AGI Agents

A streamlined interface for LangChain AI agent creation with multi-modal support (text and images).

Features

• Simple API: Easy-to-use interface for creating AI agents
• Multi-modal Support: Handle both text and image inputs seamlessly
• LangChain Integration: Built on top of the powerful LangChain framework
• Flexible Models: Support for OpenAI, Anthropic, and other LLM providers
• Document Processing: PDF and image processing capabilities
• Context Management: Advanced context engineering utilities
• Async Support: Both synchronous and asynchronous execution

Installation

pip install agi-agents

Quick Start

from agi_agents import Agents
from langchain_openai import ChatOpenAI

# Initialize your model
llm = ChatOpenAI(model_name='gpt-4o-mini')

# Create a simple text processing chain
chain = Agents.chain_create(
    model=llm,
    text_prompt_template="Answer this question: {question}",
)

# Use the chain
response = Agents.chain_batch_generator(
    chain, 
    {"question": "What is artificial intelligence?"}
)
print(response)

Multi-modal Example

# Create a chain that processes both text and images
chain = Agents.chain_create(
    model=llm,
    text_prompt_template="Describe this image: {description}",
    image_prompt_template=True
)

# Process an image
base64_image = Agents.normalize_image_to_base64("path/to/image.jpg")
response = Agents.chain_batch_generator(
    chain,
    {
        "description": "What do you see?",
        "base64_image": base64_image,
        "detail_parameter": "high"
    }
)

Main Classes

Agents

The core class for creating and managing AI agents:

• chain_create(): Create LangChain processing chains
• chain_batch_generator(): Execute chains synchronously
• chain_stream_generator(): Stream responses in real-time
• continue_chain_batch_generator(): Handle long conversations with continuation

Contexts

Utility class for context engineering:

• create_context_layer(): Create context templates
• compress_context(): Compress contexts to fit token limits

Document

Document processing utilities:

• extract_text_from_pdf(): Extract text from PDFs
• convert_pdf_to_images(): Convert PDFs to images
• pdf_to_base64_images(): Convert PDFs to base64 for LLM processing

Requirements

• Python 3.8+
• langchain-openai
• langchain-anthropic
• langchain-core
• PyMuPDF
• Pillow
• pillow-heif

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.