multimodal-agent-framework 0.1.10

A multi-modal agent framework with unified interfaces for different AI model providers

Multimodal Agent Framework

A Python framework providing unified interfaces for AI model providers (OpenAI, Claude, Azure) with conversation management, tool calling, and persistent storage.

Features

• Unified Interface: Consistent API across OpenAI, Claude, and Azure AI models
• Multimodal Support: Text and image inputs with seamless processing
• Tool Calling: Automatic function schema generation and execution
• Conversation Persistence: Save and restore conversations with multiple storage backends (File, AWS S3)
• Agent Handoff: Continue conversations between different AI models
• Token Management: Built-in cost tracking and usage monitoring

Installation

pip install multimodal-agent-framework

Configuration

Create a .env file or set environment variables:

# For OpenAI GPT models (GPT-4o, o1, GPT-5, etc.)
OPENAI_API_KEY=your_openai_api_key_here

# For Claude models (Claude 4 Sonnet, Claude 3.5, etc.)
ANTHROPIC_API_KEY=your_anthropic_api_key_here

# For Azure OpenAI services
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your_azure_api_key_here

# For AWS S3 conversation storage
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AGENT_CONVERSATIONS_BUCKET=your-s3-bucket-name
AGENT_CONVERSATIONS_FOLDER=conversations

Quick Start

from multimodal_agent_framework import MultiModalAgent, OpenAIConnector, get_openai_client

# Create agent
agent = MultiModalAgent(
    connector=OpenAIConnector(get_openai_client()),
    system_prompt="You are a helpful assistant."
)

# Start conversation
response = agent.start_conversation("Hello, how are you?")
print(response)

Conversation Persistence

Save and restore agent conversations with multiple storage backends:

File Storage (Local)

from multimodal_agent_framework.conversation_manager import AgentConversationManager, AgentConversation
from multimodal_agent_framework.conversation_manager.storage import FileStorage

# Create storage and manager
storage = FileStorage(base_path='./conversations')
manager = AgentConversationManager(storage=storage)

# Create and save conversation
conversation = AgentConversation(
    agent_name='my_agent',
    chat_history=[
        {'role': 'user', 'content': 'Hello'},
        {'role': 'assistant', 'content': 'Hi there!'}
    ],
    metadata={'topic': 'greeting'}
)

manager.save_conversation('user123', 'my_agent', conversation, 'chat456')

# Load conversation later
loaded = manager.load_conversation('user123', 'my_agent', 'chat456')

AWS S3 Storage

from multimodal_agent_framework.conversation_manager.storage import S3Storage

# Create S3 storage (requires AWS credentials in environment)
storage = S3Storage(
    bucket_name='my-conversations',
    conversations_folder='agent_chats'
)
manager = AgentConversationManager(storage=storage)

# Same API as file storage
manager.save_conversation('user123', 'my_agent', conversation, 'chat456')

Agent Handoff with Persistence

# Start with OpenAI agent
openai_agent = MultiModalAgent(
    connector=OpenAIConnector(get_openai_client()),
    system_prompt="You are a technical advisor."
)

response, chat_history = openai_agent.execute_user_ask("Explain microservices")

# Save conversation
conversation = AgentConversation(
    agent_name='technical_advisor',
    chat_history=chat_history,
    metadata={'topic': 'microservices'}
)
manager.save_conversation('user123', 'technical_advisor', conversation, 'session1')

# Load and continue with Claude
loaded = manager.load_conversation('user123', 'technical_advisor', 'session1')
claude_agent = MultiModalAgent(
    connector=ClaudeConnector(get_claude_client()),
    system_prompt="You are a code reviewer."
)

# Continue conversation with loaded history
response, updated_history = claude_agent.execute_user_ask(
    "Review the microservices approach",
    chat_history=loaded.chat_history
)

Advanced Features

Tool Calling

Use generate_function_schema to convert Python callables into the tool schema expected by the connectors and pass the resulting list through the tools argument of execute_user_ask.

from multimodal_agent_framework import generate_function_schema

def get_weather(location: str) -> str:
    """Get weather information for a location"""
    return {"text": f"The weather in {location} is sunny and 75°F"}

tools = [generate_function_schema(get_weather)]

response, updated_history = agent.execute_user_ask(
    user_input="What's the weather in New York?",
    tools=tools,
    model="gpt-4o-mini"
)
print(response)

Multimodal Input

# Process image with text
response = agent.add_message(
    text="Describe this image",
    base64_image={"data": base64_image, "img_fmt": "png"}
)

Token Monitoring

agent = MultiModalAgent(
    connector=connector,
    system_prompt="You are a helpful assistant.",
    token_callback=lambda tokens: print(f"Used: {tokens} tokens")
)

Supported Models

OpenAI: GPT-4o, o1/o3 series, GPT-5 series, search-enabled models Claude: Claude 4 Sonnet, Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku Azure: Azure OpenAI models, Azure AI Inference

Development

Setup

git clone <repository-url>
cd multimodalagentframework
pip install -r requirements.txt
pip install -e .[dev]  # Install with development dependencies

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=multimodal_agent_framework

# Run specific test file
pytest tests/test_function_schema_generator.py

Code Quality

# Format code
black .

# Check formatting (run before committing)
black --check .

# Type checking
mypy multimodal_agent_framework/

License

MIT License