AI & MemoryMCP Registrypypi13 toolsA · 83

genesys-memory MCP server

Name: genesys-memory
Author: Astrix-Labs

io.github.Astrix-Labs · Updated 3mo ago

Open-source causal memory for AI agents: persistent, explainable, MCP-native memory (13 tools).

At a glance

genesys-memory is an MCP server in the AI & Memory category. It runs locally over stdio via `uvx genesys-memory`, and needs OPENAI_API_KEY and ANTHROPIC_API_KEY. It exposes 13 tools, including memory_store, memory_amend and memory_recall. It scores A (83/100) on the MCP Skills Hub health score as of Oct 28, 2026.

Source

One-click install

VS Code VS Code Insiders Cursor

Opens your editor and pre-fills the config. Your editor will ask before adding it.

How to install genesys-memory in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install genesys-memory in Claude CodeAnthropic's agentic CLI

Add the server

bash

claude mcp add genesys-memory -e 'OPENAI_API_KEY=<OPENAI_API_KEY>' -e 'ANTHROPIC_API_KEY=<ANTHROPIC_API_KEY>' -- uvx genesys-memory

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
3
Verify it's connected
bash
```
claude mcp list
```
Sharing it with your team?
Add `--scope project` to write it to .mcp.json in the repository root, which you can commit (with the secrets left as environment variables).
Use it in a session
Ask Claude to use the tools this server exposes (e.g. memory_store, memory_amend, memory_recall). It discovers them automatically.

How to install genesys-memory in Claude DesktopDesktop app (MCP config)

Open your MCP config
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this server

json

{
  "mcpServers": {
    "genesys-memory": {
      "command": "uvx",
      "args": [
        "genesys-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
      }
    }
  }
}

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
Restart Claude Desktop
The tools appear under the 🔌 menu once the app reloads.

How to install genesys-memory in CursorAI-first code editor

Create / edit .cursor/mcp.json in your project

json

{
  "mcpServers": {
    "genesys-memory": {
      "command": "uvx",
      "args": [
        "genesys-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
      }
    }
  }
}

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
Enable it
Cursor Settings → MCP → toggle the server on. Tools become available to the Composer/Agent.

How to install genesys-memory in Codex CLIOpenAI coding agent

Add to ~/.codex/config.toml

toml

[mcp_servers.genesys-memory]
command = "uvx"
args = ["genesys-memory"]
env = { OPENAI_API_KEY = "<OPENAI_API_KEY>", ANTHROPIC_API_KEY = "<ANTHROPIC_API_KEY>" }

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
Run Codex
Codex loads MCP servers from config.toml on startup and exposes their tools to the agent.

How to install genesys-memory in WindsurfCodeium's agentic IDE

Edit ~/.codeium/windsurf/mcp_config.json

json

{
  "mcpServers": {
    "genesys-memory": {
      "command": "uvx",
      "args": [
        "genesys-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
      }
    }
  }
}

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
Reload
Windsurf → Cascade → refresh MCP servers. Cascade can now call the tools.

How to install genesys-memory in ClineVS Code autonomous agent

Open Cline → MCP Servers → Configure
Adds to cline_mcp_settings.json.

Add the server

json

{
  "mcpServers": {
    "genesys-memory": {
      "command": "uvx",
      "args": [
        "genesys-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
      }
    }
  }
}

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.

How to install genesys-memory in VS CodeCopilot / MCP support

Create .vscode/mcp.json

json

{
  "servers": {
    "genesys-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "genesys-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
      }
    }
  }
}

Fill in your credentials
Replace <OPENAI_API_KEY>, <ANTHROPIC_API_KEY> with your real values. Keep secrets out of any config file you commit.
Start it
VS Code shows a ▶ Start action above the server entry. Agent mode (Copilot Chat) can then use the tools.

What does genesys-memory need from you?

API key — OpenAI API key for embedding generation (optional — use GENESYS_EMBEDDER=local for no API key)

OPENAI_API_KEYoptionalsecretEnvironment variable
OpenAI API key for embedding generation (optional — use GENESYS_EMBEDDER=local for no API key)
ANTHROPIC_API_KEYoptionalsecretEnvironment variable
Anthropic API key for LLM-based memory processing (optional)
GENESYS_EMBEDDERoptionalEnvironment variable
Embedding provider: 'openai' or 'local' (default: openai)

Pass secrets through your shell environment rather than committing them into a config file.

What tools does genesys-memory expose? (13)

Taken from the project's README — the server itself was not queried, so check its docs for the exact schemas.

memory_storeStore a new memory. Use `related` for writer-specified typed edges (`{id, type}`); `related_to` is legacy and always creates `caused_by`. Optional `category`. May return `possible_conflicts` (heuristic hints).
memory_amendRecord a correction: creates a new memory that supersedes an existing one. The old memory is kept (decayed in recall), not deleted.
memory_recallRecall memories by natural language query (vector + keyword + graph spreading activation). Supports `verbosity: "concise"` for lightweight payloads.
memory_searchFiltered vector search by status, category, date (`since`), last-active date (`active_since`), or entity. Pass an empty query to enumerate by recency instead (no embedder needed) — with `since`/`active_since` this answers "what's new since
memory_traverseWalk the causal graph from a node. Returns reachable nodes and the edges of the induced subgraph (`source/target/type/weight/created_by`) — a superset of the BFS tree, so paths can be reconstructed. Honors `edge_types`.
memory_explainExplain a memory's score. Includes a `score_model` block (formula + live per-force breakdown + staleness note) and `removal_impact`.
memory_statsGet memory system statistics
pin_memoryPin a memory so it's never forgotten
unpin_memoryUnpin a previously pinned memory
delete_memoryPermanently delete a memory
list_core_memoriesList core memories, optionally filtered by category
set_core_preferencesSet user preferences for core memory categories
promote_to_orgPromote a private memory to org visibility

What the code can do

Source analysis currently covers npm packages only. This server ships as a Python package, which needs a different parser.

Before you install

Signals gathered from the source repo and package registry — not a safety verdict.

What it can reach

Runs code on your machine — Executes as a local process with your user's permissions, including filesystem access.
Downloads at launch — The package is fetched from its registry each run unless you pin a version.

Client compatibility

Claude Code: NativeClaude Desktop: NativeCursor: NativeCodex CLI: NativeWindsurf: NativeCline: NativeVS Code: Native

Health score

A · 83

Maintenance, adoption, transparency and packaging — not a safety audit. How this is calculated

Maintenance30%not measurable
No commit history available.
Adoption20%not measurable
No usage data available.
Transparency20%75%
3/4: ✓ public source repo, ✗ declared license, ✓ documentation, ✓ discoverable tool list.
Provenance15%75%
Links to its own repository; package linkage unverified.
Packaging15%100%
Publishes a pinned version (0.5.2).

Documentation

Full README

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)

| System | Score | Protocol | |---|---|---| | Papez | 85.55 ± 0.37 | Frozen: gpt-4o-mini answerer + judge, temp 0, n=1,540, cats 1–4, 10 runs (July 2026) | | Zep | 75.14 | Comparable published setup | | Mem0 | 66.9 | Comparable published setup (Mem0 paper) |

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

Excerpt — the full document is on GitHub.

Alternatives to genesys-memory

Maintain genesys-memory? Add a badge to your README

Shows its health grade, and links readers to install steps for every client.

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