AI

AstrBot provides built-in support for multiple Large Language Model (LLM) providers and offers a unified interface, making it convenient for plugin developers to access various LLM services.

You can use the LLM / Agent interfaces provided by AstrBot to implement your own intelligent agents.

Starting from version v4.5.7, we've made significant improvements to the way LLM providers are invoked. We recommend using the new approach, which is more concise and supports additional features. Of course, you can still use the legacy invocation method.

Getting the Chat Model ID for the Current Session

TIP

Added in v4.5.7

umo = event.unified_msg_origin
provider_id = await self.context.get_current_chat_provider_id(umo=umo)

Invoking Large Language Models

TIP

Added in v4.5.7

llm_resp = await self.context.llm_generate(
    chat_provider_id=provider_id, # Chat model ID
    prompt="Hello, world!",
)
# print(llm_resp.completion_text) # Get the returned text

Defining Tools

Tools enable large language models to invoke external capabilities.

from pydantic import Field
from pydantic.dataclasses import dataclass

from astrbot.core.agent.run_context import ContextWrapper
from astrbot.core.agent.tool import FunctionTool, ToolExecResult
from astrbot.core.astr_agent_context import AstrAgentContext


@dataclass
class BilibiliTool(FunctionTool[AstrAgentContext]):
    name: str = "bilibili_videos"  # Tool name
    description: str = "A tool to fetch Bilibili videos."  # Tool description
    parameters: dict = Field(
        default_factory=lambda: {
            "type": "object",
            "properties": {
                "keywords": {
                    "type": "string",
                    "description": "Keywords to search for Bilibili videos.",
                },
            },
            "required": ["keywords"],
        }
    )

    async def call(
        self, context: ContextWrapper[AstrAgentContext], **kwargs
    ) -> ToolExecResult:
        return "1. Video Title: How to Use AstrBot\nVideo Link: xxxxxx"

Invoking Agents

TIP

Added in v4.5.7

An Agent can be defined as a combination of system_prompt + tools + llm, enabling more sophisticated intelligent behavior.

After defining the Tool above, you can invoke an Agent as follows:

llm_resp = await self.context.tool_loop_agent(
    event=event,
    chat_provider_id=prov_id,
    prompt="Search for videos related to AstrBot on Bilibili.",
    tools=ToolSet([BilibiliTool()]),
    max_steps=30, # Maximum agent execution steps
    tool_call_timeout=60, # Tool invocation timeout
)
# print(llm_resp.completion_text) # Get the returned text

tool_loop_agent() method automatically handles the loop of tool invocations and LLM requests until the model stops calling tools or the maximum number of steps is reached.

Multi-Agent

TIP

Added in v4.5.7

Multi-Agent systems decompose complex applications into multiple specialized agents that collaborate to solve problems. Unlike relying on a single agent to handle every step, multi-agent architectures allow smaller, more focused agents to be composed into coordinated workflows. We implement multi-agent systems using the agent-as-tool pattern.

In the example below, we define a Main Agent responsible for delegating tasks to different Sub-Agents based on user queries. Each Sub-Agent focuses on specific tasks, such as retrieving weather information.

Define Tools:

@dataclass
class AssignAgentTool(FunctionTool[AstrAgentContext]):
    """Main agent uses this tool to decide which sub-agent to delegate a task to."""

    name: str = "assign_agent"
    description: str = "Assign an agent to a task based on the given query"
    parameters: dict = field(
        default_factory=lambda: {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The query to call the sub-agent with.",
                },
            },
            "required": ["query"],
        }
    )

    async def call(
        self, context: ContextWrapper[AstrAgentContext], **kwargs
    ) -> str | CallToolResult:
        # Here you would implement the actual agent assignment logic.
        # For demonstration purposes, we'll return a dummy response.
        return "Based on the query, you should assign agent 1."


@dataclass
class WeatherTool(FunctionTool[AstrAgentContext]):
    """In this example, sub agent 1 uses this tool to get weather information."""

    name: str = "weather"
    description: str = "Get weather information for a location"
    parameters: dict = field(
        default_factory=lambda: {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "The city to get weather information for.",
                },
            },
            "required": ["city"],
        }
    )

    async def call(
        self, context: ContextWrapper[AstrAgentContext], **kwargs
    ) -> str | CallToolResult:
        city = kwargs["city"]
        # Here you would implement the actual weather fetching logic.
        # For demonstration purposes, we'll return a dummy response.
        return f"The current weather in {city} is sunny with a temperature of 25°C."


@dataclass
class SubAgent1(FunctionTool[AstrAgentContext]):
    """Define a sub-agent as a function tool."""

    name: str = "subagent1_name"
    description: str = "subagent1_description"
    parameters: dict = field(
        default_factory=lambda: {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The query to call the sub-agent with.",
                },
            },
            "required": ["query"],
        }
    )

    async def call(
        self, context: ContextWrapper[AstrAgentContext], **kwargs
    ) -> str | CallToolResult:
        ctx = context.context.context
        event = context.context.event
        logger.info(f"the llm context messages: {context.messages}")
        llm_resp = await ctx.tool_loop_agent(
            event=event,
            chat_provider_id=await ctx.get_current_chat_provider_id(
                event.unified_msg_origin
            ),
            prompt=kwargs["query"],
            tools=ToolSet([WeatherTool()]),
            max_steps=30,
        )
        return llm_resp.completion_text


@dataclass
class SubAgent2(FunctionTool[AstrAgentContext]):
    """Define a sub-agent as a function tool."""

    name: str = "subagent2_name"
    description: str = "subagent2_description"
    parameters: dict = field(
        default_factory=lambda: {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The query to call the sub-agent with.",
                },
            },
            "required": ["query"],
        }
    )

    async def call(
        self, context: ContextWrapper[AstrAgentContext], **kwargs
    ) -> str | CallToolResult:
        return "I am useless :(, you shouldn't call me :("

Then, similarly, invoke the Agent using the tool_loop_agent() method:

@filter.command("test")
async def test(self, event: AstrMessageEvent):
    umo = event.unified_msg_origin
    prov_id = await self.context.get_current_chat_provider_id(umo)
    llm_resp = await self.context.tool_loop_agent(
        event=event,
        chat_provider_id=prov_id,
        prompt="Test calling sub-agent for Beijing's weather information.",
        system_prompt=(
            "You are the main agent. Your task is to delegate tasks to sub-agents based on user queries."
            "Before delegating, use the 'assign_agent' tool to determine which sub-agent is best suited for the task."
        ),
        tools=ToolSet([SubAgent1(), SubAgent2(), AssignAgentTool()]),
        max_steps=30,
    )
    yield event.plain_result(llm_resp.completion_text)

Conversation Manager

Getting the Current LLM Conversation History for a Session

from astrbot.core.conversation_mgr import Conversation

uid = event.unified_msg_origin
conv_mgr = self.context.conversation_manager
curr_cid = await conv_mgr.get_curr_conversation_id(uid)
conversation = await conv_mgr.get_conversation(uid, curr_cid)  # Conversation

Conversation 类型定义

@dataclass
class Conversation:
    """The conversation entity representing a chat session."""

    platform_id: str
    """The platform ID in AstrBot"""
    user_id: str
    """The user ID associated with the conversation."""
    cid: str
    """The conversation ID, in UUID format."""
    history: str = ""
    """The conversation history as a string."""
    title: str | None = ""
    """The title of the conversation. For now, it's only used in WebChat."""
    persona_id: str | None = ""
    """The persona ID associated with the conversation."""
    created_at: int = 0
    """The timestamp when the conversation was created."""
    updated_at: int = 0
    """The timestamp when the conversation was last updated."""

Main Methods

new_conversation

• Usage
  Create a new conversation in the current session and automatically switch to it.
• Arguments
  • unified_msg_origin: str – In the format platform_name:message_type:session_id
  • platform_id: str | None – Platform identifier, defaults to parsing from unified_msg_origin
  • content: list[dict] | None – Initial message history
  • title: str | None – Conversation title
  • persona_id: str | None – Associated persona ID
• Returns
  str – Newly generated UUID conversation ID

switch_conversation

• Usage
  Switch the session to a specified conversation.
• Arguments
  • unified_msg_origin: str
  • conversation_id: str
• Returns
  None

delete_conversation

• Usage
  Delete a conversation from the session; if conversation_id is None, deletes the current conversation.
• Arguments
  • unified_msg_origin: str
  • conversation_id: str | None
• Returns
  None

get_curr_conversation_id

• Usage
  Get the conversation ID currently in use by the session.
• Arguments
  • unified_msg_origin: str
• Returns
  str | None – Current conversation ID, returns None if it doesn't exist

get_conversation

• Usage
  Get the complete object for a specified conversation; automatically creates it if it doesn't exist and create_if_not_exists=True.
• Arguments
  • unified_msg_origin: str
  • conversation_id: str
  • create_if_not_exists: bool = False
• Returns
  Conversation | None

get_conversations

• Usage
  Retrieve the complete list of conversations for a user or platform.
• Arguments
  • unified_msg_origin: str | None – When None, does not filter by user
  • platform_id: str | None
• Returns
  List[Conversation]

update_conversation

• Usage
  Update the title, history, or persona_id of a conversation.
• Arguments
  • unified_msg_origin: str
  • conversation_id: str | None – Uses the current conversation when None
  • history: list[dict] | None
  • title: str | None
  • persona_id: str | None
• Returns
  None

Persona Manager

PersonaManager is responsible for unified loading, caching, and providing CRUD interfaces for all Personas, while maintaining compatibility with the legacy persona format (v3) from before AstrBot 4.x.
During initialization, it automatically reads all personas from the database and generates v3-compatible data for seamless use with legacy code.

persona_mgr = self.context.persona_manager

Main Methods

get_persona

• Usage Get persona data by persona ID.
• Arguments
  • persona_id: str – Persona ID
• ReturnsPersona – Persona data, returns None if it doesn't exist
• RaisesValueError – Raised when it doesn't exist

get_all_personas

• Usage
  Retrieve all personas from the database at once.
• Returns
  list[Persona] – Persona list, may be empty

create_persona

• Usage
  Create a new persona and immediately write it to the database; automatically refreshes the local cache upon success.
• Arguments
  • persona_id: str – New persona ID (unique)
  • system_prompt: str – System prompt
  • begin_dialogs: list[str] – Optional, opening dialogs (even number of entries, alternating user/assistant)
  • tools: list[str] – Optional, list of allowed tools; None=all tools, []=disable all
• Returns
  Persona – Newly created persona object
• Raises
  ValueError – If persona_id already exists

update_persona

• Usage
  Update any fields of an existing persona and synchronize to database and cache.
• Arguments
  • persona_id: str – Persona ID to update
  • system_prompt: str – Optional, new system prompt
  • begin_dialogs: list[str] – Optional, new opening dialogs
  • tools: list[str] – Optional, new tool list; semantics same as create_persona
• Returns
  Persona – Updated persona object
• Raises
  ValueError – If persona_id doesn't exist

delete_persona

• Usage
  Delete the specified persona and clean up both database and cache.
• Arguments
  • persona_id: str – Persona ID to delete
• Raises
  ValueError – If persona_id doesn't exist

get_default_persona_v3

• Usage
  Get the default persona (v3 format) to use based on the current session configuration.
  Falls back to DEFAULT_PERSONALITY if configuration doesn't specify one or the specified persona doesn't exist.
• Arguments
  • umo: str | MessageSession | None – Session identifier, used to read user-level configuration
• Returns
  Personality – Default persona object in v3 format

Persona / Personality 类型定义

class Persona(SQLModel, table=True):
    """Persona is a set of instructions for LLMs to follow.

    It can be used to customize the behavior of LLMs.
    """

    __tablename__ = "personas"

    id: int = Field(primary_key=True, sa_column_kwargs={"autoincrement": True})
    persona_id: str = Field(max_length=255, nullable=False)
    system_prompt: str = Field(sa_type=Text, nullable=False)
    begin_dialogs: Optional[list] = Field(default=None, sa_type=JSON)
    """a list of strings, each representing a dialog to start with"""
    tools: Optional[list] = Field(default=None, sa_type=JSON)
    """None means use ALL tools for default, empty list means no tools, otherwise a list of tool names."""
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    updated_at: datetime = Field(
        default_factory=lambda: datetime.now(timezone.utc),
        sa_column_kwargs={"onupdate": datetime.now(timezone.utc)},
    )

    __table_args__ = (
        UniqueConstraint(
            "persona_id",
            name="uix_persona_id",
        ),
    )


class Personality(TypedDict):
    """LLM Persona class.

    Starting from v4.0.0 and later, it's recommended to use the Persona class above. Additionally, the mood_imitation_dialogs field has been deprecated.
    """

    prompt: str
    name: str
    begin_dialogs: list[str]
    mood_imitation_dialogs: list[str]
    """Mood imitation dialog preset. Deprecated since v4.0.0 and later."""
    tools: list[str] | None
    """Tool list. None means use all tools, empty list means don't use any tools"""