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.
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.

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
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
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