angie-ai 0.1.1

Angie — 24/7 personal AI assistant

Angie — Personal AI Assistant

Your 24/7 personal AI assistant. Always on. Always working.

Angie is a self-hosted, event-driven AI assistant that runs as a persistent background daemon on your infrastructure. Angie connects to your real-world tools — email, calendar, smart home devices, music, GitHub, Slack, Discord, and more — and acts on your behalf through a fleet of specialized agents powered by GitHub Copilot's LLM.

Unlike chat-only AI tools, Angie is proactive and persistent: Angie wakes up on a schedule, monitors your channels, executes multi-step workflows, and reports back without being asked. Angie remembers context about you through a layered prompt hierarchy, so every interaction is personalized to your preferences, communication style, and routines.

What Angie can do

• Real-time chat interface — Chat directly with Angie and your fleet of AI agents from the terminal or the web UI.
• Developer workflows — Query GitHub issues, open PRs, summarize repository activity, and turn issues into pull requests autonomously.
• Web browsing — Browse URLs, take screenshots, extract content, and summarize web pages.
• Weather — Get current conditions, forecasts, and severe weather alerts.
• Unified inbox — (planned) Connect Slack, Discord, iMessage, and email in one place. Angie routes incoming messages to the right agent automatically.
• Scheduled tasks — Set cron jobs that run agents on a schedule ("every weekday at 8am, summarize my email and post to Slack").
• Multi-step workflows — Chain agents together: check calendar → summarize emails → control smart lights → send morning briefing.
• Smart home control — (planned) Adjust Philips Hue lighting and Home Assistant automations via natural language.
• Media control — (planned) Control Spotify playback, switch playlists, and manage your queue.
• Network management — (planned) Inspect your UniFi network, connected devices, and bandwidth stats.
• Personalized context — Onboarding builds a private profile (personality, communication style, preferences) that shapes every LLM interaction.
• REST API + Web UI — Full FastAPI backend with a Next.js dashboard for managing agents, teams, workflows, tasks, and events in real time.

---

Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                          Channels                               │
│  Slack · Discord · iMessage · Email · Angie UI Chat             │
└───────────────────────────┬─────────────────────────────────────┘
                            │ Events: infer task from user input
                            ▼
┌─────────────────────────────────────────────────────────────────┐
│                       Angie Daemon Loop                         │
│   EventRouter  →  TaskDispatcher  →  Celery Queue  →  Worker    │
│          ↑                                  ↑                   │
│     CronEngine                       AgentRegistry              │
└─────────────────────────────────────────────────────────────────┘
                            │ Task: units of work, assigned to agents/teams
                            ▼
┌─────────────────────────────────────────────────────────────────┐
│                         Agent Fleet                             │
│  System: cron · task-manager · workflow-manager · event-manager │
│  Dev: github · software-dev                                     │
│  Productivity: web   Lifestyle: weather                         │
└─────────────────────────────────────────────────────────────────┘
                            │ Agents & Teams: single teams of AI agents
                            ▼
┌───────────┐   ┌───────────────────┐   ┌───────────────────────┐
│   MySQL   │-->│   FastAPI (API)   │<->│     Redis Cache       │
│    DB     │<--│     /api/v1/*     │<->│    Celery broker      │
└───────────┘   └───────────────────┘   └───────────────────────┘
                           │ 
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│              Next.js Web UI  (frontend/)                        │
│  Dashboard · Agents · Teams · Workflows · Tasks · Events        │
│  Real-time Chat · Settings                                      │
└─────────────────────────────────────────────────────────────────┘

Key Concepts

Concept	Description
Event	Any trigger: user message, cron tick, webhook, task completion
Task	A unit of work dispatched from an event to an agent
Agent	A pydantic-ai powered worker that can handle specific task types
Team	A named group of agents that collaborate on related tasks
Workflow	An ordered sequence of steps across agents/teams for a goal
Prompt Hierarchy	SYSTEM → ANGIE → AGENT/USER — layered context fed to every LLM call

---

Tech Stack

Layer	Technology
Language	Python 3.12, uv
AI framework	pydantic-ai (GitHub Models, OpenAI, Anthropic)
API	FastAPI + SQLAlchemy 2.0 async
Database	MySQL 8 (aiomysql driver)
Cache / Queue	Redis + Celery
Scheduler	APScheduler
Channels	Slack SDK · discord.py · BlueBubbles REST · SMTP/IMAP
CLI	Click
Frontend	Next.js 15 · TypeScript · Tailwind CSS v3
Build	PyInstaller (standalone angie binary)
CI/CD	GitHub Actions
Dev	Docker Compose, Ruff, pytest

---

Quick Start

Prerequisites

• Python 3.12+, uv
• Docker Desktop (for MySQL + Redis)
• Node.js 20+ (for frontend)

1. Clone and install

git clone https://github.com/your-org/angie.git
cd angie
make install          # uv sync --dev --all-extras
cp .env.example .env  # fill in SECRET_KEY, DB_PASSWORD, GITHUB_TOKEN

2. Start backing services

make docker-up        # starts MySQL + Redis containers
make migrate          # runs Alembic migrations

3. Run Angie

# Start the Angie daemon (event loop only)
angie daemon

# Or start services individually:
uvicorn angie.api.app:create_app --factory --reload   # API on :8000
celery -A angie.queue.celery_app worker -l info       # Worker

# Frontend dev server:
cd frontend && npm run dev    # UI on :3000

4. Onboarding

On first run, Angie needs to learn about you:

angie setup

This asks a series of questions and generates personalized prompts/user/<id>/ markdown files that become part of every LLM interaction.

---

CLI Reference

angie --help

# Daemon
angie daemon                 # Start background daemon (event loop)

# One-shot queries
angie ask "What's on my calendar today?"

# Interactive chat
angie chat "Hello Angie"             # Chat from the terminal
angie chat --agent github "List PRs" # Route directly to a specific agent

# Channel configuration
angie config slack           # Set Slack bot token
angie config discord         # Set Discord bot token
angie config imessage        # Set BlueBubbles URL
angie config email           # Set SMTP/IMAP credentials
angie config channels        # Show status of all configured channels

# Unified configuration wizard
angie configure keys slack   # Set API keys for a service
angie configure list         # Show all configured keys grouped by service
angie configure model        # Select the LLM model
angie configure seed         # Seed the database with demo data

# Onboarding
angie setup                  # Interactive first-run onboarding

# Status
angie status                 # Show active tasks, registered agents

---

Agent Fleet

Agents are pydantic-ai powered workers. Each declares:

• slug — unique identifier used for routing
• capabilities — keywords that trigger auto-selection
• execute(task) — async method that does the work

Built-in Agents

Slug	Category (group)	Description
cron	System	Create, delete, and list cron scheduled tasks
task-manager	System	List, cancel, and retry Angie tasks
workflow-manager	System	Manage and trigger Angie workflows
event-manager	System	Query, filter, and manage Angie events
github	Dev	GitHub repository and PR management
software-dev	Dev	Turn GitHub issues into pull requests autonomously
web	Productivity	Browse URLs, take screenshots, extract and summarize web pages
weather	Lifestyle	Weather conditions, forecasts, and severe weather alerts

More agents are planned — see the environment variables section below for services Angie will support.

Adding a New Agent

# src/angie/agents/my_category/my_agent.py
from angie.agents.base import BaseAgent

class MyAgent(BaseAgent):
    name = "My Agent"
    slug = "my-agent"
    description = "Does something useful"
    category = "General"
    capabilities = ["useful", "something"]

    async def execute(self, task: dict) -> dict:
        # Use the underlying SDK / API here
        return {"status": "success", "result": "done"}

Then add to AGENT_MODULES in src/angie/agents/registry.py.

---

Teams & Workflows

Creating a Team (API)

curl -X POST http://localhost:8000/api/v1/teams/ \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"name":"Dev Team","slug":"dev","agent_slugs":["github","software-dev"]}'

Defining a Workflow (API)

curl -X POST http://localhost:8000/api/v1/workflows/ \
  -H "Authorization: Bearer $TOKEN" \
  -d '{
    "name": "Morning Briefing",
    "slug": "morning-briefing",
    "trigger_event": "cron",
    "steps": [
      {"order": 1, "name": "Check weather", "agent_slug": "weather"},
      {"order": 2, "name": "Browse news", "agent_slug": "web"},
      {"order": 3, "name": "Report status", "agent_slug": "task-manager"}
    ]
  }'

---

Prompt Hierarchy

Every LLM call is built from layered prompts:

prompts/
  system.md          ← Core safety/persona rules (SYSTEM_PROMPT)
  angie.md           ← Angie's personality and behavior (ANGIE_PROMPT)
  user/<id>/         ← Generated from onboarding (USER_PROMPTS)
    personality.md
    communication.md
    preferences.md

For agent tasks: SYSTEM → ANGIE → AGENT_PROMPT For user interactions: SYSTEM → ANGIE → USER_PROMPTS

Reconfigure at any time:

angie setup           # re-run full onboarding

---

Development

make help             # list all targets

make install          # install deps
make check            # lint + format check
make fix              # auto-fix lint + format
make md-check         # check Markdown formatting
make md-fix           # auto-format Markdown files
make test             # run all tests (unit + e2e)
make test-cov         # with coverage report
make test-single K=test_name  # single test

make migrate-new MSG="add column"  # new migration
make migrate          # apply migrations

make docker-up        # start MySQL + Redis
make docker-down      # stop
make docker-reset     # stop + wipe volumes

make build            # PyInstaller standalone binary → dist/angie
make dist             # build sdist + wheel into dist/
make clean-dist       # remove distribution artifacts
make clean            # remove all build artifacts

Project Structure

angie/
├── src/angie/
│   ├── agents/         # Agent implementations + registry + teams
│   ├── api/            # FastAPI app + routers
│   ├── cache/          # Redis client + @cached decorator
│   ├── channels/       # Slack, Discord, iMessage, Email, WebChat
│   ├── cli/            # Click CLI commands
│   ├── core/           # Events, tasks, prompts, cron, loop, feedback
│   ├── db/             # SQLAlchemy session + generic repository
│   ├── models/         # SQLAlchemy ORM models
│   └── queue/          # Celery app + workers
├── frontend/           # Next.js 15 web UI
├── tests/
│   ├── e2e/            # End-to-end flow tests
│   ├── integration/    # (future) API integration tests
│   └── unit/           # Unit tests
├── prompts/            # Jinja2 prompt templates
├── alembic/            # DB migrations
├── docker/             # Dockerfiles
├── docker-compose.yml
├── Makefile
└── angie.spec          # PyInstaller spec

---

Environment Variables & API Key Permissions

Copy .env.example to .env. The sections below describe every credential, where to obtain it, and the exact permissions/scopes required.

---

Core (required)

Variable	Description
SECRET_KEY	JWT signing secret. Generate with openssl rand -hex 32. Keep this private.
DB_PASSWORD	MySQL password for the angie database user.

---

LLM Provider Selection

Angie supports three LLM providers. Set LLM_PROVIDER to choose:

Provider	LLM_PROVIDER	Required Variables
GitHub Models API (default)	github	GITHUB_TOKEN, COPILOT_MODEL
OpenAI	openai	OPENAI_API_KEY, COPILOT_MODEL
Anthropic Claude	anthropic	ANTHROPIC_API_KEY, ANTHROPIC_MODEL

---

LLM — GitHub Models API (default)

Variable	Description
GITHUB_TOKEN	GitHub OAuth token used to obtain a short-lived Copilot session token.
COPILOT_MODEL	Model to use (default: gpt-4o). Other options: gpt-4o-mini, o1-mini.
COPILOT_API_BASE	Copilot OpenAI-compatible endpoint (default: https://api.githubcopilot.com).

How to get GITHUB_TOKEN:

1. Go to GitHub → Settings → Developer Settings → Personal Access Tokens → Tokens (classic)
2. Click Generate new token (classic)
3. Under Scopes, enable:
   • read:user — required to identify your account
   • No additional scopes are needed; Copilot access is governed by your GitHub Copilot subscription, not token scopes
4. You must have an active GitHub Copilot Individual, Business, or Enterprise subscription
5. Paste the ghp_... token as GITHUB_TOKEN

Alternative: Run gh auth token after authenticating with the GitHub CLI to get a token that already has the right access.

---

OpenAI

Variable	Description
OPENAI_API_KEY	OpenAI API key (sk-...).
COPILOT_MODEL	Model to use (default: gpt-4o).

Get from: platform.openai.com → API Keys → Create new secret key. No special permissions needed — any key with access to gpt-4o works.

---

Anthropic Claude

Variable	Description
ANTHROPIC_API_KEY	Anthropic API key (sk-ant-...).
ANTHROPIC_MODEL	Model to use (default: claude-sonnet-4-20250514). Other options: claude-opus-4-20250514, claude-haiku-4-20250514.

Get from: console.anthropic.com → API Keys → Create Key.

---

Slack (optional)

Variable	Description
SLACK_BOT_TOKEN	Bot OAuth token (xoxb-...) — for posting messages and reading channel events
SLACK_APP_TOKEN	App-level token (xapp-...) — required for Socket Mode (real-time events without a public URL)
SLACK_SIGNING_SECRET	Used to verify that incoming webhooks are from Slack

How to create a Slack app:

1. Go to api.slack.com/apps → Create New App → From scratch
2. Under OAuth & Permissions → Bot Token Scopes, add:
   • channels:history — read messages in public channels
   • channels:read — list channels
   • chat:write — post messages as the bot
   • im:history — read direct messages
   • im:read — list DM conversations
   • im:write — open DM conversations
   • users:read — look up user info (for @-mentioning)
   • app_mentions:read — receive @Angie mentions
3. Under Event Subscriptions, enable and subscribe to:
   • message.channels, message.im, app_mention
4. Under Socket Mode, enable Socket Mode and generate an App-Level Token with scope connections:write → this becomes SLACK_APP_TOKEN
5. Install the app to your workspace → copy the Bot User OAuth Token → SLACK_BOT_TOKEN
6. Under Basic Information → Signing Secret → SLACK_SIGNING_SECRET

---

Discord (optional)

Variable	Description
DISCORD_BOT_TOKEN	Bot token from the Discord Developer Portal

How to create a Discord bot:

1. Go to discord.com/developers/applications → New Application
2. Under Bot, click Add Bot → copy the Token → DISCORD_BOT_TOKEN
3. Under Bot → Privileged Gateway Intents, enable:
   • Message Content Intent — required to read message text
   • Server Members Intent — required to look up user info
   • Presence Intent — optional, for presence-aware responses
4. Under OAuth2 → URL Generator, select scopes:
   • bot with permissions: Send Messages, Read Message History, View Channels, Add Reactions
5. Use the generated URL to invite the bot to your server

---

iMessage via BlueBubbles (optional)

Variable	Description
BLUEBUBBLES_URL	URL of your BlueBubbles server (e.g. https://your-server.ngrok.io)
BLUEBUBBLES_PASSWORD	BlueBubbles server password

Requirements:

• A Mac that stays on with iMessage signed in
• BlueBubbles Server installed and running on that Mac
• A way to expose the server publicly (ngrok, Cloudflare Tunnel, or static IP)
• No Apple credentials needed — BlueBubbles uses the Mac's existing iMessage session

---

GitHub Agent (optional — separate from Copilot API token)

This should be set from the Connections page in the Angie web UI. If not, we have:

Variable	Description
GITHUB_PAT	Personal Access Token for the GitHub agent (repo queries, PRs, issues)

This is separate from GITHUB_TOKEN (which is for LLM). Create a fine-grained token at GitHub → Settings → Developer Settings → Fine-grained tokens with:

• Contents: Read — read repository files
• Issues: Read and Write — create/update issues
• Pull requests: Read and Write — create/update PRs
• Metadata: Read — required for all fine-grained tokens

---

CI/CD

Three GitHub Actions workflows:

• ci.yml — runs on every push/PR: lint → format → markdown format → test → security → Docker build
• deploy.yml — runs on v*.*.* tags: build PyInstaller binary → GitHub Release + publish angie-ai to PyPI (via PYPI_TOKEN)

---

License

MIT