yourmemory 1.4.11

Persistent memory for Claude — Ebbinghaus forgetting curve, semantic deduplication, MCP-native

YourMemory

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

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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agents a persistent memory layer that works the way human memory does — important things stick, forgotten things fade, outdated facts get replaced automatically. One command to install, zero infrastructure required. Memory starts working the moment you add it to your AI client.

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How Well Does It Work?

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.

The 59% result used all-mpnet-base-v2. The current default model (multi-qa-mpnet-base-dot-v1) scores 55% on LoCoMo session-summary retrieval — see BENCHMARKS.md for details.

LongMemEval-S — 500 questions, ~53 sessions each

System	Recall-all@5	nDCG@5
YourMemory (full stack · multi-qa-mpnet-base-dot-v1)	85%	87%

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

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Quick Start

Supports Python 3.11–3.14. No Docker, no database setup, no external services.

Step 1 — Install

pip install yourmemory

Step 2 — Get your config path

yourmemory-path

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

Step 3 — 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" }
    }
  }
}

Windsurf / OpenCode / any MCP client

YourMemory is a standard stdio MCP server. Use the full path from yourmemory-path if the client doesn't inherit shell PATH.

{
  "mcpServers": {
    "yourmemory": {
      "command": "/full/path/to/yourmemory",
      "env": { "YOURMEMORY_USER": "your_name" }
    }
  }
}

First start is automatic. On the first run, YourMemory initialises your database, downloads the language model, and injects memory workflow instructions into your AI client config — no manual setup needed.

Step 4 — Start remembering

That's it. On the first MCP start, YourMemory automatically:

• Initialises your local database at ~/.yourmemory/memories.duckdb
• Downloads the spaCy language model in the background
• Injects the memory workflow rules into your AI client

Your AI now recalls what it learned in previous sessions, without you telling it to.

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Memory Dashboard

Every YourMemory instance ships with a built-in browser UI. When the MCP server is running, open:

http://localhost:3033/ui

Browse your memories by agent, filter by category, sort by strength, and see which memories are fading.

What you'll see

• Strength bars — how close each memory is to being pruned
• Agent tabs — switch between All / User / per-agent views
• Category badges — fact · strategy · assumption · failure
• Stats — total, strong (≥ 50%), fading (5–50%), near prune (< 10%)

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MCP Tools

Three tools, called by your AI automatically.

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

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How It Works

Ebbinghaus 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 × days) × (1 + recall_count × 0.2), 0, 1)
hybrid_score = 0.4 × bm25_norm + 0.6 × cosine_similarity

Decay (strength) is used for pruning only — not ranking. This prevents old-but-valid memories from being buried below newer irrelevant ones. Memories below strength 0.05 are pruned automatically every 24 hours.

Hybrid Retrieval: Vector + BM25 + Graph

Retrieval runs in two rounds to surface related context that vocabulary-based search misses:

Round 1 — Hybrid search: cosine similarity + BM25 keyword scoring, 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.

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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

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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 (multi-qa-mpnet-base-dot-v1, 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

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Architecture

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

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Contributing

PRs are welcome. See CONTRIBUTORS.md for the people who have already improved YourMemory.

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Dataset Reference

Benchmarks use the LoCoMo dataset by Snap Research.

Maharana et al. (2024). LoCoMo: Long Context Multimodal Benchmark for Dialogue. Snap Research.

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License

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: [e-mail]