panel-of-agents 0.1.0
A framework for orchestrating multiple AI agents in collaborative discussions and tasks
Requires Python: >=3.10
Keywords: agents, ai, automation, llms

# PanelOfAgents

A flexible and powerful framework for orchestrating multiple AI agents in domain-specific applications, with support for any LLM backend.

## Introduction

This framework enables seamless orchestration of multiple AI agents for domain-specific applications. Inspired by OpenAI's swarm framework but addressing its limitations, our solution offers greater flexibility in agent interactions without being tied to specific LLM implementations.

### Purpose

- Create a simple yet powerful multi-agent orchestration framework
- Enable flexible task handoffs between agents in single-domain applications
- Provide a production-ready alternative to experimental frameworks
- Support integration with various LLM implementations

### Why This Framework?

Unlike OpenAI's swarm framework, which is limited to GPT models and requires predefined handoffs, this framework offers:
- Model-agnostic implementation
- Dynamic agent interactions
- Production-ready architecture
- Flexible integration options

## Features

- **🚀 Flexible Multi-agent Orchestration** - Dynamic task distribution and collaboration between agents
- **⚡️ Real-time Communication** - Token-by-token streaming with minimal latency
- **🔄 Rich Context Sharing** - Comprehensive context including conversation history, action results, and artifacts
- **🔌 Universal LLM Support** - Compatible with any LangChain BaseChatModel implementation
- **🎯 Targeted Agent Usage** - Smart agent selection based on capabilities rather than predefined rules
- **⚙️ Developer-First Design** - Flexible state management and easy service integration

## Installation

```bash
pip install panel-of-agents
```

## Usage

### Context

The `Context` object is the central object in the framework. It contains the state of the conversation, the artifacts, and any custom properties. It is shared between all agents.

The `Context` can be initialized with the following parameters:
- `current_question`: The current question to be answered.
- `conversation_history`: A list of messages between the user and the agents. This is a list of `HumanMessage` and `AIMessage` objects from LangChain.
- `artifacts`: A dictionary of artifacts. The key is the index of the conversation turn and the value is an `Artifact` object.
- `target_agent` (optional): The agent that will respond to the user.
- `custom_props` (optional): A dictionary of custom properties. The key is the name of the property and the value is a list of `CustomProp` objects.

#### Initializing an empty context

```python
from panel_of_agents.context import Context

context = Context("Hi", [], {})
```

#### Initializing a context with a conversation history

```python
from langchain_core.messages import HumanMessage, AIMessage

context = Context("Hi", [HumanMessage("Hi"), AIMessage("Hi, how can I help you today?")], {})
```

### Decorators

`panel_of_agents` uses decorators to indicate the actions/tools that an agent can use.

#### `capability`

The `capability` decorator is used to indicate that a function is a capability.

```python
from panel_of_agents.decorators import capability

class SomeAgent(Agent):
    ...

    @capability
    def my_capability(self, context: Context) -> CapabilityResult:
        return CapabilityResult(result="My capability was successful", artifact=None)
```

Capabilities which are the actions that an agent can perform must return a `CapabilityResult` object.

The `CapabilityResult` object contains the following parameters:
- `result`: The result of the capability. The output of the function.
- `artifact`: An optional artifact.

#### Artifacts

Artifacts are outputs of capabilities that may be stored and persisted across multiple conversation turns.

```python
from panel_of_agents.types.context import Artifact

artifact = Artifact(author="SomeAgent", data={"some_data": "some_data"}, artifact_type=ArtifactType.INTERNAL)
```

The `artifact_type` can be either `INTERNAL` or `USER`.

- `INTERNAL`: The artifact is internal to the framework and is not visible to the user.
- `USER`: The artifact is visible to the user and can be used by other capabilities.

Capabilities which create artifacts must be decorated with the `creates_artifact` decorator.

```python
from panel_of_agents.decorators import capability, creates_artifact

class SomeAgent(Agent):
    ...

    @creates_artifact("A list of users")
    @capability
    def my_capability(self, context: Context) -> CapabilityResult:
        artifact = Artifact(author=self.name, data={"some_data": "some_data"}, artifact_type=ArtifactType.INTERNAL)
        return CapabilityResult(result="My capability was successful", artifact=artifact)
```

### Agents

An agent is a self-contained entity that can perform various related actions. In `panel_of_agents`, custom agents can be created by extending the `Agent` class.

An agent requires the following parameters:
- `name`: The name of the agent.
- `personal_biography`: A short biography of the agent.
- `public_biography`: A short biography of the agent that is visible to other agents.
- `model`: The model to use for the agent.
- `max_tries` (optional): The maximum number of tries for an agent to perform an action. Defaults to 3.

#### Creating an agent

```python

class AdditionAgent(Agent):
    def __init__(self, model: BaseChatModel, max_tries: int = 3):
        personal_biography = "You are an agent that performs addition on two numbers."
        public_biography = "An agent that performs addition on two numbers."

        super().__init__(
            name="Addition Agent",
            personal_biograhpy=personal_biography,
            public_biograhpy=public_biography,
            model=model,
            max_tries=max_tries
        )

    @agent_capability
    def add(self, a: float, b: float) -> CapabilityResult:
        result = a + b
        return CapabilityResult(result=result, artifact=None)


addition_agent = AdditionAgent(model, max_tries=5)
```

### Moderator

The `Moderator` is responsible for orchestrating the conversation between the agents. It is responsible for selecting the initial agent (when `target_agent` is not set), and for controlling the number of times agents execute to answer a given question.

#### Creating a moderator

The `Moderator` requires the following parameters:
- `panel_of_agents`: A list of agents to be used in the conversation. These are instances of the subclassed `Agent` class.
- `leader`: The agent that will respond to the user.
- `moderator_cast_vote`: Whether the moderator will cast a vote on whether an agent is capable of answering a given question. If not set, each agent will vote on whether they are capable of answering the question.

```python
from panel_of_agents.moderator import Moderator

moderator = Moderator(
    panel_of_agents=[AdditionAgent(), SubtractionAgent()],
    leader="Addition Agent",
    moderator_cast_vote=True
)
```

### Transmitter

The `Transmitter` is responsible for transmitting the conversation between the agents. It is responsible for formatting the conversation for the user and for streaming the conversation to the user.

#### Creating a transmitter

```python
from panel_of_agents.transmitter import Transmitter

transmitter = Transmitter(moderator)
```

#### Invoking the transmitter

##### Streaming
```python

for token in transmitter.invoke_moderator(context, stream=True):
    print(token, end="", flush=True)
```

##### Non-streaming

```python
response = transmitter.invoke_moderator(context, stream=False)
```

#### Raw Feed

The `panel_of_agents` framework utilizes a custom communication protocol, for consistent performance, output containing the raw response should be used as the conversation history.

The `raw_feed` can be accessed from the `transmitter` object after an invocation is complete.

```python
response = transmitter.invoke_moderator(context, stream=False)

conversation_history.append(AIMessage(content=transmitter.raw_feed))
```