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Genesys Memory MCP server

by Astrix-Labs·io.github.Astrix-Labs/genesys-memory·v0.5.2·28 stars
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Open-source causal memory for AI agents: persistent, explainable, MCP-native memory (13 tools).

B82/100grade B
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B82/100

full report

Adoption
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28 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

Genesys Memory tools (13, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • delete_memorywrite action

    Permanently delete a memory node and all its edges.

  • list_core_memories

    List all core memories, optionally filtered by category.

  • memory_amend

    Record a correction: creates a new memory that supersedes an existing one. The old memory is kept (decayed in recall results), not deleted.

  • memory_explain

    Explain a memory's score breakdown.

  • memory_recall

    Recall memories using hybrid search (vector + keyword + graph spreading activation).

  • memory_search

    Filtered vector search by status, category, date, or entity. Pass an EMPTY query to enumerate by recency instead (no vector search, no embedder needed): with since/active_since this answers 'what's new/changed since <ts>' without knowing what to query for.

  • memory_stats

    Get graph statistics.

  • memory_store

    Store a new memory in the causal memory graph. Use `related` for writer-specified typed edges (each {id, type}); `related_to` is legacy and always creates caused_by edges. May return `possible_conflicts` — heuristic hints, not verified contradictions.

  • memory_traverse

    Traverse the memory graph from a starting node. Returns reachable nodes AND the edges of the induced subgraph among them (source/target/type/weight/created_by) — a superset of the BFS tree, so paths can be reconstructed.

  • pin_memory

    Pin a memory to core status.

  • promote_to_org

    Promote a private memory to org visibility. Caller must own the node and belong to the target org.

  • set_core_preferences

    Configure core memory category preferences.

  • unpin_memory

    Unpin a memory and re-evaluate core eligibility.

Public scan report

scanner v0.1.9 · 2026-09-25 · same rubric, same numbers if you re-run it

no findings
  • Code scan35 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 18 days ago15/15
  • Maintainer identityregistry namespace matches repository owner7/10
Overall 82/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Genesys Memory compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
82/100
higher than 63% of 48,255 servers
Tools
13
the typical server has 7
Tools that change things
8%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
28
more than 92% of 28,688 servers

What its tools let an agent do: 11 read · 1 change · 1 delete

Install Genesys Memory in Claude Code, Cursor or VS Code

claude mcp add genesys-memory -- uvx genesys-memory
Add to Cursor

What the publisher says

From the Genesys Memory repository's README, as published. We do not edit it. Read it on GitHub

<!-- mcp-name: io.github.Astrix-Labs/papez -->

Papez

The intelligence layer for AI memory.

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

LoCoMo benchmark (certified)

Self-reported vendor figures above ~90 use different answerers/judges and are not comparable — the oracle retrieval ceiling under this frozen protocol is 94.9. Reproduce it yourself: Astrix-Labs/locomo-harness · full methodology · per-run results.

Hosted product: papez.ai — your personal memory for AI, carried across ChatGPT, Claude, and every MCP app · Pricing · Developer docs · Benchmark methodology (85.55 on LoCoMo, certified over 10 runs, receipts published)

What is this

Papez 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 (papez) 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 api.papez.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. Papez fixes that.

Quick Start

Requires Python 3.11 or newer.

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

pip install papez

Optional extras:

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

Run the stdio MCP server directly:

python3 -m papez

From source

git clone https://github.com/Astrix-Labs/papez.git
cd papez
pip install -e '.[dev]'
pytest tests/

Connect to your AI

Claude Code

claude mcp add papez -- python -m papez

Claude Desktop

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Genesys Memory: common questions

Is Genesys Memory MCP server safe?
Mostly: it is graded B (82/100). Read the Genesys Memory safety report
How do I install Genesys Memory?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Genesys Memory need an API key?
Yes. The registry entry asks for OPENAI_API_KEY, ANTHROPIC_API_KEY.
Is Genesys Memory maintained?
The last commit was 58 days ago (2026-09-07). The latest release is v0.5.2.
What can I use instead of Genesys Memory?
Servers from other publishers that do the same job: State Memory MCP server and Knowl MCP server.
How does Genesys Memory compare with other MCP servers?
safety score 82/100 (higher than 63% of 48,255 servers); tools 13 (the typical server has 7); tools that change things 8% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Genesys Memory

Same job from other publishers: the closest match first, then the best rated.

  • State Memory MCP
    Persistent workflow graph server tracking tasks, decisions, spatial entities, and blockers.
    A
  • Knowl
    Persistent memory for AI agents, for code, research and everyday life. Stale facts retire.
    A