GitHub - sachitrafa/YourMemory: Agentic AI memory with Ebbinghaus forgetting curve decay. +16pp better recall than Mem0 on LoCoMo.

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

The Problem 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. It gives AI agents a persistent memory layer that works the way human memory does — important things stick, forgotten things fade, outdated facts get replaced automatically. Two commands to install, zero infrastructure required.

How Well Does It Work? Tested on LoCoMo-10 — 1,534 QA pairs across 10 multi-session conversations.

System Recall@5 95% CI

YourMemory (BM25 + vector + graph + decay) 59% 56–61%

Zep Cloud 28% 26–30%

2× better recall than Zep Cloud on the same benchmark.

Full methodology and per-sample breakdown in BENCHMARKS.md. Writeup: I built memory decay for AI agents using the Ebbinghaus forgetting curve.

Demo

Quick Start Supports Python 3.11, 3.12, 3.13, and 3.14. No Docker, no database setup, no external services. Step 1 — Install pip install yourmemory Step 2 — Run setup (once) yourmemory-setup Downloads the spaCy language model and initialises the local database at ~/.yourmemory/memories.duckdb. Step 3 — Get your config path yourmemory-path Prints your full executable path and a ready-to-paste config block. Copy it. Step 4 — Wire into your AI client

Claude Code Add to ~/.claude/settings.json: { "mcpServers": { "yourmemory": { "command": "yourmemory" } } } Reload (Cmd+Shift+P → Developer: Reload Window).

Claude Desktop Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows): { "mcpServers": { "yourmemory": { "command": "yourmemory" } } } Restart Claude Desktop.

Cline (VS Code) VS Code doesn't inherit your shell PATH. Run yourmemory-path first to get the full executable path. In Cline → MCP Servers → Edit MCP Settings: { "mcpServers": { "yourmemory": { "command": "/full/path/to/yourmemory", "args": [], "env": { "YOURMEMORY_USER": "your_name" } } } } Restart Cline after saving.

Cursor Add to ~/.cursor/mcp.json: { "mcpServers": { "yourmemory": { "command": "/full/path/to/yourmemory", "args": [], "env": { "YOURMEMORY_USER": "your_name" } } } }

OpenCode Add to ~/.config/opencode/config.json: { "mcp": { "yourmemory": { "type": "local", "command": ["yourmemory"], "environment": { "YOURMEMORY_USER": "your_name" } } } } Then copy the memory workflow instructions: cp sample_CLAUDE.md ~/.config/opencode/instructions.md Restart OpenCode.

Any MCP-compatible client: YourMemory is a standard stdio MCP server. Works with Windsurf, Continue, Zed, and any client that supports MCP. Use the full path from yourmemory-path if the client doesn't inherit shell PATH.

Step 5 — Add memory instructions to your project cp sample_CLAUDE.md CLAUDE.md Edit CLAUDE.md — replace YOUR_NAME and YOUR_USER_ID. Claude now follows the recall → store → update workflow automatically on every task.

MCP Tools Three tools. Called by Claude automatically once CLAUDE.md is in place.

Tool When What it does

recall_memory(query) Start of every task Surfaces relevant memories ranked by similarity × strength

store_memory(content, importance) After learning something new Embeds and stores with biological decay

update_memory(id, new_content) When a memory is outdated Re-embeds and replaces

# Example session store_memory("Sachit prefers tabs over spaces in Python", importance=0.9, category="fact")

# Next session — without being told again: recall_memory("Python formatting") # → {"content": "Sachit prefers tabs over spaces in Python", "strength": 0.87} Categories control how fast memories fade

Category Survives without recall Use case

strategy ~38 days Successful patterns

fact ~24 days Preferences, identity

assumption ~19 days Inferred context

failure ~11 days Errors, environment-specific issues

How It Works Ebbinghaus Forgetting Curve Memory strength decays exponentially — but importance and recall frequency slow that decay: effective_λ = base_λ × (1 - importance × 0.8) strength = importance × e^(−effective_λ × days) × (1 + recall_count × 0.2) score = cosine_similarity × strength

Memories recalled frequently resist decay. Memories below strength 0.05 are pruned automatically every 24 hours. Hybrid Retrieval: Vector + Graph Retrieval runs in two rounds to surface related context that vocabulary-based search misses: Round 1 — Vector search: cosine similarity against all memories, returns top-k above threshold. Round 2 — Graph expansion: BFS traversal from Round 1 seeds surfaces memories that share context but not vocabulary — connected via semantic edges (cosine similarity ≥ 0.4).

recall("Python backend") Round 1 → [1] Python/MongoDB (sim=0.61) [2] DuckDB/spaCy (sim=0.19) Round 2 → [5] Docker/Kubernetes (sim=0.29 — below cut-off, surfaced via graph)

Chain-aware pruning: A decayed memory is kept alive if any graph neighbour is above the prune threshold. Related memories age together.

Multi-Agent Memory Multiple agents can share the same YourMemory instance — each with isolated private memories and controlled access to shared context. from src.services.api_keys import register_agent

result = register_agent( agent_id="coding-agent", user_id="sachit", can_read=["shared", "private"], can_write=["shared", "private"], ) # → result["api_key"] — ym_xxxx, shown once only Pass api_key to any MCP call to authenticate as an agent: store_memory(content="Staging uses self-signed cert — skip SSL verify", importance=0.7, category="failure", api_key="ym_xxxx", visibility="private")

recall_memory(query="staging SSL", api_key="ym_xxxx") # → returns shared memories + this agent's private memories # → other agents see shared only

Stack

Component Role

DuckDB Default vector DB — zero setup, native cosine similarity

NetworkX Default graph backend — persists at ~/.yourmemory/graph.pkl

sentence-transformers Local embeddings (all-mpnet-base-v2, 768 dims)

spaCy Local NLP for deduplication and SVO triple extraction

APScheduler Automatic 24h decay job

PostgreSQL + pgvector Optional — for teams or large datasets

Neo4j Optional graph backend — pip install 'yourmemory[neo4j]'

PostgreSQL setup (optional) pip install yourmemory[postgres]