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docs: remove OpenMemory references from docs, README, and issue templates (#4520)

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This commit is contained in:
Saket Aryan
2026-03-25 04:24:13 +05:30
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parent 5431badfd4
commit 2868bfe749
12 changed files with 13 additions and 479 deletions
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.github/ISSUE_TEMPLATE/bug_report.yml
-1
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@@ -14,7 +14,6 @@ body:
- Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
- Graph Memory (Neo4j, Memgraph, etc.)
- Ollama / Local Models
- OpenMemory (MCP Server)
- OpenClaw
- REST API
- Other
.github/ISSUE_TEMPLATE/feature_request.yml
-1
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@@ -14,7 +14,6 @@ body:
- Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
- Graph Memory (Neo4j, Memgraph, etc.)
- Ollama / Local Models
- OpenMemory (MCP Server)
- OpenClaw
- REST API
- Benchmarks / Evals
.github/advanced-issue-labeler.yml
-2
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@@ -12,8 +12,6 @@ component:
matcher: "Graph Memory"
- label: "ollama"
matcher: "Ollama"
- label: "openmemory"
matcher: "OpenMemory"
- label: "openclaw"
matcher: "OpenClaw"
- label: "rest-api"
README.md
-2
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@@ -15,8 +15,6 @@
<a href="https://mem0.dev/DiG">Join Discord</a>
·
<a href="https://mem0.dev/demo">Demo</a>
·
<a href="https://mem0.dev/openmemory">OpenMemory</a>
</p>
<p align="center">
docs/docs.json
+12 -15
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@@ -296,21 +296,6 @@
}
]
},
{
"tab": "OpenMemory",
"groups": [
{
"group": "Overview & Quickstart",
"icon": "square-terminal",
"pages": [
"openmemory/overview",
"vibecoding",
"openmemory/quickstart",
"openmemory/integrations"
]
}
]
},
{
"tab": "Cookbooks",
"groups": [
@@ -1056,6 +1041,18 @@
{
"source": "/cookbooks/customer-support-agent",
"destination": "/cookbooks/operations/support-inbox"
},
{
"source": "/openmemory/overview",
"destination": "/introduction"
},
{
"source": "/openmemory/quickstart",
"destination": "/introduction"
},
{
"source": "/openmemory/integrations",
"destination": "/introduction"
}
]
}
docs/introduction.mdx
+1 -26
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@@ -31,7 +31,7 @@ mode: "custom"
</h2>
</div>
<div className="grid gap-6 sm:grid-cols-2 lg:grid-cols-3">
<div className="grid gap-6 sm:grid-cols-2">
<a
href="/platform/overview"
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
@@ -84,31 +84,6 @@ mode: "custom"
</div>
</a>
<a
href="/openmemory/overview"
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
>
<img
className="block dark:hidden aspect-[4/3] w-full object-cover"
src="/images/docs thumbnails/light/mem0_openmemory.png"
alt="OpenMemory thumbnail"
style={{pointerEvents: "none"}}
/>
<img
className="hidden dark:block aspect-[4/3] w-full object-cover"
src="/images/docs thumbnails/dark/mem0_openmemory.png"
alt="OpenMemory thumbnail"
style={{pointerEvents: "none"}}
/>
<div className="flex flex-1 flex-col gap-3 px-5 pb-6 pt-5 text-left">
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
OpenMemory
</h3>
<p className="text-sm text-gray-600 dark:text-zinc-400">
Workspace-based memory for teams collaborating across agents and projects.
</p>
</div>
</a>
</div>
</section>
docs/llms.txt
-1
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@@ -213,4 +213,3 @@ Key differentiators:
- [FAQs](https://docs.mem0.ai/platform/faqs): Frequently asked questions about Mem0's Platform capabilities and implementation details
- [Changelog](https://docs.mem0.ai/changelog): Detailed product updates and version history for tracking new features and improvements
- [Contributing Guide](https://docs.mem0.ai/contributing/development): Guidelines for contributing to Mem0's open-source development
- [OpenMemory](https://docs.mem0.ai/openmemory/overview): Open-source memory infrastructure for research and experimentation
docs/openmemory/integrations.mdx
-56
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@@ -1,56 +0,0 @@
---
title: MCP Client Integration Guide
description: "Connect MCP-compatible clients to a locally running OpenMemory server for seamless memory integration."
icon: "plug"
iconType: "solid"
---
## Connecting an MCP Client
Once your OpenMemory server is running locally, you can connect any compatible MCP client to your personal memory stream. This enables a seamless memory layer integration for AI tools and agents.
Ensure the following environment variables are correctly set in your configuration files:
**In `/ui/.env`:**
```env
NEXT_PUBLIC_API_URL=http://localhost:8765
NEXT_PUBLIC_USER_ID=<user-id>
```
**In `/api/.env`:**
```env
OPENAI_API_KEY=sk-xxx
USER=<user-id>
```
These values define where your MCP server is running and which user's memory is accessed.
### MCP Client Setup
Use the following one-step command to configure OpenMemory Local MCP to a client. The general command format is as follows:
```bash
npx @openmemory/install local http://localhost:8765/mcp/<client-name>/sse/<user-id> --client <client-name>
```
Replace `<client-name>` with the desired client name and `<user-id>` with the value specified in your environment variables.
### Example Commands for Supported Clients
| Client | Command |
|-------------|---------|
| Claude | `npx install-mcp http://localhost:8765/mcp/claude/sse/<user-id> --client claude` |
| Cursor | `npx install-mcp http://localhost:8765/mcp/cursor/sse/<user-id> --client cursor` |
| Cline | `npx install-mcp http://localhost:8765/mcp/cline/sse/<user-id> --client cline` |
| RooCline | `npx install-mcp http://localhost:8765/mcp/roocline/sse/<user-id> --client roocline` |
| Windsurf | `npx install-mcp http://localhost:8765/mcp/windsurf/sse/<user-id> --client windsurf` |
| Witsy | `npx install-mcp http://localhost:8765/mcp/witsy/sse/<user-id> --client witsy` |
| Enconvo | `npx install-mcp http://localhost:8765/mcp/enconvo/sse/<user-id> --client enconvo` |
| Augment | `npx install-mcp http://localhost:8765/mcp/augment/sse/<user-id> --client augment` |
| Claude Code | `claude mcp add -s user -t sse OpenMemory http://localhost:8765/mcp/openmemory/sse/<user-id>` |
### What This Does
Running one of the above commands registers the specified MCP client and connects it to your OpenMemory server. This enables the client to stream and store contextual memory for the provided user ID.
The connection status and memory activity can be monitored via the OpenMemory UI at [http://localhost:3000](http://localhost:3000).
docs/openmemory/overview.mdx
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@@ -1,124 +0,0 @@
---
title: Overview
description: "Overview of OpenMemory, a local and hosted memory infrastructure powered by Mem0 with MCP server support."
icon: "info"
iconType: "solid"
---
## Hosted OpenMemory MCP Now Available
#### Sign Up Now - [app.openmemory.dev](https://app.openmemory.dev)
Everything you love about OpenMemory MCP but with zero setup.
- Works with all MCP-compatible tools (Claude Desktop, Cursor, etc.)
- Same standard memory operations: `add_memories`, `search_memory`, etc.
- One-click provisioning, no Docker required
- Powered by Mem0
Add shared, persistent, low-friction memory to your MCP-compatible clients in seconds.
### Get Started Now
Sign up and get your access key at [app.openmemory.dev](https://app.openmemory.dev).
Example installation: `npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key`
OpenMemory is a local memory infrastructure powered by Mem0 that lets you carry your memory across any AI app. It provides a unified memory layer that stays with you, enabling agents and assistants to remember what matters across applications.
<img src="https://github.com/user-attachments/assets/3c701757-ad82-4afa-bfbe-e049c2b4320b" alt="OpenMemory UI" />
## What is the OpenMemory MCP Server
The OpenMemory MCP Server is a private, local-first memory server that creates a shared, persistent memory layer for your MCP-compatible tools. It runs entirely on your machine, enabling seamless context handoff across tools. Whether you're switching between development, planning, or debugging environments, your AI assistants can access relevant memory without needing repeated instructions.
The OpenMemory MCP Server ensures all memory stays local, structured, and under your control with no cloud sync or external storage.
## OpenMemory Easy Setup
### Prerequisites
- Docker
- OpenAI API Key
You can quickly run OpenMemory by running the following command:
```bash
curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
```
You should set the `OPENAI_API_KEY` as a global environment variable:
```bash
export OPENAI_API_KEY=your_api_key
```
You can also set the `OPENAI_API_KEY` as a parameter to the script:
```bash
curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | OPENAI_API_KEY=your_api_key bash
```
This will start the OpenMemory server and the OpenMemory UI. Deleting the container will lead to the deletion of the memory store. We suggest you follow the instructions [here](/openmemory/quickstart#setting-up-openmemory) to set up OpenMemory on your local machine with a more persistent memory store.
## How the OpenMemory MCP Server Works
Built around the Model Context Protocol (MCP), the OpenMemory MCP Server exposes a standardized set of memory tools:
- `add_memories`: Store new memory objects
- `search_memory`: Retrieve relevant memories
- `list_memories`: View all stored memory
- `delete_all_memories`: Clear memory entirely
Any MCP-compatible tool can connect to the server and use these APIs to persist and access memory.
## What It Enables
### Cross-Client Memory Access
Store context in Cursor and retrieve it later in Claude or Windsurf without repeating yourself.
### Fully Local Memory Store
All memory is stored on your machine. Nothing goes to the cloud. You maintain full ownership and control.
### Unified Memory UI
The built-in OpenMemory dashboard provides a central view of everything stored. Add, browse, delete, and control memory access to clients directly from the dashboard.
## Supported Clients
The OpenMemory MCP Server is compatible with any client that supports the Model Context Protocol. This includes:
- Cursor
- Claude Desktop
- Windsurf
- Cline
- And more
As more AI systems adopt MCP, your private memory becomes more valuable.
## Real-World Examples
### Scenario 1: Cross-Tool Project Flow
Define technical requirements of a project in Claude Desktop. Build in Cursor. Debug issues in Windsurf - all with shared context passed through OpenMemory.
### Scenario 2: Preferences That Persist
Set your preferred code style or tone in one tool. When you switch to another MCP client, it can access those same preferences without redefining them.
### Scenario 3: Project Knowledge
Save important project details once, then access them from any compatible AI tool - no more repetitive explanations.
## Conclusion
The OpenMemory MCP Server brings memory to MCP-compatible tools without giving up control or privacy. It solves a foundational limitation in modern LLM workflows: the loss of context across tools, sessions, and environments.
By standardizing memory operations and keeping all data local, it reduces token overhead, improves performance, and unlocks more intelligent interactions across the growing ecosystem of AI assistants.
This is just the beginning. The MCP server is the first core layer in the OpenMemory platform, a broader effort to make memory portable, private, and interoperable across AI systems.
## Getting Started Today
- Repository: [GitHub](https://github.com/mem0ai/mem0/tree/main/openmemory)
- Join our community: [Discord](https://discord.gg/6PzXDgEjG5)
With OpenMemory, your AI memories stay private, portable, and under your control, exactly where they belong.
OpenMemory: Your memories, your control.
## Contributing
OpenMemory is open source and we welcome contributions. Please see the [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/openmemory/CONTRIBUTING.md) file for more information.
docs/openmemory/quickstart.mdx
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@@ -1,197 +0,0 @@
---
title: Quickstart
description: "Get started with hosted or self-hosted OpenMemory MCP, including API key setup and client installation."
icon: "terminal"
iconType: "solid"
---
## Hosted OpenMemory MCP Now Available
#### Sign Up Now - [app.openmemory.dev](https://app.openmemory.dev)
Everything you love about OpenMemory MCP but with zero setup.
- Works with all MCP-compatible tools (Claude Desktop, Cursor, etc.)
- Same standard memory operations: `add_memories`, `search_memory`, etc.
- One-click provisioning, no Docker required
- Powered by Mem0
Add shared, persistent, low-friction memory to your MCP-compatible clients in seconds.
### Get Started Now
Sign up and get your access key at [app.openmemory.dev](https://app.openmemory.dev).
Example installation: `npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key`
## Getting Started with Hosted OpenMemory
The fastest way to get started is with our hosted version - no setup required.
### 1. Get Your API Key
Visit [app.openmemory.dev](https://app.openmemory.dev) to sign up and get your `OPENMEMORY_API_KEY`.
### 2. Install and Connect to Your Preferred Client
Example commands (replace `your-key` with your actual API key):
**For Claude Desktop:**
```bash
npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key
```
**For Cursor:**
```bash
npx @openmemory/install --client cursor --env OPENMEMORY_API_KEY=your-key
```
**For Windsurf:**
```bash
npx @openmemory/install --client windsurf --env OPENMEMORY_API_KEY=your-key
```
That's it! Your AI client now has persistent memory across sessions.
## Local Setup (Self-Hosted)
Prefer to run OpenMemory locally? Follow the instructions below for a self-hosted setup.
## OpenMemory Easy Setup
### Prerequisites
- Docker
- OpenAI API Key
You can quickly run OpenMemory by running the following command:
```bash
curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
```
You should set the `OPENAI_API_KEY` as a global environment variable:
```bash
export OPENAI_API_KEY=your_api_key
```
You can also set the `OPENAI_API_KEY` as a parameter to the script:
```bash
curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | OPENAI_API_KEY=your_api_key bash
```
This will start the OpenMemory server and the OpenMemory UI. Deleting the container will lead to the deletion of the memory store. We suggest you follow the instructions below to set up OpenMemory on your local machine with a more persistent memory store.
## Setting Up OpenMemory
Getting started with OpenMemory is straightforward and takes just a few minutes to set up on your local machine. Follow these steps:
### 1. Clone the Repository
```bash
# Clone the repository
git clone https://github.com/mem0ai/mem0.git
cd mem0/openmemory
```
### 2. Set Up Environment Variables
Before running the project, you need to configure environment variables for both the API and the UI.
You can do this in one of the following ways:
- **Manually:** Create a `.env` file in each of the following directories:
- `/api/.env`
- `/ui/.env`
- **Using `.env.example` files:** Copy and rename the example files:
```bash
cp api/.env.example api/.env
cp ui/.env.example ui/.env
```
- **Using Makefile** (if supported): Run:
```bash
make env
```
#### Example `/api/.env`
```bash
OPENAI_API_KEY=sk-xxx
USER=<user-id> # The User ID you want to associate the memories with
```
#### LLM Configuration (optional)
By default, OpenMemory uses OpenAI (`gpt-4o-mini`) for the LLM and embedder. You can configure a different provider by adding these variables to `/api/.env`:
| Variable | Description | Default |
|---|---|---|
| `LLM_PROVIDER` | LLM provider (`openai`, `ollama`, `anthropic`, `groq`, `together`, `deepseek`, etc.) | `openai` |
| `LLM_MODEL` | Model name for the LLM provider | `gpt-4o-mini` (OpenAI) / `llama3.1:latest` (Ollama) |
| `LLM_API_KEY` | API key for the LLM provider | `OPENAI_API_KEY` env var |
| `LLM_BASE_URL` | Custom base URL for the LLM API | Provider default |
| `OLLAMA_BASE_URL` | Ollama-specific base URL (takes precedence over `LLM_BASE_URL` for Ollama) | `http://localhost:11434` |
| `EMBEDDER_PROVIDER` | Embedder provider (defaults to `ollama` when LLM is Ollama, otherwise `openai`) | `openai` |
| `EMBEDDER_MODEL` | Model name for the embedder | `text-embedding-3-small` (OpenAI) / `nomic-embed-text` (Ollama) |
| `EMBEDDER_API_KEY` | API key for the embedder provider | `OPENAI_API_KEY` env var |
| `EMBEDDER_BASE_URL` | Custom base URL for the embedder API | Provider default |
**Example: Using Ollama (fully local)**
```bash
LLM_PROVIDER=ollama
LLM_MODEL=llama3.1:latest
EMBEDDER_PROVIDER=ollama
EMBEDDER_MODEL=nomic-embed-text
OLLAMA_BASE_URL=http://localhost:11434
```
**Example: Using Anthropic**
```bash
LLM_PROVIDER=anthropic
LLM_MODEL=claude-sonnet-4-20250514
LLM_API_KEY=sk-ant-xxx
```
#### Example `/ui/.env`
```bash
NEXT_PUBLIC_API_URL=http://localhost:8765
NEXT_PUBLIC_USER_ID=<user-id> # Same as the user ID for environment variable in api
```
### 3. Build and Run the Project
You can run the project using the following two commands:
```bash
make build # Builds the MCP server and UI
make up # Runs OpenMemory MCP server and UI
```
After running these commands, you will have:
- OpenMemory MCP server running at http://localhost:8765 (API documentation available at http://localhost:8765/docs)
- OpenMemory UI running at http://localhost:3000
#### UI Not Working on http://localhost:3000?
If the UI does not start properly on http://localhost:3000, try running it manually:
```bash
cd ui
pnpm install
pnpm dev
```
You can configure the MCP client using the following command (replace `username` with your username):
```bash
npx @openmemory/install local "http://localhost:8765/mcp/cursor/sse/username" --client cursor
```
**For Claude Code (CLI):**
```bash
claude mcp add -s user -t sse OpenMemory "http://localhost:8765/mcp/openmemory/sse/username"
```
The OpenMemory dashboard will be available at http://localhost:3000. From here, you can view and manage your memories and check connection status with your MCP clients.
Once set up, OpenMemory runs locally on your machine, ensuring all your AI memories remain private and secure while being accessible across any compatible MCP client.
## Getting Started Today
GitHub Repository: https://github.com/mem0ai/mem0/tree/main/openmemory
docs/vibecoding.mdx
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@@ -30,58 +30,10 @@ npx skills add https://github.com/mem0ai/mem0 --skill mem0
Works with Claude Code, Cursor, Windsurf, and any assistant that supports skills. Once installed, your assistant understands Mem0's full API, framework integrations, and common patterns.
## Claude Code Plugin
The [OpenMemory plugin](https://github.com/mem0ai/claude-code-plugin) gives Claude Code **persistent memory across sessions, projects, and teams** — automatically.
<Steps>
<Step title="Get your API key">
Sign up at [app.openmemory.dev](https://app.openmemory.dev).
</Step>
<Step title="Install the plugin">
```bash
/plugin add mem0ai/claude-code-plugin
```
</Step>
<Step title="Set your environment variable">
```bash
export OPENMEMORY_API_KEY="your-key-here"
```
</Step>
<Step title="Start coding">
The plugin activates automatically. It captures decisions at session end, preserves context during compaction, and retrieves relevant memories at session start.
</Step>
</Steps>
## MCP Server Setup
Connect Cursor, Windsurf, Claude Desktop, or any MCP-compatible client to Mem0.
<Tabs>
<Tab title="OpenMemory Hosted">
Sign up at [app.openmemory.dev](https://app.openmemory.dev), then pick your client:
<CodeGroup>
```bash Claude Desktop
npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key
```
```bash Cursor
npx @openmemory/install --client cursor --env OPENMEMORY_API_KEY=your-key
```
```bash Windsurf
npx @openmemory/install --client windsurf --env OPENMEMORY_API_KEY=your-key
```
</CodeGroup>
For full setup options, see [OpenMemory Quickstart](/openmemory/quickstart).
</Tab>
<Tab title="Mem0 Platform MCP">
Get your API key from [app.mem0.ai](https://app.mem0.ai), then add to your MCP config:
```json
@@ -100,8 +52,6 @@ Get your API key from [app.mem0.ai](https://app.mem0.ai), then add to your MCP c
```
For Docker, Smithery, and advanced options, see [Mem0 MCP Setup](/platform/mem0-mcp).
</Tab>
</Tabs>
## Universal Starter Prompt
skills/mem0/scripts/mem0_doc_search.py
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@@ -62,10 +62,6 @@ SECTION_MAP = {
"/open-source/features/rest-api",
"/open-source/configure-components",
],
"openmemory": [
"/openmemory/overview",
"/openmemory/quickstart",
],
"sdks": [
"/sdks/python",
"/sdks/js",