GitHub - VinvAI/supermemory: Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

State-of-the-art memory and context engine for AI. And yes - you can use it as a company/personal brain.

Docs · Quickstart · Self-host · Dashboard · Discord

English · 简体中文

#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.


🧠 Memory	Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting.
👤 User Profiles	Auto-maintained user context — stable facts + recent activity. One call, ~50ms.
🔍 Hybrid Search	RAG + Memory in a single query. Knowledge base docs and personalized context together.
🔌 Connectors	Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks.
📄 Multi-modal Extractors	PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works.

All of this is in our single memory structure and ontology.

Use Supermemory

🧑‍💻 I use AI tools

Build your own personal supermemory by using our app. Builds persistent memory graph across every conversation.

Your AI remembers your preferences, projects, past discussions — and gets smarter over time.

→ Jump to User setup

🔧 I'm building AI products

Add memory, RAG, user profiles, and connectors to your agents and apps with a single API.

No vector DB config. No embedding pipelines. No chunking strategies.

→ Jump to developer quickstart

🖥️ I want to run it myself

State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama.

curl -fsSL https://supermemory.ai/install | bash

→ Jump to Supermemory local

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode, OpenClaw, and Hermes.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
Claude code plugin: https://github.com/supermemoryai/claude-supermemory
OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

MCP

Server URL:

https://mcp.supermemory.ai/mcp

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

Tool	What it does
`memory`	Save or forget information. Your AI calls this automatically when you share something worth remembering.
`recall`	Search memories by query. Returns relevant memories + your user profile summary.
`context`	Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type `/context`.

How it works

Once installed, Supermemory runs in the background:

You talk to your AI normally. Share preferences, mention projects, discuss problems.
Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

{
"mcpServers": {
"supermemory": {
"url": "https://mcp.supermemory.ai/mcp"
}
}
}

Build with Supermemory (API)

If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

npm install supermemory # or: pip install supermemory

Quickstart

importSupermemoryfrom"supermemory";constclient=newSupermemory();// Store a conversationawaitclient.add({content: "User loves TypeScript and prefers functional patterns",containerTag: "user_123",});// Get user profile + relevant memories in one callconst{ profile, searchResults }=awaitclient.profile({containerTag: "user_123",q: "What programming style does the user prefer?",});// profile.static → ["Loves TypeScript", "Prefers functional patterns"]// profile.dynamic → ["Working on API integration"]// searchResults → Relevant memories ranked by similarity

fromsupermemoryimportSupermemoryclient=Supermemory()
client.add(
content="User loves TypeScript and prefers functional patterns",
container_tag="user_123"
)
result=client.profile(container_tag="user_123", q="programming style")
print(result.profile.static) # Long-term factsprint(result.profile.dynamic) # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

// Vercel AI SDKimport{withSupermemory}from"@supermemory/tools/ai-sdk";constmodel=withSupermemory(openai("gpt-4o"),{containerTag: "user_123",customId: "conv-1"});// Mastraimport{withSupermemory}from"@supermemory/tools/mastra";constagent=newAgent(withSupermemory(config,"user-123",{mode: "full"}));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

// Hybrid (default) — RAG + Memory in one queryconstresults=awaitclient.search({q: "how do I deploy?",containerTag: "user_123",searchMode: "hybrid",});// Returns deployment docs (RAG) + user's deploy preferences (Memory)// Memories onlyconstresults=awaitclient.search({q: "user preferences",containerTag: "user_123",searchMode: "memories",});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

const{ profile }=awaitclient.profile({containerTag: "user_123"});// profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Method	Purpose
`client.add()`	Store content — text, conversations, URLs, HTML
`client.profile()`	User profile + optional search in one call
`client.search()`	Hybrid search across memories and documents (`searchMode`)
`client.search.documents()`	Document search with metadata filters (legacy v3 response shape)
`client.documents.uploadFile()`	Upload PDFs, images, videos, code
`client.documents.list()`	List and filter documents
`client.settings.update()`	Configure memory extraction and chunking

Full API reference → supermemory.ai/docs

Supermemory local — run it yourself

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

constclient=newSupermemory({apiKey: "sm_...",baseURL: "http://localhost:6767",// that's the only change});

Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

Benchmark	What it measures	Result
LongMemEval	Long-term memory across sessions with knowledge updates	#1
LoCoMo	Fact recall across extended conversations (single-hop, multi-hop, temporal, adversarial)	#1
ConvoMem	Personalization and preference learning	#1

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

Your app / AI tool
↓
Supermemory
│
├── Memory Engine Extracts facts, tracks updates, resolves contradictions,
│ auto-forgets expired info
├── User Profiles Static facts + dynamic context built from engine, always fresh
├── Hybrid Search RAG + Memory in one query
├── Connectors Real-time sync from Google Drive, Gmail, Notion, GitHub...
└── File Processing PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that "I just moved to SF" supersedes "I live in NYC." Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts ("I have an exam tomorrow") expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

Give your AI a memory. It's about time..

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
apps		apps
packages		packages
skills/supermemory		skills/supermemory
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
biome.json		biome.json
bun.lock		bun.lock
package.json		package.json
portless.json		portless.json
turbo.json		turbo.json

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
apps		apps
packages		packages
skills/supermemory		skills/supermemory
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
biome.json		biome.json
bun.lock		bun.lock
package.json		package.json
portless.json		portless.json
turbo.json		turbo.json

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
apps		apps
packages		packages
skills/supermemory		skills/supermemory
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
biome.json		biome.json
bun.lock		bun.lock
package.json		package.json
portless.json		portless.json
turbo.json		turbo.json

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
apps		apps
packages		packages
skills/supermemory		skills/supermemory
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
biome.json		biome.json
bun.lock		bun.lock
package.json		package.json
portless.json		portless.json
turbo.json		turbo.json

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
apps		apps
packages		packages
skills/supermemory		skills/supermemory
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
biome.json		biome.json
bun.lock		bun.lock
package.json		package.json
portless.json		portless.json
turbo.json		turbo.json

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Use Supermemory

🧑‍💻 I use AI tools

🔧 I'm building AI products

🖥️ I want to run it myself

Give your AI memory

The app

Supermemory Plugins

MCP

What your AI gets

How it works

Supported clients

Manual configuration

Build with Supermemory (API)

Install

Quickstart

Framework integrations

Search modes

User profiles

Connectors

API at a glance

Supermemory local — run it yourself

Benchmarks

Benchmarking your own memory solution

How memory works under the hood

Links

About

Resources

Contributing

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Uh oh!

Latest commit

History

Folders and files

Repository files navigation

Use Supermemory

🧑‍💻 I use AI tools

🔧 I'm building AI products

🖥️ I want to run it myself

Give your AI memory

The app

Supermemory Plugins

MCP

What your AI gets

How it works

Supported clients

Manual configuration

Build with Supermemory (API)

Install

Quickstart

Framework integrations

Search modes

User profiles

Connectors

API at a glance

Supermemory local — run it yourself

Benchmarks

Benchmarking your own memory solution

How memory works under the hood

Links

About

Resources

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages