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

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agents a persistent memory layer that works the way human memory does — important things stick, forgotten things fade, outdated facts get replaced automatically.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

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.

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

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

{
  "mcpServers": {
    "yourmemory": {
      "command": "yourmemory"
    }
  }
}

{
  "mcpServers": {
    "yourmemory": {
      "command": "yourmemory"
    }
  }
}

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

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

{
  "mcp": {
    "yourmemory": {
      "type": "local",
      "command": ["yourmemory"],
      "environment": { "YOURMEMORY_USER": "your_name" }
    }
  }
}

cp sample_CLAUDE.md ~/.config/opencode/instructions.md

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.

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.

# 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}

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

pip install yourmemory[postgres]

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

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

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: mishrasachit1@gmail.com