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 v0.16.0 — Lossless Memory
**Most memory layers store an LLM's paraphrase of what you said. Remembra now keeps the receipts.**
- **🧾 Verbatim source records** — the exact original text is preserved as an
immutable record whenever facts are derived from it. Never LLM-merged, never rewritten.
- **🔗 Receipts on every fact** — each derived fact carries `metadata.source_id`
pointing back to its source. Recall a fact, fetch its evidence.
- **🛡️ Hallucination flagging** — every derived fact is verified against its source;
facts that don't overlap the original are stored flagged `verified: false`, not silently trusted.
- **⚡ Fast writes (opt-in)** — `REMEMBRA_ASYNC_ENRICHMENT=true` stores the verbatim
source instantly and runs extraction in the background.
- **🩺 Production reliability** — request IDs on every response, honest 429/502 upstream
error mapping, embedding cache (~8× faster repeat recalls), litestream backups, and
the opaque store-500 class of failures fixed at the root.
### Previous highlights
- **🧠 Brain layer (v0.15+)** — GraphRAG-style community detection over your entity graph; 2D/3D knowledge graph in the dashboard
- **🌐 Remote MCP** — multi-tenant streamable-HTTP MCP: connect any agent with just a URL + API key
- **🔐 Dashboard v2** — 2FA, teams, admin console, audit log, entity browser
### Supported Agents (6+)
Claude Desktop • Claude Code • Codex CLI • Cursor • Windsurf • Gemini
---
## 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