Genesys

The intelligence layer for AI memory.

Genesys doesn't just remember what happened; it remembers why. A scoring engine + causal graph + lifecycle manager for AI agent memory. Speaks MCP natively.

What is this

Genesys is a scoring engine, causal graph, and lifecycle manager for AI memory. Memories are scored by a multiplicative formula (relevance × connectivity × reactivation), connected in a causal graph, and actively forgotten when they become irrelevant.

This package (genesys-memory) is the core library: an in-memory causal graph engine with optional JSON persistence, plus a stdio MCP server. It has no database dependency and no REST API. A hosted product built on top of this library — with Postgres, additional storage backends, and a REST/HTTP MCP API — is available separately at genesys-api.astrixlabs.ai; it is not part of this package.

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 the package. The base install has zero database dependencies — state lives in memory and is optionally persisted to a JSON file.

pip install genesys-memory

Optional extras:

pip install 'genesys-memory[openai]'      # OpenAI embeddings
pip install 'genesys-memory[local]'       # Local embeddings (sentence-transformers, no API key)
pip install 'genesys-memory[anthropic]'   # LLM-based causal inference (consolidation, contradiction detection)

Run the stdio MCP server directly:

python3 -m genesys_memory

From source

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

Connect to your AI

Claude Code

claude mcp add genesys -- python -m genesys_memory

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "genesys": {
      "command": "python",
      "args": ["-m", "genesys_memory"]
    }
  }
}

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 → DORMANT → FADING → PRUNED
           ↑                    │
           └── reactivation ────┘
                                  (only if score=0, orphan, not pinned)

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

Benchmark Results

We've run internal evaluations against the LoCoMo long-conversation memory benchmark during development. These are self-reported, run with our own harness (category 5 — adversarial questions with disputed ground truth — excluded), and not independently reproduced, so treat them as directional rather than a verified claim. Reproduction scripts are in benchmarks/ if you want to run your own numbers.

Storage backend

This package ships one storage backend: an in-memory causal graph (storage/memory.py) with optional JSON persistence via GENESYS_PERSIST_PATH. No database is required.

Additional backends — Postgres/pgvector, FalkorDB, MongoDB, and an Obsidian vault adapter — along with a REST API, OAuth, and multi-user auth, are part of the hosted product at genesys-api.astrixlabs.ai and are not included in this repo.

Want a different storage backend for the open-source library? Implement the provider protocols in storage/base.py and bring your own.

Configuration

Copy .env.example to .env and set:

Variable	Required	Description
OPENAI_API_KEY	Unless GENESYS_EMBEDDER=local	Embeddings
ANTHROPIC_API_KEY	No	Enables LLM-based causal inference (consolidation, contradiction detection). Off by default — without it, causal edges only come from edges the caller explicitly declares in memory_store plus cosine-similarity linking.
GENESYS_EMBEDDER	No	openai (default) or local (sentence-transformers, no API key)
GENESYS_PERSIST_PATH	No	JSON file path to persist state across restarts (in-memory otherwise)
GENESYS_USER_ID	No	Default user ID for single-tenant mode

See .env.example for all options.

Built by

Genesys is built by Rishi Meka at Astrix Labs. It came out of frustration with re-explaining project context to Claude every session. The goal is the intelligence layer between your LLM and your memory — fully open source.

Contributing

See CONTRIBUTING.md.

License

AGPL-3.0-or-later

Note: Genesys releases prior to v0.3.6 were documented as Apache 2.0 in error. The LICENSE file has always contained the AGPLv3 text. From v0.3.6 onward, all documentation correctly references AGPL-3.0-or-later with a Contributor License Agreement.