept="application/json",
                     contentType="application/json",
-            )
+                )
 
         return self._parse_response(response, tools)

mem0/llms/configs.py

@@ -24,6 +24,7 @@ class LlmConfig(BaseModel):
             "gemini",
             "deepseek",
             "xai",
+            "sarvam",
             "lmstudio",
             "langchain",
         ):

mem0/llms/sarvam.py

@@ -0,0 +1,87 @@
+import os
+import requests
+from typing import Dict, List, Optional
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.base import LLMBase
+
+
+class SarvamLLM(LLMBase):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config)
+
+        # Set default model if not provided
+        if not self.config.model:
+            self.config.model = "sarvam-m"
+
+        # Get API key from config or environment variable
+        self.api_key = self.config.api_key or os.getenv("SARVAM_API_KEY")
+
+        if not self.api_key:
+            raise ValueError(
+                "Sarvam API key is required. Set SARVAM_API_KEY environment variable " "or provide api_key in config."
+            )
+
+        # Set base URL - use config value or environment or default
+        self.base_url = (
+            getattr(self.config, "sarvam_base_url", None) or os.getenv("SARVAM_API_BASE") or "https://api.sarvam.ai/v1"
+        )
+
+    def generate_response(self, messages: List[Dict[str, str]], response_format=None) -> str:
+        """
+        Generate a response based on the given messages using Sarvam-M.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response.
+                                                     Currently not used by Sarvam API.
+
+        Returns:
+            str: The generated response.
+        """
+        url = f"{self.base_url}/chat/completions"
+
+        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
+
+        # Prepare the request payload
+        params = {
+            "messages": messages,
+            "model": self.config.model if isinstance(self.config.model, str) else "sarvam-m",
+        }
+
+        # Add standard parameters that already exist in BaseLlmConfig
+        if self.config.temperature is not None:
+            params["temperature"] = self.config.temperature
+
+        if self.config.max_tokens is not None:
+            params["max_tokens"] = self.config.max_tokens
+
+        if self.config.top_p is not None:
+            params["top_p"] = self.config.top_p
+
+        # Handle Sarvam-specific parameters if model is passed as dict
+        if isinstance(self.config.model, dict):
+            # Extract model name
+            params["model"] = self.config.model.get("name", "sarvam-m")
+
+            # Add Sarvam-specific parameters
+            sarvam_specific_params = ["reasoning_effort", "frequency_penalty", "presence_penalty", "seed", "stop", "n"]
+
+            for param in sarvam_specific_params:
+                if param in self.config.model:
+                    params[param] = self.config.model[param]
+
+        try:
+            response = requests.post(url, headers=headers, json=params, timeout=30)
+            response.raise_for_status()
+
+            result = response.json()
+
+            if "choices" in result and len(result["choices"]) > 0:
+                return result["choices"][0]["message"]["content"]
+            else:
+                raise ValueError("No response choices found in Sarvam API response")
+
+        except requests.exceptions.RequestException as e:
+            raise RuntimeError(f"Sarvam API request failed: {e}")
+        except KeyError as e:
+            raise ValueError(f"Unexpected response format from Sarvam API: {e}")

mem0/memory/graph_memory.py

@@ -35,11 +35,25 @@ class MemoryGraph:
             self.config.graph_store.config.password,
             self.config.graph_store.config.database,
             refresh_schema=False,
-            driver_config={"notifications_min_severity":"OFF"},
+            driver_config={"notifications_min_severity": "OFF"},
         )
         self.embedding_model = EmbedderFactory.create(
             self.config.embedder.provider, self.config.embedder.config, self.config.vector_store.config
         )
+        self.node_label = ":`__Entity__`" if self.config.graph_store.config.base_label else ""
+
+        if self.config.graph_store.config.base_label:
+            # Safely add user_id index
+            try:
+                self.graph.query(f"CREATE INDEX entity_single IF NOT EXISTS FOR (n {self.node_label}) ON (n.user_id)")
+            except Exception:
+                pass
+            try:  # Safely try to add composite index (Enterprise only)
+                self.graph.query(
+                    f"CREATE INDEX entity_composite IF NOT EXISTS FOR (n {self.node_label}) ON (n.name, n.user_id)"
+                )
+            except Exception:
+                pass
 
         self.llm_provider = "openai_structured"
         if self.config.llm.provider:
@@ -108,8 +122,8 @@ class MemoryGraph:
         return search_results
 
     def delete_all(self, filters):
-        cypher = """
-        MATCH (n {user_id: $user_id})
+        cypher = f"""
+        MATCH (n {self.node_label} {{user_id: $user_id}})
         DETACH DELETE n
         """
         params = {"user_id": filters["user_id"]}
@@ -127,10 +141,9 @@ class MemoryGraph:
                 - 'contexts': The base data store response for each memory.
                 - 'entities': A list of strings representing the nodes and relationships
         """
-
         # return all nodes and relationships
-        query = """
-        MATCH (n {user_id: $user_id})-[r]->(m {user_id: $user_id})
+        query = f"""
+        MATCH (n {self.node_label} {{user_id: $user_id}})-[r]->(m {self.node_label} {{user_id: $user_id}})
         RETURN n.name AS source, type(r) AS relationship, m.name AS target
         LIMIT $limit
         """
@@ -224,22 +237,21 @@ class MemoryGraph:
     def _search_graph_db(self, node_list, filters, limit=100):
         """Search similar nodes among and their respective incoming and outgoing relations."""
         result_relations = []
-
         for node in node_list:
             n_embedding = self.embedding_model.embed(node)
 
-            cypher_query = """
-            MATCH (n)
+            cypher_query = f"""
+            MATCH (n {self.node_label})
             WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
             WITH n, round(2 * vector.similarity.cosine(n.embedding, $n_embedding) - 1, 4) AS similarity // denormalize for backward compatibility
             WHERE similarity >= $threshold
-            CALL (n) {
+            CALL (n) {{
                 MATCH (n)-[r]->(m) 
                 RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id
                 UNION
                 MATCH (m)-[r]->(n) 
                 RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id
-            }
+            }}
             WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity //deduplicate
             RETURN source, source_id, relationship, relation_id, destination, destination_id, similarity
             ORDER BY similarity DESC
@@ -294,9 +306,9 @@ class MemoryGraph:
 
             # Delete the specific relationship between nodes
             cypher = f"""
-            MATCH (n {{name: $source_name, user_id: $user_id}})
+            MATCH (n {self.node_label} {{name: $source_name, user_id: $user_id}})
             -[r:{relationship}]->
-            (m {{name: $dest_name, user_id: $user_id}})
+            (m {self.node_label} {{name: $dest_name, user_id: $user_id}})
             DELETE r
             RETURN 
                 n.name AS source,
@@ -323,7 +335,11 @@ class MemoryGraph:
 
             # types
             source_type = entity_type_map.get(source, "__User__")
+            source_label = self.node_label if self.node_label else f":`{source_type}`"
+            source_extra_set = f", source:`{source_type}`" if self.node_label else ""
             destination_type = entity_type_map.get(destination, "__User__")
+            destination_label = self.node_label if self.node_label else f":`{destination_type}`"
+            destination_extra_set = f", destination:`{destination_type}`" if self.node_label else ""
 
             # embeddings
             source_embedding = self.embedding_model.embed(source)
@@ -340,10 +356,11 @@ class MemoryGraph:
                     WHERE elementId(source) = $source_id
                     SET source.mentions = coalesce(source.mentions, 0) + 1
                     WITH source
-                    MERGE (destination:{destination_type} {{name: $destination_name, user_id: $user_id}})
+                    MERGE (destination {destination_label} {{name: $destination_name, user_id: $user_id}})
                     ON CREATE SET
                         destination.created = timestamp(),
                         destination.mentions = 1
+                        {destination_extra_set}
                     ON MATCH SET
                         destination.mentions = coalesce(destination.mentions, 0) + 1
                     WITH source, destination
@@ -370,10 +387,11 @@ class MemoryGraph:
                     WHERE elementId(destination) = $destination_id
                     SET destination.mentions = coalesce(destination.mentions, 0) + 1
                     WITH destination
-                    MERGE (source:{source_type} {{name: $source_name, user_id: $user_id}})
+                    MERGE (source {source_label} {{name: $source_name, user_id: $user_id}})
                     ON CREATE SET
                         source.created = timestamp(),
                         source.mentions = 1
+                        {source_extra_set}
                     ON MATCH SET
                         source.mentions = coalesce(source.mentions, 0) + 1
                     WITH source, destination
@@ -420,24 +438,26 @@ class MemoryGraph:
                 }
             else:
                 cypher = f"""
-                    MERGE (n:{source_type} {{name: $source_name, user_id: $user_id}})
-                    ON CREATE SET n.created = timestamp(),
-                                  n.mentions = 1
-                    ON MATCH SET n.mentions = coalesce(n.mentions, 0) + 1
-                    WITH n
-                    CALL db.create.setNodeVectorProperty(n, 'embedding', $source_embedding)
-                    WITH n
-                    MERGE (m:{destination_type} {{name: $dest_name, user_id: $user_id}})
-                    ON CREATE SET m.created = timestamp(),
-                                  m.mentions = 1
-                    ON MATCH SET m.mentions = coalesce(m.mentions, 0) + 1
-                    WITH n, m
-                    CALL db.create.setNodeVectorProperty(m, 'embedding', $source_embedding)
-                    WITH n, m
-                    MERGE (n)-[rel:{relationship}]->(m)
+                    MERGE (source {source_label} {{name: $source_name, user_id: $user_id}})
+                    ON CREATE SET source.created = timestamp(),
+                                  source.mentions = 1
+                                  {source_extra_set}
+                    ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1
+                    WITH source
+                    CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
+                    WITH source
+                    MERGE (destination {destination_label} {{name: $dest_name, user_id: $user_id}})
+                    ON CREATE SET destination.created = timestamp(),
+                                  destination.mentions = 1
+                                  {destination_extra_set}
+                    ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1
+                    WITH source, destination
+                    CALL db.create.setNodeVectorProperty(destination, 'embedding', $source_embedding)
+                    WITH source, destination
+                    MERGE (source)-[rel:{relationship}]->(destination)
                     ON CREATE SET rel.created = timestamp(), rel.mentions = 1
                     ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1
-                    RETURN n.name AS source, type(rel) AS relationship, m.name AS target
+                    RETURN source.name AS source, type(rel) AS relationship, destination.name AS target
                     """
                 params = {
                     "source_name": source,
@@ -458,8 +478,8 @@ class MemoryGraph:
         return entity_list
 
     def _search_source_node(self, source_embedding, user_id, threshold=0.9):
-        cypher = """
-            MATCH (source_candidate)
+        cypher = f"""
+            MATCH (source_candidate {self.node_label})
             WHERE source_candidate.embedding IS NOT NULL 
             AND source_candidate.user_id = $user_id
 
@@ -484,8 +504,8 @@ class MemoryGraph:
         return result
 
     def _search_destination_node(self, destination_embedding, user_id, threshold=0.9):
-        cypher = """
-            MATCH (destination_candidate)
+        cypher = f"""
+            MATCH (destination_candidate {self.node_label})
             WHERE destination_candidate.embedding IS NOT NULL 
             AND destination_candidate.user_id = $user_id
 

mem0/memory/memgraph_memory.py

@@ -5,16 +5,12 @@ from mem0.memory.utils import format_entities
 try:
     from langchain_memgraph import Memgraph
 except ImportError:
-    raise ImportError(
-        "langchain_memgraph is not installed. Please install it using pip install langchain-memgraph"
-    )
+    raise ImportError("langchain_memgraph is not installed. Please install it using pip install langchain-memgraph")
 
 try:
     from rank_bm25 import BM25Okapi
 except ImportError:
-    raise ImportError(
-        "rank_bm25 is not installed. Please install it using pip install rank-bm25"
-    )
+    raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25")
 
 from mem0.graphs.tools import (
     DELETE_MEMORY_STRUCT_TOOL_GRAPH,
@@ -74,22 +70,14 @@ class MemoryGraph:
             filters (dict): A dictionary containing filters to be applied during the addition.
         """
         entity_type_map = self._retrieve_nodes_from_data(data, filters)
-        to_be_added = self._establish_nodes_relations_from_data(
-            data, filters, entity_type_map
-        )
-        search_output = self._search_graph_db(
-            node_list=list(entity_type_map.keys()), filters=filters
-        )
-        to_be_deleted = self._get_delete_entities_from_search_output(
-            search_output, data, filters
-        )
+        to_be_added = self._establish_nodes_relations_from_data(data, filters, entity_type_map)
+        search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
+        to_be_deleted = self._get_delete_entities_from_search_output(search_output, data, filters)
 
         # TODO: Batch queries with APOC plugin
         # TODO: Add more filter support
         deleted_entities = self._delete_entities(to_be_deleted, filters["user_id"])
-        added_entities = self._add_entities(
-            to_be_added, filters["user_id"], entity_type_map
-        )
+        added_entities = self._add_entities(to_be_added, filters["user_id"], entity_type_map)
 
         return {"deleted_entities": deleted_entities, "added_entities": added_entities}
 
@@ -108,16 +96,13 @@ class MemoryGraph:
                 - "entities": List of related graph data based on the query.
         """
         entity_type_map = self._retrieve_nodes_from_data(query, filters)
-        search_output = self._search_graph_db(
-            node_list=list(entity_type_map.keys()), filters=filters
-        )
+        search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
 
         if not search_output:
             return []
 
         search_outputs_sequence = [
-            [item["source"], item["relationship"], item["destination"]]
-            for item in search_output
+            [item["source"], item["relationship"], item["destination"]] for item in search_output
         ]
         bm25 = BM25Okapi(search_outputs_sequence)
 
@@ -126,9 +111,7 @@ class MemoryGraph:
 
         search_results = []
         for item in reranked_results:
-            search_results.append(
-                {"source": item[0], "relationship": item[1], "destination": item[2]}
-            )
+            search_results.append({"source": item[0], "relationship": item[1], "destination": item[2]})
 
         logger.info(f"Returned {len(search_results)} search results")
 
@@ -161,9 +144,7 @@ class MemoryGraph:
         RETURN n.name AS source, type(r) AS relationship, m.name AS target
         LIMIT $limit
         """
-        results = self.graph.query(
-            query, params={"user_id": filters["user_id"], "limit": limit}
-        )
+        results = self.graph.query(query, params={"user_id": filters["user_id"], "limit": limit})
 
         final_results = []
         for result in results:
@@ -208,13 +189,8 @@ class MemoryGraph:
                 f"Error in search tool: {e}, llm_provider={self.llm_provider}, search_results={search_results}"
             )
 
-        entity_type_map = {
-            k.lower().replace(" ", "_"): v.lower().replace(" ", "_")
-            for k, v in entity_type_map.items()
-        }
-        logger.debug(
-            f"Entity type map: {entity_type_map}\n search_results={search_results}"
-        )
+        entity_type_map = {k.lower().replace(" ", "_"): v.lower().replace(" ", "_") for k, v in entity_type_map.items()}
+        logger.debug(f"Entity type map: {entity_type_map}\n search_results={search_results}")
         return entity_type_map
 
     def _establish_nodes_relations_from_data(self, data, filters, entity_type_map):
@@ -223,9 +199,7 @@ class MemoryGraph:
             messages = [
                 {
                     "role": "system",
-                    "content": EXTRACT_RELATIONS_PROMPT.replace(
-                        "USER_ID", filters["user_id"]
-                    ).replace(
+                    "content": EXTRACT_RELATIONS_PROMPT.replace("USER_ID", filters["user_id"]).replace(
                         "CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}"
                     ),
                 },
@@ -235,9 +209,7 @@ class MemoryGraph:
             messages = [
                 {
                     "role": "system",
-                    "content": EXTRACT_RELATIONS_PROMPT.replace(
-                        "USER_ID", filters["user_id"]
-                    ),
+                    "content": EXTRACT_RELATIONS_PROMPT.replace("USER_ID", filters["user_id"]),
                 },
                 {
                     "role": "user",
@@ -304,9 +276,7 @@ class MemoryGraph:
     def _get_delete_entities_from_search_output(self, search_output, data, filters):
         """Get the entities to be deleted from the search output."""
         search_output_string = format_entities(search_output)
-        system_prompt, user_prompt = get_delete_messages(
-            search_output_string, data, filters["user_id"]
-        )
+        system_prompt, user_prompt = get_delete_messages(search_output_string, data, filters["user_id"])
 
         _tools = [DELETE_MEMORY_TOOL_GRAPH]
         if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
@@ -379,12 +349,8 @@ class MemoryGraph:
             # search for the nodes with the closest embeddings; this is basically
             # comparison of one embedding to all embeddings in a graph -> vector
             # search with cosine similarity metric
-            source_node_search_result = self._search_source_node(
-                source_embedding, user_id, threshold=0.9
-            )
-            destination_node_search_result = self._search_destination_node(
-                dest_embedding, user_id, threshold=0.9
-            )
+            source_node_search_result = self._search_source_node(source_embedding, user_id, threshold=0.9)
+            destination_node_search_result = self._search_destination_node(dest_embedding, user_id, threshold=0.9)
 
             # TODO: Create a cypher query and common params for all the cases
             if not destination_node_search_result and source_node_search_result:
@@ -424,9 +390,7 @@ class MemoryGraph:
                     """
 
                 params = {
-                    "destination_id": destination_node_search_result[0][
-                        "id(destination_candidate)"
-                    ],
+                    "destination_id": destination_node_search_result[0]["id(destination_candidate)"],
                     "source_name": source,
                     "source_embedding": source_embedding,
                     "user_id": user_id,
@@ -445,9 +409,7 @@ class MemoryGraph:
                     """
                 params = {
                     "source_id": source_node_search_result[0]["id(source_candidate)"],
-                    "destination_id": destination_node_search_result[0][
-                        "id(destination_candidate)"
-                    ],
+                    "destination_id": destination_node_search_result[0]["id(destination_candidate)"],
                     "user_id": user_id,
                 }
             else:

mem0/memory/storage.py

@@ -1,110 +1,151 @@
+import logging
 import sqlite3
 import threading
 import uuid
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
 
 
 class SQLiteManager:
-    def __init__(self, db_path=":memory:"):
-        self.connection = sqlite3.connect(db_path, check_same_thread=False)
+    def __init__(self, db_path: str = ":memory:"):
+        self.db_path = db_path
+        self.connection = sqlite3.connect(self.db_path, check_same_thread=False)
         self._lock = threading.Lock()
         self._migrate_history_table()
         self._create_history_table()
 
-    def _migrate_history_table(self):
+    def _migrate_history_table(self) -> None:
+        """
+        If a pre-existing history table had the old group-chat columns,
+        rename it, create the new schema, copy the intersecting data, then
+        drop the old table.
+        """
         with self._lock:
-            with self.connection:
-                cursor = self.connection.cursor()
+            try:
+                # Start a transaction
+                self.connection.execute("BEGIN")
+                cur = self.connection.cursor()
 
-                cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='history'")
-                table_exists = cursor.fetchone() is not None
+                cur.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='history'")
+                if cur.fetchone() is None:
+                    self.connection.execute("COMMIT")
+                    return  # nothing to migrate
 
-                if table_exists:
-                    # Get the current schema of the history table
-                    cursor.execute("PRAGMA table_info(history)")
-                    current_schema = {row[1]: row[2] for row in cursor.fetchall()}
+                cur.execute("PRAGMA table_info(history)")
+                old_cols = {row[1] for row in cur.fetchall()}
 
-                    # Define the expected schema
-                    expected_schema = {
-                        "id": "TEXT",
-                        "memory_id": "TEXT",
-                        "old_memory": "TEXT",
-                        "new_memory": "TEXT",
-                        "new_value": "TEXT",
-                        "event": "TEXT",
-                        "created_at": "DATETIME",
-                        "updated_at": "DATETIME",
-                        "is_deleted": "INTEGER",
-                    }
+                expected_cols = {
+                    "id",
+                    "memory_id",
+                    "old_memory",
+                    "new_memory",
+                    "event",
+                    "created_at",
+                    "updated_at",
+                    "is_deleted",
+                    "actor_id",
+                    "role",
+                }
 
-                    # Check if the schemas are the same
-                    if current_schema != expected_schema:
-                        # Rename the old table
-                        cursor.execute("ALTER TABLE history RENAME TO old_history")
+                if old_cols == expected_cols:
+                    self.connection.execute("COMMIT")
+                    return
 
-                        cursor.execute(
-                            """
-                            CREATE TABLE IF NOT EXISTS history (
-                                id TEXT PRIMARY KEY,
-                                memory_id TEXT,
-                                old_memory TEXT,
-                                new_memory TEXT,
-                                new_value TEXT,
-                                event TEXT,
-                                created_at DATETIME,
-                                updated_at DATETIME,
-                                is_deleted INTEGER
-                            )
-                        """
-                        )
+                logger.info("Migrating history table to new schema (no convo columns).")
 
-                        # Copy data from the old table to the new table
-                        cursor.execute(
-                            """
-                            INSERT INTO history (id, memory_id, old_memory, new_memory, new_value, event, created_at, updated_at, is_deleted)
-                            SELECT id, memory_id, prev_value, new_value, new_value, event, timestamp, timestamp, is_deleted
-                            FROM old_history
-                        """  # noqa: E501
-                        )
+                # Clean up any existing history_old table from previous failed migration
+                cur.execute("DROP TABLE IF EXISTS history_old")
 
-                        cursor.execute("DROP TABLE old_history")
+                # Rename the current history table
+                cur.execute("ALTER TABLE history RENAME TO history_old")
 
-                        self.connection.commit()
-
-    def _create_history_table(self):
-        with self._lock:
-            with self.connection:
-                self.connection.execute(
+                # Create the new history table with updated schema
+                cur.execute(
                     """
-                    CREATE TABLE IF NOT EXISTS history (
-                        id TEXT PRIMARY KEY,
-                        memory_id TEXT,
-                        old_memory TEXT,
-                        new_memory TEXT,
-                        new_value TEXT,
-                        event TEXT,
-                        created_at DATETIME,
-                        updated_at DATETIME,
-                        is_deleted INTEGER
+                    CREATE TABLE history (
+                        id           TEXT PRIMARY KEY,
+                        memory_id    TEXT,
+                        old_memory   TEXT,
+                        new_memory   TEXT,
+                        event        TEXT,
+                        created_at   DATETIME,
+                        updated_at   DATETIME,
+                        is_deleted   INTEGER,
+                        actor_id     TEXT,
+                        role         TEXT
                     )
                 """
                 )
 
-    def add_history(
-        self,
-        memory_id,
-        old_memory,
-        new_memory,
-        event,
-        created_at=None,
-        updated_at=None,
-        is_deleted=0,
-    ):
+                # Copy data from old table to new table
+                intersecting = list(expected_cols & old_cols)
+                if intersecting:
+                    cols_csv = ", ".join(intersecting)
+                    cur.execute(f"INSERT INTO history ({cols_csv}) SELECT {cols_csv} FROM history_old")
+
+                # Drop the old table
+                cur.execute("DROP TABLE history_old")
+
+                # Commit the transaction
+                self.connection.execute("COMMIT")
+                logger.info("History table migration completed successfully.")
+
+            except Exception as e:
+                # Rollback the transaction on any error
+                self.connection.execute("ROLLBACK")
+                logger.error(f"History table migration failed: {e}")
+                raise
+
+    def _create_history_table(self) -> None:
         with self._lock:
-            with self.connection:
+            try:
+                self.connection.execute("BEGIN")
                 self.connection.execute(
                     """
-                    INSERT INTO history (id, memory_id, old_memory, new_memory, event, created_at, updated_at, is_deleted)
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                    CREATE TABLE IF NOT EXISTS history (
+                        id           TEXT PRIMARY KEY,
+                        memory_id    TEXT,
+                        old_memory   TEXT,
+                        new_memory   TEXT,
+                        event        TEXT,
+                        created_at   DATETIME,
+                        updated_at   DATETIME,
+                        is_deleted   INTEGER,
+                        actor_id     TEXT,
+                        role         TEXT
+                    )
+                """
+                )
+                self.connection.execute("COMMIT")
+            except Exception as e:
+                self.connection.execute("ROLLBACK")
+                logger.error(f"Failed to create history table: {e}")
+                raise
+
+    def add_history(
+        self,
+        memory_id: str,
+        old_memory: Optional[str],
+        new_memory: Optional[str],
+        event: str,
+        *,
+        created_at: Optional[str] = None,
+        updated_at: Optional[str] = None,
+        is_deleted: int = 0,
+        actor_id: Optional[str] = None,
+        role: Optional[str] = None,
+    ) -> None:
+        with self._lock:
+            try:
+                self.connection.execute("BEGIN")
+                self.connection.execute(
+                    """
+                    INSERT INTO history (
+                        id, memory_id, old_memory, new_memory, event,
+                        created_at, updated_at, is_deleted, actor_id, role
+                    )
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                 """,
                     (
                         str(uuid.uuid4()),
@@ -115,30 +156,63 @@ class SQLiteManager:
                         created_at,
                         updated_at,
                         is_deleted,
+                        actor_id,
+                        role,
                     ),
                 )
+                self.connection.execute("COMMIT")
+            except Exception as e:
+                self.connection.execute("ROLLBACK")
+                logger.error(f"Failed to add history record: {e}")
+                raise
 
-    def get_history(self, memory_id):
+    def get_history(self, memory_id: str) -> List[Dict[str, Any]]:
         with self._lock:
-            cursor = self.connection.execute(
+            cur = self.connection.execute(
                 """
-                SELECT id, memory_id, old_memory, new_memory, event, created_at, updated_at
+                SELECT id, memory_id, old_memory, new_memory, event,
+                       created_at, updated_at, is_deleted, actor_id, role
                 FROM history
                 WHERE memory_id = ?
-                ORDER BY updated_at ASC
+                ORDER BY created_at ASC, DATETIME(updated_at) ASC
             """,
                 (memory_id,),
             )
-            rows = cursor.fetchall()
-            return [
-                {
-                    "id": row[0],
-                    "memory_id": row[1],
-                    "old_memory": row[2],
-                    "new_memory": row[3],
-                    "event": row[4],
-                    "created_at": row[5],
-                    "updated_at": row[6],
-                }
-                for row in rows
-            ]
+            rows = cur.fetchall()
+
+        return [
+            {
+                "id": r[0],
+                "memory_id": r[1],
+                "old_memory": r[2],
+                "new_memory": r[3],
+                "event": r[4],
+                "created_at": r[5],
+                "updated_at": r[6],
+                "is_deleted": bool(r[7]),
+                "actor_id": r[8],
+                "role": r[9],
+            }
+            for r in rows
+        ]
+
+    def reset(self) -> None:
+        """Drop and recreate the history table."""
+        with self._lock:
+            try:
+                self.connection.execute("BEGIN")
+                self.connection.execute("DROP TABLE IF EXISTS history")
+                self.connection.execute("COMMIT")
+                self._create_history_table()
+            except Exception as e:
+                self.connection.execute("ROLLBACK")
+                logger.error(f"Failed to reset history table: {e}")
+                raise
+
+    def close(self) -> None:
+        if self.connection:
+            self.connection.close()
+            self.connection = None
+
+    def __del__(self):
+        self.close()