YourMemory

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.

▶ Try the live interactive demo · Website · Benchmarks

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

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.

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

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

HotpotQA — 200 multi-hop questions

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

🚀 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:

# 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

One executable handles every command: register, setup, ask "<question>", path, and (with no args) starts the server.

Fully self-contained & offline — the binary bundles Python, every dependency, and both ML models (the embedding model + spaCy). Nothing is downloaded on first run. The trade-off is size (~2 GB). Build your own with a single command — ./build-binary.sh — and multi-platform release binaries are produced automatically by the build workflow.

🧠 How Memory Works

YourMemory treats memory as a living system — it grows, consolidates, forgets, and connects, the way a brain does.

Consolidation — N → 1

Most memory tools just keep growing. YourMemory watches for clusters of related facts and, once enough accumulate, compresses them into a single clean summary — archiving the originals (never deleting, so nothing is lost).

flowchart LR
    subgraph before [Related facts pile up]
        A1["Railway uses Nixpacks"]
        A2["Railway on Pro plan"]
        A3["Railway env vars hold<br/>the Postgres URL"]
        A4["Deploys on Railway<br/>with Postgres"]
    end
    before --> C{"cluster +<br/>LLM summarize"}
    C --> S["✨ Summary<br/>Deploys on Railway (Pro,<br/>Nixpacks) with Postgres<br/>via env vars"]
    C -.->|"archived, recoverable"| ARC[("archive")]
    style S fill:#0a2540,stroke:#5eead4,color:#fff
    style C fill:#0c2b3a,stroke:#19cdff,color:#fff

Real example from one production store: 444 memories → 16 summaries — same knowledge, a fraction of the noise. Consolidation is event-driven (triggered when related memories pile up), not a blind nightly job.

Decay — the forgetting curve

Memory strength decays exponentially. Importance and recall frequency slow that decay:

effective_λ  = base_λ × (1 − importance × 0.8)
strength     = clamp(importance × e^(−effective_λ × active_days) × (1 + recall_count × 0.2), 0, 1)

active_days counts only days you w