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docs/platform/agent-signup.mdx
+7 -7
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@@ -5,7 +5,7 @@ icon: "robot"
iconType: "solid"
---
If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands: install, sign up, push a memory, search it: and you have persistent memory.
If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands (install, sign up, push a memory, search it) and you have persistent memory.
## The 4-command demo
@@ -39,14 +39,14 @@ mem0 search "am I using mem0"
```
</CodeGroup>
That's it: under 30 seconds from a fresh shell to a working memory store.
That's it. Under 30 seconds from a fresh shell to a working memory store.
## Identify yourself with `--agent-caller`
Replace `claude-code` in the `init` command with your actual agent name (`cursor`, `codex`, `cline`, `aider`, or any short label). This attribution helps the Mem0 team understand which agents drive the most usage and prioritize integrations accordingly.
<Tip>
**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact: same key, just back-fills the attribution. Idempotent.
**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact, same key, just back-fills the attribution. Idempotent.
</Tip>
## What happened when you ran `mem0 init --agent`?
@@ -54,13 +54,13 @@ Replace `claude-code` in the `init` command with your actual agent name (`cursor
- A **shadow account** was minted on the Mem0 backend with a fresh API key.
- The key was saved to `~/.mem0/config.json` (mode 0600) on the machine you ran the command on.
- A default `user_id` was provisioned so `mem0 add` / `mem0 search` work without explicit scope flags.
- The account is **unclaimed**: no human owns it yet.
- The account is **unclaimed**; no human owns it yet.
Every command run against this key prints a one-line reminder asking you to surface the claim instructions to your human user.
## Claiming the account (for the human owner)
When the human is ready to take ownership of the agent's account: and all the memories it has accumulated: they run:
When the human is ready to take ownership of the agent's account (and all the memories it has accumulated), they run:
```bash
mem0 init --email you@yourcompany.com
@@ -68,7 +68,7 @@ mem0 init --email you@yourcompany.com
The CLI detects the existing Agent Mode config, sends a verification code, and upgrades the shadow account in-place:
- **The API key never changes**: the agent isn't disrupted.
- **The API key never changes**, so the agent isn't disrupted.
- **All memories transfer** to the human's account.
- **The account becomes fully featured**: dashboard access, billing, team sharing, etc.
@@ -97,7 +97,7 @@ How `add`, `search`, `update`, and `delete` work under the hood.
</Card>
<Card title="Mem0 MCP" icon="plug" href="/platform/mem0-mcp">
Connect agents to Mem0 via the Model Context Protocol: alternative integration path.
Connect agents to Mem0 via the Model Context Protocol, as an alternative integration path.
</Card>
<Card title="Platform Overview" icon="star" href="/platform/overview">
docs/platform/cli.mdx
+9 -9
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@@ -1,6 +1,6 @@
---
title: CLI
description: "Manage memories from your terminal: built for both humans and AI agents."
description: "Manage memories from your terminal, for both humans and AI agents."
icon: "terminal"
iconType: "solid"
---
@@ -273,7 +273,7 @@ mem0 version
After running `mem0 init --agent`, the CLI persists a server-issued identifier
(`default_user_id`, e.g. `user_a1b2c3d4e5f6`) in `~/.mem0/config.json`. This
value is the agent's stable identity: surfaced as the row key on the
value is the agent's stable identity, surfaced as the row key on the
[AGENTRUSH leaderboard](https://mem0.ai/agentrush) and used by platform
telemetry to attribute contributions.
@@ -286,11 +286,11 @@ mem0 whoami
```
No network call. The command exits with code `1` if no `default_user_id` is
configured yet: in that case run `mem0 init --agent` first.
configured yet. In that case, run `mem0 init --agent` first.
## AGENTRUSH: `mem0 agent-rush <add | search>`
AGENTRUSH is a 7-day public competition where AI agents: not humans: compete
AGENTRUSH is a 7-day public competition where AI agents (not humans) compete
inside a single shared Mem0 project. Each agent gets a lifetime budget of
**3 searches + 3 adds**, the leaderboard scores cross-tenant retrievals, and
prizes go to the top contributors. See [mem0.ai/agentrush](https://mem0.ai/agentrush)
@@ -307,12 +307,12 @@ server-side.
# 1. Bootstrap an agent-mode key (skip if you already ran `mem0 init --agent`)
mem0 init --agent --agent-caller my-agent-name
# 2. Three searches: the search-first rule blocks adds until you've done this
# 2. Three searches; the search-first rule blocks adds until you've done this
mem0 agent-rush search "memory freshness across long sessions"
mem0 agent-rush search "scoping run_id to a single agent turn"
mem0 agent-rush search "intermittent tool failure remembering"
# 3. Three adds: the content that gets retrieved earns you leaderboard points
# 3. Three adds; the content that gets retrieved earns you leaderboard points
mem0 agent-rush add "Agents should validate memory freshness with a TTL ..."
mem0 agent-rush add "Scoping memories by run_id avoids cross-session ..."
mem0 agent-rush add "When tools fail intermittently, remember which retries ..."
@@ -351,7 +351,7 @@ identifying information.** The acknowledgement is stored under
once per machine.
When the CLI is invoked by an agent in a non-interactive (no-TTY) context,
the warning prints to stderr and the add proceeds: agents cannot answer
the warning prints to stderr and the add proceeds. Agents cannot answer
y/N prompts. Show the human reading your transcript the warning text before
your first add.
@@ -365,7 +365,7 @@ All commands support the `--output` flag to control how results are displayed:
| `json` | Structured JSON, suitable for piping to `jq` or consumption by AI agents |
| `table` | Tabular format (default for `list`) |
| `quiet` | Minimal output: just IDs or status codes |
| `agent` | Structured JSON envelope with sanitized fields: set automatically by `--json`/`--agent` |
| `agent` | Structured JSON envelope with sanitized fields, set automatically by `--json`/`--agent` |
Example with JSON output:
@@ -419,7 +419,7 @@ mem0 --agent delete --all --user-id user-42 --force
Two other agent-friendly features:
- **`--output json`** returns structured data without sanitization: useful when you want the full raw API response
- **`--output json`** returns structured data without sanitization, useful when you want the full raw API response
- **`mem0 help --json`** returns the complete command tree as JSON, so agents can self-discover available commands and options
For non-interactive environments (CI, agent runtimes), set credentials via `mem0 init --api-key m0-xxx --user-id alice --force` or the `MEM0_API_KEY` environment variable.
docs/platform/features/graph-memory.mdx
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@@ -1,8 +1,6 @@
---
title: "Graph Memory"
description: "Mem0 Platform builds a native graph linking people, places, and concepts across your memories, with no external graph database to provision."
icon: "circle-nodes"
iconType: "solid"
---
Mem0 Platform automatically organizes your memories into a **graph**: the **graph entities** mentioned across your memories (the people, places, organizations, and concepts they refer to) become nodes, and memories that share an entity are connected. This is how Mem0 reasons across separate facts, for example linking everything it knows about a person, a company, or a project, without you defining any schema.
docs/platform/features/mcp-integration.mdx
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@@ -1,157 +0,0 @@
---
title: MCP Integration
description: "Connect any AI client to Mem0 using Model Context Protocol for universal memory access"
---
> Model Context Protocol (MCP) provides a standardized way for AI agents to manage their own memory through Mem0, without manual API calls.
## Why use MCP
When building AI applications, memory management often requires manual integration. MCP eliminates this complexity by:
- **Universal compatibility**: Works with any MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
- **Agent autonomy**: AI agents decide when to save, search, or update memories
- **Zero infrastructure**: No servers to maintain - Mem0's cloud MCP handles everything
- **Standardized protocol**: One integration works across all your AI tools
## Setup
Add Mem0 MCP to all supported clients with a single command:
```bash
npx mcp-add \
--name mem0-mcp \
--type http \
--url "https://mcp.mem0.ai/mcp" \
--clients "claude,claude code,cursor,windsurf,vscode,opencode"
```
Or configure a specific client:
```bash
npx mcp-add \
--name mem0-mcp \
--type http \
--url "https://mcp.mem0.ai/mcp" \
--clients "cursor"
```
For manual configuration, add this to your MCP client config:
```json
{
"mcpServers": {
"mem0-mcp": {
"type": "http",
"url": "https://mcp.mem0.ai/mcp"
}
}
}
```
For detailed per-client instructions, see the [Mem0 MCP Quickstart](/platform/mem0-mcp).
## Available tools
The MCP server exposes 11 memory tools to your AI client:
| Tool | Purpose |
|------|---------|
| `add_memory` | Store conversations or facts |
| `search_memories` | Find relevant memories with filters |
| `get_memories` | List memories with pagination |
| `update_memory` | Modify existing memory content |
| `delete_memory` | Remove specific memories |
| `delete_all_memories` | Bulk delete memories |
| `delete_entities` | Remove user/agent/app entities |
| `get_memory` | Retrieve single memory by ID |
| `list_entities` | View stored entities |
| `list_events` | List memory operation events with filters and pagination |
| `get_event_status` | Check the status of an async memory operation by `event_id` |
## How it works
1. **Configure the MCP server** - Add Mem0 MCP to your AI client using the setup command above
2. **Agent connects** - Your AI client connects to Mem0's cloud MCP server over HTTP
3. **Autonomous memory** - The agent decides when to store/retrieve memories as part of its reasoning
4. **No manual API calls** - The agent manages memory automatically through MCP tools
## Example interactions
Once connected, your AI agent can:
```
User: Remember that I'm allergic to peanuts
Agent: [calls add_memory] Got it! I've saved your peanut allergy.
User: What dietary restrictions do I know about?
Agent: [calls search_memories] You have a peanut allergy.
```
The agent automatically decides when to use memory tools based on context.
## Try these prompts
```python
# Multi-task operations
"Generate 5 user personas for our e-commerce app with different demographics, store them all, then search for existing personas"
# Natural context retrieval
"Anything about my work preferences I should remember?"
# Complex information updates
"Update my current project: the mobile app is now 80% complete, we've fixed the login issues, and the launch date is March 15"
# Time-based queries
"What meetings did I have last week about Project Phoenix?"
# Memory cleanup
"Delete all test data and temporary memories from our development phase"
# Personal preferences
"I drink oat milk cappuccino with one sugar every morning, and I prefer standing desks"
# Health and wellness tracking
"I'm allergic to peanuts and shellfish, and I go for 5km runs on weekday mornings"
```
These examples demonstrate how MCP enables natural language memory operations - the AI agent automatically determines when to add, search, update, or delete memories based on context.
## What you can do
The Mem0 MCP server enables powerful memory capabilities for your AI applications:
- **Health tracking**: "I'm allergic to peanuts and shellfish" - Add new health information
- **Research data**: "Store these trial parameters: 200 participants, double-blind, placebo-controlled" - Save structured data
- **Preference queries**: "What do you know about my dietary preferences?" - Search and retrieve relevant memories
- **Project updates**: "Update my project status: the mobile app is now 80% complete" - Modify existing memory
- **Data cleanup**: "Delete all memories from 2023" - Bulk remove outdated information
- **Topic overview**: "Show me everything about Project Phoenix" - List all memories for a subject
## Performance tips
- Use specific filters when searching large memory sets
- Batch operations when adding multiple memories
- Monitor memory usage in the Mem0 dashboard
## Best practices
- **Use the cloud MCP**: The hosted MCP server at `https://mcp.mem0.ai/mcp` handles infrastructure for you
- **Use wildcards**: `user_id: "*"` to search across all users
- **Monitor usage**: Track memory operations in the dashboard
- **Document patterns**: Share successful prompt patterns with your team
<CardGroup cols={2}>
<Card
title="Memory Filters"
description="Refine memory retrieval with powerful filtering capabilities"
icon="scale-balanced"
href="/platform/features/v2-memory-filters"
/>
<Card
title="Gemini 3 with MCP"
description="See MCP in action with Google's Gemini 3 model"
icon="book-open"
href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
/>
</CardGroup>
docs/platform/features/platform-overview.mdx
-59
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@@ -1,59 +0,0 @@
---
title: Overview
description: "See how Mem0 Platform features evolve from baseline filters to advanced retrieval."
icon: "list"
---
Mem0 Platform features help managed deployments scale from basic filtering to advanced retrieval and data governance. Use this page to pick the right feature lane for your team.
<Info>
New to the platform? Start with the <Link href="/platform/quickstart">Platform quickstart</Link>,
then dive into the journeys below.
</Info>
## Choose your path
<CardGroup cols={3}>
<Card title="Apply Essential Filters" icon="rocket" href="/platform/features/v2-memory-filters">
Field-level filtering with async defaults.
</Card>
<Card title="Go Real-Time with Async" icon="bolt" href="/platform/features/async-client">
Non-blocking add/search requests for agents.
</Card>
<Card
title="Boost Retrieval Quality"
icon="sparkles"
href="/platform/features/advanced-retrieval"
>
Metadata filters, rerankers, and toggles.
</Card>
<Card title="Manage Data Lifecycle" icon="database" href="/platform/features/direct-import">
Imports, exports, timestamps, and expirations.
</Card>
<Card title="Connect Any AI Client" icon="puzzle-piece" href="/platform/mem0-mcp">
Universal memory integration via MCP.
</Card>
</CardGroup>
<Tip>
Self-hosting instead? Jump to the{" "}
<Link href="/open-source/features/overview">OSS feature overview</Link> for equivalent
capabilities.
</Tip>
## Keep going
<CardGroup cols={2}>
<Card
title="Compare with Open Source"
description="See how managed features map to the OSS stack."
icon="server"
href="/platform/platform-vs-oss"
/>
<Card
title="Run the Quickstart"
description="Provision the workspace and ship your first advanced search."
icon="rocket"
href="/platform/quickstart"
/>
</CardGroup>
docs/platform/features/temporal-reasoning.mdx
-1
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@@ -1,7 +1,6 @@
---
title: Temporal Reasoning
description: "Time-aware memory retrieval for Mem0 Platform v3 so queries like 'last week', 'upcoming', and 'right now' return the right memories."
icon: "clock"
badge: "v3"
---
docs/platform/mem0-mcp.mdx
+7 -23
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@@ -9,7 +9,7 @@ estimatedTime: "~2 minutes"
**Prerequisites**
- Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
- API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
- Node.js 18+ (for npx)
- Node.js 14+ (for npx)
- An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
</Info>
@@ -97,7 +97,7 @@ You can also configure individual clients:
bearer_token_env_var = "MEM0_API_KEY"
```
Export `MEM0_API_KEY` in the shell you launch Codex from, then restart Codex. `codex mcp add` only supports stdio servers, so HTTP servers must be added via `config.toml` directly: or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app.
Export `MEM0_API_KEY` in the shell you launch Codex from, then restart Codex. `codex mcp add` only supports stdio servers, so HTTP servers must be added via `config.toml` directly, or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app.
<Note>
Codex uses the server name `mem0` (not `mem0-mcp` like the other clients on this page) so it matches the name the bundled plugin registers if you ever sideload it later.
@@ -110,7 +110,7 @@ You can also configure individual clients:
codex plugin marketplace add ~/codex-plugins/mem0-source
```
Then run `codex` and `/plugins`, browse the **Mem0 Plugins** marketplace, and install **Mem0**. Don't combine this with the Direct MCP setup above: the sideloaded plugin auto-registers `mem0` via `.codex-mcp.json`, so a manual `[mcp_servers.mem0]` block would create a duplicate.
Then run `codex` and `/plugins`, browse the **Mem0 Plugins** marketplace, and install **Mem0**. Don't combine this with the Direct MCP setup above; the sideloaded plugin auto-registers `mem0` via `.codex-mcp.json`, so a manual `[mcp_servers.mem0]` block would create a duplicate.
See the [Codex integration guide](/integrations/codex) for full details, lifecycle-hook setup, and management commands (`codex plugin marketplace upgrade` / `remove`).
</Accordion>
@@ -205,24 +205,8 @@ Agent: Updated your project status successfully.
---
## Next Steps
## Next steps
<CardGroup cols={2}>
<Card
title="MCP Integration Feature"
description="Learn about MCP configuration options and advanced patterns"
icon="plug"
href="/platform/features/mcp-integration"
/>
<Card
title="Gemini 3 with Mem0 MCP"
description="See how to integrate Gemini 3 with Mem0 MCP server"
icon="book-open"
href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
/>
</CardGroup>
## Additional Resources
- **[Platform Quickstart](/platform/quickstart)** - Direct API integration guide
- **[MCP Specification](https://modelcontextprotocol.io)** - Learn about MCP protocol
- [Platform Quickstart](/platform/quickstart) - direct SDK/API integration guide
- [MCP Specification](https://modelcontextprotocol.io) - the Model Context Protocol standard
- [Gemini with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp) - example integration cookbook
docs/platform/overview.mdx
+35 -62
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@@ -1,89 +1,62 @@
---
title: "Overview"
description: "Managed memory layer for AI agents - production-ready in minutes"
description: "Managed memory layer for AI agents, production-ready in minutes"
icon: "cloud"
---
# Mem0 Platform Overview
Mem0 Platform is the fully managed memory layer for your AI apps and agents. Your users stop repeating themselves and your agents keep context across sessions, with no vector store, reranker, or infrastructure to run.
Mem0 is the memory engine that keeps conversations contextual so users never repeat themselves and your agents respond with continuity. Mem0 Platform delivers that experience as a fully managed service: scaling, securing, and enriching memories without any infrastructure work on your side.
## Why teams pick the Platform
## Why it matters
- **Personalized replies.** Memories persist across users and agents, cutting prompt bloat and repeat questions.
- **Zero infrastructure.** Mem0 runs the vector store and rerankers, so there is nothing to provision, tune, or maintain.
- **Enterprise-ready.** Audit logs and workspace governance ship by default.
- **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions.
- **Hosted stack**: Mem0 runs the vector store and rerankers: no provisioning, tuning, or maintenance.
- **Enterprise controls**: Audit logs and workspace governance ship by default for production readiness.
## How it works
<Steps>
<Step title="Add">
Send Mem0 your messages and conversations.
</Step>
<Step title="Extract and store">
Mem0 distills them into facts and links entities across memories.
</Step>
<Step title="Recall">
At query time, Mem0 returns only the most relevant memories.
</Step>
</Steps>
For the full pipeline, see [How Mem0 works](/core-concepts/how-it-works).
<AccordionGroup>
<Accordion title="What you get with Mem0 Platform" icon="sparkles">
| Feature | Why it helps |
| --- | --- |
| Fast setup | Add a few lines of code and you’re production-ready: no vector database or LLM configuration required. |
| Fast setup | Add a few lines of code and you're production-ready, with no vector database or LLM configuration required. |
| Production scale | Automatic scaling, high availability, and managed infrastructure so you focus on product work. |
| Advanced features | webhooks, multimodal support, and custom categories are ready to enable. |
| Advanced features | Webhooks, multimodal support, and custom categories are ready to enable. |
| Enterprise ready | Audit logs, workspace governance, and dedicated support keep security and governance covered. |
</Accordion>
</AccordionGroup>
<Info>
Start with the <Link href="/platform/quickstart">Platform quickstart</Link> to provision your workspace, then pick the journey below that matches your next milestone.
</Info>
## Choose your path
<CardGroup cols={3}>
<Card title="Launch Your Workspace" icon="rocket" href="/platform/quickstart">
Create project and ship first memory.
</Card>
<Card title="Connect Any AI Client" icon="puzzle-piece" href="/platform/mem0-mcp">
Use MCP for universal AI integration.
</Card>
<Card title="Understand Memory Types" icon="brain" href="/core-concepts/memory-types">
User, agent, and session memory behavior.
</Card>
</CardGroup>
<CardGroup cols={3}>
<Card title="Master Core Operations" icon="circle-check" href="/core-concepts/memory-operations/add">
Add, search, update, and delete workflows.
</Card>
<Card title="Explore Platform Features" icon="sparkles" href="/platform/features/platform-overview">
async clients and rerankers.
</Card>
<Card title="Configure Advanced Operations" icon="bolt" href="/platform/advanced-memory-operations">
Metadata filters and per-request toggles.
</Card>
</CardGroup>
## Explore the Platform
<CardGroup cols={2}>
<Card title="Connect Integrations" icon="plug" href="/integrations">
LangChain, CrewAI, Vercel AI SDK.
<Card title="How Mem0 works" icon="diagram-project" href="/core-concepts/how-it-works">
The extraction and retrieval pipeline, end to end.
</Card>
<Card title="Monitor in the Dashboard" icon="presentation" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=platform-overview">
Track activity and manage workspaces.
<Card title="Run the quickstart" icon="rocket" href="/platform/quickstart">
Get an API key and save your first memory.
</Card>
<Card title="Understand memory types" icon="brain" href="/core-concepts/memory-types">
How user, agent, run, and session memory differ.
</Card>
<Card title="Add, search, and update" icon="layer-group" href="/core-concepts/memory-operations/add">
The core memory operations, end to end.
</Card>
</CardGroup>
<Tip>
Evaluating self-hosting instead? Jump to the <Link href="/platform/platform-vs-oss">Platform vs OSS comparison</Link> to see trade-offs before you commit.
Deciding whether to self-host? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link>, or switch tabs to the Open Source docs to run Mem0 yourself.
</Tip>
## Keep going
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
title="Compare with Open Source"
description="Review feature parity, migration paths, and when to stay managed."
icon="arrows-left-right"
href="/platform/platform-vs-oss"
/>
<Card
title="Run the Quickstart"
description="Provision your workspace, install the SDK, and persist your first memory."
icon="rocket"
href="/platform/quickstart"
/>
</CardGroup>
docs/platform/platform-vs-oss.mdx
+18 -42
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@@ -60,6 +60,10 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
| **Multimodal support** | ✅ | ✅ |
| **Custom categories** | ✅ | Limited |
| **Advanced retrieval** | ✅ | ✅ |
| **Criteria retrieval** | ✅ | ❌ |
| **Temporal reasoning** | ✅ (v3) | ❌ |
| **Memory decay** | ✅ (v3) | ❌ |
| **Graph memory** | ✅ Built-in | ✅ External graph store |
| **Memory filters v2** | ✅ | ⚠️ (via metadata) |
| **Webhooks** | ✅ | ❌ |
| **Memory export** | ✅ | ❌ |
@@ -71,8 +75,8 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
| **Hosting** | Managed by Mem0 | Self-hosted |
| **Auto-scaling** | ✅ | Manual |
| **High availability** | ✅ Built-in | DIY setup |
| **Vector DB choice** | Managed | Qdrant, Chroma, Pinecone, Milvus, +20 more |
| **LLM choice** | Managed (optimized) | OpenAI, Anthropic, Ollama, Together, +10 more |
| **Vector DB choice** | Managed | 20+ stores: Qdrant, Pinecone, Chroma, Weaviate, Milvus, pgvector |
| **LLM choice** | Managed (optimized) | 15+ providers: OpenAI, Anthropic, Gemini, Groq, Ollama, Together |
| **Data residency** | US (expandable) | Your choice |
</Accordion>
@@ -88,10 +92,10 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
<Accordion title="Development & Integration" icon="code">
| Feature | Platform | Open Source |
|---------|----------|-------------|
| **REST API** | ✅ | ✅ (via feature flag) |
| **REST API** | ✅ | ✅ (self-hosted server) |
| **Python SDK** | ✅ | ✅ |
| **JavaScript SDK** | ✅ | ✅ |
| **Framework integrations** | LangChain, CrewAI, LlamaIndex, +15 | Same |
| **Framework integrations** | LangChain, CrewAI, LlamaIndex, and 20+ more | Same |
| **Dashboard** | ✅ Web-based | ❌ |
| **Analytics** | ✅ Built-in | DIY |
</Accordion>
@@ -101,45 +105,17 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
## Decision Guide
### Choose **Platform** if you want:
**Choose Platform if you want:**
- Fast time to market: get your AI app with memory live in hours, not weeks.
- Production-ready hosting: auto-scaling, high availability, and managed infrastructure.
- Built-in analytics: track memory usage, query patterns, and user engagement through the dashboard.
- Advanced features: webhooks, memory export, custom categories, and priority support.
<CardGroup cols={2}>
<Card icon="bolt" title="Fast Time to Market">
Get your AI app with memory live in hours, not weeks. No infrastructure setup needed.
</Card>
<Card icon="shield" title="Production-Ready">
Auto-scaling, high availability, and managed infrastructure out of the box.
</Card>
<Card icon="chart-line" title="Built-in Analytics">
Track memory usage, query patterns, and user engagement through our dashboard.
</Card>
<Card icon="webhook" title="Advanced Features">
Access to webhooks, memory export, custom categories, and priority support.
</Card>
</CardGroup>
### Choose **Open Source** if you need:
<CardGroup cols={2}>
<Card icon="lock" title="Full Data Control">
Host everything on your infrastructure. Complete data residency and privacy control.
</Card>
<Card icon="wrench" title="Custom Configuration">
Choose your own vector DB, LLM provider, embedder, and deployment strategy.
</Card>
<Card icon="code" title="Extensibility">
Modify the codebase, add custom features, and contribute back to the community.
</Card>
<Card icon="dollar-sign" title="Cost Optimization">
Use local LLMs (Ollama), self-hosted vector DBs, and optimize for your specific use case.
</Card>
</CardGroup>
**Choose Open Source if you need:**
- Full data control: host everything on your infrastructure with complete data residency.
- Custom configuration: choose your own vector DB, LLM provider, embedder, and deployment strategy.
- Extensibility: modify the codebase, add custom features, and contribute back to the community.
- Cost optimization: use local LLMs (Ollama), self-hosted vector DBs, and optimize for your use case.
---
docs/platform/quickstart.mdx
+10 -12
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@@ -147,24 +147,22 @@ mem0 search "What are my dietary restrictions?" --user-id user123
**Pro Tip**: Want AI agents to manage their own memory automatically? Use <Link href="/platform/mem0-mcp">Mem0 MCP</Link> to let LLMs decide when to save, search, and update memories.
</Callout>
## What's Next?
## What's next?
You stored and searched your first memory. Keep going:
<CardGroup cols={3}>
<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
Learn how to search, update, and delete memories with complete CRUD operations
<Card title="How it works" icon="diagram-project" href="/core-concepts/how-it-works">
See how Mem0 extracts, stores, and retrieves memories under the hood.
</Card>
<Card title="Platform Features" icon="star" href="/platform/features/platform-overview">
Explore advanced features like metadata filtering and webhooks
<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
Go beyond add and search: update, delete, and the full memory lifecycle.
</Card>
<Card title="API Reference" icon="code" href="/api-reference/memory/add-memories">
See complete API documentation and integration examples
<Card title="Build an AI companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
Put it to work in a real app, end to end, in about 10 minutes.
</Card>
</CardGroup>
## Additional Resources
- **[Platform vs OSS](/platform/platform-vs-oss)** - Understand the differences between Platform and Open Source
- **[Troubleshooting](/platform/faqs)** - Common issues and solutions
- **[Integration Examples](/cookbooks/companions/quickstart-demo)** - See Mem0 in action
Stuck on setup? See the [FAQs and troubleshooting](/platform/faqs).