• 2026-02-17 🎉 Released v0.1.4 — MCP support, progress streaming, new providers. See nanobot releases.
• 2026-02-14 🔌 MCP support added! See MCP section for details.
• 2026-02-09 💬 Added Slack, Email, and QQ support.
• 2026-02-02 🎉 nanobot officially launched!

1. Create a bot

• Open Telegram, search @BotFather
• Send /newbot, follow prompts
• Copy the token

2. Configure

{
  "channels": {
    "telegram": {
      "enabled": true,
      "token": "YOUR_BOT_TOKEN",
      "allowFrom": ["YOUR_USER_ID"]
    }
  }
}

You can find your User ID in Telegram settings. It is shown as @yourUserId. Copy this value without the @ symbol and paste it into the config file.

3. Run

banabot gateway

Uses Socket.IO WebSocket by default, with HTTP polling fallback.

1. Ask banabot to set up Mochat for you

Simply send this message to banabot (replace xxx@xxx with your real email):

Read https://raw.githubusercontent.com/HKUDS/MoChat/refs/heads/main/skills/nanobot/skill.md and register on MoChat. My Email account is xxx@xxx Bind me as your owner and DM me on MoChat.

banabot will automatically register, configure ~/.banabot/config.json, and connect to Mochat.

2. Restart gateway

banabot gateway

That's it — banabot handles the rest!

{
  "channels": {
    "mochat": {
      "enabled": true,
      "base_url": "https://mochat.io",
      "socket_url": "https://mochat.io",
      "socket_path": "/socket.io",
      "claw_token": "claw_xxx",
      "agent_user_id": "6982abcdef",
      "sessions": ["*"],
      "panels": ["*"],
      "reply_delay_mode": "non-mention",
      "reply_delay_ms": 120000
    }
  }
}

1. Create a bot

• Go to https://discord.com/developers/applications
• Create an application → Bot → Add Bot
• Copy the bot token

2. Enable intents

• In the Bot settings, enable MESSAGE CONTENT INTENT
• (Optional) Enable SERVER MEMBERS INTENT if you plan to use allow lists based on member data

3. Get your User ID

• Discord Settings → Advanced → enable Developer Mode
• Right-click your avatar → Copy User ID

4. Configure

{
  "channels": {
    "discord": {
      "enabled": true,
      "token": "YOUR_BOT_TOKEN",
      "allowFrom": ["YOUR_USER_ID"]
    }
  }
}

5. Invite the bot

• OAuth2 → URL Generator
• Scopes: bot
• Bot Permissions: Send Messages, Read Message History
• Open the generated invite URL and add the bot to your server

6. Run

banabot gateway

Requires Node.js ≥18.

1. Link device

banabot channels login
# Scan QR with WhatsApp → Settings → Linked Devices

2. Configure

{
  "channels": {
    "whatsapp": {
      "enabled": true,
      "allowFrom": ["+1234567890"]
    }
  }
}

3. Run (two terminals)

# Terminal 1
banabot channels login

# Terminal 2
banabot gateway

Uses WebSocket long connection — no public IP required.

1. Create a Feishu bot

• Visit Feishu Open Platform
• Create a new app → Enable Bot capability
• Permissions: Add im:message (send messages)
• Events: Add im.message.receive_v1 (receive messages)
  • Select Long Connection mode (requires running banabot first to establish connection)
• Get App ID and App Secret from "Credentials & Basic Info"
• Publish the app

2. Configure

{
  "channels": {
    "feishu": {
      "enabled": true,
      "appId": "cli_xxx",
      "appSecret": "xxx",
      "encryptKey": "",
      "verificationToken": "",
      "allowFrom": []
    }
  }
}

encryptKey and verificationToken are optional for Long Connection mode. allowFrom: Leave empty to allow all users, or add ["ou_xxx"] to restrict access.

3. Run

banabot gateway

Uses botpy SDK with WebSocket — no public IP required. Currently supports private messages only.

1. Register & create bot

• Visit QQ Open Platform → Register as a developer (personal or enterprise)
• Create a new bot application
• Go to 开发设置 (Developer Settings) → copy AppID and AppSecret

2. Set up sandbox for testing

• In the bot management console, find 沙箱配置 (Sandbox Config)
• Under 在消息列表配置, click 添加成员 and add your own QQ number
• Once added, scan the bot's QR code with mobile QQ → open the bot profile → tap "发消息" to start chatting

3. Configure

• allowFrom: Leave empty for public access, or add user openids to restrict. You can find openids in the banabot logs when a user messages the bot.
• For production: submit a review in the bot console and publish. See QQ Bot Docs for the full publishing flow.

{
  "channels": {
    "qq": {
      "enabled": true,
      "appId": "YOUR_APP_ID",
      "secret": "YOUR_APP_SECRET",
      "allowFrom": []
    }
  }
}

4. Run

banabot gateway

Now send a message to the bot from QQ — it should respond!

Uses Stream Mode — no public IP required.

1. Create a DingTalk bot

• Visit DingTalk Open Platform
• Create a new app -> Add Robot capability
• Configuration:
  • Toggle Stream Mode ON
• Permissions: Add necessary permissions for sending messages
• Get AppKey (Client ID) and AppSecret (Client Secret) from "Credentials"
• Publish the app

2. Configure

{
  "channels": {
    "dingtalk": {
      "enabled": true,
      "clientId": "YOUR_APP_KEY",
      "clientSecret": "YOUR_APP_SECRET",
      "allowFrom": []
    }
  }
}

allowFrom: Leave empty to allow all users, or add ["staffId"] to restrict access.

3. Run

banabot gateway

Uses Socket Mode — no public URL required.

1. Create a Slack app

• Go to Slack API → Create New App → "From scratch"
• Pick a name and select your workspace

2. Configure the app

• Socket Mode: Toggle ON → Generate an App-Level Token with connections:write scope → copy it (xapp-...)
• OAuth & Permissions: Add bot scopes: chat:write, reactions:write, app_mentions:read
• Event Subscriptions: Toggle ON → Subscribe to bot events: message.im, message.channels, app_mention → Save Changes
• App Home: Scroll to Show Tabs → Enable Messages Tab → Check "Allow users to send Slash commands and messages from the messages tab"
• Install App: Click Install to Workspace → Authorize → copy the Bot Token (xoxb-...)

3. Configure banabot

{
  "channels": {
    "slack": {
      "enabled": true,
      "botToken": "xoxb-...",
      "appToken": "xapp-...",
      "groupPolicy": "mention"
    }
  }
}

4. Run

banabot gateway

DM the bot directly or @mention it in a channel — it should respond!

Give banabot its own email account. It polls IMAP for incoming mail and replies via SMTP — like a personal email assistant.

1. Get credentials (Gmail example)

• Create a dedicated Gmail account for your bot (e.g. my-nanobot@gmail.com)
• Enable 2-Step Verification → Create an App Password
• Use this app password for both IMAP and SMTP

2. Configure

• consentGranted must be true to allow mailbox access. This is a safety gate — set false to fully disable.
• allowFrom: Leave empty to accept emails from anyone, or restrict to specific senders.
• smtpUseTls and smtpUseSsl default to true / false respectively, which is correct for Gmail (port 587 + STARTTLS). No need to set them explicitly.
• Set "autoReplyEnabled": false if you only want to read/analyze emails without sending automatic replies.

{
  "channels": {
    "email": {
      "enabled": true,
      "consentGranted": true,
      "imapHost": "imap.gmail.com",
      "imapPort": 993,
      "imapUsername": "my-nanobot@gmail.com",
      "imapPassword": "your-app-password",
      "smtpHost": "smtp.gmail.com",
      "smtpPort": 587,
      "smtpUsername": "my-nanobot@gmail.com",
      "smtpPassword": "your-app-password",
      "fromAddress": "my-nanobot@gmail.com",
      "allowFrom": ["your-real-email@gmail.com"]
    }
  }
}

3. Run

banabot gateway

Codex uses OAuth instead of API keys. Requires a ChatGPT Plus or Pro account.

1. Login:

banabot provider login openai-codex

2. Set model (merge into ~/.banabot/config.json):

{
  "agents": {
    "defaults": {
      "model": "openai-codex/gpt-5.1-codex"
    }
  }
}

3. Chat:

banabot agent -m "Hello!"

Docker users: use docker run -it for interactive OAuth login.

Connects directly to any OpenAI-compatible endpoint — LM Studio, llama.cpp, Together AI, Fireworks, Azure OpenAI, or any self-hosted server. Bypasses LiteLLM; model name is passed as-is.

{
  "providers": {
    "custom": {
      "apiKey": "your-api-key",
      "apiBase": "https://api.your-provider.com/v1"
    }
  },
  "agents": {
    "defaults": {
      "model": "your-model-name"
    }
  }
}

For local servers that don't require a key, set apiKey to any non-empty string (e.g. "no-key").

Run your own model with vLLM or any OpenAI-compatible server, then add to config:

1. Start the server (example):

vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

2. Add to config (partial — merge into ~/.banabot/config.json):

Provider (key can be any non-empty string for local):

{
  "providers": {
    "vllm": {
      "apiKey": "dummy",
      "apiBase": "http://localhost:8000/v1"
    }
  }
}

Model:

{
  "agents": {
    "defaults": {
      "model": "meta-llama/Llama-3.1-8B-Instruct"
    }
  }
}

banabot uses a Provider Registry (banabot/providers/registry.py) as the single source of truth. Adding a new provider only takes 2 steps — no if-elif chains to touch.

Step 1. Add a ProviderSpec entry to PROVIDERS in banabot/providers/registry.py:

ProviderSpec(
    name="myprovider",                   # config field name
    keywords=("myprovider", "mymodel"),  # model-name keywords for auto-matching
    env_key="MYPROVIDER_API_KEY",        # env var for LiteLLM
    display_name="My Provider",          # shown in `banabot status`
    litellm_prefix="myprovider",         # auto-prefix: model → myprovider/model
    skip_prefixes=("myprovider/",),      # don't double-prefix
)

Step 2. Add a field to ProvidersConfig in banabot/config/schema.py:

class ProvidersConfig(BaseModel):
    ...
    myprovider: ProviderConfig = ProviderConfig()

That's it! Environment variables, model prefixing, config matching, and banabot status display will all work automatically.

Common ProviderSpec options:

# Add a job
banabot cron add --name "daily" --message "Good morning!" --cron "0 9 * * *"
banabot cron add --name "hourly" --message "Check status" --every 3600

# List jobs
banabot cron list

# Remove a job
banabot cron remove <job_id>

1. Create banabot/agent/tools/mytool.py extending the Tool base class
2. Implement name, description, parameters (JSON schema), and execute(**kwargs)
3. Register it in the AgentLoop tool setup

from nanobot.agent.tools.base import Tool

class MyTool(Tool):
    name = "my_tool"
    description = "Does something useful"
    parameters = {
        "type": "object",
        "properties": {
            "input": {"type": "string", "description": "The input value"}
        },
        "required": ["input"]
    }

    async def execute(self, **kwargs):
        return f"Result: {kwargs['input']}"

1. Create banabot/channels/myservice.py extending Channel
2. Implement start(), stop(), and message sending logic
3. Subscribe to the inbound message bus
4. Add a config class to banabot/config/schema.py
5. Register in ChannelManager.start_all()

Skills are Markdown files that give the agent domain-specific instructions:

1. Create ~/.banabot/workspace/skills/myskill/SKILL.md
2. Write instructions, examples, and notes in Markdown
3. The agent will auto-discover and use it

See banabot/skills/README.md for the full skill format.