c73531ec38
New src/agents/headless.ts wraps the turn runtime in the old headless
calling convention: startHeadlessAgent returns the turn id immediately
(callers record it in pointer files / bus events before completion) and
a done promise settling with { outcome, state, summary };
runHeadlessAgent awaits it. throwOnError reproduces the old
waitForRunCompletion({ throwOnError }) semantics via HeadlessRunError;
summary reproduces extractAgentResponse (last assistant text);
toolInputPaths replaces the run-bus tool-invocation subscriptions by
reading invoked calls from durable turn state. Model overrides pair the
caller's model id with the app-default provider. Unit-tested against an
injected fake runtime (7 tests).
Migrated all nine callers:
- background-tasks/runner: handle start wrapped in withUseCase so tools
(notify-user) read the use case via AsyncLocalStorage
- knowledge/live-note/runner: same shape, gains withUseCase
- pre_built/runner, knowledge/agent_notes: run-and-wait
- knowledge/tag_notes, label_emails, build_graph: edited/created paths
now come from turn state (toolInputPaths) instead of bus streaming
- knowledge/inline_tasks (both sites): summary text feeds the existing
marker parsing unchanged
- agent-schedule/runner: fire-and-forget start with
AbortSignal.timeout(TIMEOUT_MS); dropped the now-unused
runsRepo/agentRuntime/idGenerator plumbing
Code-mode sessions remain on the runs infrastructure (deliberate
carve-out until stage 7 scoping); agents/utils.ts stays for
launch-code-task's extractAgentResponse. The notify-user useCase gate
already prefers ALS with a best-effort fetchRun fallback, so it works
unchanged for turn ids.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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| .github/workflows | ||
| apps | ||
| assets | ||
| .env.example | ||
| .gitattributes | ||
| .gitignore | ||
| AGENTS.md | ||
| build-electron.sh | ||
| CLAUDE.md | ||
| docker-compose.yml | ||
| Dockerfile.qdrant | ||
| google-setup.md | ||
| LICENSE | ||
| README.md | ||
| start.sh | ||
Rowboat
A desktop AI coworker with a memory of your work and built-in surfaces to act on it.
Rowboat indexes your work into a living knowledge graph and uses that to get work done on your machine. It includes work surfaces for collaborating with AI: email client, notes, browser, code mode, meeting note taker, and workspaces for different projects.
Download latest for Mac/Windows/Linux: Download
Demo - email to code · Demo - knowledge graph
⭐ If you find Rowboat useful, please star the repo. It helps more people find it.
Overview
BrainRowboat indexes email, meetings, slack and assistant conversations into a living Obsidian-style backlinked knowledge graph. |
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Background agentsYou can set up background agents that run on events like new email or on schedule like every day at 8am. They can connect to tools, search the web, use the browser and write code using Claude Code or Codex. |
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Built-in BrowserRowboat includes a browser that lets you and assistant collaborate on web tasks. Because its isolated from your main browser, you can log in only to the accounts that want the assistant to access. |
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Meeting NotesA local meeting note-taker that taps into mic & speaker, produces live transcript and summarizes the meeting in a markdown file and updates the knowledge graph. |
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Code ModeCode mode lets you spin up parallel coding agents with Claude Code or Codex, and have Rowboat drive them with all the work context where needed. |
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IntegrationsIncludes one-click integrations to most popular products. |
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Installation
Download latest for Mac/Windows/Linux: Download
All release files: https://github.com/rowboatlabs/rowboat/releases/latest
Google setup
To connect Google services (Gmail, Calendar, and Drive), follow Google setup.
Voice input
To enable voice input and voice notes (optional), add a Deepgram API key in ~/.rowboat/config/deepgram.json
Voice output
To enable voice output (optional), add an ElevenLabs API key in ~/.rowboat/config/elevenlabs.json
Web search
To use Exa research search (optional), add the Exa API key in ~/.rowboat/config/exa-search.json
External tools
To enable external tools (optional), you can add any MCP server or use Composio tools by adding an API key in ~/.rowboat/config/composio.json
All API key files use the same format:
{
"apiKey": "<key>"
}
How it’s different
Most AI tools reconstruct context on demand by searching transcripts or documents.
Rowboat maintains long-lived knowledge instead:
- context accumulates over time
- relationships are explicit and inspectable
- notes are editable by you, not hidden inside a model
- everything lives on your machine as plain Markdown
The result is memory that compounds, rather than retrieval that starts cold every time.
Bring your own model
Rowboat works with the model setup you prefer:
- Local models via Ollama or LM Studio
- Hosted models (bring your own API key/provider)
- Swap models anytime — your data stays in your local Markdown vault
Extend Rowboat with tools (MCP)
Rowboat can connect to external tools and services via Model Context Protocol (MCP). That means you can plug in (for example) search, databases, CRMs, support tools, and automations - or your own internal tools.
Examples: Exa (web search), Twitter/X, ElevenLabs (voice), Slack, Linear/Jira, GitHub, and more.
Local-first by design
- All data is stored locally as plain Markdown
- No proprietary formats or hosted lock-in
- You can inspect, edit, back up, or delete everything at any time