Helmo

Helmo

A self-hosted work dashboard for AI agent teams, where agents create and manage tickets through MCP tools and humans review an awaiting-human queue backed by an append-only event log. Enables ticket lifecycle management and evidence-based provenance tracking.

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README

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.

The Helmo view: a question awaiting the human with options and an agent recommendation, a hygiene flag, workstream steering, and work in motion

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.

Status

Walking skeleton. Store + MCP server + plain read-only view. Being dogfooded on its own development.

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:

npm install && npm run build

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: helmo_create_ticket, helmo_get_ticket, helmo_list_tickets, helmo_update_ticket, helmo_link_tickets, helmo_return_to_human, helmo_answer_ticket. The tool descriptions teach correct usage; no separate convention doc is required.

The view (read-only)

npm run view    # http://localhost:4400

Run 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   # end-to-end MCP stdio round trip
npm run demo    # stage the fictional board behind the screenshot above, in a throwaway db

Note: 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

MIT

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