AI & MemoryMCP Registrynpm5 toolsA · 83

RCLL MCP server

Name: RCLL
Author: rcll.ai

ai.rcll · Updated 2mo ago

Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path.

At a glance

RCLL is an MCP server in the AI & Memory category. It runs locally over stdio via `npx -y fleet-memory-mcp`, and declares no required credentials. It exposes 5 tools, including memory_retain, memory_recall and memory_reflect. It scores A (83/100) on the MCP Skills Hub health score as of Oct 28, 2026.

Source 0

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 RCLL in your AI client

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

How to install RCLL in Claude CodeAnthropic's agentic CLI

Add the server

bash

claude mcp add fleet-memory -- npx -y fleet-memory-mcp

2
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_retain, memory_recall, memory_reflect). It discovers them automatically.

How to install RCLL 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": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}

Restart Claude Desktop
The tools appear under the 🔌 menu once the app reloads.

How to install RCLL in CursorAI-first code editor

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

json

{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}

Enable it
Cursor Settings → MCP → toggle the server on. Tools become available to the Composer/Agent.

How to install RCLL in Codex CLIOpenAI coding agent

Add to ~/.codex/config.toml

toml

[mcp_servers.fleet-memory]
command = "npx"
args = ["-y", "fleet-memory-mcp"]

Run Codex
Codex loads MCP servers from config.toml on startup and exposes their tools to the agent.

How to install RCLL in WindsurfCodeium's agentic IDE

Edit ~/.codeium/windsurf/mcp_config.json

json

{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}

Reload
Windsurf → Cascade → refresh MCP servers. Cascade can now call the tools.

How to install RCLL in ClineVS Code autonomous agent

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

Add the server

json

{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}

How to install RCLL in VS CodeCopilot / MCP support

Create .vscode/mcp.json

json

{
  "servers": {
    "fleet-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}

Start it
VS Code shows a ▶ Start action above the server entry. Agent mode (Copilot Chat) can then use the tools.

What does RCLL need from you?

Configuration only — Declares config values but no credentials.

FLEET_URLoptionalEnvironment variable
Base URL of the fleet-memory backend (self-hosted). Defaults to http://127.0.0.1:5100. The pre-rebrand name HINDSIGHT_URL is still read as a fallback.
FLEET_BANKoptionalEnvironment variable
Default memory bank ID for retain/recall. The pre-rebrand variable MEMPALACE_BANK is still read as a fallback. Migrating from hindsight-mempalace-mcp? Set this to mempalace-main to keep reading your existing bank; see mcp-server/README.md.

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

What tools does RCLL expose? (5)

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

memory_retainSave a memory with automatic room/hall classification
memory_recallScoped semantic search with room/hall/layer filters
memory_reflectDeep reasoning — synthesize facts, find patterns, answer with citations
memory_compressCreate closet summaries from accumulated facts
memory_bridgeCross-bank tunnels between related memories

What the code can do

Read from the published package source — never executed. Parsed 1 of 1 source file.

This server can make network requests and read your environment variables.

How it was published

Published by a verified CI identity (github trusted publishing) rather than a personal token.
Has a provenance attestation tying this artifact to the commit it was built from.
1 npm maintainer — a single point of compromise · last published by GitHub Actions.

Maintainer of this package and think something here is wrong? Tell us and we'll correct it.

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.

Last commit 2 months ago — Actively maintained.
Repository created 2 months ago — Very new project with little track record yet.
0 GitHub stars — Very little community usage so far.
48 weekly downloads — Very few installs — you would be an early user.

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%96%
Last commit 61 days ago.
Adoption20%20%
0 stars, 48 weekly downloads.
Transparency20%100%
4/4: ✓ public source repo, ✓ declared license, ✓ documentation, ✓ discoverable tool list.
Provenance15%100%
Package and registry entry point at the same repository.
Packaging15%100%
Publishes a pinned version (0.1.0).

Documentation

Full README

RCLL

Self-hosted shared memory for a _team_ of AI agents. Storage + structure in one system.

RCLL — team memory for agent fleets. Built on Hindsight (github.com/vectorize-io/hindsight, MIT).

[!IMPORTANT] You are looking at a read-only mirror. The canonical repository is `godcrm.ai/git/holetron-lab/fleet-memory` — self-hosted, public, clonable anonymously with no account anywhere in the chain. Everything here is pushed out from there, so a merge performed on GitHub is overwritten by the next sync, usually within the hour. Issues and stars belong here and are read. A pull request is welcome here as well — it gets merged on the canonical side and arrives back here on the next sync.

RCLL is a fork of `vectorize-io/hindsight` (MIT). It keeps Hindsight's storage engine and adds rooms — topic scoping over one shared store, which is selectivity rather than isolation — plus a hierarchical depth model (L0–L3). The room/hall/layer taxonomy is prior art in the hierarchical-memory space; the implementation here is our own.

RCLL is recall with the vowels dropped — the one operation every agent in the fleet performs before it does anything else. The tool is literally called memory_recall; the product is named after the call.

Its one structural property worth remembering: the read path never invokes a language model. A recall costs CPU and zero model tokens — see architecture.

Status

The source is public and MIT. There is no packaged release yet: fleet-memory-mcp is not published on npm and no container image is pushed. Running RCLL today means building from this tree, which the quick start below does. Don't quote an install command as working until rcll.ai shows one.

| | | |---|---| | Site, measured numbers | rcll.ai · benchmarks | | Written for an AI agent, not a human | rcll.ai/agents.md | | Where we branched from upstream, and how to take the next release | FORK.md | | Architecture spec | RCLL.md |

How it works

text

┌──────────────────────────────────────────────────────┐
│                         RCLL                         │
│                                                      │
│  ┌─── Room: auth ───┐  ┌─── Room: pipeline ──┐       │
│  │ Hall: facts      │  │ Hall: decisions     │       │
│  │ Hall: procedures │  │ Hall: events        │       │
│  │ Hall: warnings   │  │ Hall: facts         │       │
│  │                  │  │                     │       │
│  │  L0 ████ always  │  │  L0 ████ always     │       │
│  │  L1 ███░ warm    │  │  L1 ███░ warm       │       │
│  │  L2 ██░░ cold    │  │  L2 ██░░ cold       │       │
│  │  L3 █░░░ archive │  │  L3 █░░░ archive    │       │
│  └──────────────────┘  └─────────────────────┘       │
│           │                      │                   │
│           └──── Tunnel ──────────┘                   │
│                (cross-bank bridge)                   │
│                                                      │
│  Closets: compressed summaries + source pointers     │
└──────────────────────┬───────────────────────────────┘
                       │
              Hindsight vector store
              (embeddings + semantic search)

Rooms — topic isolation. Auth, pipeline, infrastructure, schema — each topic in its own room. An agent searching for auth facts won't wade through 500 deploy memories.

Excerpt — the full document is on GitHub.

Alternatives to RCLL

Maintain RCLL? Add a badge to your README

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

markdown

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Catalog data refreshed Oct 28, 2026. Something wrong? Report it.