Update version to 0.1.114 (#3107 )

Add Amazon Neptune Analytics graph_store configuration & integration (#2949 )
Fix CI issues related to missing dependency (#3096 )
2025-07-04 16:28:39 -07:00 · 2025-07-04 16:26:21 -07:00 · 2025-07-03 18:52:50 -07:00 · 2025-07-03 14:41:10 -07:00 · 2025-07-03 14:40:39 -07:00 · 2025-07-03 14:37:20 -07:00
210 changed files with 13694 additions and 1754 deletions
@@ -52,16 +52,16 @@ jobs:
        with:
          path: .venv
          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
+      - name: Install GEOS Libraries
+        run: sudo apt-get update && sudo apt-get install -y libgeos-dev
      - name: Install dependencies
        run: |
-          make install_all
-          pip install -e ".[test]"
-          pip install pinecone pinecone-text
+          pip install --upgrade pip
+          pip install -e ".[test,graph,vector_stores,llms,extras]"
+          pip install ruff
        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
-      - name: Run Formatting
-        run: |
-          mkdir -p .ruff_cache && chmod -R 777 .ruff_cache
-          hatch run format
+      - name: Run Linting
+        run: make lint
      - name: Run tests and generate coverage report
        run: make test

@@ -102,4 +102,4 @@ jobs:
        with:
          file: coverage.xml
        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
@@ -16,18 +16,19 @@ To make a contribution, follow these steps:
 For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).


-### 📦 Package manager
+### 📦 Development Environment

-We use `poetry` as our package manager. You can install poetry by following the instructions [here](https://python-poetry.org/docs/#installation).
-
-Please DO NOT use pip or conda to install the dependencies. Instead, use poetry:
+We use `hatch` for managing development environments. To set up:

 ```bash
-make install_all
+# Activate environment for specific Python version:
+hatch shell dev_py_3_9   # Python 3.9
+hatch shell dev_py_3_10  # Python 3.10  
+hatch shell dev_py_3_11  # Python 3.11

-#activate
-
-poetry shell
+# The environment will automatically install all dev dependencies
+# Run tests within the activated shell:
+make test
 ```

 ### 📌 Pre-commit
@@ -40,16 +41,21 @@ pre-commit install

 ### 🧪 Testing

-We use `pytest` to test our code. You can run the tests by running the following command:
+We use `pytest` to test our code across multiple Python versions. You can run tests using:

 ```bash
-poetry run pytest tests
-
-# or
-
+# Run tests with default Python version
 make test
+
+# Test specific Python versions:
+make test-py-3.9   # Python 3.9 environment
+make test-py-3.10  # Python 3.10 environment
+make test-py-3.11  # Python 3.11 environment
+
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
 ```

-Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass. Make sure that all tests pass before submitting a pull request.
+Make sure that all tests pass across all supported Python versions before submitting a pull request.

-We look forward to your pull requests and can't wait to see your contributions!
+We look forward to your pull requests and can't wait to see your contributions!
@@ -12,8 +12,8 @@ install:

 install_all:
 	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 langchain-neo4j rank-bm25
+	            google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
+							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j langchain-aws rank-bm25 pymochow pymongo

 # Format code with ruff
 format:
@@ -41,3 +41,12 @@ clean:

 test:
 	hatch run test
+
+test-py-3.9:
+	hatch run dev_py_3_9:test
+
+test-py-3.10:
+	hatch run dev_py_3_10:test
+
+test-py-3.11:
+	hatch run dev_py_3_11:test
@@ -0,0 +1,3 @@
+<Note type="info">
+  🔐 Mem0 is now <strong>SOC 2</strong> and <strong>HIPAA</strong> compliant! We're committed to the highest standards of data security and privacy, enabling secure memory for enterprises, healthcare, and beyond.
+</Note>
@@ -4,7 +4,7 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 provides a powerful set of APIs that allow you to integrate advanced memory management capabilities into your applications. Our APIs are designed to be intuitive, efficient, and scalable, enabling you to create, retrieve, update, and delete memories across various entities such as users, agents, apps, and runs.

@@ -3,11 +3,132 @@ title: "Product Updates"
 mode: "wide"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 <Tabs>
 <Tab title="Python">

+<Update label="2025-06-19" description="v0.1.109">
+
+**New Features:**
+- **AgentOps:** Added AgentOps integration
+- **LM Studio:** Added response_format parameter for LM Studio configuration
+- **Examples:** Added Memory agent powered by voice (Cartesia + Agno)
+
+**Improvements:**
+- **AI SDK:** Added output_format parameter
+- **Client:** Enhanced update method to support metadata
+- **Google:** Added Google Genai library support
+
+**Bug Fixes:**
+- **Build:** Fixed Build CI failure
+- **Pinecone:** Fixed pinecone for async memory
+
+</Update>
+
+<Update label="2025-06-14" description="v0.1.108">
+
+**New Features:**
+- **MongoDB:** Added MongoDB Vector Store support
+- **Client:** Added client support for summary functionality
+
+**Improvements:**
+- **Pinecone:** Fixed pinecone version issues
+- **OpenSearch:** Added logger support
+- **Testing:** Added python version test environments
+
+</Update>
+
+<Update label="2025-06-11" description="v0.1.107">
+
+**Improvements:**
+- **Documentation:** 
+  - Updated Livekit documentation migration
+  - Updated OpenMemory hosted version documentation
+- **Core:** Updated categorization flow
+- **Storage:** Fixed migration issues
+
+</Update>
+
+<Update label="2025-06-09" description="v0.1.106">
+
+**New Features:**
+- **Cloudflare:** Added Cloudflare vector store support
+- **Search:** Added threshold parameter to search functionality
+- **API:** Added wildcard character support for v2 Memory APIs
+
+**Improvements:**
+- **Documentation:** Updated README docs for OpenMemory environment setup
+- **Core:** Added support for unique user IDs
+
+**Bug Fixes:**
+- **Core:** Fixed error handling exceptions
+
+</Update>
+
+<Update label="2025-06-03" description="v0.1.104">
+
+**Bug Fixes:**
+- **Vector Stores:** Fixed GET_ALL functionality for FAISS and OpenSearch
+
+</Update>
+
+<Update label="2025-06-02" description="v0.1.103">
+
+**New Features:**
+- **LLM:** Added support for OpenAI compatible LLM providers with baseUrl configuration
+
+**Improvements:**
+- **Documentation:** 
+  - Fixed broken links
+  - Improved Graph Memory features documentation clarity
+  - Updated enable_graph documentation
+- **TypeScript SDK:** Updated Google SDK peer dependency version
+- **Client:** Added async mode parameter
+
+</Update>
+
+<Update label="2025-05-26" description="v0.1.102">
+
+**New Features:**
+- **Examples:** Added Neo4j example
+- **AI SDK:** Added Google provider support
+- **OpenMemory:** Added LLM and Embedding Providers support
+
+**Improvements:**
+- **Documentation:** 
+  - Updated memory export documentation
+  - Enhanced role-based memory attribution rules documentation
+  - Updated API reference and messages documentation
+  - Added Mastra and Raycast documentation
+  - Added NOT filter documentation for Search and GetAll V2
+  - Announced Claude 4 support
+- **Core:** 
+  - Removed support for passing string as input in client.add()
+  - Added support for sarvam-m model
+- **TypeScript SDK:** Fixed types from message interface
+
+**Bug Fixes:**
+- **Memory:** Prevented saving prompt artifacts as memory when no new facts are present
+- **OpenMemory:** Fixed typos in MCP tool description
+
+</Update>
+
+<Update label="2025-05-15" description="v0.1.101">
+
+**New Features:**
+- **Neo4j:** Added base label configuration support
+
+**Improvements:**
+- **Documentation:** 
+  - Updated Healthcare example index
+  - Enhanced collaborative task agent documentation clarity
+  - Added criteria-based filtering documentation
+- **OpenMemory:** Added cURL command for easy installation
+- **Build:** Migrated to Hatch build system
+
+</Update>
+
 <Update label="2025-05-10" description="v0.1.100">

 **New Features:**
@@ -288,6 +409,26 @@ mode: "wide"

 <Tab title="TypeScript">

+<Update label="2025-07-03" description="v2.1.34">
+**New Features:**
+- **OSS:** Added Gemini support
+</Update>
+
+<Update label="2025-06-24" description="v2.1.33">
+**Improvement :**
+- **Client:** Added `immutable` param to `add` method.
+</Update>
+
+<Update label="2025-06-20" description="v2.1.32">
+**Improvement :**
+- **Client:** Made `api_version` V2 as default.
+</Update>
+
+<Update label="2025-06-17" description="v2.1.31">
+**Improvement :**
+- **Client:** Added param `filter_memories`.
+</Update>
+
 <Update label="2025-06-06" description="v2.1.30">
 **New Features:**
 - **OSS:** Added Cloudflare support
@@ -437,6 +578,104 @@ mode: "wide"

 <Tab title="Platform">

+<Update label="2025-06-19" description="">
+
+**New Features:**
+- **Rate Limiting:** Implemented comprehensive rate limiting system
+
+**Improvements:**
+- **Performance:** Added performance indexes for memory stats query
+
+**Bug Fixes:**
+- **Search:** Fixed search events not respecting top-k parameter
+
+</Update>
+
+<Update label="2025-06-18" description="">
+
+**New Features:**
+- **Memory Management:** Implemented OpenAI Batch API for Memory Cleaning with fallback
+- **Playground:** Added Claude 4 support on Playground
+
+**Improvements:**
+- **Memory:** Added ability to update memory metadata
+
+</Update>
+
+<Update label="2025-06-17" description="">
+
+**New Features:**
+- **UI:** New Memories Page UI design
+
+</Update>
+
+<Update label="2025-06-16" description="">
+
+**Improvements:**
+- **Infrastructure:** Migrated to Application Load Balancer (ALB)
+
+</Update>
+
+<Update label="2025-06-13" description="">
+
+**Improvements:**
+- **Memory Management:** Enhanced Memory Management with Cosine Similarity Fallback
+
+</Update>
+
+<Update label="2025-06-11" description="">
+
+**New Features:**
+- **OMM:** Added OMM Script and UI functionality
+
+**Improvements:**
+- **API:** Added filters validation to semantic_search_v2 endpoint
+
+</Update>
+
+<Update label="2025-06-09" description="">
+
+**New Features:**
+- **Intercom:** Set Intercom events for ADD and SEARCH operations
+- **OpenMemory:** Added Posthog integration and feedback functionality
+- **MCP:** New JavaScript MCP Server with feedback support
+
+**Improvements:**
+- **Structured Data:** Enhanced structured data handling in memory management
+
+</Update>
+
+<Update label="2025-06-06" description="">
+
+**New Features:**
+- **OAuth:** Added Mem0 OAuth integration
+- **OMM:** Added OMM-Mem0 sync for deleted memories
+
+</Update>
+
+<Update label="2025-06-05" description="">
+
+**New Features:**
+- **Filters:** Implemented Wildcard Filters and refactored filter logic in V2 Views
+
+</Update>
+
+<Update label="2025-06-02" description="">
+
+**New Features:**
+- **OpenMemory Cloud:** Added OpenMemory Cloud support
+- **Structured Data:** Added 'structured_attributes' field to Memory model
+
+</Update>
+
+<Update label="2025-05-30" description="">
+
+**New Features:**
+- **Projects:** Added version and enable_graph to project views
+- **OpenMemory:** Added Postgres support for OpenMemory
+
+</Update>
+
 <Update label="2025-05-19" description="">

 **Bug Fixes:**
@@ -444,254 +683,15 @@ mode: "wide"

 </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:**
- **Performance:** Parallelized embedding calls
- **Monitoring:** Added timing for LLM calls
- **Search:** Added category checking in Search V2
- **Bug Fixes:** Fixed issues with ADD filters
- **Graph:** Implemented new graph updates
-
-</Update>
-
-<Update label="2025-04-25" description="">
-
-**Improvements:**
- **Memory:** Fixed memory export functionality
- **Analytics:** Added logging for project
-
-</Update>
-
-<Update label="2025-04-24" description="">
-
-**Improvements:**
- **Output:** Added memory_type display for ADD output
-
-</Update>
-
-<Update label="2025-04-23" description="">
-
-**New Features:**
- **UI:** Added new Pricing Component
- **Memory:** Implemented Long/Short term memory categorization
- **Output:** Modified serializer to hide memory_type
-
-**Documentation:**
- Updated README for deployment
-
-</Update>
-
-<Update label="2025-04-22" description="">
-
-**New Features:**
- **Memory:** Added timestamp to ADD call
-
-**Bug Fixes:**
- Fixed issues with coreV2
-
-</Update>
-
-<Update label="2025-04-21" description="">
-
-**New Features:**
- **Memory:** Implemented backdating with migrations and backfilling script
-
-</Update>
-
-<Update label="2025-04-17" description="">
-
-**New Features:**
- **Billing:** Integrated Stripe Billing Dashboard
- **Admin:** Added webhook creation functionality
-
-**Bug Fixes:**
- Fixed Users Page issues
- Fixed Custom Categories
- Fixed Table components
- Updated Stripe configuration
-
-</Update>
-
-<Update label="2025-04-16" description="">
-
-**Improvements:**
- **Performance:** Made Admin panel and Memory Page faster
- **Security:** Implemented active session cancellation
- **Analytics:** Added Stripe customer ID capture
-
-</Update>
-
-<Update label="2025-04-12" description="">
-
-**New Features:**
- **Memory Management:** 
-  - Added ability to delete memories from Project level with filters
-  - Added delete memories capability on Memories Page
- **Memory Visualization:** Released V1 Graph Memory Visualization
- **Graph Playground:** Enabled for @mem0.ai users
- **Notifications:** Added email alerts to organization owners when new members join
- **Memory Export:** Added date support for filtering memory exports
-
-**Improvements:**
- **Performance:** 
-  - Optimized graph for better performance
-  - Optimized database calls in ADD method
- **Analytics:** Added flagging of paid users in Posthog
- **CI/CD:** Improved CI pipeline and fixed lint issues
-
-</Update>
-
-<Update label="2025-04-10" description="">
-
-**New Features:**
- **Notifications:** Implemented email notifications for organization owners when new members join
-
-**Improvements:**
- **CI/CD:** Fixed Dockerfile for CI tests
-
-</Update>
-
-<Update label="2025-04-09" description="">
-
-**Improvements:**
- **Integrations:** Updated chat model for Together Qwen
- **Platform:** Removed older platforms
- **Bug Fixes:** Fixed FILTER_MAPPING
-
-</Update>
-
-<Update label="2025-04-03" description="">
-
-**New Features:**
- **Memory:** Added implicit memory capabilities
- **API:** Improved implicit lambda and get_all v2 functionality
-
-</Update>
-
-<Update label="2025-04-02" description="">
-
-**New Features:**
- **Integrations:** Added Clay integration
-
-**Improvements:**
- **Integrations:** Removed deepseek coder from Together
- **API:** Added custom instructions for add v2
-
-</Update>
-
-<Update label="2025-03-31" description="">
-
-**Security:**
- **Validation:** Added key validation in messages
-
-</Update>
-
-<Update label="2025-03-28" description="">
- **Updated Playground Prompt**
- **Send Email on User Addition to Org/Proj**
- **Fix Search Entity**
-</Update>
-
-<Update label="2025-03-19" description="">
- **General Stability & Performance Improvements**
-</Update>
-
 </Tab>

 <Tab title="Vercel AI SDK">

+<Update label="2025-06-15" description="v1.0.6">
+**New Features:**
+- **Vercel AI SDK:** Added param `filter_memories`.
+</Update>
+
 <Update label="2025-05-23" description="v1.0.5">
 **New Features:**
 - **Vercel AI SDK:** Added support for Google provider.
@@ -4,7 +4,7 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.

@@ -39,5 +39,5 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `models/text-embedding-004` |
-| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
 | `api_key` | The Gemini API key | `None` |
@@ -4,7 +4,7 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.

@@ -4,7 +4,7 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## How to define configurations?

@@ -58,6 +58,7 @@ config = {

 m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
+
 ```

 ```typescript TypeScript
@@ -76,6 +77,7 @@ const config = {
 const memory = new Memory(config);
 await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
 ```
+
 </CodeGroup>

 ## Why is Config Needed?
@@ -2,9 +2,9 @@
 title: Anthropic
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

-To use anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
+To use Anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).

 ## Usage

@@ -2,7 +2,7 @@
 title: AWS Bedrock
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
@@ -2,7 +2,7 @@
 title: Azure OpenAI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>

@@ -2,7 +2,7 @@
 title: DeepSeek
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").

@@ -2,39 +2,74 @@
 title: Gemini
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

-To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
+To use the Gemini model, set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
+
+> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
+
+> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.

 ## Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

-os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
-os.environ["GEMINI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
+os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

 config = {
    "llm": {
        "provider": "gemini",
        "config": {
-            "model": "gemini-1.5-flash-latest",
+            "model": "gemini-2.0-flash-001",
            "temperature": 0.2,
            "max_tokens": 2000,
+            "top_p": 1.0
        }
    }
 }

 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."}
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
 ]
+
 m.add(messages, user_id="alice", metadata={"category": "movies"})
+
 ```
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    llm: {
+        // You can also use "google" as provider ( for backward compatibility )
+        provider: "gemini",
+        config: {
+            model: "gemini-2.0-flash-001",
+            temperature: 0.1
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+    { role: "user", content: "I’m not a big fan of thrillers, but I love sci-fi movies." },
+    { role: "assistant", content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead." }
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>

 ## Config

@@ -2,7 +2,7 @@
 title: Google AI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)

@@ -2,7 +2,7 @@
 title: Groq
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.

@@ -2,7 +2,7 @@
 title: LangChain
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 supports LangChain as a provider to access a wide range of LLM models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various LLM providers through a consistent interface.

@@ -1,4 +1,4 @@
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.

@@ -2,7 +2,7 @@
 title: LM Studio
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.

@@ -23,6 +23,7 @@ config = {
            "temperature": 0.2,
            "max_tokens": 2000,
            "lmstudio_base_url": "http://localhost:1234/v1", # default LM Studio API URL
+            "lmstudio_response_format": {"type": "json_schema", "json_schema": {"type": "object", "schema": {}}},
        }
    }
 }
@@ -2,7 +2,7 @@
 title: Mistral AI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use mistral's models, please obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.

@@ -1,4 +1,4 @@
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.

@@ -2,7 +2,7 @@
 title: OpenAI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).

@@ -2,7 +2,7 @@
 title: Sarvam AI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.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.

@@ -1,4 +1,4 @@
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).

@@ -0,0 +1,109 @@
+---
+title: vLLM
+---
+
+<Snippet file="security-compliance.mdx" />
+
+[vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.
+
+## Prerequisites
+
+1. **Install vLLM**:
+
+   ```bash
+   pip install vllm
+   ```
+
+2. **Start vLLM server**:
+
+   ```bash
+   # For testing with a small model
+   vllm serve microsoft/DialoGPT-medium --port 8000
+
+   # For production with a larger model (requires GPU)
+   vllm serve Qwen/Qwen2.5-32B-Instruct --port 8000
+   ```
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"  # used for embedding model
+
+config = {
+    "llm": {
+        "provider": "vllm",
+        "config": {
+            "model": "Qwen/Qwen2.5-32B-Instruct",
+            "vllm_base_url": "http://localhost:8000/v1",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+## Configuration Parameters
+
+| Parameter       | Description                       | Default                       | Environment Variable |
+| --------------- | --------------------------------- | ----------------------------- | -------------------- |
+| `model`         | Model name running on vLLM server | `"Qwen/Qwen2.5-32B-Instruct"` | -                    |
+| `vllm_base_url` | vLLM server URL                   | `"http://localhost:8000/v1"`  | `VLLM_BASE_URL`      |
+| `api_key`       | API key (dummy for local)         | `"vllm-api-key"`              | `VLLM_API_KEY`       |
+| `temperature`   | Sampling temperature              | `0.1`                         | -                    |
+| `max_tokens`    | Maximum tokens to generate        | `2000`                        | -                    |
+
+## Environment Variables
+
+You can set these environment variables instead of specifying them in config:
+
+```bash
+export VLLM_BASE_URL="http://localhost:8000/v1"
+export VLLM_API_KEY="your-vllm-api-key"
+export OPENAI_API_KEY="your-openai-api-key"  # for embeddings
+```
+
+## Benefits
+
+- **High Performance**: 2-24x faster inference than standard implementations
+- **Memory Efficient**: Optimized memory usage with PagedAttention
+- **Local Deployment**: Keep your data private and reduce API costs
+- **Easy Integration**: Drop-in replacement for other LLM providers
+- **Flexible**: Works with any model supported by vLLM
+
+## Troubleshooting
+
+1. **Server not responding**: Make sure vLLM server is running
+
+   ```bash
+   curl http://localhost:8000/health
+   ```
+
+2. **404 errors**: Ensure correct base URL format
+
+   ```python
+   "vllm_base_url": "http://localhost:8000/v1"  # Note the /v1
+   ```
+
+3. **Model not found**: Check model name matches server
+
+4. **Out of memory**: Try smaller models or reduce `max_model_len`
+
+   ```bash
+   vllm serve Qwen/Qwen2.5-32B-Instruct --max-model-len 4096
+   ```
+
+## Config
+
+All available parameters for the `vllm` config are present in [Master List of All Params in Config](../config).
@@ -2,7 +2,7 @@
 title: xAI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 [xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.

@@ -4,7 +4,7 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.

@@ -14,7 +14,9 @@ To use a llm, you must provide a configuration to customize its usage. If no con

 For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).

-To view all supported llms, visit the [Supported LLMs](./models).
+## Supported LLMs
+
+See the list of supported LLMs below.

 <Note>
  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
@@ -4,7 +4,7 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## How to define configurations?

@@ -0,0 +1,67 @@
+---
+title: Baidu VectorDB (Mochow)
+---
+
+[Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "baidu",
+        "config": {
+            "endpoint": "http://your-mochow-endpoint:8287",
+            "account": "root",
+            "api_key": "your-api-key",
+            "database_name": "mem0",
+            "table_name": "mem0_table",
+            "embedding_model_dims": 1536,
+            "metric_type": "COSINE"
+        }
+    }
+}
+
+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 movie? 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="alice", metadata={"category": "movies"})
+```
+
+### Config
+
+Here are the available parameters for the `mochow` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |
+
+### Distance Metrics
+
+The following distance metrics are supported:
+
+- `L2`: Euclidean distance (default)
+- `IP`: Inner product
+- `COSINE`: Cosine similarity
+
+### Index Configuration
+
+The vector index is automatically configured with the following HNSW parameters:
+
+- `m`: 16 (number of connections per element)
+- `efconstruction`: 200 (size of the dynamic candidate list)
+- `auto_build`: true (automatically build index)
+- `auto_build_index_policy`: Incremental build with 10000 rows increment
@@ -0,0 +1,45 @@
+# MongoDB
+
+[MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "mongodb",
+        "config": {
+            "db_name": "mem0-db",
+            "collection_name": "mem0-collection",
+            "mongo_uri":"mongodb://username:password@localhost:27017"
+        }
+    }
+}
+
+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="alice", metadata={"category": "movies"})
+```
+
+## Config
+
+Here are the parameters available for configuring MongoDB:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| db_name | Name of the MongoDB database | `"mem0_db"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
+| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
+| mongo_uri | The mongo URI connection string | mongodb://username:password@localhost:27017 |
+
+> **Note**: If Mongo_uri is not provided it will default to mongodb://username:password@localhost:27017.
@@ -4,7 +4,7 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.

@@ -23,6 +23,7 @@ See the list of supported vector databases below.
  <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
  <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
  <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
+  <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
  <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
  <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
  <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
@@ -3,7 +3,7 @@ title: Development
 icon: "code"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 # Development Contributions

@@ -31,13 +31,18 @@ For detailed guidance on pull requests, refer to [GitHub's documentation](https:

 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:
+⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, follow these steps in order:

 ```bash
-make install_all
+# 1. Install base dependencies
+make install

-# Activate virtual environment
-hatch shell
+# 2. Activate virtual environment (this will install deps.)
+hatch shell (for default env)
+hatch -e dev_py_3_11 shell (for dev_py_3_11) (differences are mentioned in pyproject.toml)
+
+# 3. Install all optional dependencies
+make install_all
 ```

 ---
@@ -3,7 +3,7 @@ title: Documentation
 icon: "book"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 # Documentation Contributions

@@ -5,7 +5,7 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 provides two core operations for managing memories in AI applications: adding new memories and searching existing ones. This guide covers how these operations work and how to use them effectively in your application.

@@ -0,0 +1,154 @@
+---
+title: Add Memory
+description: Add memory into the Mem0 platform by storing user-assistant interactions and facts for later retrieval.
+icon: "plus"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+The `add` operation is how you store memory into Mem0. Whether you're working with a chatbot, a voice assistant, or a multi-agent system, this is the entry point to create long-term memory.
+
+Memories typically come from a **user-assistant interaction** and Mem0 handles the extraction, transformation, and storage for you.
+
+Mem0 offers two implementation flows:
+
+- **Mem0 Platform** (Managed, scalable, with dashboard + API)
+- **Mem0 Open Source** (Lightweight, fully local, flexible SDKs)
+
+Each supports the same core memory operations, but with slightly different setup. Below, we walk through examples for both.
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the process of adding memories.">
+  <img src="../../images/add_architecture.png" />
+</Frame>
+
+When you call `add`, Mem0 performs the following steps under the hood:
+
+1. **Information Extraction**
+   The input messages are passed through an LLM that extracts key facts, decisions, preferences, or events worth remembering.
+
+2. **Conflict Resolution**
+   Mem0 compares the new memory against existing ones to detect duplication or contradiction and handles updates accordingly.
+
+3. **Memory Storage**
+   The result is stored in a vector database (for semantic search) and optionally in a graph structure (for relationship mapping).
+
+You don’t need to handle any of this manually, Mem0 takes care of it with a single API call or SDK method.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+messages = [
+    {"role": "user", "content": "I'm planning a trip to Tokyo next month."},
+    {"role": "assistant", "content": "Great! I’ll remember that for future suggestions."}
+]
+
+client.add(
+    messages=messages,
+    user_id="alice",
+    version="v2"
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const messages = [
+  { role: "user", content: "I'm planning a trip to Tokyo next month." },
+  { role: "assistant", content: "Great! I’ll remember that for future suggestions." }
+];
+
+await client.add({
+  messages,
+  user_id: "alice",
+  version: "v2"
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+m = Memory()
+
+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."}
+]
+
+# Store inferred memories (default behavior)
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+
+# Optionally store raw messages without inference
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+
+const messages = [
+  {
+    role: "user",
+    content: "I like to drink coffee in the morning and go for a walk"
+  }
+];
+
+const result = memory.add(messages, {
+  userId: "alice",
+  metadata: { category: "preferences" }
+});
+```
+</CodeGroup>
+
+---
+
+## When Should You Add Memory?
+
+Add memory whenever your agent learns something useful:
+
+- A new user preference is shared
+- A decision or suggestion is made
+- A goal or task is completed
+- A new entity is introduced
+- A user gives feedback or clarification
+
+Storing this context allows the agent to reason better in future interactions.
+
+
+### More Details
+
+For full list of supported fields, required formats, and advanced options, see the
+[Add Memory API Reference](/api-reference/memory/add-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>
@@ -0,0 +1,143 @@
+---
+title: Delete Memory
+description: Remove memories from Mem0 either individually, in bulk, or via filters.
+icon: "trash"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
+
+1. **Delete a Single Memory**: Using a specific memory ID
+2. **Batch Delete**: Delete multiple known memory IDs (up to 1000)
+3. **Filtered Delete**: Delete memories matching a filter (e.g., `user_id`, `metadata`, `run_id`)
+
+This page walks through code example for each method.
+
+
+## Use Cases
+
+- Forget a user’s past preferences by request
+- Remove outdated or incorrect memory entries
+- Clean up memory after session expiration
+- Comply with data deletion requests (e.g., GDPR)
+
+---
+
+## 1. Delete a Single Memory by ID
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.delete(memory_id=memory_id)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.delete("your_memory_id")
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 2. Batch Delete Multiple Memories
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+delete_memories = [
+    {"memory_id": "id1"},
+    {"memory_id": "id2"}
+]
+
+response = client.batch_delete(delete_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const deleteMemories = [
+  { memory_id: "id1" },
+  { memory_id: "id2" }
+];
+
+client.batchDelete(deleteMemories)
+  .then(response => console.log('Batch delete response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 3. Delete Memories by Filter (e.g., user_id)
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Delete all memories for a specific user
+client.delete_all(user_id="alice")
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.deleteAll({ user_id: "alice" })
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+You can also filter by other parameters such as:
+- `agent_id`
+- `run_id`
+- `metadata` (as JSON string)
+
+---
+
+## Key Differences
+
+| Method                | Use When                                | IDs Needed | Filters |
+|----------------------|-------------------------------------------|------------|----------|
+| `delete(memory_id)`  | You know exactly which memory to remove   | ✔          | ✘        |
+| `batch_delete([...])`| You have a known list of memory IDs       | ✔          | ✘        |
+| `delete_all(...)`    | You want to delete by user/agent/run/etc | ✘          | ✔        |
+
+
+### More Details
+
+For request/response schema and additional filtering options, see:
+- [Delete Memory API Reference](/api-reference/memory/delete-memory)
+- [Batch Delete API Reference](/api-reference/memory/batch-delete)
+- [Delete Memories by Filter Reference](/api-reference/memory/delete-memories)
+
+You’ve now seen how to add, search, update, and delete memories in Mem0.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>
@@ -0,0 +1,126 @@
+---
+title: Search Memory
+description: Retrieve relevant memories from Mem0 using powerful semantic and filtered search capabilities.
+icon: "magnifying-glass"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+The `search` operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior.
+
+Mem0 supports:
+- Semantic similarity search
+- Metadata filtering (with advanced logic)
+- Reranking and thresholds
+- Cross-agent, multi-session context resolution
+
+This applies to both:
+- **Mem0 Platform** (hosted API with full-scale features)
+- **Mem0 Open Source** (local-first with LLM inference and local vector DB)
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the memory search process.">
+  <img src="../../images/search_architecture.png" />
+</Frame>
+
+The search flow follows these steps:
+
+1. **Query Processing**
+   An LLM refines and optimizes your natural language query.
+
+2. **Vector Search**
+   Semantic embeddings are used to find the most relevant memories using cosine similarity.
+
+3. **Filtering & Ranking**
+   Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
+
+4. **Results Delivery**
+   Relevant memories are returned with associated metadata and timestamps.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+query = "What do you know about me?"
+filters = {
+   "OR": [
+      {"user_id": "alice"},
+      {"agent_id": {"in": ["travel-assistant", "customer-support"]}}
+   ]
+}
+
+results = client.search(query, version="v2", filters=filters)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const query = "I'm craving some pizza. Any recommendations?";
+const filters = {
+  AND: [
+    { user_id: "alice" }
+  ]
+};
+
+const results = await client.search(query, {
+  version: "v2",
+  filters
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+from mem0 import Memory
+
+m = Memory()
+related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
+```
+</CodeGroup>
+
+---
+
+## Tips for Better Search
+
+- Use descriptive natural queries (Mem0 can interpret intent)
+- Apply filters for scoped, faster lookup
+- Use `version: "v2"` for enhanced results
+- Consider wildcard filters (e.g., `run_id: "*"`) for broader matches
+- Tune with `top_k`, `threshold`, or `rerank` if needed
+
+
+### More Details
+
+For the full list of filter logic, comparison operators, and optional search parameters, see the
+[Search Memory API Reference](/api-reference/memory/v2-search-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>
@@ -0,0 +1,119 @@
+---
+title: Update Memory
+description: Modify an existing memory by updating its content or metadata.
+icon: "pencil"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+User preferences, interests, and behaviors often evolve over time. The `update` operation lets you revise a stored memory, whether it's updating facts and memories, rephrasing a message, or enriching metadata.
+
+Mem0 supports both:
+- **Single Memory Update** for one specific memory using its ID
+- **Batch Update** for updating many memories at once (up to 1000)
+
+This guide includes usage for both single update and batch update of memories through **Mem0 Platform**
+
+
+## Use Cases
+
+- Refine a vague or incorrect memory after a correction
+- Add or edit memory with new metadata (e.g., categories, tags)
+- Evolve factual knowledge as the user’s profile changes
+- A user profile evolves: “I love spicy food” → later says “Actually, I can’t handle spicy food.”
+
+Updating memory ensures your agents remain accurate, adaptive, and personalized.
+
+---
+
+## Update Memory
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.update(
+    memory_id=memory_id,
+    text="Updated memory content about the user",
+    metadata={"category": "profile-update"}
+)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+const memory_id = "your_memory_id";
+
+client.update(memory_id, {
+  text: "Updated memory content about the user",
+  metadata: { category: "profile-update" }
+})
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Batch Update
+
+Update up to 1000 memories in one call.
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+update_memories = [
+    {"memory_id": "id1", "text": "Watches football"},
+    {"memory_id": "id2", "text": "Likes to travel"}
+]
+
+response = client.batch_update(update_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const updateMemories = [
+  { memoryId: "id1", text: "Watches football" },
+  { memoryId: "id2", text: "Likes to travel" }
+];
+
+client.batchUpdate(updateMemories)
+  .then(response => console.log('Batch update response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Tips
+
+- You can update both `text` and `metadata` in the same call.
+- Use `batchUpdate` when you're applying similar corrections at scale.
+- If memory is marked `immutable`, it must first be deleted and re-added.
+- Combine this with feedback mechanisms (e.g., user thumbs-up/down) to self-improve memory.
+
+
+### More Details
+
+Refer to the full [Update Memory API Reference](/api-reference/memory/update-memory) and [Batch Update Reference](/api-reference/memory/batch-update) for schema and advanced fields.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>
@@ -5,7 +5,7 @@ icon: "memory"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 To build useful AI applications, we need to understand how different memory systems work together. This guide explores the fundamental types of memory in AI systems and shows how Mem0 implements these concepts.

@@ -19,10 +19,10 @@
            "tab": "Documentation",
            "groups": [
              {
-                "group": "Get Started",
+                "group": "Getting Started",
                "icon": "rocket",
                "pages": [
-                  "overview",
+                  "what-is-mem0",
                  "quickstart",
                  "faqs"
                ]
@@ -32,7 +32,16 @@
                "icon": "brain",
                "pages": [
                  "core-concepts/memory-types",
-                  "core-concepts/memory-operations"
+                  {
+                    "group": "Memory Operations",
+                    "icon": "gear",
+                    "pages": [
+                      "core-concepts/memory-operations/add",
+                      "core-concepts/memory-operations/search",
+                      "core-concepts/memory-operations/update",
+                      "core-concepts/memory-operations/delete"
+                    ]
+                  }
                ]
              },
              {
@@ -46,21 +55,20 @@
                    "icon": "star",
                    "pages": [
                      "platform/features/platform-overview",
+                      "platform/features/contextual-add",
+                      "platform/features/async-client",
                      "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/timestamp",
+                      "platform/features/expiration-date",
                      "platform/features/webhooks",
-                      "platform/features/graph-memory",
                      "platform/features/feedback-mechanism",
-                      "platform/features/expiration-date"
+                      "platform/features/group-chat"
                    ]
                  }
                ]
@@ -69,12 +77,12 @@
                "group": "Open Source",
                "icon": "code-branch",
                "pages": [
-                  "open-source/quickstart",
+                  "open-source/overview",
                  "open-source/python-quickstart",
                  "open-source/node-quickstart",
                  {
                    "group": "Features",
-                    "icon": "wrench",
+                    "icon": "star",
                    "pages": [
                      "open-source/features/async-memory",
                      "open-source/features/openai_compatibility",
@@ -117,7 +125,8 @@
                          "components/llms/models/xAI",
                          "components/llms/models/sarvam",
                          "components/llms/models/lmstudio",
-                          "components/llms/models/langchain"
+                          "components/llms/models/langchain",
+                          "components/llms/models/vllm"
                        ]
                      }
                    ]
@@ -137,6 +146,7 @@
                          "components/vectordbs/dbs/pgvector",
                          "components/vectordbs/dbs/milvus",
                          "components/vectordbs/dbs/pinecone",
+                          "components/vectordbs/dbs/mongodb",
                          "components/vectordbs/dbs/azure",
                          "components/vectordbs/dbs/redis",
                          "components/vectordbs/dbs/elasticsearch",
@@ -145,7 +155,8 @@
                          "components/vectordbs/dbs/vertex_ai",
                          "components/vectordbs/dbs/weaviate",
                          "components/vectordbs/dbs/faiss",
-                          "components/vectordbs/dbs/langchain"
+                          "components/vectordbs/dbs/langchain",
+                          "components/vectordbs/dbs/baidu"
                        ]
                      }
                    ]
@@ -191,7 +202,8 @@
            "icon": "square-terminal",
            "pages": [
              "openmemory/overview",
-              "openmemory/quickstart"
+              "openmemory/quickstart",
+              "openmemory/integrations"
            ]
          },
          {
@@ -235,23 +247,25 @@
                "icon": "plug",
                "pages": [
                  "integrations",
-                  "integrations/vercel-ai-sdk",
-                  "integrations/flowise",
-                  "integrations/crewai",
-                  "integrations/autogen",
                  "integrations/langchain",
                  "integrations/langgraph",
                  "integrations/llama-index",
-                  "integrations/langchain-tools",
-                  "integrations/dify",
-                  "integrations/mcp-server",
-                  "integrations/livekit",
-                  "integrations/elevenlabs",
-                  "integrations/pipecat",
                  "integrations/agno",
+                  "integrations/autogen",
+                  "integrations/crewai",
+                  "integrations/openai-agents-sdk",
+                  "integrations/google-ai-adk",
+                  "integrations/mastra",
+                  "integrations/vercel-ai-sdk",
+                  "integrations/livekit",
+                  "integrations/pipecat",
+                  "integrations/elevenlabs",
+                  "integrations/flowise",
+                  "integrations/langchain-tools",
+                  "integrations/agentops",
                  "integrations/keywords",
-                  "integrations/raycast",
-                  "integrations/mastra"
+                  "integrations/dify",
+                  "integrations/raycast"
                ]
              }
            ]
@@ -346,31 +360,6 @@
            ]
          }
        ]
-      },
-      {
-        "anchor": "Your Dashboard",
-        "href": "https://app.mem0.ai",
-        "icon": "chart-simple"
-      },
-      {
-        "anchor": "Demo",
-        "href": "https://mem0.dev/demo",
-        "icon": "play"
-      },
-      {
-        "anchor": "Discord",
-        "href": "https://mem0.dev/DiD",
-        "icon": "discord"
-      },
-      {
-        "anchor": "GitHub",
-        "href": "https://github.com/mem0ai/mem0",
-        "icon": "github"
-      },
-      {
-        "anchor": "Support",
-        "href": "mailto:founders@mem0.ai",
-        "icon": "envelope"
      }
    ]
  },
@@ -3,7 +3,7 @@ title: Overview
 description: How to use mem0 in your existing applications?
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />


 With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
@@ -2,7 +2,7 @@
 title: AI Companion
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -2,7 +2,7 @@
 title: AI Companion in Node.js
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -2,7 +2,7 @@
 title: AWS Bedrock and AOSS
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.

@@ -1,6 +1,6 @@
 # Mem0 Chrome Extension

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Enhance your AI interactions with **Mem0**, a Chrome extension that introduces a universal memory layer across platforms like `ChatGPT`, `Claude`, and `Perplexity`. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.

@@ -2,7 +2,7 @@
 title: Multi-User Collaboration with Mem0
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Overview

@@ -2,7 +2,7 @@
 title: Customer Support AI Agent
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -1,7 +1,7 @@
 ---
 title: Document Editing with Mem0
 ---
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 This guide demonstrates how to leverage **Mem0** to edit documents efficiently, ensuring they align with your unique writing style and preferences.

@@ -2,7 +2,7 @@
 title: Eliza OS Character
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalised Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -2,7 +2,7 @@
 title: Email Processing with Mem0
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.

@@ -1,7 +1,7 @@
 ---
 title: LlamaIndex ReAct Agent
 ---
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.

@@ -22,7 +22,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-4o")
 ```

-Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/quickstart).
+Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/api-keys). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"

@@ -2,7 +2,7 @@
 title: Mem0 as an Agentic Tool
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
 You can create agents that remember past conversations and use that context to provide better responses.
@@ -3,7 +3,7 @@ title: Mem0 Demo
 ---


-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.

@@ -3,7 +3,7 @@ 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" />
+<Snippet file="security-compliance.mdx" />

 # Healthcare Assistant with Memory

@@ -2,7 +2,7 @@
 title: Mem0 with Mastra
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 In this example you'll learn how to use the Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use.
 This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).
@@ -3,7 +3,7 @@ title: 'Mem0 with OpenAI Agents SDK for Voice'
 description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 # Building Voice Agents with Memory using Mem0 and OpenAI Agents SDK

@@ -2,7 +2,7 @@
 title: Mem0 with Ollama
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Running Mem0 Locally with Ollama

@@ -2,7 +2,7 @@
 title: Multimodal Demo with Mem0
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Enhance your AI interactions with **Mem0**'s multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.

@@ -2,7 +2,7 @@
 title: OpenAI Inbuilt Tools
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.

@@ -2,7 +2,7 @@
 title: Personalized AI Tutor
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -2,7 +2,7 @@
 title: Personal AI Travel Assistant
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.

@@ -2,7 +2,7 @@
 title: Personalized Deep Research
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.

@@ -2,7 +2,7 @@
 title: YouTube Assistant Extension
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Enhance your YouTube experience with Mem0's **YouTube Assistant**, a Chrome extension that brings AI-powered chat directly to your YouTube videos. Get instant, personalized answers about video content while leveraging your own knowledge and memories - all without leaving the page.

@@ -4,7 +4,7 @@ icon: "question"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 <AccordionGroup>
    <Accordion title="How does Mem0 work?">
@@ -4,7 +4,7 @@ icon: "wrench"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Core features

@@ -3,7 +3,7 @@ title: Overview
 description: How to integrate Mem0 into other frameworks
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 seamlessly integrates with popular AI frameworks and tools to enhance your LLM-based applications with persistent memory capabilities. By integrating Mem0, your applications benefit from:

@@ -18,6 +18,23 @@ Here are the available integrations for Mem0:
 ## Integrations

 <CardGroup cols={2}>
+  <Card
+    title="AgentOps"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="25"
+        height="26"
+        viewBox="0 0 30 36"
+        fill="none"
+      >
+      <path d="M10.4659 6.47277C10.45 6.37428 10.4381 6.27986 10.4303 6.18101L10.4285 6.16388C10.4212 6.09482 10.414 6.02566 10.4106 5.95626L1.18538 21.8752C0.505422 23.0493 0.323356 24.4208 0.675227 25.7289C0.849119 26.3869 1.14971 26.9859 1.55323 27.5098C1.95675 28.0338 2.46282 28.4751 3.05175 28.8143C3.83464 29.2675 4.70856 29.5 5.59028 29.5C6.03318 29.5 6.4798 29.4408 6.91899 29.3226C8.23581 28.972 9.3349 28.1326 10.0152 26.9545L15.9268 16.749V16.7449L16.5001 15.7637L17.6431 13.7936L16.5001 11.8234L15.9309 10.8381L15.9268 10.8341L13.7836 7.13406C13.6651 6.933 13.5741 6.72418 13.5109 6.51165C13.2817 5.80223 13.3292 5.04172 13.6097 4.37599L13.8115 4.02535C14.3532 3.09155 15.31 2.53987 16.3184 2.47692C16.3738 2.46915 16.4251 2.46915 16.4804 2.46915C16.5421 2.46915 16.6038 2.47257 16.6654 2.47599L16.6822 2.47692C17.6906 2.53987 18.6474 3.09155 19.1892 4.02535L21.2216 7.52838L21.8146 8.55289L21.8421 8.60399L30.1024 22.8601C30.5174 23.5814 30.6281 24.4167 30.4148 25.2205C30.1975 26.0244 29.6832 26.6942 28.9598 27.1081C28.2364 27.5258 27.3977 27.6361 26.5911 27.4195C25.7844 27.2066 25.1123 26.6905 24.6968 25.9696L18.2119 14.7788L17.069 16.7449L22.9847 26.9545C23.6646 28.1326 24.7641 28.972 26.0809 29.3226C26.5197 29.4408 26.9626 29.5 27.4096 29.5C28.2914 29.5 29.1612 29.2675 29.9482 28.8143C31.1264 28.1367 31.9728 27.0411 32.3247 25.7289C32.6766 24.4208 32.4949 23.0493 31.8145 21.8752L21.1261 3.43034C20.7029 2.51617 20.0033 1.72011 19.0621 1.18027C18.5281 0.877027 17.9708 0.675975 17.3975 0.581189C17.3027 0.565268 17.2076 0.549717 17.1129 0.537868C17.0099 0.52602 16.9074 0.518244 16.8045 0.510469C16.6027 0.498621 16.3972 0.494548 16.1914 0.510469C16.0885 0.518244 15.9859 0.52639 15.883 0.537868C15.795 0.54887 15.7067 0.563384 15.6187 0.577852L15.5984 0.581189C15.0291 0.675605 14.4673 0.876657 13.9375 1.18027C12.9885 1.72789 12.2766 2.53579 11.8537 3.46181C11.7742 3.63473 11.707 3.81282 11.6471 3.99314C11.6361 4.02668 11.6269 4.06051 11.6177 4.09435C11.612 4.11503 11.6064 4.13579 11.6003 4.15642C11.5624 4.28601 11.5275 4.41634 11.4996 4.54853C11.4885 4.60231 11.4794 4.65668 11.4703 4.71111L11.4666 4.73329C11.4443 4.86399 11.4264 4.99543 11.4145 5.12762C11.4093 5.18686 11.4045 5.24573 11.4012 5.30534C11.3934 5.44567 11.3923 5.58637 11.3963 5.72744C11.3969 5.74403 11.3962 5.76062 11.3956 5.7772C11.3949 5.79616 11.3942 5.81512 11.3952 5.83407C11.3952 5.86184 11.3952 5.88924 11.3993 5.92071C11.3998 5.9291 11.4006 5.93736 11.4014 5.94564C11.402 5.95125 11.4026 5.95687 11.403 5.96255C11.4045 5.98181 11.4064 6.00106 11.4082 6.02031C11.4097 6.03577 11.4109 6.05122 11.4122 6.06674C11.4142 6.09134 11.4163 6.11621 11.419 6.14139L11.4428 6.32282C11.4506 6.38983 11.4625 6.46092 11.4744 6.52757C11.5063 6.68863 11.5468 6.84896 11.5936 7.0078C11.5944 7.0102 11.5949 7.0127 11.5955 7.0152C11.5958 7.01662 11.5961 7.01804 11.5965 7.01944C11.5967 7.02051 11.597 7.02157 11.5974 7.02261C11.6483 7.19293 11.7081 7.36177 11.7787 7.52838C11.8619 7.72943 11.9607 7.92641 12.0715 8.11932L12.3245 8.5566V8.56067L12.4984 8.85614L12.7199 9.24232H12.7239L12.728 9.25417L14.7802 12.7927V12.7968L14.7883 12.805V12.809L15.3576 13.7943L14.7883 14.7796L8.30344 25.9703C7.88431 26.6912 7.21216 27.2077 6.40921 27.4202C6.14019 27.4913 5.86338 27.5306 5.59474 27.5306C5.053 27.5306 4.51906 27.3888 4.04085 27.1089C3.31705 26.6953 2.79909 26.0251 2.58581 25.2213C2.36845 24.4174 2.47917 23.5821 2.89829 22.8609L11.1585 8.60473L11.186 8.56141V8.55734C11.1266 8.45478 11.0753 8.35629 11.024 8.25409C11.0105 8.22496 10.9969 8.19611 10.9834 8.16739C10.9458 8.08735 10.9086 8.00836 10.8739 7.92715C10.8718 7.92504 10.8708 7.92194 10.8698 7.91887C10.8688 7.91602 10.8679 7.91319 10.8661 7.91123V7.90346C10.8423 7.8483 10.8186 7.79311 10.7989 7.73795C10.7476 7.60799 10.7041 7.47803 10.6644 7.3477C10.6012 7.15479 10.5536 6.96152 10.518 6.76861C10.4942 6.67012 10.4786 6.5757 10.4667 6.47684C10.4667 6.47684 10.47 6.47684 10.4659 6.47277Z" fill="currentColor"></path>
+      </svg>
+    }
+    href="/integrations/agentops"
+  >
+    Monitor and analyze Mem0 operations with comprehensive AI agent analytics and LLM observability.
+  </Card>
  <Card
    title="LangChain"
    icon={
@@ -305,6 +322,7 @@ Here are the available integrations for Mem0:
  >
    Build autonomous agents with memory using Agno framework.
  </Card>
+
  <Card
    title="Keywords AI"
    icon={
@@ -0,0 +1,172 @@
+---
+title: AgentOps
+---
+<Snippet file="security-compliance.mdx" />
+
+Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.
+
+## Overview
+
+1. Automatic monitoring of Mem0 operations and performance metrics
+2. Real-time tracking of memory add, search, and retrieval operations
+3. Analytics dashboard with memory usage patterns and insights
+4. Error tracking and debugging capabilities for memory operations
+
+## Prerequisites
+
+Before setting up Mem0 with AgentOps, ensure you have:
+
+1. Installed the required packages:
+```bash
+pip install mem0ai agentops
+```
+
+2. Valid API keys:
+   - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
+   - OpenAI API Key (for LLM operations)
+   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys) (optional, for cloud operations)
+
+## Basic Integration Example
+
+The following example demonstrates how to integrate Mem0 with AgentOps monitoring for comprehensive memory operation tracking:
+
+```python
+#Import the required libraries for local memory management with Mem0
+from mem0 import Memory, AsyncMemory
+import os
+import asyncio
+import logging
+from dotenv import load_dotenv
+import agentops
+
+#Set up environment variables for API keys
+os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+#Set up the configuration for local memory storage and define sample user data. 
+local_config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        },
+    }
+}
+user_id = "alice_demo"
+agent_id = "assistant_demo"
+run_id = "session_001"
+
+sample_messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller? 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.",
+    },
+]
+
+sample_preferences = [
+    "I prefer dark roast coffee over light roast",
+    "I exercise every morning at 6 AM",
+    "I'm vegetarian and avoid all meat products",
+    "I love reading science fiction novels",
+    "I work in software engineering",
+]
+
+#This function demonstrates sequential memory operations using the synchronous Memory class
+def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id):
+    """
+    Demonstrate synchronous Memory class operations.
+    """
+
+    agentops.start_trace("mem0_memory_example", tags=["mem0_memory_example"])
+    try:
+        
+        memory = Memory.from_config(local_config)
+
+        result = memory.add(
+            sample_messages, user_id=user_id, metadata={"category": "movie_preferences", "session": "demo"}
+        )
+
+        for i, preference in enumerate(sample_preferences):
+            result = memory.add(preference, user_id=user_id, metadata={"type": "preference", "index": i})
+       
+        search_queries = [
+            "What movies does the user like?",
+            "What are the user's food preferences?",
+            "When does the user exercise?",
+        ]
+
+        for query in search_queries:
+            results = memory.search(query, user_id=user_id)
+        
+            if results and "results" in results:
+                for j, result in enumerate(results): 
+                    print(f"Result {j+1}: {result.get('memory', 'N/A')}")
+            else:
+                print("No results found")
+
+        all_memories = memory.get_all(user_id=user_id)
+        if all_memories and "results" in all_memories:
+            print(f"Total memories: {len(all_memories['results'])}")
+
+        delete_all_result = memory.delete_all(user_id=user_id)
+        print(f"Delete all result: {delete_all_result}")
+
+        agentops.end_trace(end_state="success")
+    except Exception as e:
+        agentops.end_trace(end_state="error")
+
+# Execute sync demonstrations
+demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id)
+
+```
+
+For detailed information on this integration, refer to the official [Agentops Mem0 integration documentation](https://docs.agentops.ai/v2/integrations/mem0).
+
+
+## Key Features
+
+### 1. Automatic Operation Tracking
+
+AgentOps automatically monitors all Mem0 operations:
+
+- **Memory Operations**: Track add, search, get_all, delete operations and much more
+- **Performance Metrics**: Monitor response times and success rates
+- **Error Tracking**: Capture and analyze operation failures
+
+### 2. Real-time Analytics Dashboard
+
+Access comprehensive analytics through the AgentOps dashboard:
+
+- **Usage Patterns**: Visualize memory usage trends over time
+- **User Behavior**: Analyze how different users interact with memory
+- **Performance Insights**: Identify bottlenecks and optimization opportunities
+
+### 3. Session Management
+
+Organize your monitoring with structured sessions:
+
+- **Session Tracking**: Group related operations into logical sessions
+- **Success/Failure Rates**: Track session outcomes for reliability monitoring
+- **Custom Metadata**: Add context to sessions for better analysis
+
+## Best Practices
+
+1. **Initialize Early**: Always initialize AgentOps before importing Mem0 classes
+2. **Session Management**: Use meaningful session names and end sessions appropriately
+3. **Error Handling**: Wrap operations in try-catch blocks and report failures
+4. **Tagging**: Use tags to organize different types of memory operations
+5. **Environment Separation**: Use different projects or tags for dev/staging/prod
+
+## Help & Resources
+
+- [AgentOps Documentation](https://docs.agentops.ai/)
+- [AgentOps Dashboard](https://app.agentops.ai/)
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" /> 
+ 
@@ -1,7 +1,7 @@
 ---
 title: Agno
 ---
-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno), a Python framework for building autonomous agents. This integration enables Agno agents to access persistent memory across conversations, enhancing context retention and personalization.

@@ -1,6 +1,6 @@
 Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Overview

@@ -2,7 +2,7 @@
 title: CrewAI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Build an AI system that combines CrewAI's agent-based architecture with Mem0's memory capabilities. This integration enables persistent memory across agent interactions and personalized task execution based on user history.

@@ -164,7 +164,7 @@ By combining CrewAI with Mem0, you can create sophisticated AI systems that main

 ## Help

- For CrewAI documentation, visit [CrewAI Documentation](https://docs.crewai.com/)
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/)
+- [CrewAI Documentation](https://docs.crewai.com/)
+- [Mem0 Platform](https://app.mem0.ai/)

 <Snippet file="get-help.mdx" />
@@ -2,7 +2,7 @@
 title: Dify
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 # Integrating Mem0 with Dify AI

@@ -2,7 +2,7 @@
 title: ElevenLabs
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables persistent, context-aware voice interactions that remember past conversations.

@@ -2,7 +2,7 @@
 title: Flowise
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.

@@ -0,0 +1,284 @@
+---
+title: Google Agent Development Kit
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google Agent Development Kit (ADK)](https://github.com/google/adk-python), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
+
+## Overview
+
+1. Store and retrieve memories from Mem0 within Google ADK agents
+2. Multi-agent workflows with shared memory across hierarchies
+3. Retrieve relevant memories from past conversations
+4. Personalized responses
+
+## Prerequisites
+
+Before setting up Mem0 with Google ADK, ensure you have:
+
+1. Installed the required packages:
+```bash
+pip install google-adk mem0ai
+```
+
+2. Valid API keys:
+   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys)
+   - Google AI Studio API Key
+
+## Basic Integration Example
+
+The following example demonstrates how to create a Google ADK agent with Mem0 memory integration:
+
+```python
+import os
+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
+
+# Set up environment variables
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Initialize Mem0 client
+mem0 = MemoryClient()
+
+# Define memory function tools
+def search_memory(query: str, user_id: str) -> dict:
+    """Search through past conversations and memories"""
+    memories = mem0.search(query, user_id=user_id)
+    if memories:
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories])
+        return {"status": "success", "memories": memory_context}
+    return {"status": "no_memories", "message": "No relevant memories found"}
+
+def save_memory(content: str, user_id: str) -> dict:
+    """Save important information to memory"""
+    try:
+        mem0.add([{"role": "user", "content": content}], user_id=user_id)
+        return {"status": "success", "message": "Information saved to memory"}
+    except Exception as e:
+        return {"status": "error", "message": f"Failed to save memory: {str(e)}"}
+
+# Create agent with memory capabilities
+personal_assistant = Agent(
+    name="personal_assistant",
+    model="gemini-2.0-flash",
+    instruction="""You are a helpful personal assistant with memory capabilities.
+    Use the search_memory function to recall past conversations and user preferences.
+    Use the save_memory function to store important information about the user.
+    Always personalize your responses based on available memory.""",
+    description="A personal assistant that remembers user preferences and past interactions",
+    tools=[search_memory, save_memory]
+)
+
+def chat_with_agent(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with automatic memory integration.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The agent's response
+    """
+    # Set up session and runner
+    session_service = InMemorySessionService()
+    session = session_service.create_session(
+        app_name="memory_assistant",
+        user_id=user_id,
+        session_id=f"session_{user_id}"
+    )
+    runner = Runner(agent=personal_assistant, app_name="memory_assistant", session_service=session_service)
+
+    # Create content and run agent
+    content = types.Content(role='user', parts=[types.Part(text=user_input)])
+    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
+
+    # Extract final response
+    for event in events:
+        if event.is_final_response():
+            response = event.content.parts[0].text
+
+            return response
+
+    return "No response generated"
+
+# Example usage
+if __name__ == "__main__":
+    response = chat_with_agent(
+        "I love Italian food and I'm planning a trip to Rome next month",
+        user_id="alice"
+    )
+    print(response)
+```
+
+## Multi-Agent Hierarchy with Shared Memory
+
+Create specialized agents in a hierarchy that share memory:
+
+```python
+from google.adk.tools.agent_tool import AgentTool
+
+# Travel specialist agent
+travel_agent = Agent(
+    name="travel_specialist",
+    model="gemini-2.0-flash",
+    instruction="""You are a travel planning specialist. Use get_user_context to
+    understand the user's travel preferences and history before making recommendations.
+    After providing advice, use store_interaction to save travel-related information.""",
+    description="Specialist in travel planning and recommendations",
+    tools=[search_memory, save_memory]
+)
+
+# Health advisor agent
+health_agent = Agent(
+    name="health_advisor",
+    model="gemini-2.0-flash",
+    instruction="""You are a health and wellness advisor. Use get_user_context to
+    understand the user's health goals and dietary preferences.
+    After providing advice, use store_interaction to save health-related information.""",
+    description="Specialist in health and wellness advice",
+    tools=[search_memory, save_memory]
+)
+
+# Coordinator agent that delegates to specialists
+coordinator_agent = Agent(
+    name="coordinator",
+    model="gemini-2.0-flash",
+    instruction="""You are a coordinator that delegates requests to specialist agents.
+    For travel-related questions (trips, hotels, flights, destinations), delegate to the travel specialist.
+    For health-related questions (fitness, diet, wellness, exercise), delegate to the health advisor.
+    Use get_user_context to understand the user before delegation.""",
+    description="Coordinates requests between specialist agents",
+    tools=[
+        AgentTool(agent=travel_agent, skip_summarization=False),
+        AgentTool(agent=health_agent, skip_summarization=False)
+    ]
+)
+
+def chat_with_specialists(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with specialist agent delegation and memory.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The specialist agent's response
+    """
+    session_service = InMemorySessionService()
+    session = session_service.create_session(
+        app_name="specialist_system",
+        user_id=user_id,
+        session_id=f"session_{user_id}"
+    )
+    runner = Runner(agent=coordinator_agent, app_name="specialist_system", session_service=session_service)
+
+    content = types.Content(role='user', parts=[types.Part(text=user_input)])
+    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
+
+    for event in events:
+        if event.is_final_response():
+            response = event.content.parts[0].text
+
+            # Store the conversation in shared memory
+            conversation = [
+                {"role": "user", "content": user_input},
+                {"role": "assistant", "content": response}
+            ]
+            mem0.add(conversation, user_id=user_id)
+
+            return response
+
+    return "No response generated"
+
+# Example usage
+response = chat_with_specialists("Plan a healthy meal for my Italy trip", user_id="alice")
+print(response)
+```
+
+
+
+## Quick Start Chat Interface
+
+Simple interactive chat with memory and Google ADK:
+
+```python
+def interactive_chat():
+    """Interactive chat interface with memory and ADK"""
+    user_id = input("Enter your user ID: ") or "demo_user"
+    print(f"Chat started for user: {user_id}")
+    print("Type 'quit' to exit")
+    print("=" * 50)
+
+    while True:
+        user_input = input("\nYou: ")
+
+        if user_input.lower() == 'quit':
+            print("Goodbye! Your conversation has been saved to memory.")
+            break
+        else:
+            response = chat_with_specialists(user_input, user_id)
+            print(f"Assistant: {response}")
+
+if __name__ == "__main__":
+    interactive_chat()
+```
+
+## Key Features
+
+### 1. Memory-Enhanced Function Tools
+- **Function Tools**: Standard Python functions that can search and save memories
+- **Tool Context**: Access to session state and memory through function parameters
+- **Structured Returns**: Dictionary-based returns with status indicators for better LLM understanding
+
+### 2. Multi-Agent Memory Sharing
+- **Agent-as-a-Tool**: Specialists can be called as tools while maintaining shared memory
+- **Hierarchical Delegation**: Coordinator agents route to specialists based on context
+- **Memory Categories**: Store interactions with metadata for better organization
+
+### 3. Flexible Memory Operations
+- **Search Capabilities**: Retrieve relevant memories through conversation history
+- **User Segmentation**: Organize memories by user ID
+- **Memory Management**: Built-in tools for saving and retrieving information
+
+## Configuration Options
+
+Customize memory behavior and agent setup:
+
+```python
+# Configure memory search with metadata
+memories = mem0.search(
+    query="travel preferences",
+    user_id="alice",
+    limit=5,
+    filters={"category": "travel"}  # Filter by category if supported
+)
+
+# Configure agent with custom model settings
+agent = Agent(
+    name="custom_agent",
+    model="gemini-2.0-flash",  # or use LiteLLM for other models
+    instruction="Custom agent behavior",
+    tools=[memory_tools],
+    # Additional ADK configurations
+)
+
+# Use Google Cloud Vertex AI instead of AI Studio
+os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
+os.environ["GOOGLE_CLOUD_PROJECT"] = "your-project-id"
+os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"
+```
+
+## Help
+
+- [Google ADK Documentation](https://google.github.io/adk-docs/)
+- [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" />
@@ -2,7 +2,7 @@
 title: Keywords AI
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Keywords AI.

@@ -135,8 +135,8 @@ Integrating Mem0 with Keywords AI provides a powerful combination for building A

 ## Help

-For more information on using Mem0 and Keywords AI together, refer to:
- [Mem0 Documentation](https://docs.mem0.ai)
+For more information, refer to:
 - [Keywords AI Documentation](https://docs.keywordsai.co)
+- [Mem0 Platform]((https://app.mem0.ai/))

 <Snippet file="get-help.mdx" />
@@ -3,7 +3,7 @@ title: Langchain Tools
 description: 'Integrate Mem0 with LangChain tools to enable AI agents to store, search, and manage memories through structured interfaces'
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Overview

@@ -2,7 +2,7 @@
 title: Langchain
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Build a personalized Travel Agent AI using LangChain for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient travel planning experiences.

@@ -152,7 +152,7 @@ By integrating LangChain with Mem0, you can build a personalized Travel Agent AI
 ## Help

 - For more details on LangChain, visit the [LangChain documentation](https://python.langchain.com/).
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- [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" />
@@ -2,7 +2,7 @@
 title: LangGraph
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Build a personalized Customer Support AI Agent using LangGraph for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient support experiences.

@@ -140,7 +140,7 @@ By integrating LangGraph with Mem0, you can build a personalized Customer Suppor
 ## Help

 - For more details on LangGraph, visit the [LangChain documentation](https://python.langchain.com/docs/langgraph).
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- [Mem0 Platform](https://app.mem0.ai/).
 - If you need further assistance, please feel free to reach out to us through following methods:

 <Snippet file="get-help.mdx" />
@@ -2,7 +2,7 @@
 title: Livekit
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 This guide demonstrates how to create a memory-enabled voice assistant using LiveKit, Deepgram, OpenAI, and Mem0, focusing on creating an intelligent, context-aware travel planning agent.

@@ -12,10 +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-agents[voice] \
-livekit-plugins-silero \
-livekit-plugins-deepgram \
-livekit-plugins-openai
+pip install livekit-agents[silero,openai,deepgram]
 ```

 2. Installed Mem0 SDK:
@@ -382,4 +379,12 @@ logging.basicConfig(
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger("memory_voice_agent")
-```
+```
+
+## Help & Resources
+
+- [LiveKit Documentation](https://docs.livekit.io/)
+- [Mem0 Platform](https://app.mem0.ai/)
+- Need assistance? Reach out through:
+
+<Snippet file="get-help.mdx" />
@@ -2,7 +2,7 @@
 title: LlamaIndex
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-index-memory-mem0). In this guide, we'll show you how to use it.

@@ -63,7 +63,7 @@ context = {
 Set your Mem0 OSS by providing configuration details:

 <Note type="info">
-  To know more about Mem0 OSS, read [Mem0 OSS Quickstart](https://docs.mem0.ai/open-source/quickstart).
+  To know more about Mem0 OSS, read [Mem0 OSS Quickstart](https://docs.mem0.ai/open-source/overview).
 </Note>

 ```python
@@ -202,7 +202,7 @@ By integrating LlamaIndex with Mem0, you can build a personalized agent that can
 ## Help

 - For more details on LlamaIndex, visit the [LlamaIndex documentation](https://llamahub.ai/l/memory/llama-index-memory-mem0).
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- [Mem0 Platform](https://app.mem0.ai/).
 - If you need further assistance, please feel free to reach out to us through following methods:

 <Snippet file="get-help.mdx" />
@@ -2,7 +2,7 @@
 title: Mastra
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.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.

@@ -130,7 +130,7 @@ By integrating Mastra with Mem0, you can build intelligent agents that learn and
 ## 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/).
+- [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" /> 
@@ -2,7 +2,7 @@
 title: MCP Server
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Integrating mem0 as an MCP Server in Cursor
 [mem0](https://github.com/mem0ai/mem0-mcp) is a powerful tool designed to enhance AI-driven workflows, particularly in code generation and contextual memory. In this guide, we'll walk through integrating mem0 as an **MCP (Model Context Protocol) server** within [Cursor](https://cursor.sh/), an AI-powered coding editor.
@@ -2,7 +2,7 @@
 title: MultiOn
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Build a personal browser agent that remembers user preferences and automates web tasks. It integrates Mem0 for memory management with MultiOn for executing browser actions, enabling personalized and efficient web interactions.

@@ -0,0 +1,236 @@
+---
+title: OpenAI Agents SDK
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), a lightweight framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
+
+## Overview
+
+1. Store and retrieve memories from Mem0 within OpenAI agents
+2. Multi-agent workflows with shared memory
+3. Retrieve relevant memories for past conversations
+4. Personalized responses based on user history
+
+## Prerequisites
+
+Before setting up Mem0 with OpenAI Agents SDK, ensure you have:
+
+1. Installed the required packages:
+```bash
+pip install openai-agents mem0ai
+```
+
+2. Valid API keys:
+   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys)
+   - [OpenAI API Key](https://platform.openai.com/api-keys)
+
+## Basic Integration Example
+
+The following example demonstrates how to create an OpenAI agent with Mem0 memory integration:
+
+```python
+import os
+from agents import Agent, Runner, function_tool
+from mem0 import MemoryClient
+
+# Set up environment variables
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Initialize Mem0 client
+mem0 = MemoryClient()
+
+# Define memory tools for the agent
+@function_tool
+def search_memory(query: str, user_id: str) -> str:
+    """Search through past conversations and memories"""
+    memories = mem0.search(query, user_id=user_id, limit=3)
+    if memories:
+        return "\n".join([f"- {mem['memory']}" for mem in memories])
+    return "No relevant memories found."
+
+@function_tool
+def save_memory(content: str, user_id: str) -> str:
+    """Save important information to memory"""
+    mem0.add([{"role": "user", "content": content}], user_id=user_id)
+    return "Information saved to memory."
+
+# Create agent with memory capabilities
+agent = Agent(
+    name="Personal Assistant",
+    instructions="""You are a helpful personal assistant with memory capabilities.
+    Use the search_memory tool to recall past conversations and user preferences.
+    Use the save_memory tool to store important information about the user.
+    Always personalize your responses based on available memory.""",
+    tools=[search_memory, save_memory],
+    model="gpt-4o"
+)
+
+def chat_with_agent(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with automatic memory integration.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The agent's response
+    """
+    # Run the agent (it will automatically use memory tools when needed)
+    result = Runner.run_sync(agent, user_input)
+
+    return result.final_output
+
+# Example usage
+if __name__ == "__main__":
+
+    # preferences will be saved in memory (using save_memory tool)
+    response_1 = chat_with_agent(
+        "I love Italian food and I'm planning a trip to Rome next month",
+        user_id="alice"
+    )
+    print(response_1)
+
+    # memory will be retrieved using search_memory tool to answer the user query
+    response_2 = chat_with_agent(
+        "Give me some recommendations for food",
+        user_id="alice"
+    )
+    print(response_2)
+```
+
+## Multi-Agent Workflow with Handoffs
+
+Create multiple specialized agents with proper handoffs and shared memory:
+
+```python
+from agents import Agent, Runner, handoffs, function_tool
+
+# Specialized agents
+travel_agent = Agent(
+    name="Travel Planner",
+    instructions="""You are a travel planning specialist. Use get_user_context to
+    understand the user's travel preferences and history before making recommendations.
+    After providing your response, use store_conversation to save important details.""",
+    tools=[search_memory, save_memory],
+    model="gpt-4o"
+)
+
+health_agent = Agent(
+    name="Health Advisor",
+    instructions="""You are a health and wellness advisor. Use get_user_context to
+    understand the user's health goals and dietary preferences.
+    After providing advice, use store_conversation to save relevant information.""",
+    tools=[search_memory, save_memory],
+    model="gpt-4o"
+)
+
+# Triage agent with handoffs
+triage_agent = Agent(
+    name="Personal Assistant",
+    instructions="""You are a helpful personal assistant that routes requests to specialists.
+    For travel-related questions (trips, hotels, flights, destinations), hand off to Travel Planner.
+    For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
+    For general questions, you can handle them directly using available tools.""",
+    handoffs=[travel_agent, health_agent],
+    model="gpt-4o"
+)
+
+def chat_with_handoffs(user_input: str, user_id: str) -> str:
+    """
+    Handle user input with automatic agent handoffs and memory integration.
+
+    Args:
+        user_input: The user's message
+        user_id: Unique identifier for the user
+
+    Returns:
+        The agent's response
+    """
+    # Run the triage agent (it will automatically handoff when needed)
+    result = Runner.run_sync(triage_agent, user_input)
+
+    # Store the original conversation in memory
+    conversation = [
+        {"role": "user", "content": user_input},
+        {"role": "assistant", "content": result.final_output}
+    ]
+    mem0.add(conversation, user_id=user_id)
+
+    return result.final_output
+
+# Example usage
+response = chat_with_handoffs("Plan a healthy meal for my Italy trip", user_id="alex")
+print(response)
+```
+
+## Quick Start Chat Interface
+
+Simple interactive chat with memory:
+
+```python
+def interactive_chat():
+    """Interactive chat interface with memory and handoffs"""
+    user_id = input("Enter your user ID: ") or "demo_user"
+    print(f"Chat started for user: {user_id}")
+    print("Type 'quit' to exit\n")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == 'quit':
+            break
+
+        response = chat_with_handoffs(user_input, user_id)
+        print(f"Assistant: {response}\n")
+
+if __name__ == "__main__":
+    interactive_chat()
+```
+
+## Key Features
+
+### 1. Automatic Memory Integration
+- **Tool-Based Memory**: Agents use function tools to search and save memories
+- **Conversation Storage**: All interactions are automatically stored
+- **Context Retrieval**: Agents can access relevant past conversations
+
+### 2. Multi-Agent Memory Sharing
+- **Shared Context**: Multiple agents access the same memory store
+- **Specialized Agents**: Create domain-specific agents with shared memory
+- **Seamless Handoffs**: Agents maintain context across handoffs
+
+### 3. Flexible Memory Operations
+- **Retrieve Capabilities**: Retrieve relevant memories from previous conversation
+- **User Segmentation**: Organize memories by user ID
+- **Memory Management**: Built-in tools for saving and retrieving information
+
+## Configuration Options
+
+Customize memory behavior:
+
+```python
+# Configure memory search
+memories = mem0.search(
+    query="travel preferences",
+    user_id="alex",
+    limit=5  # Number of memories to retrieve
+)
+
+# Add metadata to memories
+mem0.add(
+    messages=[{"role": "user", "content": "I prefer luxury hotels"}],
+    user_id="alex",
+    metadata={"category": "travel", "importance": "high"}
+)
+```
+
+## Help
+
+- [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)
+- [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" />
@@ -3,7 +3,7 @@ title: 'Pipecat'
 description: 'Integrate Mem0 with Pipecat for conversational memory in AI agents'
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 # Pipecat Integration

@@ -3,11 +3,11 @@ title: "Raycast Extension"
 description: "Mem0 Raycast extension for intelligent memory management"
 ---

-# Mem0
+<Snippet file="security-compliance.mdx" />

 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
+## Getting Started

 **Get your API Key**: You'll need a Mem0 API key to use this extension:

@@ -2,7 +2,7 @@
 title: Vercel AI SDK
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-provider) is a library developed by **Mem0** to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.

@@ -225,6 +225,18 @@ const memories = await getMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-x

 The `getMemories` function will return an object with two keys: `results` and `relations`, if `enable_graph` is set to `true`. Otherwise, it will return an array of objects.

+## Supported LLM Providers
+
+| Provider | Configuration Value |
+|----------|-------------------|
+| OpenAI | openai |
+| Anthropic | anthropic |
+| Gemini | gemini |
+| Google | google |
+| Mistral | mistral |
+| Groq | groq |
+
+> **Note**: You can use `google` as provider for Gemini (Google) models. They are same and internally they use `@ai-sdk/google` package.

 ## Key Features

@@ -246,8 +258,8 @@ Mem0’s Vercel AI SDK enables the creation of intelligent, context-aware applic

 ## Help

- For more details on Vercel AI SDK, visit the [Vercel AI SDK documentation](https://sdk.vercel.ai/docs/introduction).
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
+- For more details on Vercel AI SDK, visit the [Vercel AI SDK documentation](https://sdk.vercel.ai/docs/introduction)
+- [Mem0 Platform](https://app.mem0.ai/)
 - If you need further assistance, please feel free to reach out to us through following methods:

 <Snippet file="get-help.mdx" />
@@ -5,6 +5,8 @@ icon: "bolt"
 iconType: "solid"
 ---

+<Snippet file="security-compliance.mdx" />
+
 ## AsyncMemory

 The `AsyncMemory` class is a direct asynchronous interface to Mem0's in-process memory operations. Unlike the memory, which interacts with an API, `AsyncMemory` works directly with the underlying storage systems. This makes it ideal for applications where you want to embed Mem0 directly into your codebase.
@@ -5,7 +5,7 @@ icon: "pencil"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 ## Introduction to Custom Fact Extraction Prompt

@@ -4,7 +4,7 @@ icon: "pencil"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Update memory prompt is a prompt used to determine the action to be performed on the memory. 
 By customizing this prompt, you can control how the memory is updated.  
@@ -5,7 +5,7 @@ icon: "image"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.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.

@@ -4,7 +4,7 @@ icon: "code"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 can be easily integrated into chat applications to enhance conversational agents with structured memory. Mem0's APIs are designed to be compatible with OpenAI's, with the goal of making it easy to leverage Mem0 in applications you may have already built.

@@ -4,6 +4,8 @@ icon: "server"
 iconType: "solid"
 ---

+<Snippet file="security-compliance.mdx" />
+
 Mem0 provides a REST API server (written using FastAPI). Users can perform all operations through REST endpoints. The API also includes OpenAPI documentation, accessible at `/docs` when the server is running.

 <Frame caption="APIs supported by Mem0 REST API Server">
@@ -5,7 +5,7 @@ icon: "list-check"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Graph Memory is a powerful feature that allows users to create and utilize complex relationships between pieces of information.

@@ -1,11 +1,11 @@
 ---
 title: Overview
 description: 'Enhance your memory system with graph-based knowledge representation and retrieval'
-icon: "database"
+icon: "info"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 now supports **Graph Memory**.
 With Graph Memory, users can now create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. 
@@ -232,22 +232,90 @@ m = Memory.from_config(config_dict=config)
 ```
 </CodeGroup>

+### Initialize Neptune Analytics
+
+Mem0 now supports Amazon Neptune Analytics as a graph store provider. This integration allows you to use Neptune Analytics for storing and querying graph-based memories.
+
+#### Instance Setup
+
+Create an Amazon Neptune Analytics instance in your AWS account following the [AWS documentation](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/get-started.html).
+- Public connectivity is not enabled by default, and if accessing from outside a VPC, it needs to be enabled.
+- Once the Amazon Neptune Analytics instance is available, you will need the graph-identifier to connect.
+- The Neptune Analytics instance must be created using the same vector dimensions as the embedding model creates. See: https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-index.html
+
+#### Attach Credentials
+
+ Configure your AWS credentials with access to your Amazon Neptune Analytics resources by following the [Configuration and credentials precedence](https://docs.aws.amazon.com/cli/v1/userguide/cli-chap-configure.html#configure-precedence).
+- For example, add your SSH access key session token via environment variables:
+```bash
+export AWS_ACCESS_KEY_ID=your-access-key
+export AWS_SECRET_ACCESS_KEY=your-secret-key
+export AWS_SESSION_TOKEN=your-session-token
+export AWS_DEFAULT_REGION=your-region
+```
+- The IAM user or role making the request must have a policy attached that allows one of the following IAM actions in that neptune-graph:
+  - neptune-graph:ReadDataViaQuery
+  - neptune-graph:WriteDataViaQuery
+  - neptune-graph:DeleteDataViaQuery
+
+#### Usage
+
+The Neptune memory store uses AWS LangChain Python API to connect to Neptune instances.  For additional configuration options for connecting to your Amazon Neptune Analytics instance see [AWS LangChain API documentation](https://python.langchain.com/api_reference/aws/graphs/langchain_aws.graphs.neptune_graph.NeptuneAnalyticsGraph.html).
+
+<CodeGroup>
+```python Python
+from mem0 import Memory
+
+# This example must connect to a neptune-graph instance with 1536 vector dimensions specified.
+config = {
+    "embedder": {
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-large", "embedding_dims": 1536},
+    },
+    "graph_store": {
+        "provider": "neptune",
+        "config": {
+            "endpoint": "neptune-graph://<GRAPH_ID>",
+        },
+    },
+}
+
+m = Memory.from_config(config_dict=config)
+```
+</CodeGroup>
+
+#### Troubleshooting
+
+- For issues connecting to Amazon Neptune Analytics, please refer to the [Connecting to a graph guide](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/gettingStarted-connecting.html).
+
+- For issues related to authentication, refer to the [boto3 client configuration options](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html).
+
+- For more details on how to connect, configure, and use the graph_memory graph store, see the [Neptune Analytics example notebook](examples/graph-db-demo/neptune-analytics-example.ipynb).
+
 ## Graph Operations
 The Mem0's graph supports the following operations:

 ### Add Memories

 <Note>
-If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. Use `userId` in NodeSDK.
+Mem0 with Graph Memory supports both "user_id" and "agent_id" parameters. You can use either or both to organize your memories. Use "userId" and "agentId" in NodeSDK.
 </Note>

 <CodeGroup>
 ```python Python
+# Using only user_id
 m.add("I like pizza", user_id="alice")
+
+# Using both user_id and agent_id
+m.add("I like pizza", user_id="alice", agent_id="food-assistant")
 ```

 ```typescript TypeScript
+// Using only userId
 memory.add("I like pizza", { userId: "alice" });
+
+// Using both userId and agentId
+memory.add("I like pizza", { userId: "alice", agentId: "food-assistant" });
 ```

 ```json Output
@@ -260,11 +328,19 @@ memory.add("I like pizza", { userId: "alice" });

 <CodeGroup>
 ```python Python
+# Get all memories for a user
 m.get_all(user_id="alice")
+
+# Get all memories for a specific agent belonging to a user
+m.get_all(user_id="alice", agent_id="food-assistant")
 ```

 ```typescript TypeScript
+// Get all memories for a user
 memory.getAll({ userId: "alice" });
+
+// Get all memories for a specific agent belonging to a user
+memory.getAll({ userId: "alice", agentId: "food-assistant" });
 ```

 ```json Output
@@ -277,7 +353,8 @@ memory.getAll({ userId: "alice" });
            'metadata': None,
            'created_at': '2024-08-20T14:09:27.588719-07:00',
            'updated_at': None,
-            'user_id': 'alice'
+            'user_id': 'alice',
+            'agent_id': 'food-assistant'
        }
    ],
    'entities': [
@@ -295,11 +372,19 @@ memory.getAll({ userId: "alice" });

 <CodeGroup>
 ```python Python
+# Search memories for a user
 m.search("tell me my name.", user_id="alice")
+
+# Search memories for a specific agent belonging to a user
+m.search("tell me my name.", user_id="alice", agent_id="food-assistant")
 ```

 ```typescript TypeScript
+// Search memories for a user
 memory.search("tell me my name.", { userId: "alice" });
+
+// Search memories for a specific agent belonging to a user
+memory.search("tell me my name.", { userId: "alice", agentId: "food-assistant" });
 ```

 ```json Output
@@ -312,7 +397,8 @@ memory.search("tell me my name.", { userId: "alice" });
            'metadata': None,
            'created_at': '2024-08-20T14:09:27.588719-07:00',
            'updated_at': None,
-            'user_id': 'alice'
+            'user_id': 'alice',
+            'agent_id': 'food-assistant'
        }
    ],
    'entities': [
@@ -331,11 +417,19 @@ memory.search("tell me my name.", { userId: "alice" });

 <CodeGroup>
 ```python Python
+# Delete all memories for a user
 m.delete_all(user_id="alice")
+
+# Delete all memories for a specific agent belonging to a user
+m.delete_all(user_id="alice", agent_id="food-assistant")
 ```

 ```typescript TypeScript
+// Delete all memories for a user
 memory.deleteAll({ userId: "alice" });
+
+// Delete all memories for a specific agent belonging to a user
+memory.deleteAll({ userId: "alice", agentId: "food-assistant" });
 ```
 </CodeGroup>

@@ -516,6 +610,44 @@ memory.search("Who is spiderman?", { userId: "alice123" });

 > **Note:** The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.

+## Using Multiple Agents with Graph Memory
+
+
+When working with multiple agents, you can use the "agent_id" parameter to organize memories by both user and agent. This allows you to:
+
+
+1. Create agent-specific knowledge graphs
+2. Share common knowledge between agents
+3. Isolate sensitive or specialized information to specific agents
+
+### Example: Multi-Agent Setup
+
+<CodeGroup>
+```python Python
+# Add memories for different agents
+m.add("I prefer Italian cuisine", user_id="bob", agent_id="food-assistant")
+m.add("I'm allergic to peanuts", user_id="bob", agent_id="health-assistant")
+m.add("I live in Seattle", user_id="bob")  # Shared across all agents
+
+# Search within specific agent context
+food_preferences = m.search("What food do I like?", user_id="bob", agent_id="food-assistant")
+health_info = m.search("What are my allergies?", user_id="bob", agent_id="health-assistant")
+location = m.search("Where do I live?", user_id="bob")  # Searches across all agents
+```
+
+```typescript TypeScript
+// Add memories for different agents
+memory.add("I prefer Italian cuisine", { userId: "bob", agentId: "food-assistant" });
+memory.add("I'm allergic to peanuts", { userId: "bob", agentId: "health-assistant" });
+memory.add("I live in Seattle", { userId: "bob" });  // Shared across all agents
+
+// Search within specific agent context
+const foodPreferences = memory.search("What food do I like?", { userId: "bob", agentId: "food-assistant" });
+const healthInfo = memory.search("What are my allergies?", { userId: "bob", agentId: "health-assistant" });
+const location = memory.search("Where do I live?", { userId: "bob" });  // Searches across all agents
+```
+</CodeGroup>
+
 If you want to use a managed version of Mem0, please check out [Mem0](https://mem0.dev/pd). If you have any questions, please feel free to reach out to us using one of the following methods:

-<Snippet file="get-help.mdx" />
+<Snippet file="get-help.mdx" />
@@ -4,7 +4,7 @@ icon: "image"
 iconType: "solid"
 ---

-<Snippet file="paper-release.mdx" />
+<Snippet file="security-compliance.mdx" />

 Mem0 extends its capabilities beyond text by supporting multimodal data, including images. Users can seamlessly integrate images into their interactions, allowing Mem0 to extract pertinent information from visual content and enrich the memory system.

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Deshraj Yadav	c0a930a7d3	Update version to 0.1.114 (#3107 )	2025-07-04 16:28:39 -07:00
Andrew Carbonetto	05c404d8d3	Add Amazon Neptune Analytics graph_store configuration & integration (#2949 )	2025-07-04 16:26:21 -07:00
Deshraj Yadav	7484eed4b2	Fix CI issues related to missing dependency (#3096 )	2025-07-03 18:52:50 -07:00
Mingxiangyu	2c496e6376	Fix the error that occurs when VLLM is called (#3076 )	2025-07-03 14:41:10 -07:00
Jainish	a20b68fcec	Fixes: Mem0 Setup, Logging, Docs (#3080 )	2025-07-03 14:40:39 -07:00
Sakshi Srivastava	eb7c712aa6	Fix: Add missing OpenAI import in vLLM module (#3091 )	2025-07-03 14:37:20 -07:00
Saket Aryan	7476c39257	Add Gemini Model Support to Vercel AI SDK Provider (#3094 )	2025-07-03 09:54:11 -07:00
Saket Aryan	5b0f1a7cf8	feat: Add Gemini support to TypeScript SDK (#3093 )	2025-07-03 09:53:52 -07:00
Antaripa Saha	b336cdf018	Image fixes (#3089 )	2025-07-02 11:40:55 -07:00
Antaripa Saha	60e4e8a662	Google AI ADK Integration Docs (#3086 )	2025-07-02 10:23:37 -07:00
Chaithanya Kumar	6d4a78b7c7	Enhance documentation: Add group chat feature to the list of platform… (#3077 )	2025-07-02 11:13:01 +05:30
Antaripa Saha	d39a1d5541	Openai agents sdk added (#3081 )	2025-07-01 17:02:59 -07:00
Parshva Daftari	044ad4f131	Reverting the changes of pip install (#3010 )	2025-07-01 15:49:39 +05:30
Antaripa Saha	75482fdb29	Docs SOC2 and HIPAA update (#3075 )	2025-07-01 01:21:53 -07:00
Antaripa Saha	6c69599db9	Docs Update Images (#3072 )	2025-07-01 00:36:30 -07:00
Kade Shockey	b79bfb7c1e	MongoDB Vector Store misaligned strings and classes (#3064 )	2025-07-01 11:12:28 +05:30
Dev Khant	5a1083b709	Fix: Gemini embedder config and version bump -> 0.1.113 (#3070 )	2025-06-30 13:31:51 +05:30
Dev Khant	ac085db500	version bump -> 0.1.112 (#3058 )	2025-06-27 15:19:57 +05:30
Dev Khant	2cc253341c	Fix mongodb config name (#3052 )	2025-06-26 23:06:21 +05:30
Dev Khant	e3e2da6d45	Fix: Gemini Embeddings and LLM (#3050 )	2025-06-26 21:05:00 +05:30
Dev Khant	acf7a30d32	Doc: Add async_mode (#3037 )	2025-06-25 10:51:49 -07:00
Saket Aryan	a4f6751741	fix(ui-backend): resolve provider name format inconsistency in form configuration (#3041 )	2025-06-25 09:48:22 -07:00
Ryan Rozich	6f3fbd087d	fix: Fix memory categorization by updating dependencies and correcting API usage (#3005 ) Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Saket Aryan <94069182+whysosaket@users.noreply.github.com>	2025-06-25 22:02:26 +05:30
Antaripa Saha	a98842422b	doc: Broken links fixed in docs (#3034 )	2025-06-25 17:18:29 +05:30
Antaripa Saha	aaf879322c	Platform feature docs revamp (#3007 )	2025-06-25 00:57:08 -07:00
Laith Al-Saadoon	8139b5887f	fix: bedrock llm, embeddings, tools, temporary creds (#3023 )	2025-06-24 20:46:06 +05:30
Saket Aryan	b4b27f099e	Add immutable param to add method and bump version (#3022 )	2025-06-24 05:03:35 +05:30
Dev Khant	dc877fd3ba	version bump -> 0.1.111 (#3016 )	2025-06-23 21:52:03 +05:30
Akshat Jain	2bb0653e67	Add: Json Parsing to solve Hallucination Errors (#3013 )	2025-06-23 21:50:16 +05:30
Akshat Jain	eb24b92227	Add : Openmemory Local Support using New Library (#3014 ) Co-authored-by: Saket Aryan <saketaryan2002@gmail.com>	2025-06-23 20:45:46 +05:30
Akshat Jain	a5ec286fd4	Add: Openmemory Augment support (#3015 )	2025-06-23 20:45:01 +05:30
NiLAy	89499aedbe	Feature/vllm support (#2981 )	2025-06-23 13:18:38 +05:30
Akshat Jain	386d8b87ae	Fix: Migrate Gemini Embeddings (#3002 ) Co-authored-by: Dev-Khant <devkhant24@gmail.com>	2025-06-23 13:16:10 +05:30
Akshat Jain	c173ec32d0	Improve Docs: Agent Id - Mem0 OSS Graph Memory (#2969 )	2025-06-21 23:34:28 +05:30
Akshat Jain	dd6f6f7a2e	Fix: Add MCP Client Integration Guide and update installation commands (#2956 )	2025-06-20 22:09:10 +05:30
Dev Khant	b6684b96f7	version bump -> 0.1.110 (#3001 )	2025-06-20 20:30:51 +05:30
Akarsha Sehwag	1fa0f0a157	fix(opensearch): update logger warning (#2999 )	2025-06-20 20:28:51 +05:30
Saket Aryan	2754f45387	Make V2 Add as Default (#2997 )	2025-06-20 16:56:42 +05:30
Parshva Daftari	ecd4d91046	Fix failing CI pipeline (#2979 )	2025-06-20 15:19:11 +05:30
Prateek Chhikara	a5a247b161	Update client.update() method documentation in OpenAPI specification (#2990 )	2025-06-19 14:04:12 -07:00
Dev Khant	d47cb8d284	Doc: Fix example in quickstart page (#2986 )	2025-06-19 13:51:20 +05:30
Dev Khant	fa15db089d	Update Changelog (#2985 )	2025-06-19 12:32:33 +05:30
Shili Cao	d35065c887	Feature: baidu vector db integration (#2929 )	2025-06-19 11:12:12 +05:30
Prateek Chhikara	cdee6a4ff0	Enhance update method to support metadata (#2976 )	2025-06-18 10:07:18 -07:00
Dev Khant	9eb4e77c75	Fix pinecone for async memory (#2975 )	2025-06-18 01:37:45 +05:30
Akshat Jain	c700d790db	Fix Build CI Failure (#2973 )	2025-06-17 09:39:19 -07:00
Antaripa Saha	a90b572389	Memory agent powered by voice (Cartesia + Agno) (#2970 )	2025-06-17 18:54:53 +05:30
i-sun	62c330e5b3	feat(LM Studio): Add response_format param for LM Studio to config (#2502 )	2025-06-17 17:55:18 +05:30
Akshat Jain	c70dc7614b	Fix: Add Google Genai library support (#2941 )	2025-06-17 17:47:09 +05:30
Fenil Faldu	e0003247c3	feat: add AgentOps integration (#2898 )	2025-06-17 11:38:39 +05:30
Saket Aryan	888ee766c5	TS SDK - filter memories param (#2971 )	2025-06-17 10:54:35 +05:30
Saket Aryan	c7e91171a0	Added Param `output_format` in AI SDK (#2960 )	2025-06-15 06:42:56 +05:30
Dev Khant	18c870ec79	version bump -> 0.1.108 (#2958 )	2025-06-14 21:58:12 +05:30
Dev Khant	3e5f68ee90	Add logger in Opensearch (#2957 )	2025-06-14 21:55:22 +05:30
Fabian Valle	a0cd4065d9	+MongoDB Vector Support (#2367 ) Co-authored-by: Divya Gupta <divya.gupta@mongodb.com>	2025-06-14 17:57:06 +05:30
John Lockwood	7c0c4a03c4	Feat/add python version test envs (#2774 )	2025-06-14 17:43:16 +05:30
John Lockwood	a8ace18607	Fix/pin pinecone issue #2772 (#2773 )	2025-06-14 17:38:32 +05:30