# Set Up Workflow: AI Workflow Automation Platforms Overview
## What This Is
This guide introduces platforms like n8n, Dify, and Langflow that enable you to automate complex business processes using AI. These tools allow you to visually build workflows that connect different applications and use AI models to make decisions, handle tasks like customer support triage, or generate reports automatically.
Source: https://blog.bytebytego.com/p/top-ai-github-repositories-in-2026
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://blog.bytebytego.com/p/top-ai-github-repositories-in-2026) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.
- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files
## Additional Context
- Analyze the user's project `README.md` and existing dependencies to determine the most suitable workflow automation platform from the list (n8n, Dify, Langflow, Trigger.dev, GitHub Agentic Workflows).
- Based on the analysis, recommend a platform. If the user agrees to try an open-source option like n8n, create a `docker-compose.yml` file in the project root to configure the n8n service for self-hosting.
- Scan the user's environment variables (`.env` file) for existing AI provider keys (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`). Generate an initial workflow JSON for the chosen platform that uses a credential placeholder and instruct the user on how to add their key to the platform's credential store.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it