triobot 0.2.3

trio.ai - train your own AI, deploy it everywhere

trio.ai

Train your own AI. Deploy it everywhere. Own it forever.

The open-source agent framework that lets you train custom LLMs and run them across 17 chat platforms with 3,876 built-in skills, 12 tools, and a 5-layer security guardrail.

Quick Start • Models • Skills • Channels • Commands • Architecture • Comparison

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Why trio.ai

Most AI agent frameworks lock you into one provider, one platform, and someone else's model. trio.ai is different.

• Train your own LLM — built-in transformer training pipeline (nano to pro, 1M to 30B params)
• Use any provider — Ollama, OpenAI, Claude, Gemini, Groq, DeepSeek, OpenRouter, GitHub Models
• Deploy anywhere — 17 chat channels (Discord, Telegram, Slack, WhatsApp, Teams, SMS, Email, and more)
• 3,876 built-in skills — coding, marketing, DevOps, security, finance, data science, legal, health, and more
• 5-layer guardrails — input/output filtering, jailbreak detection, content safety, rate limiting
• Smart routing — automatically picks local first, free APIs next, paid only if you allow
• MCP compatible — works with Claude Code, Cursor, Zed, Continue, and any MCP client

Stop paying per-token. Stop trusting closed-source models. Stop being locked in.

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⚡ Quick Start

Install

pip install trio-ai
trio onboard

That's it. trio onboard walks you through provider setup, model download, and channel configuration in under 2 minutes.

Start chatting

trio agent

Or use the web UI

trio serve
# Open http://localhost:28337

From source

git clone https://github.com/iampopye/trio.git
cd trio
python install.py
trio onboard

New to AI agents? Run trio help for a guided tour of every command.

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🧠 Model Tiers

trio.ai ships with 6 built-in model tiers you can train, download, or run locally via Ollama:

Tier	Params	Size (Q4)	Hardware	Best For
trio-nano	~1M	600 MB	CPU, 4 GB RAM	Embedded devices, IoT, testing
trio-small	~125M	1.2 GB	CPU/GPU, 8 GB RAM	Lightweight chat, edge deployment
trio-medium	~350M	2.5 GB	GPU/Apple Silicon	Personal assistant, coding helper
trio-high	~750M	5.0 GB	RTX 3060+, M2+	Production workloads
trio-max	~3B	5.6 GB	RTX 4070+, M3+	Enterprise tasks, complex reasoning
trio-pro	~30B (MoE)	18 GB	RTX 4090, A100	Research, advanced agentic workflows

Switch models on the fly

# Set default model
trio provider set --model trio-max

# Or use any external provider
trio provider set --provider openai --model gpt-4o
trio provider set --provider anthropic --model claude-opus-4-6
trio provider set --provider gemini --model gemini-2.5-pro
trio provider set --provider ollama --model llama3.1:8b

# In-chat: switch with the /provider slash command
trio agent
> /provider                       # Open provider picker
> /model trio-max                 # Quick model switch

Train your own

pip install trio-ai[model]
trio train --setup                 # Download pre-quantized models
trio train                          # Train from scratch (resume with Ctrl+C)

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🎯 Skills

trio.ai includes 3,876 community-curated skills across 13 categories. Skills are markdown files that teach the agent how to do specific tasks.

Category	Count	Examples
General	415	Email drafting, summarization, brainstorming
Coding	418	Debug Python, refactor JS, write SQL, code review
SysAdmin	204	Docker, K8s, Nginx, systemd, log analysis
Productivity	167	GTD workflows, meeting notes, task triage
Marketing	163	SEO, ad copy, social media, email campaigns
Web Dev	159	React, Next.js, Tailwind, deployment
Data Science	102	Pandas, scikit-learn, visualization, ML pipelines
Security	80	Pentest, OWASP, log analysis, hardening
Creative	79	Storytelling, copywriting, ideation
Finance	47	Bookkeeping, invoicing, P&L analysis
Legal	29	Contract review, NDA drafting, IP basics
Education	25	Tutoring, lesson plans, quiz generation
Health	21	Wellness, fitness plans, nutrition (informational)

Browse and install skills

trio skill list                          # Show installed skills
trio hub search "python"                 # Search the community registry
trio hub trending                         # Most popular skills
trio skill install python_debugger        # Install a specific skill
trio skill install codex_review devops_toolkit  # Install multiple
trio skill remove python_debugger        # Remove a skill

Create your own skill

mkdir -p ~/.trio/skills
cat > ~/.trio/skills/my_skill.md <<EOF
---
name: my_skill
description: What this skill does
tags: [coding, python]
---

# Instructions
When the user asks about X, do Y by following these steps...
EOF

trio.ai picks up new skills automatically — no restart needed.

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📡 Channels

Deploy your AI on any messaging platform:

Channel	Type	Setup
CLI	Terminal	Built-in
Web UI	Browser	trio serve
Discord	Bot	Bot token
Telegram	Bot	Bot token
Slack	Workspace bot	Socket Mode tokens
WhatsApp	Business API	Meta tokens + webhook
Microsoft Teams	Bot Framework	App ID + password
Google Chat	Service account	Service account JSON
Signal	Private messenger	Phone number
Matrix / Element	Federated chat	Homeserver + token
iMessage	macOS only	AppleScript
SMS	Twilio	Account SID + token
Instagram DM	Meta Graph API	Access token
Facebook Messenger	Page bot	Page access token
LINE	LINE Bot SDK	Channel token + secret
Reddit	Bot	Client credentials
Email	IMAP / SMTP	Username + password

Enable channels

trio onboard                              # Interactive channel setup
trio gateway                              # Start all enabled channels
trio daemon install                       # Auto-start as system service
trio daemon status                        # Check daemon health

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🛠 Tools

trio.ai includes 12 built-in tools that the agent can use autonomously:

Tool	Description
web_search	DuckDuckGo / Firecrawl web search
browser	Playwright browser automation (navigate, click, screenshot)
shell	Sandboxed shell execution (allowlist-based)
file_ops	Read, write, append, list files (workspace-restricted)
email	Send/receive via SMTP/IMAP
calendar	Schedule and manage events
notes	Persistent note-taking
screenshot	Capture screen regions
rag_search	Semantic search over local documents
math_solver	Symbolic math via SymPy
delegate	Spawn sub-agents for complex tasks
mcp_client	Connect to any MCP server (code-review-graph, etc.)

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🤝 Sub-Agents

trio.ai delegates complex tasks to specialized sub-agents:

Agent	Role	Tools
researcher	Web search, browsing, RAG synthesis	web_search, browser, rag_search
coder	Write, run, and debug code	shell, file_ops
reviewer	Code review, bug detection	read-only
planner	Task breakdown, architecture design	LLM-only
summarizer	Condense long documents	LLM-only

Each sub-agent can use a different model for cost optimization (e.g., DeepSeek for research, Claude for coding).

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📋 Commands

Get help anytime

trio help                                 # Show all commands with examples
trio help <command>                       # Detailed help for a command
trio --version                            # Show version info

Common commands

# Setup
trio onboard                              # Interactive setup wizard
trio doctor                               # Diagnose system issues
trio doctor --fix                         # Auto-repair common issues
trio status                               # System overview

# Chat
trio agent                                # Interactive chat
trio agent -m "summarize this PR"         # Single message
trio serve                                # Browser UI on port 28337

# Models & Providers
trio provider list                        # Show configured providers
trio provider add                         # Add a new LLM provider
trio provider set --model trio-max        # Set default model

# Skills
trio skill list                           # Installed skills
trio skill install <name>                 # Install from TrioHub
trio hub search "<query>"                 # Search 3,876 skills
trio hub trending                         # Popular skills

# Plugins
trio plugin list                          # Installed plugins
trio plugin install <path>                # Install a plugin
trio plugin enable <name>                 # Enable a plugin

# Channels
trio gateway                              # Start all enabled channels
trio daemon install                       # Auto-start on boot
trio daemon start | stop | restart        # Control the daemon

# Training
trio train --setup                        # Download pre-trained models
trio train                                # Train from scratch
trio train --reset                        # Restart training

# Maintenance
trio update                               # Self-update
trio pairing list                         # DM access management

📖 Full command reference: COMMANDS.md

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🏗 Architecture

graph TB
    User[User] --> Channels[17 Channels<br/>Discord, Telegram, Slack, WhatsApp...]
    Channels --> Bus[MessageBus]
    Bus --> Loop[AgentLoop]
    Loop --> Context[Context Builder<br/>Memory + Skills + Tools]
    Loop --> Router[Smart Router<br/>local→free→paid]
    Router --> LLM[LLM Provider<br/>Ollama / OpenAI / Claude / Gemini...]
    Loop --> Tools[12 Built-in Tools]
    Loop --> SubAgents[5 Sub-Agents<br/>researcher, coder, reviewer...]
    Loop --> Guardrails[5-Layer Guardrails<br/>input → content → output → rate → ops]
    Guardrails --> Bus
    Bus --> Channels
    Channels --> User

    Memory[(Persistent Memory)] -.-> Context
    Skills[(3,876 Skills)] -.-> Context
    RAG[(RAG Store)] -.-> Context

How a message flows

1. User sends a message via any channel (Discord, CLI, etc.)
2. Channel adapter normalizes it into an InboundMessage
3. MessageBus routes it to the AgentLoop
4. Input guardrails check for jailbreaks, prompt injection, harmful content
5. Context builder assembles system prompt + memory + relevant skills
6. Smart router picks the cheapest available provider
7. LLM generates a response, optionally calling tools
8. Tool loop executes any tool calls (max 20 iterations) and feeds results back
9. Sub-agents handle specialized subtasks if delegated
10. Output guardrails redact sensitive info before sending
11. Channel adapter delivers the response back to the user

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🆚 Comparison

Feature	trio.ai	Claude Code	OpenClaude	LangChain
Train your own LLM	✅	❌	❌	❌
17 chat channels	✅	❌	❌	⚠️
Built-in skills (3,876+)	✅	❌	❌	❌
Multi-provider	✅	❌	✅	✅
Local-first routing	✅	❌	⚠️	❌
Sub-agents	✅	✅	✅	⚠️
5-layer guardrails	✅	⚠️	❌	❌
MCP support	✅	✅	✅	⚠️
Plugin system	✅	✅	⚠️	⚠️
Production daemon	✅	❌	❌	❌
Web UI	✅	✅	❌	❌
100% open source	✅	❌	✅	✅
One-command install	✅	✅	✅	❌

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🛡 Security

trio.ai implements a 7-layer defense model:

1. API key authentication for the web API (auto-generated, machine-local)
2. AES-128 encrypted secrets in ~/.trio/config.json (Fernet)
3. Allowlist-based shell sandbox (~70 safe commands, blocks shell interpreters)
4. Plugin checksum verification (SHA-256, refuses tampered plugins)
5. 5-layer LLM guardrails (input filtering, output redaction, content safety, rate limiting, operational limits)
6. File upload validation (extension allowlist, 50MB cap, path traversal prevention)
7. Per-IP rate limiting on all API endpoints (60 req/min)

📖 Full security policy: SECURITY.md

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🌐 Platform Support

Platform	Status	Notes
Windows 10/11	✅	NVIDIA CUDA, native installer
macOS Apple Silicon	✅	MPS Metal acceleration
macOS Intel	✅	CPU + optional eGPU
Ubuntu / Debian	✅	CUDA, ROCm
Fedora / Arch	✅	Full support
WSL2	✅	CUDA passthrough
Android	⚠️	Termux experimental
iOS	❌	Not supported

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📂 Project Structure

trio/
├── trio/                       # Agent framework
│   ├── core/                   #   Loop, Bus, Config, Memory, Sessions, Router
│   ├── providers/              #   13+ LLM provider integrations
│   ├── channels/               #   17 chat channel adapters
│   ├── tools/                  #   12 built-in tools
│   ├── skills/                 #   3,876 markdown-based skills
│   ├── plugins/                #   Plugin system
│   ├── hub/                    #   TrioHub registry client
│   ├── shared/                 #   Guardrails, pairing, security
│   ├── cli/                    #   13 CLI commands
│   └── web/                    #   Browser UI (aiohttp)
├── trio_model/                 # LLM training engine
│   ├── model/                  #   Transformer (RoPE, GQA, RMSNorm, SwiGLU)
│   ├── training/               #   Pre-train, SFT, Constitutional AI
│   └── inference/              #   FastAPI inference server
├── triohub/                    # Community skill/plugin registry
├── COMMANDS.md                 # Full command reference
├── INSTALL.md                  # Platform-specific install guides
├── BENCHMARKS.md               # Performance & cost comparisons
├── SECURITY.md                 # Security policy
└── NOTICE                      # Third-party attributions

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🤝 Contributing

trio.ai is open source and contributions are welcome. Before submitting a PR:

1. Read CONTRIBUTING.md
2. Sign the CLA on your first PR (handled automatically by the bot)
3. Run trio doctor to ensure your environment is set up
4. Follow the existing code style

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📬 Community & Support

• Issues: github.com/iampopye/trio/issues
• Discussions: github.com/iampopye/trio/discussions
• Security: See SECURITY.md for responsible disclosure

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📜 License

trio.ai is released under the MIT License. See LICENSE for details.

Copyright © 2026 Karan Garg. All rights reserved.

trio.ai is open source and free to use, modify, and distribute. The trio.ai name and brand are owned by Karan Garg.

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Built from scratch. Train it. Deploy it. Own it.

⭐ Star us on GitHub if trio.ai helps you build smarter agents.