genesys-memory 0.2.0

The intelligence layer for AI memory — scoring, causal inference, lifecycle management, and active forgetting

Genesys

The intelligence layer for AI memory.

Scoring engine + causal graph + lifecycle manager for AI agent memory. Speaks MCP natively.

What is this

Genesys is not another vector database. It's a scoring engine + causal graph + lifecycle manager that makes AI memory actually work. Memories are scored by a multiplicative formula (relevance × connectivity × reactivation), connected in a causal graph, and actively forgotten when they become irrelevant. It plugs into any storage backend and speaks MCP natively.

Why

• Flat memory doesn't scale. Dumping everything into a vector store gives you recall with zero understanding. The 500th memory buries the 5 that matter.
• No forgetting = no intelligence. Real memory systems forget. Without active pruning, your AI drowns in stale context.
• No causal reasoning. Vector similarity can't answer "why did I choose X?" — you need a graph.

Your AI remembers everything but understands nothing. Genesys fixes that.

Quick Start

Install from PyPI

pip install genesys-memory
cp .env.example .env
# Set OPENAI_API_KEY in .env

uvicorn genesys.api:app --port 8000

With Postgres + pgvector

pip install genesys-memory[postgres]
cp .env.example .env
# Set OPENAI_API_KEY and DATABASE_URL in .env

docker compose up -d postgres
alembic upgrade head
GENESYS_BACKEND=postgres uvicorn genesys.api:app --port 8000

From source

git clone https://github.com/rishimeka/genesys.git
cd genesys
pip install -e ".[dev]"

Connect to your AI

Claude Code

claude mcp add --transport http genesys http://localhost:8000/mcp

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "genesys": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Any MCP client

Point your client at the MCP endpoint:

http://localhost:8000/mcp

MCP Tools

Tool	Description
memory_store	Store a new memory, optionally linking to related memories
memory_recall	Recall memories by natural language query (vector + graph)
memory_search	Search memories with filters (status, date range, keyword)
memory_traverse	Walk the causal graph from a given memory node
memory_explain	Explain why a memory exists and its causal chain
memory_stats	Get memory system statistics
pin_memory	Pin a memory so it's never forgotten
unpin_memory	Unpin a previously pinned memory
delete_memory	Permanently delete a memory
list_core_memories	List core memories, optionally filtered by category
set_core_preferences	Set user preferences for core memory categories

How it works

Every memory is scored by three forces multiplied together:

decay_score = relevance × connectivity × reactivation

• Relevance decays over time. Old memories fade unless reinforced.
• Connectivity rewards memories with many causal links. Hub memories survive.
• Reactivation boosts memories that keep getting recalled. Frequency matters.

Because the formula is multiplicative, a memory must score on all three axes to survive. A highly connected but never-accessed memory still decays. A frequently recalled but causally orphaned memory still fades.

                    ┌─────────┐
                    │  STORE  │
                    └────┬────┘
                         │
                    ┌────▼────┐
                    │ ACTIVE  │◄──── reactivation
                    └────┬────┘
                         │ decay
                    ┌────▼────┐
                    │ DORMANT │
                    └────┬────┘
                         │ continued decay
                    ┌────▼────┐
           ┌───────│ FADING  │
           │        └─────────┘
           │ score=0, orphan,
           │ not pinned
      ┌────▼────┐
      │ PRUNED  │
      └─────────┘

Memories can also be promoted to core status — structurally important memories that are auto-pinned and never pruned.

Benchmark Results

Tested on the LoCoMo long-conversation memory benchmark (1,540 questions across 10 conversations, category 5 excluded):

Category	J-Score
Single-hop	94.3%
Temporal	87.5%
Multi-hop	69.8%
Open-domain	91.7%
Overall	89.9%

Answer model: gpt-4o-mini | Judge model: gpt-4o-mini | Retrieval k=20

Full results and reproduction steps in benchmarks/.

Storage backends

Backend	Status	Use case
memory	Built-in	Zero deps, try it out
postgres + pgvector	Production	Persistent, scalable
Obsidian	Coming soon	Local-first knowledge base
Custom	Bring your own	Implement GraphStorageProvider

Configuration

Copy .env.example to .env and set:

Variable	Required	Description
OPENAI_API_KEY	Yes	Embeddings
ANTHROPIC_API_KEY	No	LLM memory processing (consolidation, contradiction detection)
GENESYS_BACKEND	No	memory (default) or postgres
DATABASE_URL	If postgres	Postgres connection string
GENESYS_USER_ID	No	Default user ID for single-tenant mode

See .env.example for all options.

Contributing

See CONTRIBUTING.md.

License

Apache 2.0