aipype

Build AI Automation Workflows
in Plain Python

aipype is the framework for automation-heavy LLM workflows.
Create your AI agents as simple Python scripts that orchestrate complex tasks automatically.

Get Started View Examples →

Why aipype?

Write Automation, Not Boilerplate

Skip the complexity of traditional agent frameworks. With aipype, your automation workflows are just Python scripts that declare what you want to accomplish.

LLM-Native by Design

Built specifically for automation-heavy LLM workflows. Chain together search, content processing, analysis, and generation tasks effortlessly.

Simple but Powerful

Declarative pipeline orchestration with automatic dependency resolution. Focus on your logic, let aipype handle the execution flow.

Real Automation in 30 Lines

See how easy it is to build complex workflows

from aipype import PipelineAgent, SearchTask, LLMTask, TaskDependency, DependencyType

class ContentAutomationAgent(PipelineAgent):
    """Automatically research, analyze, and create content from any topic"""

    def setup_tasks(self):
        topic = self.config.get("topic", "AI automation")

        return [
            # 1. Research the topic
            SearchTask(
                name="research",
                config={"query": f"latest developments in {topic}", "max_results": 5}
            ),

            # 2. Analyze and summarize findings
            LLMTask(
                name="analyze",
                config={
                    "llm_provider": "openai",
                    "llm_model": "gpt-4o-mini",
                    "prompt_template": "Analyze these search results and create key insights about ${topic}:\n${research_data}",
                    "topic": topic
                },
                dependencies=[
                    TaskDependency("research_data", "research.results", DependencyType.REQUIRED)
                ]
            ),

            # 3. Generate final content
            LLMTask(
                name="create_content",
                config={
                    "llm_provider": "openai",
                    "llm_model": "gpt-4o-mini",
                    "prompt_template": "Write a comprehensive article about ${topic} using these insights:\n${analysis}",
                    "topic": topic
                },
                dependencies=[
                    TaskDependency("analysis", "analyze.content", DependencyType.REQUIRED)
                ]
            )
        ]

# Run your automation
agent = ContentAutomationAgent(name="content-agent", config={"topic": "AI automation"})
agent.run()
agent.display_results()

That's it. aipype automatically:

  • Executes tasks in the right order based on dependencies
  • Passes data between tasks seamlessly
  • Handles errors and retries
  • Displays formatted results

Key Features

Declarative Pipeline Orchestration

Define what you want to accomplish, not how to orchestrate it. aipype automatically resolves task dependencies and execution order.

Multiple LLM Providers

Works with OpenAI, Gemini, Ollama, and any OpenAI-compatible API. Switch providers without changing your automation logic.

Built-in Automation Tasks

SearchTask, LLMTask, URLFetchTask, FileSaveTask, TransformTask, and ConditionalTask - everything you need for complex workflows.

Smart Data Flow

Template substitution with ${variable} syntax automatically injects data from previous tasks into prompts and configurations.

Quick Start

# Core framework pip install aipype # With Google integrations pip install aipype aipype-g # With extras pip install aipype aipype-extras
export OPENAI_API_KEY=your-key-here export SERPER_API_KEY=your-serper-key # For search functionality
python my_automation.py
EXTENSIONS AVAILABLE:
  • aipype-extras - LLM log viewer and debugging tools
  • aipype-g - Gmail and Google Sheets automation