README file from
GithubOpenAugi
The Personal Intelligence Layer for Your Agents
OpenAugi is a context engineering layer and personal agent harness for your data (currently as an Obsidian plugin). It sits between your knowledge and your AI agents — gathering the right context, dispatching tasks, and shortcutting the loop from idea to action.
Your vault is full of plans, decisions, research, and context. OpenAugi makes that context available to agents so they can actually do useful work.
Works with the OpenAugi MCP server for semantic search, hub discovery, and structured access to your vault data. (MCP server coming soon.)
Join the Discord. Parent repo.
What It Does
1. Context Engineering
Gather precisely the right context from your vault — not too much, not too little.
- Link traversal — Follow wikilinks up to 3 levels deep (breadth-first)
- Backlink discovery — Find notes that reference your notes, not just notes you link to
- Journal filtering — Extract only recent sections from date-headed journal notes
- Character budgets — Stay within token limits with configurable caps
- Checkbox review — Toggle individual notes on/off before processing
2. Task Dispatch
Write a task note, link your context, and launch an agent session directly from Obsidian.
- tmux sessions — Each task gets its own persistent terminal session
- Context injection — Task note body + all linked notes are assembled and passed to the agent
- Named repo paths — Map short names to directories so
working_dir: my-apijust works - Session management — List, attach to, or kill running agent sessions
- MCP integration — Agents can search your vault and write results back to the task note
3. Note Processing
Turn raw notes into organized, atomic knowledge.
- Voice transcripts — Break voice notes into atomic notes + tasks + summary
- Distillation — Synthesize multiple linked notes into deduplicated atomic notes
- Publishing — Turn research notes into a single polished blog post
- Custom prompts — Apply different "lenses" to extract different insights from the same content
Quick Start
Setup
- Install from Obsidian Community Plugins (or manually)
- Settings → OpenAugi → Enter your OpenAI API key
- For task dispatch: install tmux (
brew install tmux)
Dispatch a Task
Create a task note:
---
task_id: fix-auth-bug
working_dir: my-repo
---
## Objective
Fix the authentication bug where sessions expire after 5 minutes.
## Context
The auth middleware is in `src/middleware/auth.ts`. [[API Design Doc]] has the spec.
Run Task dispatch: Launch or attach from the command palette. A terminal opens with your agent pre-loaded with context from the note and all linked notes.
Gather Context
- Open any note with links to content you want to process
- Run Process notes
- Configure depth, filters, and character limits
- Review discovered notes with checkboxes
- Choose: Distill (atomic notes), Publish (blog post), or Save (raw context)
Commands
| Command | Purpose |
|---|---|
| Process notes | Gather linked notes → review → distill / publish / save |
| Process recent activity | Same flow but discovers by recent modification date |
| Save context | Gather and save raw context (no AI processing) |
| Task dispatch: Launch or attach | Launch agent session from task note |
| Task dispatch: Kill session | Kill tmux session for current task note |
| Task dispatch: List active sessions | View and manage all running agent sessions |
| Parse transcript | Process voice transcript into atomic notes |
| Distill linked notes | Legacy command — use Process notes instead |
Task Dispatch
Task dispatch is the agent harness. It reads a task note, assembles context from linked notes, creates a tmux session, and launches your agent CLI with everything pre-loaded.
See Task Dispatch docs for the full reference.
Key concepts:
task_idin frontmatter identifies the task (required)working_dirsets where the agent runs — supports named repos, absolute paths, or vault-relative paths- Linked notes (
[[Design Doc]],[[API Spec]]) are automatically included in context - Sessions persist across Obsidian restarts — use "List active sessions" to reattach
- Agents can use the OpenAugi MCP to search your vault and write results back
Context Gathering
The context gathering pipeline is a three-stage flow:
- Configure — Source mode (linked notes or recent activity), depth, filters
- Review — Checkbox list of discovered notes with character/token counts
- Process — Distill to atomic notes, publish as blog post, or save raw
Features:
- Breadth-first link traversal up to 3 levels
- Bidirectional: forward links + backlinks at each depth
- Journal-style date filtering
- Dataview query support
- Custom prompt lenses
Configuration
Settings are in Settings → OpenAugi.
Core:
- OpenAI API key (required for AI processing)
- Output folders: Summaries, Notes, Published, Prompts
Context Gathering:
- Default link depth (1-3)
- Max characters (default: 100k)
- Include backlinks (default: on)
- Journal section filtering
Task Dispatch:
- Terminal app (iTerm2 or Terminal.app)
- tmux path (auto-detected or manual)
- Default working directory
- Repository path mappings
- Default agent CLI
- Max context characters (default: 200k)
Recent Activity:
- Days to look back (default: 7)
- Date header format
- Folder exclusions
Requirements
- OpenAI API key — Required for AI processing (distill, publish, parse)
- tmux — Required for task dispatch (
brew install tmux) - macOS — Task dispatch terminal opening uses AppleScript (iTerm2 or Terminal.app)
Get Involved
OpenAugi is about augmented intelligence — using AI to help you think faster and do more, not to think for you.
Open an issue, join the Discord, or check out YouTube for updates. Parent repo.