# Remembra

> Universal memory layer for AI applications. Self-host in minutes. Open source.

- **Type:** MCP server
- **Install:** `agentstack add mcp-remembra-ai-remembra`
- **Verified:** Pending review
- **Seller:** [remembra-ai](https://agentstack.voostack.com/s/remembra-ai)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [remembra-ai](https://github.com/remembra-ai)
- **Source:** https://github.com/remembra-ai/remembra
- **Website:** https://remembra.dev

## Install

```sh
agentstack add mcp-remembra-ai-remembra
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

Remembra

  The memory layer for AI that actually works.
  Persistent memory with entity resolution, temporal decay, and graph-aware recall.
  Self-host in minutes. No vendor lock-in.

  
  
  
  
  

  Documentation •
  Website •
  Quick Start •
  Why Remembra? •
  Twitter •
  Discord

---

## 🚀 What's New in v0.13.0

### Dashboard v2.0
- **🔐 Two-Factor Authentication** — TOTP-based 2FA with authenticator apps
- **👥 Team Collaboration** — Shared memory spaces with role-based access
- **🛠️ Admin Dashboard** — Full user management (delete/deactivate/reset)
- **📊 Activity Log** — Security audit trail with JSON export
- **🕵️ Entity Browser** — Visual exploration of people, places, concepts
- **⏰ Timeline Fix** — Proper timezone handling with local time display

### Core API
- **📦 npm Package** — `npm install remembra` with full TypeScript support
- **🔒 Security Fixes** — RBAC enforcement, SSRF protection, error sanitization

### Supported Agents (6+)
Claude Desktop • Claude Code • Codex CLI • Cursor • Windsurf • Gemini

### Previous (v0.12.x)
- 👤 User Profiles API with activity metrics
- 🧠 Smart Auto-Forgetting (35+ temporal patterns)
- ⏰ Event-driven expiry with `expires_at`
- 🌐 Browser Extension for AI chat interfaces

---

## The Problem

Every AI app needs memory. Your chatbot forgets users between sessions. Your agent can't recall decisions from yesterday. Your assistant asks the same questions over and over.

**Existing solutions have tradeoffs:**
- Mem0: Graph features require $249/mo plan; limited self-hosting documentation
- Zep: Academic approach, complex deployment
- Letta: Research-grade, not production-ready
- LangChain Memory: Too basic, no persistence

## The Solution

```python
from remembra import Memory

memory = Memory(user_id="user_123")

# Store — entities and facts extracted automatically
memory.store("Had a meeting with Sarah from Acme Corp. She prefers email over Slack.")

# Recall — semantic search finds relevant memories
result = memory.recall("How should I contact Sarah?")
print(result.context)
# → "Sarah from Acme Corp prefers email over Slack."

# It knows "Sarah" and "Acme Corp" are entities. It builds relationships.
# It persists across sessions, reboots, context windows. Forever.
```

---

## ⚡ Quick Start (2 Minutes)

### One Command Install

```bash
curl -sSL https://raw.githubusercontent.com/remembra-ai/remembra/main/quickstart.sh | bash
```

That's it. Remembra + Qdrant + Ollama start locally. No API keys needed.

**Or with Docker Compose directly:**

```bash
git clone https://github.com/remembra-ai/remembra && cd remembra
docker compose -f docker-compose.quickstart.yml up -d
```

**Try it:**

```bash
# Store a memory
curl -X POST http://localhost:8787/api/v1/memories \
  -H "Content-Type: application/json" \
  -d '{"content": "Alice is CEO of Acme Corp", "user_id": "demo"}'

# Recall it
curl -X POST http://localhost:8787/api/v1/memories/recall \
  -H "Content-Type: application/json" \
  -d '{"query": "Who runs Acme?", "user_id": "demo"}'
```

### Connect ALL Your AI Agents (NEW in v0.10.0)

**One command configures everything:**

```bash
pip install remembra
remembra-install --all --url http://localhost:8787
```

This auto-detects and configures: Claude Desktop, Claude Code, Codex CLI, Cursor, Windsurf, Gemini.

**Verify setup:**

```bash
remembra-doctor all
```

Manual MCP Config (if needed)

**Claude Desktop** — add to `~/Library/Application Support/Claude/claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "remembra": {
      "command": "remembra-mcp",
      "env": {
        "REMEMBRA_URL": "http://localhost:8787",
        "REMEMBRA_USER_ID": "default"
      }
    }
  }
}
```

**Claude Code:**

```bash
claude mcp add remembra -e REMEMBRA_URL=http://localhost:8787 -- remembra-mcp
```

**Cursor** — add to `.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "remembra": {
      "command": "remembra-mcp",
      "env": {
        "REMEMBRA_URL": "http://localhost:8787"
      }
    }
  }
}
```

Now ask Claude: *"Remember that Alice is CEO of Acme Corp"* — then later: *"Who runs Acme?"*

### Python SDK

```bash
pip install remembra
```

```python
from remembra import Memory

memory = Memory(user_id="user_123")
memory.store("Had a meeting with Sarah from Acme Corp. She prefers email over Slack.")
result = memory.recall("How should I contact Sarah?")
print(result.context)  # "Sarah from Acme Corp prefers email over Slack."
```

### TypeScript SDK

```bash
npm install remembra
```

```typescript
import { Remembra } from 'remembra';

const memory = new Remembra({ url: 'http://localhost:8787' });
await memory.store('User prefers dark mode');
const result = await memory.recall('preferences');
```

---

## 🔥 Why Remembra?

### Feature Comparison

| Feature | Remembra | Mem0 | Zep/Graphiti | Letta | Engram |
|---------|----------|------|-------------|-------|--------|
| **One-Command Install** | ✅ `curl \| bash` | ✅ pip | ✅ pip | ⚠️ Complex | ✅ brew |
| **Bi-Temporal Relationships** | ✅ Point-in-time | ❌ | ⚠️ Basic | ❌ | ❌ |
| **Entity Resolution** | ✅ Free | 💰 $249/mo | ✅ | ❌ | ❌ |
| **Conflict Detection** | ✅ Auto-supersede | ❌ | ❌ | ❌ | ❌ |
| **PII Detection** | ✅ Built-in | ❌ | ❌ | ❌ | ❌ |
| **Hybrid Search** | ✅ BM25+Vector | ❌ | ✅ | ❌ | ❌ |
| **6 Embedding Providers** | ✅ Hot-swap | ❌ (1-2) | ❌ (1) | ❌ | ❌ |
| **Plugin System** | ✅ | ❌ | ❌ | ✅ | ❌ |
| **Sleep-Time Compute** | ✅ | ❌ | ❌ | ✅ | ❌ |
| **Self-Host + Billing** | ✅ Stripe | ❌ | ❌ | ❌ | ❌ |
| **Memory Spaces** | ✅ Multi-tenant | ❌ | ❌ | ❌ | ❌ |
| **MCP Server** | ✅ 11 Tools | ✅ | ❌ | ❌ | ✅ |
| **Pricing** | Free / $49 / $199 | $19 → $249 | $25+ | Free | Free |
| **License** | MIT | Apache 2.0 | Apache 2.0 | Apache 2.0 | MIT |

### Core Features

🧠 **Smart Extraction** — LLM-powered fact extraction from raw text

👥 **Entity Resolution** — "Adam", "Mr. Smith", "my husband" → same person

⏱️ **Temporal Memory** — TTL, decay curves, historical queries

🔍 **Hybrid Search** — Semantic + keyword for accurate recall

🔒 **Security** — PII detection, anomaly monitoring, audit logs

📊 **Dashboard** — Visual memory browser, entity graphs, analytics

---

## 📊 Benchmark Results

Tested on the [LoCoMo benchmark](https://github.com/snap-research/locomo) (Snap Research, ACL 2024) — the standard academic benchmark for AI memory systems.

| Category | Accuracy | Questions |
|----------|----------|-----------|
| **Single-hop** (direct recall) | **100%** | 37 |
| **Multi-hop** (cross-session reasoning) | **100%** | 32 |
| **Temporal** (time-based queries) | **100%** | 13 |
| **Open-domain** (world knowledge + memory) | **100%** | 70 |
| **Overall (memory categories)** | **100%** | **152** |

> Scored with LLM judge (GPT-4o-mini). Adversarial detection not yet implemented. Run your own: `python benchmarks/locomo_runner.py --data /tmp/locomo/data/locomo10.json`

---

## 📖 Documentation

| Resource | Description |
|----------|-------------|
| [Quick Start](https://docs.remembra.dev/getting-started/quickstart/) | Get running in minutes |
| [Python SDK](https://docs.remembra.dev/guides/python-sdk/) | Full Python reference |
| [TypeScript SDK](https://docs.remembra.dev/guides/javascript-sdk/) | JavaScript/TypeScript guide |
| [MCP Server](https://docs.remembra.dev/integrations/mcp-server/) | Tool reference + setup guides for 11 tools |
| [REST API](https://docs.remembra.dev/guides/rest-api/) | API reference |
| [Self-Hosting](https://docs.remembra.dev/getting-started/docker/) | Docker deployment guide |

---

## 🛠️ MCP Server

Give any AI coding tool persistent memory with one command. Works with **Claude Code**, **Cursor**, **VS Code + Copilot**, **Windsurf**, **JetBrains**, **Zed**, **OpenAI Codex**, and any MCP-compatible client.

```bash
pip install remembra[mcp]
claude mcp add remembra -e REMEMBRA_URL=http://localhost:8787 -- remembra-mcp
```

**Available Tools (11 total):**

| Tool | Description |
|------|-------------|
| `store_memory` | Save facts, decisions, context |
| `recall_memories` | Semantic search across memories |
| `update_memory` | Update content without delete+recreate |
| `forget_memories` | GDPR-compliant deletion |
| `list_memories` | Browse stored memories |
| `search_entities` | Search the entity graph |
| `share_memory` | Cross-agent memory sharing via Spaces |
| `timeline` | Temporal browsing by entity and date |
| `relationships_at` | Point-in-time relationship queries |
| `ingest_conversation` | Auto-extract from chat history |
| `health_check` | Verify connection |

---

## 🏗️ Architecture

```
┌─────────────────────────────────────────────────────────────┐
│                    Your Application                          │
├──────────┬──────────────┬───────────────────────────────────┤
│ Python   │ TypeScript   │ MCP Server (Claude/Cursor)        │
│ SDK      │ SDK          │ remembra-mcp                      │
├──────────┴──────────────┴───────────────────────────────────┤
│                   Remembra REST API                          │
├──────────────┬──────────────┬───────────────┬───────────────┤
│  Extraction  │   Entities   │   Retrieval   │   Security    │
│  (LLM)       │  (Graph)     │ (Hybrid)      │  (PII/Audit)  │
├──────────────┴──────────────┴───────────────┴───────────────┤
│                    Storage Layer                             │
│         Qdrant (vectors) + SQLite (metadata/graph)          │
└─────────────────────────────────────────────────────────────┘
```

---

## 🤝 Contributing

We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

```bash
# Clone
git clone https://github.com/remembra-ai/remembra
cd remembra

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Start dev server
remembra-server --reload
```

---

## 📄 License

MIT License — Use it however you want.

---

## ⭐ Star History

If Remembra helps you, please star the repo! It helps others discover the project.

[](https://star-history.com/#remembra-ai/remembra&Date)

---

  Built with ❤️ by DolphyTech
  remembra.dev • docs • twitter • discord

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [remembra-ai](https://github.com/remembra-ai)
- **Source:** [remembra-ai/remembra](https://github.com/remembra-ai/remembra)
- **License:** MIT
- **Homepage:** https://remembra.dev

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-remembra-ai-remembra
- Seller: https://agentstack.voostack.com/s/remembra-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.