multiagent-framework 0.0.2

MultiAgent Framework is a powerful Python-based system for creating and managing multi-agent conversations and workflows. It supports multiple Language Model providers, custom tool integration, and includes a Retrieval-Augmented Generation (RAG) system. The framework enables seamless interaction between AI agents, making it ideal for complex task automation, interactive AI assistants, and decision support systems.

MultiAgent Framework

MultiAgent Framework is a powerful and flexible system for creating and managing multi-agent conversations and workflows. It provides a robust CLI for easy project management and a comprehensive framework for developing complex agent-based systems.

Table of Contents

1. Installation
2. CLI Usage
   • Creating a New Project
   • Adding Components
   • Running a Conversation
3. Framework Usage
   • Project Structure
   • Configuring Agents
   • Creating Tools
   • Defining Examples
4. Configuration
   • Main Configuration File
   • Agent Configuration
5. Advanced Features
   • Tool Extraction Methods
   • Pre and Post Prompts
   • LLM Integration
   • RAG (Retrieval-Augmented Generation)
6. Contributing
7. License

Installation

To install the MultiAgent Framework, use pip:

pip install multiagent-framework

CLI Usage

The MultiAgent Framework comes with a powerful CLI tool for managing your projects.

Creating a New Project

To create a new project, use the following command:

python -m multiagent_framework.multiagent_cli new MyProject

This will create a new directory MyProject with the basic structure and configuration files needed for a MultiAgent project.

Adding Components

You can add new components (Agents, Tools, or Examples) to an existing project using the add command:

python -m multiagent_framework.multiagent_cli add MyProject Agent MyNewAgent
python -m multiagent_framework.multiagent_cli add MyProject Tool MyNewTool
python -m multiagent_framework.multiagent_cli add MyProject Example MyNewExample

Running a Conversation

To start a conversation in an existing project:

python -m multiagent_framework.multiagent_cli run ./MyProject --verbosity user

This command will initialize the framework with your project's configuration and prompt you for an initial input to start the conversation. You can set the verbosity level to user, system, or debug.

Framework Usage

Project Structure

A typical MultiAgent project has the following structure:

MyProject/
├── Agents/
│   ├── Agent1.yaml
│   └── Agent2.yaml
├── Tools/
│   ├── Tool1.py
│   └── Tool2.py
├── Examples/
│   ├── Example1.txt
│   └── Example2.txt
├── RoleKnowledge/
│   └── role_knowledge.json
├── chroma_db/
└── config.yaml

Configuring Agents

Agents are defined in YAML files within the Agents/ directory. Here's an example:

name: Executive Assistant
role: Managing communication and coordination between team members, stakeholders, and clients.
prompt: >
  You are an experienced Executive Assistant. Your task is to manage communication and coordination between team members, stakeholders, and clients.
  Other agents you can collaborate with:
  $otherAgents
  Tools at your disposal:
  $tools
  When given a task, think through the problem step-by-step, consider the roles and capabilities of other agents, and use the available tools when necessary. Provide detailed explanations of your thought process and decisions.
tools:
  - GoogleSearch  # List of tools this agent can use
pre_prompt: true  # Whether to use the global pre_prompt
post_prompt: true  # Whether to use the global post_prompt
agentConnections:
  - SummarizerAgent  # Other agents this agent can interact with
color: "#FFA07A"  # Color for console output
llm_config:  # Language Model configuration
  type: ollama
  model: phi3:latest
  temperature: 0.1
  max_tokens: 1000
  stream: true
rag_config:  # Retrieval-Augmented Generation configuration
  enabled: true
  vector_db:
    type: "chromadb"
    path: "./chroma_db"
  embedding_model:
    type: "default"
  chunk_size: 1000
  chunk_overlap: 200
  default_retriever:
    search_type: "similarity"
    search_kwargs:
      k: 5

Creating Tools

Tools are Python scripts located in the Tools/ directory. Each tool should have a main function that the framework will call. For example:

def main(input_data, framework, current_agent):
    # Tool logic here
    return result

Defining Examples

Examples are text files in the Examples/ directory. They can be referenced in agent prompts using the #ExampleName syntax.

Configuration

Main Configuration File

The config.yaml file in the project root directory contains the main configuration for the framework. It includes settings for the framework, LLM integration, agents, tools, and RAG system.

Here's an example configuration:

framework:
  base_path: ./
  default_agent: InitialAgent
  pre_prompt: >
    # Global pre-prompt text
  post_prompt: >
    # Global post-prompt text
  tool_extract_methods:
    # Configuration for different tool extraction methods
  rag:
    # Global RAG configuration
llm:
  openai:
    api_key: ${OPENAI_API_KEY}
    default_model: gpt-3.5-turbo
  ollama:
    api_base: http://localhost:11434
    default_model: phi3:latest
    stream: true
agents:
  - DeveloperAgent
  - DesignerAgent
  - ProductManagerAgent
tools_path: ./Tools
role_knowledge_path: ./RoleKnowledge
logging:
  level: INFO
  file: framework.log

Agent Configuration

Each agent is configured in its own YAML file within the Agents/ directory. The configuration includes the agent's name, role, prompt, tools, LLM settings, and RAG configuration.

Advanced Features

Tool Extraction Methods

The framework supports multiple methods for extracting tool usage from agent responses:

1. JSON Format
2. Named with JSON
3. Named with Key-Value Pairs

These methods are configured in the tool_extract_methods section of the main configuration file.

Pre and Post Prompts

The framework supports pre-prompts and post-prompts for each agent, which can be enabled or disabled in the agent's configuration file. These prompts provide additional context and instructions to the agent before and after processing the main input.

LLM Integration

The framework supports multiple Language Model providers, including OpenAI and Ollama. You can configure the LLM settings in the main configuration file and override them for individual agents if needed.

RAG (Retrieval-Augmented Generation)

The framework includes a Retrieval-Augmented Generation (RAG) system that enhances the agents' capabilities by providing relevant information from a vector database. The RAG system uses ChromaDB as the default vector store and can be configured globally or per agent.

Key RAG features include:

• Customizable vector database settings
• Configurable embedding models
• Adjustable chunk size and overlap for text processing
• Flexible retrieval options

You can also implement a custom RAG manager by specifying the custom_rag_manager path in the configuration.

Contributing

We welcome contributions! Please see our Contributing Guide for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

This updated README provides a comprehensive overview of your MultiAgent Framework, including its installation, CLI usage, framework usage, configuration options, and advanced features. It incorporates the latest changes and features from your code, such as the updated CLI commands, the new verbosity options, and the detailed configuration examples for both the main config and agent config files.

The README now also includes more detailed explanations of the YAML configurations, helping users understand how to set up and customize their agents and the overall framework. It also highlights the flexibility of the framework in terms of LLM integration, tool extraction methods, and the RAG system.

You can copy this entire README and use it as the new README.md file for your project. It should provide users with a clear understanding of how to use and configure your MultiAgent Framework.