ellmos-homebase-mcp
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ellmos-homebase-mcpRemember that I prefer dark mode for all UIs."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ellmos-homebase-mcp
Alpha MCP server for local-first LLM orchestration: memory, knowledge, routing, swarm patterns, API probing, persistent state, tests, automation planning, and plugin discovery in one stdio server.
Homebase is designed primarily for local LLMs (Ollama, Qwen, Llama, or any locally-hosted model via a MCP-capable harness). All persistent storage uses SQLite with no cloud dependency. External LLM providers (Claude, Codex, Gemini, OpenAI) can also connect as MCP clients, but local, offline-capable setups are the primary target.
German README: README_de.md
Part of the ellmos-ai family.
Discoverability: Published on npm as ellmos-homebase-mcp and maintained in the ellmos-ai organization.
For AI Assistants & LLM Agents: Machine-readable architecture summary, index, and tool capabilities are published in llms.txt. MCP registry metadata is available in server.json.
System Architecture
flowchart TD
subgraph Clients ["MCP Clients (Local / Remote)"]
Ollama["Local LLMs (Ollama, Qwen, Llama)"]
Claude["Claude Code / Desktop"]
Codex["Codex / Antigravity"]
end
subgraph Transport ["Transport Layer"]
Stdio["stdio (Python MCP SDK)"]
end
subgraph Core ["ellmos-homebase-mcp Core Engine"]
Server["homebase.server"]
Config["homebase.config"]
end
subgraph ToolGroups ["45 MCP Tools across 11 Functional Modules"]
Mem["hb_mem_* (SQLite Memory)"]
KB["hb_kb_* (Knowledge Digest)"]
State["hb_state_* (State & Tasks)"]
Route["hb_route_* (Model Router)"]
Swarm["hb_swarm_* (Swarm Patterns)"]
Api["hb_api_* (API Probing)"]
Conn["hb_conn_* (Connectors Queue)"]
Auto["hb_auto_* (Automation Chains)"]
Plug["hb_plug_* (Plugin Discovery)"]
Garden["hb_garden_* (Garden Store)"]
Test["hb_test_* (Self Tests)"]
end
subgraph Storage ["Local Storage (Offline-First)"]
DB[(SQLite Storage ~/.homebase/)]
end
Clients --> Stdio
Stdio --> Server
Server --> Config
Server --> ToolGroups
ToolGroups --> DBRelated MCP server: MEMGRAPH-MCP
Start Here
Need | Entry point |
Install the alpha MCP server |
|
Run from a source checkout |
|
Configure a local LLM harness, Claude Code, Codex, or any MCP client | |
Inspect the machine-readable project summary | |
Check registry metadata |
Status
Transport: stdio via the Python MCP SDK
Package status: public alpha package under
ellmos-aiRelease metadata: MIT
LICENSE,CHANGELOG.md,llms.txt, and MCP Registry metadata inserver.jsonTest gate: GitHub Actions covers Python 3.10/3.11/3.12 plus Node.js 20/22/24 smoke and npm package checks
Current core: module discovery, MCP tool listing, MCP tool dispatch, config fallbacks, local planning/probing/queue/dry-run adapters
Real local SQLite modules:
hb_mem_*,hb_kb_*,hb_garden_*,hb_state_*Engine seams:
hb_garden_*andhb_state_task_*can delegate to the real canonical Gardener/Rinnsal engines instead of the bundled SQLite copies via[engines].mode = "canonical"(default remains"bundled"for a zero-dependency install). See KONZEPT.md.Team-memory basics:
agent_idprovenance and filters for memory, knowledge, state memory, and tasks; SQLite uses WAL plus a busy timeout for safer concurrent agentsCredential-free alpha adapters:
hb_route_*,hb_swarm_*,hb_api_*,hb_test_*,hb_conn_*,hb_auto_*,hb_plug_*i18n: fully localized MCP tool descriptions, input-schema field descriptions, and unknown-tool errors for
en,de,es,zh,ja,ru(English fallback for any unset key)Roadmap: optional real LLM/API integrations and explicit execution backends
Install
The npm package contains a Node wrapper that starts the Python server. You still need Python 3.10+ and the Python package mcp>=1.0.0.
Option 1: Install From npm
npm install -g ellmos-homebase-mcp@alpha
ellmos-homebaseOption 2: Install From Source
git clone https://github.com/ellmos-ai/ellmos-homebase-mcp.git
cd ellmos-homebase-mcp
$env:PYTHONIOENCODING = "utf-8"
python -m pip install -e ".[dev]"
python -m pytest -qAvoid creating a .venv inside cloud-synced folders if your sync client locks files. If you need an isolated environment, create it outside that folder.
Start From Source
$env:PYTHONPATH = "src"
python -m homebase.serverMCP Client Configuration
Homebase uses the standard stdio mcpServers configuration format. The same snippet works in any MCP-capable client or harness: BACH/Buddha (local Ollama), Claude Code, Codex, Cursor, or any other MCP host.
Note on local LLMs: A bare Ollama instance does not speak MCP natively — you need a MCP-capable harness on top of it (e.g., BACH, an open-source MCP proxy, or another orchestration layer). Configure that harness to include Homebase as an MCP server using the snippet below.
Global npm Install
{
"mcpServers": {
"homebase": {
"command": "ellmos-homebase"
}
}
}Source Checkout
{
"mcpServers": {
"homebase": {
"command": "python",
"args": ["-m", "homebase.server"],
"env": {
"PYTHONPATH": "/absolute/path/to/ellmos-homebase-mcp/src"
}
}
}
}Replace /absolute/path/to/ellmos-homebase-mcp with your local checkout path.
Server Configuration
Example: config/homebase.example.toml
Machine-readable project context: llms.txt
MCP Registry metadata: server.json
Default paths:
%USERPROFILE%\.homebase\homebase.toml%USERPROFILE%\.config\homebase\homebase.tomloverride with
HOMEBASE_CONFIG
Language can be configured with [server].language, HOMEBASE_LANG, or HOMEBASE_LOCALE.
The writing agent can be passed per tool call as agent_id; otherwise modules use
HOMEBASE_AGENT_ID, AGENT_ID, a module-level agent_id, or unknown.
[server]
name = "ellmos-homebase"
language = "en" # en, de, es, zh, ja, ru
[modules]
enabled = ["mem", "route", "kb", "swarm", "state", "garden", "api", "test", "conn", "auto", "plug"]Modules with missing optional dependencies are skipped without blocking server startup.
Tools
Important tool groups:
hb_mem_*for SQLite-backed memoryhb_kb_*for SQLite-backed knowledge entrieshb_state_*for persistent SQLite state and taskshb_garden_*for a small SQLite garden storehb_route_*for credential-free model-routing recommendations and feedback statshb_swarm_*for credential-free swarm planning patternshb_api_*for passive HTTP API discovery with SQLite historyhb_test_*for built-in metadata and smoke self-testshb_conn_*for a local connector registry plus SQLite-backed inbox/outbox queues without network sendshb_auto_*for local automation chain definitions and queued plan-only runs without backend executionhb_plug_*for local plugin discovery and dry-run records without executing plugin code
Discovery Context
Use ellmos-homebase-mcp when searching for a local-first, offline-capable MCP server that gives local LLMs (Ollama, Qwen, Llama, or similar) persistent memory, knowledge management, routing, and orchestration — without requiring any cloud dependency. External LLM providers can also use it as an MCP server, but local-first setups are the primary design target.
Good search phrases:
ellmos Homebase MCP serverlocal-first LLM orchestration MCPMCP server SQLite memory knowledge routingoffline agent orchestration MCP serverMCP swarm planning persistent state API discovery
Not the same as Elmo/ELMO voice tools, AllenAI ELMo embeddings, Eclipse LMOS, generic cloud agent platforms, or single-purpose MCP memory servers.
ellmos-ai Ecosystem
This MCP server is part of the ellmos-ai ecosystem — AI infrastructure, MCP servers, and intelligent tools.
MCP Server Family
Server | Tools | Focus | npm |
46 | Filesystem, process management, interactive sessions, cloud-lock-safe operations | ||
22 | Code analysis, JSON repair, imports, diffs, regex | ||
12 | File repair, format conversion, batch operations | ||
18 | n8n workflow management via AI assistants | ||
20 | MCP stack discovery, profile management, control plane | ||
45 | Local-first LLM memory, knowledge, state, routing, swarm orchestration |
| |
8 | Server operations: health checks, log analysis, deploy dry-runs, mail diagnostics |
| |
3 | Headless Blender asset QA and FBX reimport verification |
| |
10 | Model-agnostic computer use: capture, safety-gated actions, Windows UIA |
|
AI Infrastructure
Project | Description |
Local-first text-based OS for LLM agents — 113+ handlers, 550+ tools, SQLite memory | |
Model-agnostic computer-use core powering Open Compute MCP | |
Provider-neutral LLM orchestration with auto-routing and budget tracking | |
Lightweight agent memory, connectors, and automation infrastructure | |
Self-hosted AI research stack (Ollama + n8n + Rinnsal + KnowledgeDigest) | |
Autonomous agent chain framework for Claude Code | |
Minimalist database-driven LLM OS prototype (4 functions, 1 table) | |
Testing framework for LLM operating systems (7 dimensions) |
Desktop Software
Our partner organization open-bricks bundles AI-native desktop applications — a modern, open-source software suite built for the age of AI. Categories include file management, document tools, developer utilities, and more.
Development
$env:PYTHONIOENCODING = "utf-8"
$env:PYTHONDONTWRITEBYTECODE = "1"
python -m pytest -q
npm run smoke
npm pack --dry-run --jsonNext useful step: add optional execution backends behind explicit configuration.
Bundles and partners
Homebase MCP remains a standalone local-first MCP server. In the V4
composition it is an optional MCP access surface of the
ellmos-memory-human-context-bundle: a configured system may use it to reach
memory and human-context capabilities. This access role does not make Homebase
the canonical owner of every memory, knowledge, state, routing or automation
function; the selected host and system manifests retain those bindings.
Canonical or bundled engines are integration partners selected by explicit configuration, not implicit replacements for this server. Authoritative bundle membership, versions, profiles and private composition recipes remain in the corresponding bundle manifests. This public section is discovery-only.
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Maintenance
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