Helmo
Click on "Deploy 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., "@HelmoWhat's in the awaiting-human queue?"
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.
Helmo
Agent work record: agents write, humans read and meet.
A self-hosted, platform-agnostic work dashboard for AI agent teams. Agents (Claude Code, Codex, any MCP-capable agent — mostly headless bash loops) create and manage tickets through MCP tools. The human never edits: they read a view, and they work the awaiting_human queue in conversation with a summonable orchestrator. Status is self-reported, backed by evidence links; provenance comes from an append-only event log.

That view is the whole interface. Helmo exists for the moment your agents outrun your ability to re-read everything they did: what needs you is at the top, "done" without an evidence link surfaces as a flagged claim, and every line traces to who wrote it — which agent, which model, at what cost.
Product: helmo-product-description.md
Design (schema, IDs, tool surface): helmo-v0-design.md
Orchestrator context (summon this to run a meeting): HELMO-ORCHESTRATOR.md
Status
MVP. The store, MCP server, read-only view, smoke test, and explicit product
acceptance gate are dogfooded on Helmo's own development. The MVP gate is
npm run build, npm test, and npm run smoke; publication is a separate
release decision.
Related MCP server: Coordination Memory MCP
Install
Agent-led install is the primary path. Tell your agent: "I want to use Helmo — install it and set it up." and point it at AGENT-INSTALL.md. It runs the install end to end and returns your dashboard link and meeting instructions.
Manual setup, if you prefer:
git clone https://github.com/arthurcoulston/helmo.git
cd helmo
npm ci
npm run build
npm test
npm run smokeThat is the isolated cold setup: it needs no sibling project and the smoke uses a fresh temporary database. Two design-source drift comparisons report skipped when the private estate source is absent; the vendored copies are still tested. Helmo requires Node.js 20 or newer.
The store is a single SQLite file (WAL), default ~/.helmo/helmo.db, override with HELMO_DB.
Connect an agent (MCP, stdio)
Each agent's MCP config launches the server with the agent's identity:
{
"mcpServers": {
"helmo": {
"command": "node",
"args": ["/path/to/helmo/dist/server.js"],
"env": {
"HELMO_ACTOR": "{\"name\": \"builder-loop\", \"kind\": \"agent\", \"model\": \"claude-sonnet-5\", \"version\": \"1.0\"}"
}
}
}
}For Claude Code: claude mcp add helmo -e HELMO_ACTOR='{"name":"...","kind":"agent","model":"...","version":"1.0"}' -- node /path/to/helmo/dist/server.js
Tools include ticket creation, reading, updates, links, human questions and
answers, workstream budgets and seats, record-hygiene scanning and disposition, plus
helmo_record_product_completion, helmo_record_acceptance_verdict, and
helmo_check_product_acceptance. The tool descriptions teach correct usage;
no separate convention doc is required.
Product acceptance
Product acceptance is an explicit gate, separate from ordinary ticket status
and review type. A builder records the exact source under review as
repo@<full 40-character commit> with each commit's author. A non-author
reviewer records PASS or FAIL against exactly those refs. A missing or failed
verdict blocks acceptance; a new completion after remediation makes the old
verdict stale and requires a new handback. Closing a ticket or writing “PASS”
in prose does not satisfy this gate, while generic review tickets keep their
normal lifecycle.
Release scripts can require the intended manifest directly:
helmo-cli acceptance-check --ticket H-42 \
--refs '["helmo@0123456789abcdef0123456789abcdef01234567"]'The command exits zero only for an independent PASS on that exact manifest. Actor names, models, harness versions, and authors are provenance asserted by the callers; Helmo records and checks those assertions but does not authenticate their real-world identities.
The view (read-only)
npm run view # http://localhost:4400Run a meeting
In your agent session (Claude Code, Codex): "Summon helmo orchestrator" → load HELMO-ORCHESTRATOR.md as context. The orchestrator walks you through the awaiting-human queue and records your answers.
Development
npm test # includes the core invariant: tickets rebuild exactly from the event log
npm run smoke # asserted MCP lifecycle + a deliberate rejected CLI operation
npm run demo # stage the fictional board behind the screenshot above, in a throwaway dbNote: better-sqlite3 uses a prebuilt binary when one matches your Node
version; otherwise it compiles from source, which needs a C toolchain and
Python ≥ 3.8 (node-gyp). If install fails in node-gyp rebuild, an old
python3 on your PATH is the usual culprit — on macOS,
PYTHON=/usr/bin/python3 npm install fixes it.
Prior art
Helmo sits in a small family of agent work-trackers and owes a nod to beads, Steve Yegge's git-backed issue graph that gives coding agents long-horizon memory of their own work. If what you want is agent memory — epics, dependency graphs, issues that travel with the repo — use beads; it is excellent at that.
Helmo's center of gravity is the other side of the table: the human who has to
trust the work without re-reading it. Agents write; the human reads a view and
answers a queue. Hence the append-only event log with full actor provenance
(who wrote, which model, which harness), "done" without an evidence link
surfacing as a flagged claim rather than a fact, an awaiting_human queue
designed to protect the operator's attention, and per-ticket metering of what
the work actually cost. Same genus, different optimization.
License
This server cannot be deployed
Maintenance
Related MCP Connectors
Control plane for autonomous software labor. Agents claim objectives over MCP with audit trail.
- OctopadOAuthapp.octopad
The back-office workspace for your team's AIs: tasks, knowledge and context shared over MCP.
Work management where AI agents are first-class members: tasks, projects, memory over hosted MCP
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
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