AI Workflow Automator

A Python package that helps users design and automate AI-driven data pipelines by converting natural language instructions into structured, executable workflow configurations.

Overview

The AI Workflow Automator enables users to describe their desired data processing or AI workflow in plain English, and automatically converts it into a structured format that can be integrated with serverless AI and data processing tools. This allows non-technical users to create complex workflows without deep technical knowledge, focusing on the "what" rather than the "how."

Installation

pip install ai_workflow_automator

Usage

from ai_workflow_automator import ai_workflow_automator

# Basic usage with default LLM7 model
user_input = "Create a workflow that processes customer data, runs sentiment analysis, and stores results in a database"
result = ai_workflow_automator(user_input=user_input)
print(result)

Parameters

• user_input (str): The natural language description of the desired workflow
• llm (Optional[BaseChatModel]): A LangChain LLM instance (defaults to ChatLLM7)
• api_key (Optional[str]): API key for LLM7 (if using default model)

Using Different LLM Providers

You can use any LangChain-compatible LLM by passing it to the function:

OpenAI

from langchain_openai import ChatOpenAI
from ai_workflow_automator import ai_workflow_automator

llm = ChatOpenAI()
response = ai_workflow_automator(user_input="your workflow description", llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from ai_workflow_automator import ai_workflow_automator

llm = ChatAnthropic()
response = ai_workflow_automator(user_input="your workflow description", llm=llm)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from ai_workflow_automator import ai_workflow_automator

llm = ChatGoogleGenerativeAI()
response = ai_workflow_automator(user_input="your workflow description", llm=llm)

Default LLM Configuration

By default, the package uses ChatLLM7 from langchain-llm7. The free tier rate limits sufficient for most use cases. For higher rate limits:

1. Set environment variable: export LLM7_API_KEY="your_api_key"
2. Or pass directly: ai_workflow_automator(..., api_key="your_api_key")

Get a free API key at: https://token.llm7.io/

Error Handling

The function will raise a RuntimeError if the LLM call fails or the output doesn't match the expected format.

Dependencies

• llmatch-messages
• langchain-core
• langchain-llm7 (optional, for default LLM)

Contributing

Issues and contributions welcome at: https://github.com/chigwell/ai-workflow-automator

Author

Eugene Evstafev (hi@euegne.plus)