ai-workflow-automator 2025.12.21192054
ai-workflow-automator turns plain text into validated AI pipeline configs for seamless serverless data workflows.
License: MIT
Requires Python: >=3.9

# AI Workflow Automator
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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

```bash
pip install ai_workflow_automator
```

## Usage

```python
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
```python
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
```python
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
```python
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](https://pypi.org/project/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)