Peuqui/AIfred-Intelligence

# AIfred Intelligence
A self-hosted AI assistant that gets things done — tool chains, sub-agents, multi-agent debates, voice, camera surveillance and local LLM inference on your own hardware.
AIfred runs on your own machine and works for you across every channel you use: it reads and answers e-mail, books appointments, manages contacts, documents and databases, watches your cameras and talks to you through the browser, Telegram, Discord or a voice terminal in your living room. Local models, persistent memory, no cloud dependency — your data stays with you.
📺 Example showcases — exported chats: multi-agent debates, chemistry, maths, coding and web research.
> ⭐ If AIfred is useful to you, please star the repo. Self-hosters easily forget — but stars are the main signal that tells me people are actually using this. It directly affects whether I keep building.
At a glance
🔗 Autonomous tool chains across plugins — one prompt, many steps, from any channel
📬 Your mail, read properly — unread mail, several at once, PDFs and Office files as text, scans and photos through the vision model; nothing is cut off, long mails are read in steps
📅 Nextcloud as your hub — calendar, tasks, contacts, notes and files, with rights per module
🌅 Morning briefing — by mail every morning: the next seven days of appointments, birthdays, open tasks and unread mail, anything with a deadline first
🎩 Multi-agent system — AIfred, Sokrates, Salomo and your own agents debate, criticise, judge — or take delegated tasks as sub-agents
📡 Reachable everywhere — web UI, Telegram, Discord, e-mail, Echo Dot voice terminal; runs headless
🎤 Voice — speech-to-text plus the TTS-Suite service over seven engines (local, other hosts, cloud, browser), voice cloning, spoken announcements on the Echo Dot
👁️ Vision & Vigilantia — image analysis in chat and continuous camera surveillance with face recognition
⚙️ Local inference, tuned automatically — llama.cpp, vLLM and Ollama with VRAM-aware context calibration across multiple GPUs
🔒 Security built into the framework — permission tiers per channel, prompt-injection defences, credential broker, audit log
Auto-Consensus mode: AIfred answers, Sokrates criticises, Salomo synthesises — local DeepSeek-V4-Flash via vLLM, with timings per answer.
## 🔗 Complex Workflows (Tool Chains)
The real value doesn't come from individual tools but from chains that AIfred works through on its own across several plugins — from any channel: browser, voice terminal (FreeEcho.2), Telegram, Discord, e-mail.
Example (real, tested end to end): you photograph a business card with your phone and say — typed or spoken:
> "Analyze this business card, extract all the data, create the contact in Google, check next week for a free morning, book a two-hour appointment, and send me the photo along with the contact details via Telegram."
AIfred runs this as one continuous chain:
1. 👁️ Vision — the VLM reads the card (name, phone, e-mail, address, web) directly from the image
2. 📇 Google Contacts — creates the contact in structured form (google_contacts_create)
3. 📅 Google Calendar — first checks the time window for free slots (google_calendar_list_events) and only then books the appointment; an existing appointment is recognised and AIfred asks back instead of creating a duplicate