AstrBot Plugin Development

This skill provides the procedural knowledge to develop AstrBot plugins.

Quick Start: Basic Command Plugin

Create a main.py in your plugin directory:

from astrbot.api.event import filter, AstrMessageEvent
from astrbot.api.star import Context, Star
from astrbot.api import logger

class MyPlugin(Star):
    def __init__(self, context: Context):
        super().__init__(context)

    @filter.command("helloworld")
    async def helloworld(self, event: AstrMessageEvent):
        '''A hello world command'''
        user_name = event.get_sender_name()
        message_str = event.message_str
        logger.info("helloworld triggered!")
        yield event.plain_result(f"Hello, {user_name}!")

    async def terminate(self):
        '''Called when the plugin is unloaded/disabled.'''

Note: The @register decorator is deprecated in newer versions of AstrBot. Please use metadata.yaml to define plugin metadata. AstrBot automatically detects the plugin class inheriting from Star.

Core Workflows

1. Project Setup and Metadata

A complete plugin requires metadata.yaml for identification, requirements.txt for dependencies, and optionally logo.png, _conf_schema.json, and a README.md.

• Plugin names should start with astrbot_plugin_, be lowercase, have no spaces, and be short.
• See references/project-structure.md for mandatory files, dev environment setup, and publishing.

2. Registering Commands and Filters

Commands are registered using @filter.command(name). AstrBot auto-parses command parameters by type hints. You can also use command groups, command aliases, and filter by event type, platform, or user permission.

• See references/core-api.md for full list of filters, hooks, the platform compatibility matrix, and event propagation control.

3. Handling Messages and Responses

AstrBot uses a “Message Chain” system. You can respond with plain text, images, or a mix of components. Proactive (bot-initiated) messages are supported via unified_msg_origin and MessageChain.

• See references/message-components.md for how to build and send messages.

4. Advanced Integrations

• Configuration: Use _conf_schema.json for user settings (supports string, int, bool, object, list, dict, file, template_list, and special selectors).
• LLM Tools: Register tools via @filter.llm_tool decorator or Pydantic FunctionTool dataclass.
• LLM Direct Calls: Use self.context.llm_generate() to call LLMs directly (>= v4.5.7).
• Agent / Multi-Agent: Use self.context.tool_loop_agent() to run tool-loop agents or compose multi-agent systems.
• Stateful Interaction: Use session_waiter for multi-step prompts with custom session filters.
• T2I: Render text or HTML/Jinja2 templates to images.
• Conversation & Persona Managers: Access LLM conversation history and persona settings.

See references/advanced-features.md for examples.

Elegant Design Patterns

Follow these patterns for robust, user-friendly plugins:

• Use unified logging via from astrbot.api import logger.
• Handle errors gracefully to avoid bot crashes.
• Use KV storage (put_kv_data/get_kv_data) or the plugin_data directory for persistence.
• Ensure all IO operations are non-blocking (async).
• Access platform instances, loaded plugins, and protocol-level APIs when needed.

See references/patterns.md for detailed code patterns.