RCLL 7fca442903 chore(mcp-server): npm storefront metadata before the first publish

Published package metadata cannot be changed without shipping another
version, so this has to land before 0.1.0 goes out, not after.

- repository/homepage/bugs were all missing: the npm page would have
  rendered with no link back to the source at all. repository names the
  GitHub mirror deliberately — npm tooling and the --provenance
  attestation are keyed to the repo the workflow builds in; the canonical
  repository is stated in the README instead.
- README is the npm page body. Its Quick Start opened with `cd
  mcp-server && npm install`, which is the from-a-clone path — the one
  instruction that cannot work for somebody who just installed the
  package. `npx fleet-memory-mcp` first, clone path kept below it, and a
  line saying this package is the client half and does not start a store.
- LICENSE was not in the tarball. MIT text now ships with the artifact,
  Vectorize AI copyright intact.
- engines and an explicit files list, so the tarball contents are stated
  rather than inferred.

2026-08-26 10:30:03 +03:00

3.7 KiB

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RCLL MCP Server (`fleet-memory-mcp`)

Standalone MCP server that exposes RCLL memory tools to any MCP-compatible client (Claude Code, OpenClaw, etc). RCLL is self-hosted, hierarchical shared memory for a team of AI agents — topic-scoped rooms, L0–L3 depth, pgvector under the hood. The published artifact is named fleet-memory; RCLL is the product name.

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

Canonical repository: https://godcrm.ai/git/holetron-lab/fleet-memory — self-hosted, clonable anonymously. https://github.com/holetron-lab/fleet-memory is a read-only mirror of it, and is where issues and stars go.

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.

Tools

Tool	Description
`memory_retain`	Save memories with room/hall/layer classification
`memory_recall`	Scoped semantic search with room filtering
`memory_reflect`	Deep reasoning + synthesis over stored memories
`memory_compress`	Create closet summaries from accumulated facts
`memory_bridge`	Cross-bank tunnels between related memories

Quick Start

npx fleet-memory-mcp

FLEET_URL points at your own RCLL backend (default http://127.0.0.1:5100); this package is the MCP client half and does not start a store for you. Standing one up is the quickstart on rcll.ai.

From a clone instead:

cd mcp-server
npm install
FLEET_URL=http://localhost:5100 node server.js

Claude Code

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "rcll": {
      "command": "npx",
      "args": ["-y", "fleet-memory-mcp"],
      "env": {
        "FLEET_URL": "http://localhost:5100",
        "FLEET_BANK": "my-agent-bank"
      }
    }
  }
}

Environment Variables

Variable	Default	Description
`FLEET_URL`	`http://127.0.0.1:5100`	fleet-memory backend base URL
`FLEET_BANK`	`fleet-main`	Default memory bank ID. Set it explicitly.

Migrating from `hindsight-mempalace-mcp`

That package defaulted to bank mempalace-main. fleet-memory-mcp defaults to fleet-main, so an install that never set the variable would open a different, empty bank — which reads as "the update erased my memory". It does not: the old bank is still there.

Set FLEET_BANK=mempalace-main to keep reading it, or move the contents into a new bank first. When FLEET_BANK and MEMPALACE_BANK are both unset, the server prints which bank it defaulted to on stderr rather than picking one silently.

Deprecated (still read, with a notice on stderr)

Installs created before the rebrand keep working — these are used only when the FLEET_* equivalent is unset, and they will be dropped in a future major.

Legacy variable	Replaced by
`HINDSIGHT_URL`	`FLEET_URL`
`MEMPALACE_BANK`	`FLEET_BANK`

Memory Taxonomy

Rooms (topics): auth, pipeline, schema, infrastructure, ui, api, deployment, monitoring, agent, general

Halls (knowledge types — the hall field): fact, event, decision, preference, discovery, procedure, warning

Layers (depth):

L0 — Surface / identity (always at hand)
L1 — Critical (recalled by default)
L2 — Session (default for new memories)
L3 — Deepest burrow / archive (deep search only, compressed into closets)

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RCLL MCP Server (fleet-memory-mcp)