YourMemory

Persistent memory for AI agents — built on the science of how humans remember.

What Is YourMemory?

Every session, your AI assistant starts from zero. It asks the same questions, forgets your preferences, re-learns your stack. There is no memory between conversations.

YourMemory fixes that with a one-command install that plugs into Claude, Cursor, Cline, Windsurf, or any MCP client. It gives your AI a persistent memory layer modelled on human cognition:

• Things that matter stick — importance score controls how quickly a memory decays
• Outdated facts get replaced — subject-aware deduplication merges or supersedes memories automatically
• Related context surfaces together — entity graph links memories that share people, places, or concepts
• Old memories fade naturally — Ebbinghaus forgetting curve prunes stale context every 24 hours

Zero infrastructure required. SQLite by default, Postgres for teams.

Table of Contents

• Benchmarks
• Quick Start
• Memory Dashboard
• Ask Without an LLM Call
• API Proxy — Guaranteed Memory
• MCP Tools
• How It Works
• Multi-Agent Memory
• Stack
• Architecture
• Contributing

Benchmarks

Three external datasets, all scripts open source and reproducible. Full methodology in BENCHMARKS.md.

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

The hardest standard benchmark for long-term memory systems. Each question is backed by ~53 conversation sessions; the model must retrieve the right one(s) from the haystack.

By question type (Recall@5):

LoCoMo-10 — 1,534 QA pairs across 10 multi-session conversations

Conversations spanning weeks to months. Every system ingests the same session summaries in the same order.

2× better recall than Zep Cloud across all 10 samples. * Supermemory and Mem0 exhausted free-tier quotas mid-benchmark; scores computed over full 1,534 pairs using 0 for unfinished samples.

HotpotQA — 200 multi-hop questions requiring two facts from different articles

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

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

Before you install — what this does

Activation steps:

1. Visit yourmemoryai.xyz and enter your email
2. Check your inbox for a 6-digit verification code
3. Enter the code on the website — your token is shown instantly
4. Run the three commands below:

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

Memory Dashboard

Two built-in browser UIs — no extra setup, start automatically with the MCP server.

Memory Browser — http://localhost:3033/ui

A full read/write view of everything stored in memory.

Pass ?user=<id> to pre-load a specific user: http://localhost:3033/ui?user=sachit

Graph Visualiser — http://localhost:3033/graph

An interactive force-directed map of how memories connect.

http://localhost:3033/graph?memoryId=42&userId=sachit&depth=2

• Root memory as a larger cyan node; neighbours color-coded by category
• Edge thickness = connection strength
• Click any node for full content; drag, zoom, reposition freely

Ask Without Calling the API

The only memory system that can answer questions without making any LLM API call.

yourmemory ask "what database does this project use"
# → YourMemory uses DuckDB locally and Postgres in production.

yourmemory ask "what port does the dashboard run on"
# → 3033

yourmemory ask "how do I fix a kubernetes deployment"
# → Not enough memory context to answer without Claude.

When memory is strong enough, it answers instantly — zero tokens, zero cloud cost, zero latency. When it isn't, it declines cleanly rather than hallucinating.

API Proxy — Guaranteed Memory

MCP tools are called at the AI's discretion. The API proxy removes that uncertainty — it intercepts every LLM call, injects relevant memories automatically, and handles store_memory / update_memory without any model configuration.

Start the YourMemory server (yourmemory), then point your LLM client at localhost:3033:

OpenAI

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="http://localhost:3033/proxy/openai"
)

# Memory is injected automatically — no other changes needed
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What database do I use?"}]
)

Anthropic

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-ant-...",
    base_url="http://localhost:3033/proxy/anthropic"
)

response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What database do I use?"}]
)

Per-user memory

Pass X-YourMemory-User to isolate memory per person:

client = OpenAI(
    api_key="sk-...",
    base_url="http://localhost:3033/proxy/openai",
    default_headers={"X-YourMemory-User": "sachit"}
)

How it works

On every request the proxy:

1. Recalls relevant memories and injects them into the system prompt — guaranteed, no tool call needed
2. Adds store_memory and update_memory as tools — the model calls them when it learns something new
3. Executes those t