Yourmemory

<!-- mcp-name: io.github.sachitrafa/yourmemory -->
<div align="center">
<br>
<h1>YourMemory</h1>

Your AI has the memory of a goldfish. Not anymore.

Persistent, self-improving memory for AI agents — built on the science of how humans remember.

<br>

▶ Try the live interactive demo · Website · Benchmarks

</div>

---

The problem

Every morning your AI agent treats you like a stranger. Same context re-explained. Same preferences forgotten. Every session starts from zero.

Most "memory" tools bolt a vector database onto an agent and call it done — but that's just storage. It hoards every near-duplicate until retrieval drowns in noise. A goldfish with a bigger bowl.

YourMemory is different: memory that works like a brain, not a database.

flowchart LR
    A["🧠 You tell your<br/>AI something"] --> B["Extract durable<br/>facts"]
    B --> C["Dedup + embed<br/>+ graph-link"]
    C --> D[("Memory<br/>store")]
    D -->|"related facts pile up"| E["✨ Consolidate<br/>N → 1 summary"]
    D -->|"stale + unused"| F["📉 Decay<br/>+ prune"]
    D -->|"new session"| G["♻️ Recall<br/>hybrid + graph"]
    E --> D
    G --> H["🤖 Your agent<br/>picks up where<br/>it left off"]
    style D fill:#0a2540,stroke:#19cdff,color:#fff
    style E fill:#0c2b3a,stroke:#5eead4,color:#fff
    style H fill:#0c2b3a,stroke:#19cdff,color:#fff

---

✨ What makes it different

| | Feature | What it does |
|---|---|---|
| 🧠 | Consolidation | When enough related facts accumulate, they're compressed into one clean summary and the originals are archived. Memory gets sharper over time, not bloated. |
| 📉 | Biological decay | Every memory ages on an Ebbinghaus forgetting curve. Stale, unused facts fade; important and frequently-recalled ones persist. |
| 🔗 | Entity graph | Memories link by shared people, places, and concepts — so recall surfaces what you forgot to ask for. |
| ♻️ | Survives context resets | When the context window compacts, YourMemory hands the working context back — no re-reading files to figure out where you were. |
| 🔒 | Tamper-evident audit trail | Every read / write / delete is logged in a hash-chained ledger. Alter one record and the chain breaks. |
| 👥 | Team memory pools | Role-based shared memory, so a whole team's agents draw on the same institutional knowledge — with private memories kept private. |
| 🛡️ | Data rights built in | One-command export (right to access) and right-to-forget (purge), plus SOC 2-aligned controls. |
| 🔌 | MCP-native & local-first | Works with Claude, Cursor, Cline, Windsurf, or any MCP client. Runs entirely on your machine — no API key, nothing leaves your system. |

> One command to install. DuckDB by default (zero setup), Postgres + pgvector for teams.

---

Table of Contents

- 🏆 Benchmarks
- 🚀 Quick Start
- 🧠 How Memory Works
- Consolidation
- Decay
- Hybrid Retrieval
- 🔒 Trust & Audit Trail
- 👥 Team Memory Pools
- 🛡️ Data Rights & Compliance
- 🎛️ Dashboards
- 🔧 MCP Tools
- ⚡ Ask Without an LLM Call
- 🔀 API Proxy — Guaranteed Memory
- 🏗️ Architecture & Stack
- 🩺 Troubleshooting
- 🤝 Contributing

---

🏆 Benchmarks

Three external datasets. Every number independently reproducible — benchmark code lives in the repo. Full methodology in BENCHMARKS.md.

LoCoMo-10 — multi-session conversational memory

xychart-beta
    title "Recall@5 · LoCoMo-10 (higher is better)"
    x-axis ["Mem0", "Zep Cloud", "Supermemory", "YourMemory"]
    y-axis "Recall@5 percent" 0 --> 70
    bar [18, 28, 31, 59]

> 2× better recall than Zep Cloud across all 10 samples. \Supermemory and Mem0 exhausted free-tier quotas mid-benchmark; scores computed over the full 1,534 pairs.

Writeup: I built memory decay for AI agents using the Ebbinghaus forgetting curve

pip install yourmemory
yourmemory-register <your-token>
yourmemory-setup

- DuckDB — zero setup, single local file (default)*
- Postgres — shared / production; you provide a DATABASE_URL (needs the pgvector extension)

> Optional — smarter local extraction: YourMemory works out of the box with built-in heuristics. For higher-quality, fully-local fact extraction, install Ollama and yourmemory-setup pulls the model (qwen2.5:7b, ~4.7 GB) automatically. Prefer the cloud? Set YOURMEMORY_EXTRACT_BACKEND=anthropic.

Or install from a binary — no Python required

Prefer not to touch pip? Grab the standalone binary for your platform from the latest release:

| Platform | Asset |
|----------|-------|
| macOS (Apple Silicon) | yourmemory-macos-arm64.tar.gz |
| macOS (Intel) | yourmemory-macos-x86_64.tar.gz |
| Linux (x86-64) | yourmemory-linux-x86_64.tar.gz |
| Windows (x86-64) | yourmemory-windows-x86_64.exe.zip |

# macOS / Linux — download, extract, run
tar -xzf yourmemory-macos-arm64.tar.gz
./yourmemory-macos-arm64 register <your-token>
./yourmemory-macos-arm64 setup
./yourmemory-macos-arm64            # start the server

…