Operagents

Installation

Install the latest version with:

```bash pip install operagents

or use poetry

poetry add operagents

or use pdm

pdm add operagents

or use uv

uv pip install operagents ```

Concepts

Agent

An agent is a human or a language model that can act as characters and use props in the opera scenes. The agent can communicate with others by observing and acting. Every agent has a backend (e.g. user, openai api) to generate the response and own memory to store the long-term / short-term information.

Scene

A scene is a part of the opera that contains a number of characters. Every scene has a flow and a director to control the whole session process. The scene can also have a prepare section to do some initialization work before the scene starts.

Character

A character is a role in the scene. Every character has a name, a description, and a list of props. When the scene starts, an agent will act as the character and communicate with others.

Flow

The flow is used to control the order of the characters' acting in the scene.

Director

The director is used to decide whether to end the current scene and which scene to play next.

Prop

A prop is a tool that can be used by the agents to improve their acting. Agents can get external information by using props.

Timeline

The timeline is the main runtime component of the opera to manage the session process. It runs the current session and switches between sessions. The timeline also records the global information of the opera, and can be shared by all agents.

Session

A session indicates a single run of the scene. It contains an unique identifier and its corresponding scene.

Usage

The common way to use operagents is to write a config file and run the opera with the operagents command-line tool.

Start writing a config file

Create a config.yaml file with the following basic content:

```yaml

yaml-language-server: $schema=https://operagents.yyydl.top/schemas/config.schema.json

agents: opening_scene: "" scenes: ```

The first line is a comment that tells the YAML Language Server to use the schema from the specified URL. This will enable autocompletion and validation in your editor.
The schema is related to the version of the operagents framework you are using. The URL is in the format https://operagents.yyydl.top/schemas/config-<version>.schema.json, where <version> is the version of the framework, e.g. 0.0.1. If no version is specified, the latest (master) version will be used.

The Template config

Before writing the agent and scene configs, we need to learn about the template config.

Operagents uses templates to generate the context input for the language model. A template is a string in jinja format. You can use jinja2 syntax with provided context varaibles to control the input to the language model.

A template config can be in the following format:

1. simple string template

yaml user_template: |- {# some jinja template #}

1. template with custom functions

yaml user_template: content: |- {# some jinja template #} custom_functions: function_name: module_name:function_name

If you want to use custom functions in the template, you need to provide the custom_functions key, which is a dictionary of custom function names and their corresponding module paths in dot notation format.

The Agent config

The agents section is a dictionary of agents, where the key is the agent's name and the value is the agent's config.

The agents need to act as a character in the scenes and respond to others' messages. So, the first part of the agent config is the backend config, which is used to communicate with the language model or user. You can use the backend key to specify the backend type and its config.

yaml agents: Mike: backend: # user as the backend (a.k.a human-agent) type: user John: backend: # openai api as the backend type: openai model: gpt-3.5-turbo temperature: 0.5 api_key: base_url: max_retries: 2 tool_choice: type: auto prop_validation_error_template: |- {# some jinja template #}

You can also customize the backend by providing a object path of the custom backend class that implements the Backend abstract class.:

yaml agents: Mike: backend: type: custom path: module_name:CustomBackend custom_config: value

```python

module_name.py

from typing import Self

from operagents.prop import Prop from operagents.timeline import Timeline from operagents.config import CustomBackendConfig from operagents.backend import Backend, Message, GenerateResponse, GeneratePropUsage

class CustomBackend(Backend): @classmethod def from_config(cls, config: CustomBackendConfig) -> Self: return cls()

@overload
async def generate(
    self,
    timeline: Timeline,
    messages: list[Message],
    props: None = None,
) -> AsyncGenerator[GenerateResponse, None]: ...

@overload
async def generate(
    self,
    timeline: Timeline,
    messages: list[Message],
    props: list[Prop],
) -> AsyncGenerator[GenerateResponse | GeneratePropUsage, None]: ...

async def generate(
    self, timeline: Timeline, messages: list[Message], props: list[Prop] | None = None
) -> AsyncGenerator[GenerateResponse | GeneratePropUsage, None]:
    yield GenerateResponse(content="")

```

The next part of the agent config is the system/user template used to generate the context input for the language model. You can use the system_template/user_template key to specify the system/user template. Here is an example of the template config:

yaml agents: John: system_template: |- Your name is {{ agent.name }}. Current scene is {{ timeline.current_scene.name }}. {% if timeline.current_scene.description -%} {{ timeline.current_scene.description }} {%- endif -%} You are acting as {{ timeline.current_character.name }}. {% if timeline.current_character.description -%} {{ timeline.current_character.description }} {%- endif -%} Please continue the conversation on behalf of {{ agent.name }}({{ timeline.current_character.name }}) based on your known information and make your answer appear as natural and coherent as possible. Please answer directly what you want to say and keep your reply as concise as possible. user_template: |- {% for event in timeline.past_events(agent) -%} {% if event.type_ == "session_act" -%} {{ event.character.agent_name }}({{ event.character.name }}): {{ event.content }} {%- endif %} {%- endfor %}

Another part of the agent config is the session summary system/user template, which is used to generate the summary of the scene session. You can use the session_summary_system_template/session_summary_user_template key to specify the session summary system/user template. Here is an example of the template config:

yaml agents: John: session_summary_system_template: |- Your name is {{ agent.name }}. Your task is to summarize the historical dialogue records according to the current scene, and summarize the most important information. session_summary_user_template: |- {% for event in agent.memory.get_memory_for_session(session_id) -%} {% if event.type_ == "observe" -%} {{ event.content }} {%- elif event.type_ == "act" -%} {{ agent.name }}({{ event.character.name }}): {{ event.content }} {%- endif %} {%- endfor %} {% for event in timeline.session_past_events(agent, session_id) -%} {% if event.type_ == "session_act" -%} {{ event.character.agent_name }}({{ event.character.name }}): {{ event.content }} {%- endif %} {%- endfor %}

Opening scene config

The opening_scene key is used to specify the start scene of the opera. The value is the name of the opening scene.

yaml opening_scene: "Introduction"

The Scene config

The scenes section is a dictionary of scenes, where the key is the scene's name and the value is the scene's config.

The opera is composed of multiple scenes, and each scene has a number of characters. You first need to define the name, description (optional), and characters of the scene.

yaml scenes: talking: description: "The scene is about two people talking." characters: user: agent_name: "Mike" ai assistant: agent_name: "John" description: |- You are a helpful assistant. props: []

The characters in the scene must define the agent_name key, which is the name of the agent acting as the character. The description key (optional) can be used to describe the character in the agent template. The props key (optional) can be used to define the props of the character, see the Prop config for more details.

The Flow of the scene is designed to control the order of the characters' acting. You can specify the type and the parameters of the Flow.

1. order type

The order type is used to pre-define the order of the characters' acting. The characters will cycle through the order list until the scene ends.

yaml scenes: talking: flow: type: order order: - user - ai assistant

1. model type

The model type is used to specify the model to predict the next character to act. The model will predict the next character based on the current context.

yaml scenes: talking: flow: type: model backend: type: openai model: gpt-3.5-turbo temperature: 0.5 system_template: "" user_template: "" allowed_characters: # optional, the characters allowed to act - user - ai assistant begin_character: user # optional, the first character to act fallback_character: ai assistant # optional, the fallback character when the model fails to predict

1. user type

The user type allows human to choose the next character to act.

yaml scenes: talking: flow: type: user

1. custom type

The custom type allows you to define a custom flow class to control the order of the characters' acting.

yaml scenes: talking: flow: type: custom path: module_name:CustomFlow custom_config: value

```python # module_name.py

from typing import Self

from operagents.flow import Flow from operagents.timeline import Timeline from operagents.character import Character from operagents.config import CustomFlowConfig

class CustomFlow(Flow): @classmethod def from_config(cls, config: CustomFlowConfig) -> Self: return cls()

   async def begin(self, timeline: Timeline) -> Character:
       return ""

   async def next(self, timeline: Timeline) -> Character:
       return ""

```

The Director of the scene is used to control the next scene to play. You can specify the type and the parameters of the Director.

1. model type

The model type is used to specify the model to predict the next scene to play. If no finish flag found or no scene name found, the curent scene will continue to play.

yaml scenes: talking: director: type: model backend: type: openai model: gpt-3.5-turbo temperature: 0.5 system_template: "" user_template: "" allowed_scenes: # optional, the next scenes allowed to play - walking - running finish_flag: "finish" # optional, the finish flag to end the opera

1. user type

The user type allows human to choose the next scene to play.

yaml scenes: talking: director: type: user

1. never type

The never Director never ends the current scene. Useful when there is a single scene and you want to end the opera by a Prop.

yaml scenes: talking: director: type: never

1. custom type

The custom type allows you to define a custom director class to control the next scene to play.

yaml scenes: talking: director: type: custom path: module_name:CustomDirector custom_config: value

```python # module_name.py

from typing import Self

from operagents.scene import Scene from operagents.director import Director from operagents.timeline import Timeline from operagents.config import CustomDirectorConfig

class CustomDirector(Director): @classmethod def from_config(cls, config: CustomDirectorConfig) -> Self: return cls()

   async def next_scene(self, timeline: Timeline) -> Scene | None:
       return None

```

The prepare section of the scene is used to defined the preparation steps before the scene starts. You can do some initialization work here.

1. preface type

You can make the character say something before the scene starts.

yaml scenes: talking: prepare: - type: preface character_name: ai assistant content: |- Hello, I am John, your AI assistant. How can I help you today?

1. function type

The function type will call the custom function before the scene starts.

yaml scenes: talking: prepare: - type: function function: module_name:function_name

The custom function will receive one parameter of type operagents.timeline.Timeline.

```python # module_name.py

from operagents.timeline import Timeline

async def function_name(timeline: Timeline) -> None: pass ```

1. custom type

The custom type will call the custom prepare class before the scene starts.

yaml scenes: talking: prepare: - type: custom path: module_name:CustomPrepare custom_config: value

```python # module_name.py

from typing import Self

from operagents.timeline import Timeline from operagents.scene.prepare import ScenePrepare from operagents.config import CustomScenePrepareConfig

class CustomScenePrepare(ScenePrepare): @classmethod def from_config(cls, config: CustomScenePrepareConfig) -> Self: return cls()

   async def prepare(self, timeline: Timeline) -> None:
       pass

```

The Prop config

The characters in the scene can use props to improve there acting. The props section is a list of props, where each prop is a dictionary with the prop type and the prop config.

1. function Prop

The function prop will call the custom function when the prop is used.

yaml scenes: talking: characters: ai assistant: props: - type: function function: module_name:function_name exception_template: |- {# some jinja template #}

The custom function should has no arguments or one argument of type pydantic.BaseModel.

```python from pydantic import Field, BaseModel from datetime import datetime, timezone

async def current_time() -> str: """Get the current real world time.""" return datetime.now(timezone.utc).astimezone().isoformat()

class Args(BaseModel): name: str = Field(description="The name")

async def greet(args: Args) -> str: """Greet the name.""" return f"Hello, {args.name}!" ```

Note that the function's name and docstring will be used as the prop's name and description. You can also provide the description of the args by pydantic's Field. The exception template will be used to render response when the function raises an error.

1. custom Prop

The custom prop will call the custom prop class when the prop is used.

yaml scenes: talking: characters: ai assistant: props: - type: custom path: module_name:CustomProp custom_config: value

```python # module_name.py

from typing import Any, Self

from pydantic import BaseModel from operagents.prop import Prop from operagents.config import CustomPropConfig

class CustomProp(Prop): """The description of the prop"""

   params: BaseModel | None
   """The parameters of the prop"""

   @classmethod
   def from_config(cls, config: CustomPropConfig) -> Self:
       return cls()

   async def call(self, params: BaseModel | None) -> Any:
       return ""

```

The Hook config

Hooks enables you to run custom code when specific timeline events occur. The hooks section is a list of hooks, where each hook is a dictionary with the hook type and the hook config. By default, operagents enables the summary hook unless you change the hooks section.

1. summary Hook

The summary hook will call the agents to summarize the session when the session ends. You can optionally specify the agent names to summarize.

yaml hooks: - type: summary agent_names: - Mike - John

1. custom Hook

The custom hook will invoke the custom hook class when specific timeline event encounters.

yaml hooks: - type: custom path: module_name:CustomHook custom_config: value

```python # module_name.py

from typing import Self

from operagents.hook import Hook from operagents.timeline import Timeline from operagents.config import CustomHookConfig from operagents.timeline.event import ( TimelineEventEnd, TimelineEventStart, TimelineEventSessionAct, TimelineEventSessionEnd, TimelineEventSessionStart, )

class CustomHook(Hook): @classmethod def from_config(cls, config: CustomHookConfig) -> Self: return cls()

   async def on_timeline_start(
       self, timeline: Timeline, event: TimelineEventStart
   ):
       """Called when the timeline is started."""
       pass

   async def on_timeline_end(
       self, timeline: Timeline, event: TimelineEventEnd
   ):
       """Called when the timeline is ended."""
       pass

   async def on_timeline_session_start(
       self, timeline: Timeline, event: TimelineEventSessionStart
   ):
       """Called when a session is started."""
       pass

   async def on_timeline_session_end(
       self, timeline: Timeline, event: TimelineEventSessionEnd
   ):
       """Called when a session is ended."""
       pass

   async def on_timeline_session_act(
       self, timeline: Timeline, event: TimelineEventSessionAct
   ):
       """Called when a character acts in a session."""
       pass

```

The hook class may contains methods in the format of on_timeline_<event_type>, where <event_type> is the type of the timeline event.

Run the opera

operagents provides a command-line tool to easily run the opera. You can run the opera with the following command:

bash operagents run config.yaml

If you want to see the debug logs, you can set the --log-level option:

bash operagents run --log-level DEBUG config.yaml

More commands and options can be found by running operagents --help.

If you want to run the opera programmatically, you can use the opera.run function:

```python import asyncio from pathlib import Path

import yaml from operagents.opera import Opera from operagents.log import setup_logging from operagents.config import OperagentsConfig

async def main(): # if you want to setup the default logging for operagents setup_logging("INFO")

# load the opera from config file
opera = Opera.from_config(
    OperagentsConfig.model_validate(
        yaml.safe_load(Path("./config.yaml").read_text(encoding="utf-8"))
    )
)

finish_state = await opera.run()

if name == "main": asyncio.run(main()) ```

Examples

Chatbot

bash cd examples/chatbot env OPENAI_API_KEY=sk-xxx OPENAI_BASE_URL=https://api.openai.com/v1 operagents run --log-level DEBUG config.yaml

Development

Open in Codespaces (Dev Container):

Or install the development environment locally with:

bash poetry install && poetry run pre-commit install