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Your Digital Companion. Self-hosted Telegram bot orchestrating multiple AI providers (OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, Alibaba, MiniMax) with autonomous agent capabilities, MCP integrations, and async task execution. Not a tool. A partner.

Project description

Chibi Logo

Your Digital Companion. Not a Tool. A Partner.
Self-hosted, asynchronous Telegram bot that orchestrates multiple AI providers, tools, and sub-agents to get real work done.

Build CodeFactor Docker Pulls Architectures License Documentation

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Chibi is built for the moment you realize you need more than “an AI tool.” You need a partner that can coordinate models, run work in the background, and integrate with your systems - without you babysitting prompts.

Chibi is an asynchronous, self-hosted Telegram-based digital companion that orchestrates multiple AI providers and tools to deliver outcomes: code changes, research syntheses, media generation, and operational tasks.


Why Chibi

  • One interface (Telegram). Mobile/desktop/web, always with you.
  • Provider-agnostic. Use the best model for each task - without vendor lock-in.
  • Autonomous execution. Sub-agents work in parallel; long tasks run asynchronously.
  • Tool-connected. Filesystem + terminal + MCP integrations (GitHub, browser, DBs, etc.).
  • Self-hosted. Your data, your keys, your rules.

Supported AI providers (and endpoints)

Chibi supports multiple providers behind a single conversation. Add one key or many - Chibi can route per task.

LLM providers

  • OpenAI (GPT models)
  • Anthropic (Claude)
  • Google (Gemini)
  • DeepSeek
  • Alibaba Cloud (Qwen)
  • xAI (Grok)
  • Mistral AI
  • Moonshot AI
  • MiniMax
  • Cloudflare Workers AI (many open-source models)

OpenAI-compatible endpoints (self-host / local)

  • Ollama
  • vLLM
  • LM Studio
  • Any OpenAI-compatible API

Multimodal providers (optional)

  • Images: Google (Imagen, Nano Banana), OpenAI (DALL·E), Alibaba (Qwen Image), xAI (Grok Image), Wan
  • Music: Suno
  • Voice: ElevenLabs, MiniMax, OpenAI (Whisper)

Exact model availability depends on your configured provider keys and enabled features.


🚀 Quick Start (pip)

Install Chibi via pip and run it as a command-line application:

# Install the package
pip install chibi

# Set up the agent (add API keys, update settings, etc)
chibi config

# Start the bot
chibi start

The bot will run as a background service. Use CLI commands to manage it.

CLI Commands

Command Description
chibi start Start the bot as a background service
chibi stop Stop the running bot
chibi restart Restart the bot
chibi config Generate or edit configuration
chibi logs View bot logs

🚀 Quick start (Docker)

Create docker-compose.yml:

version: '3.8'

services:
  chibi:
    image: pysergio/chibi:latest
    restart: unless-stopped
    environment:
      TELEGRAM_BOT_TOKEN: ${TELEGRAM_BOT_TOKEN}  # Required
      OPENAI_API_KEY: ${OPENAI_API_KEY}          # Or any other provider
      # Add more API keys as needed
    volumes:
      - chibi_data:/app/data

volumes:
  chibi_data: {}
  1. Get a bot token from @BotFather

  2. Put secrets into .env

  3. Run:

docker-compose up -d

Next:


🔑 Getting API Keys

Each provider requires its own API key. Here are the direct links:

Major Providers:

Creative Tools:

📖 Full guide with setup instructions: chibi.bot/guides/get-api-keys


Try this in the first 5 minutes

Paste these into Telegram after you deploy.

  1. Planning + execution

Ask me 3 questions to clarify my goal, then propose a plan and execute step 1.

  1. Parallel work (sub-agents)

Spawn 3 sub-agents: one to research options, one to draft a recommendation, one to list risks. Return a single decision.

  1. Agent mode (tools)

Inspect the project files and summarize what this repo does. Then propose 5 improvements and open a checklist.

  1. Background task

Start a background task: gather sources on X and deliver a synthesis in 30 minutes. Keep me updated.


What makes Chibi different

🎭 Multi-provider orchestration

Chibi can keep context while switching providers mid-thread, or choose the best model per step - balancing cost, capability, and speed.

🤖 Autonomous agent capabilities

  • Recursive delegation: spawn sub-agents that can spawn their own sub-agents
  • Background processing: long-running tasks execute asynchronously
  • Filesystem access: read/write/search/organize files
  • Terminal execution: run commands with LLM-moderated security
  • Persistent memory: conversation history survives restarts with context management/summarization

🔌 Extensible via MCP (Model Context Protocol)

Connect Chibi to external tools and services (or build your own):

  • GitHub (PRs, issues, code review)
  • Browser automation
  • Docker / cloud services
  • Databases
  • Creative tools (Blender, Figma)

If a tool can be exposed via MCP, Chibi can learn to use it.

🎨 Rich content generation

  • Images: Nano Banana, Imagen, Qwen, Wan, DALL·E, Grok
  • Music: Suno (including custom mode: style/lyrics/vocals)
  • Voice: transcription + text-to-speech (ElevenLabs, MiniMax, OpenAI)

Use cases

Developers

You: “Run the tests and fix what’s broken. I’ll work on the frontend.”
Chibi: *spawns sub-agent, executes tests, analyzes failures, proposes fixes*

Researchers

You: “Research the latest developments in quantum computing. I need a synthesis by tomorrow.”
Chibi: *spawns multiple research agents, aggregates sources, delivers a report*

Creators

You: “Generate a cyberpunk cityscape and compose a synthwave track to match.”
Chibi: *generates an image, creates music, delivers both*

Teams

You: “Review this PR and update the documentation accordingly.”
Chibi: *analyzes changes, suggests improvements, updates docs via MCP*

Privacy, control, and safety

  • Self-hosted: your data stays on your infrastructure
  • Public Mode: users can bring their own API keys (no shared master key required)
  • Access control: whitelist users/groups/models
  • Storage options: local volumes, Redis, or DynamoDB
  • Tool safety: agent tools are configurable; terminal execution is moderated and can be restricted

Documentation


System requirements

  • Minimum: Raspberry Pi 4 / AWS EC2 t4g.nano (2 vCPU, 512MB RAM)
  • Architectures: linux/amd64, linux/arm64
  • Dependencies: Docker (and optionally Docker Compose)

Contributing

Please read CONTRIBUTING.md before submitting.


License

MIT - see LICENSE.


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