Show HN: AI memory with biological decay (52% recall)

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agents a persistent memory layer that works the way human memory does — important things stick, forgotten things fade, outdated facts get replaced automatically.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.

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.

Supports Python 3.11, 3.12, 3.13, and 3.14. No Docker, no database setup, no external services.

pip install yourmemory

yourmemory-setup

Downloads the spaCy language model and initialises the local database at ~/.yourmemory/memories.duckdb

.

yourmemory-path

Prints your full executable path and a ready-to-paste config block. Copy it.

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 fromyourmemory-path

if the client doesn't inherit shell PATH.

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.

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}

| 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 |

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.

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.

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

| 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]

Create a .env

file:

DATABASE_URL=postgresql://YOUR_USER@localhost:5432/yourmemory

macOS

brew install postgresql@16 pgvector && brew services start postgresql@16
createdb yourmemory

Ubuntu / Debian

sudo apt install postgresql postgresql-contrib postgresql-16-pgvector
createdb yourmemory

Claude / Cline / Cursor / Any MCP client
│
├── recall_memory(query, api_key?)
│ └── embed → vector similarity (Round 1)
│ → graph BFS expansion (Round 2)
│ → score = sim × strength → top-k
│ → recall propagation → boost neighbours
│
├── store_memory(content, importance, category?, visibility?, api_key?)
│ └── question? → reject
│ contradiction check → update if conflict
│ embed() → INSERT → index_memory() → graph node + edges
│
└── update_memory(id, new_content, importance)
└── embed(new_content) → UPDATE → refresh graph node
Vector DB (Round 1) Graph DB (Round 2)
DuckDB (default) NetworkX (default)
memories.duckdb graph.pkl
├── embedding FLOAT[768] ├── nodes: memory_id, strength
├── importance FLOAT └── edges: sim × verb_weight ≥ 0.4
├── recall_count INTEGER
├── visibility VARCHAR Neo4j (opt-in)
└── agent_id VARCHAR └── bolt://localhost:7687

Benchmarks use the LoCoMo dataset by Snap Research.

Maharana et al. (2024).

LoCoMo: Long Context Multimodal Benchmark for Dialogue.Snap Research.

Copyright 2026 Sachit Misra — Licensed under CC-BY-NC-4.0.

Free for: personal use, education, academic research, open-source projects.

Not permitted: commercial use without a separate written agreement.

Commercial licensing: [email protected]