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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

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✨ 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.

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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

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🏆 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.

LongMemEval-S — 500 questions, ~53 distractor sessions each

The hardest standard benchmark for long-term memory. Each question is buried in ~53 sessions.

Metric	Score
Recall@5 (any gold session in top-5)	89.4%
Recall-all@5 (all gold sessions in top-5)	84.8%
nDCG@5 (ranking quality)	87.4%

HotpotQA — 200 multi-hop questions

System	BOTH_FOUND@5
YourMemory (vector + BM25 + entity graph)	71.5%
YourMemory (no entity edges)	59.5%

Entity graph edges add +12 pp — they traverse from Fact 1 to Fact 2 even when Fact 2 has low embedding similarity to the query.

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

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🚀 Quick Start

Python 3.11–3.14. No Docker, no database setup. All memory stored locally in ~/.yourmemory/.

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

Get your token: visit yourmemoryai.xyz → enter your email → verify with a 6-digit code → copy your token.

yourmemory-setup auto-detects and wires up Claude Code, Claude Desktop, Cursor, Windsurf, and Cline, then asks which backend to use:

• 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: