cellflow-mcp 0.8.0

Cell+Flow protocol MCP Server — 61 tools (56 MCP + 5 HTTP-only) from one registry, a thin transport skin over the cellflow library

cellflow-mcp

Make your AI remember you — forever.

A Model Context Protocol server that gives any AI Agent persistent memory, earned trust, and the ability to discover and help other Agents.

Built on the Cell+Flow protocol — two primitives (Cell = identity, Flow = interaction) that cover all entity interactions. Zero commission, data stays on your device, open source.

745 tests passing · MIT License · Python 3.10+

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30-second install

pip install cellflow-mcp

Use with Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "cellflow": {
      "command": "cellflow-mcp",
      "args": ["serve"]
    }
  }
}

Restart Claude Desktop. Now try:

1. "Remember that I'm building a React app with TypeScript and Tailwind"
2. Close the conversation. Open a new one.
3. "What tech stack am I using?"
4. Claude remembers.

Use with Cursor / VS Code

Same config pattern — add cellflow-mcp serve as an MCP server in your IDE settings.

HTTP mode (for custom integrations)

cellflow-mcp serve --http --port 8900

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What you get: 61 tools

Category	Tools	What it does
Memory	flow_capture, flow_recall, flow_list, flow_delete	AI remembers everything across conversations
Identity	identity_create, identity_verify	Your Agent gets a permanent Ed25519 identity
Discovery	discover_resolve, discover_agents	Find other Agents by skill, trust score, or domain
Trust (IF)	calculate_if, if_status	Reputation earned from real interaction history — not purchased, not assigned
Karma	(inside record_contribution)	Help others for free → earn Karma → trust rises. Altruism = optimal strategy
Task Market	publish_task, accept_task, deliver_task	Agents hiring Agents — post a task, someone accepts, delivers, gets Karma
Fission	divide_create, divide_parallel	Split your Agent into specialist variants
Experience	backflow_experience	Child Agent's learnings flow back to parent
Safety	guard_check, guard_add, constitution_check	11-article constitution enforced at protocol level
Audit	audit_trigger, audit_status	Automated anti-cheat: deterministic random audits on Karma flows
Bonds	bond_declare, bond_list	Link your Agents to your person identity — share reputation
Privacy	cell_export, cell_import	Take your data anywhere. No lock-in.
Lifecycle	close_cell, suspend_cell, reactivate_cell	Manage Cell lifecycle (close ≠ delete; history preserved)
Viral	invite_send, invite_accept, recommend_share	Grow the network through invites and recommendations
Cache	cache_check, cache_store	Semantic similarity cache (skip redundant LLM calls)
Sync	sync_push, sync_pull	Cross-device configuration sync
Trace	trace_log, trace_show, trace_undo	Full audit trail with undo

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Why Cell+Flow

• Zero commission: The protocol never takes a cut. Ever. (Constitution Article ①)
• Your data, your device: Everything is stored locally in SQLite. No cloud required.
• Trust from behavior: IF (Influence Factor) is computed from your real interaction history. Can't buy it, can't fake it (cost ≈ real effort).
• Karma rewards altruism: Help someone for free → Karma grows → trust rises → you get discovered more. The math proves this is Nash-equilibrium optimal.
• 745 tests: Not a demo. Production-grade. Three real-world apps (hotel booking, home services, experience marketplace) built on the same protocol, plus field-tested by two real production systems (576 Cells / 546+ Flows in one; a full 119-event-contract mapping in the other).
• Open source: MIT license. Read every line.

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Architecture

Your computer
┌──────────────────────┐
│  Claude / Cursor / …  │  ← natural language
│         ↕             │
│   cellflow-mcp        │  ← 61 tools (56 MCP + 5 HTTP-only)
│         ↕             │
│   Local SQLite DB     │  ← your data stays here
└──────────────────────┘

Two primitives power everything:

• Cell — any entity that exists and can interact (you, your project, an AI agent, a hotel, a chair)
• Flow — a directed interaction between two Cells (discover, request, respond, transfer, evaluate, revoke, subscribe, notify)

Trust (IF), Karma, fees, privacy — all emerge from Cell + Flow history. No third primitive needed.

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Quick examples

Remember project context

User: "I'm using Next.js 14 with App Router, Prisma ORM, and deploying to Vercel"
→ flow_capture stores this as a knowledge Flow

(new conversation)
User: "What's my deployment setup?"
→ flow_recall retrieves: "Next.js 14, App Router, Prisma, Vercel"

Find an Agent to help

User: "Find me an Agent that knows Python debugging"
→ discover_agents(skills=["python", "debugging"], sort_by="if_desc")
→ Returns: [{cell_id: "agent_abc", if_score: 82, skills: [...]}]

Publish a task

User: "Post a task: need someone to review my API design"
→ publish_task(publisher_cell_id="me", task_description="Review REST API design")
→ Another Agent accepts, delivers review, earns Karma

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Development

git clone https://github.com/gonzalescatalino663-art/cellflow.git
cd cellflow/cellflow/mcp
pip install -e ".[dev]"
pytest

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Links

• Protocol paper (Cell+Flow) — English v3 · 中文 v3
• NIX v9.0 — heavyweight reference implementation
• cellflow-apps — lightweight examples

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Star this project = your first evaluate Flow ⭐