Should I Build My AI Agents with n8n or Python?

Key Concepts

• N8N: A no-code workflow automation tool, often used for building AI agents and integrations.
• Python: A popular programming language, widely used for AI development, offering more control and flexibility.
• No-code vs. Code: The fundamental distinction between using visual tools like N8N and writing code in languages like Python for building AI agents.
• Integration-heavy: Refers to the ability to connect and automate workflows across numerous different services.
• Proof of Concepts (POCs): Initial, often simplified, implementations of an idea to test its feasibility.
• Production Scale Applications: Robust, scalable systems designed for real-world, high-volume use.
• Speed, Control, and Flexibility: Key advantages offered by coding solutions.
• Learning Curve: The amount of time and effort required to become proficient with a tool or technology.
• Visual Agent Builder: A user interface that allows for the creation of agents and workflows through a graphical representation.
• Nodes: Individual components or steps within an N8N workflow.
• Integrations: Pre-built connectors that allow N8N to interact with various third-party services.
• Templates: Pre-designed workflows that can be used as a starting point for new projects.
• Q mode: An N8N feature that allows for scaling to thousands of users.
• Open-source and Self-hostable: Software that is freely available for use, modification, and distribution, and can be installed on one's own servers.
• Commercialization License Fee: A cost associated with using N8N for commercial purposes.
• Node Performance Overhead: The inherent inefficiency of node-based platforms compared to direct code execution.
• Integration Box: The limitation of being restricted to the integrations provided by a no-code tool.
• HTTP Request Node: An N8N node used to make custom API calls when a direct integration is not available.
• Code Nodes: N8N nodes that allow for the injection of custom JavaScript or Python code.
• Version Control (Git): A system for tracking changes to code over time, enabling collaboration and rollback capabilities.
• JSON Diffs: The visual representation of changes in JSON files, which can be difficult to interpret for complex workflows.
• Large File Handling: The ability of a system to process and manage large data files, crucial for RAG (Retrieval Augmented Generation) pipelines.
• AI Coding Assistance: Tools like Codeex and Claude Code that help developers write code more efficiently.
• Parallel Execution: The ability to run multiple tasks or processes simultaneously.
• Token Streaming: A feature that allows for the gradual display of generated text, improving user experience.
• Open-Source AI Tools and Libraries: Freely available software components for AI development (e.g., Graffiti, Docling, Crawl for AI).
• Hybrid Approach: Combining no-code and code-based solutions to leverage the strengths of both.
• Orchestration: Managing and coordinating the execution of different tasks or services.
• Microservices: Small, independent services that perform specific functions.

N8N vs. Python for AI Agents: A Decision Framework

This video aims to provide a clear, use-case-driven decision framework for choosing between building AI agents with N8N (no-code) or Python (code). The presenter emphasizes that both approaches have their merits and that a hybrid strategy is also viable.

General Recommendation

The presenter's high-level recommendation is as follows:

• Use N8N for:

  • Integration-heavy internal tools: Automating workflows across various services for internal team use.
  • Proof of Concepts (POCs): Rapidly prototyping and testing AI agent ideas due to its speed of development.
  • Team collaboration across skill levels: Its lower learning curve makes it accessible to a wider range of users.

• Use Python for:

  • Production-scale applications: When robustness, scalability, and high performance are critical.
  • Projects requiring speed, control, and flexibility: Allowing for precise design and optimization of solutions.

The presenter notes that many users start with N8N for its ease of use and then transition to Python as their projects mature and require more advanced capabilities. The presenter also utilizes both in their own workflow, using N8N for quick ideation and Python for scaling and advanced features.

N8N: Pros and Cons

Pros:

• Ease of Getting Started & Low Learning Curve: N8N offers a visually intuitive interface, making it easy to build AI agents and automations. The workflow builder is described as a "canvas" for creating agents.
• Hundreds of Integrations and Thousands of Templates: N8N provides pre-built connectors to numerous services (e.g., PostgreSQL, Google Drive), simplifying integration. A vast library of templates allows users to start projects without building from scratch.
• Scalability (Q mode): Despite being a no-code platform, N8N can scale to thousands of users through its "Q mode" feature.
• Open-Source and Self-Hostable: N8N is open-source, allowing for local deployment and enhanced security, especially for sensitive data.

Cons:

• Commercialization License Fee: While open-source, commercial use of N8N workflows requires a license fee. This is a key reason for its use in internal tools and POCs rather than commercial products.
• Node Performance Overhead: Node-based platforms like N8N inherently have performance overhead compared to directly executing code. Python, even as a relatively slower language, is more efficient.
• Integration Box Limitations: Users are limited to the integrations provided by N8N. If a required integration is missing, custom integration via an HTTP request node becomes necessary, which can be complex.
• Incomplete Integrations: Even existing integrations may lack specific functionalities. For example, the Google Drive integration can watch for file creations/updates but not deletions, which is problematic for RAG pipelines needing synchronized knowledge bases.
• Inefficient Code Nodes: While N8N allows injecting custom code (JavaScript/Python), these code nodes are not very efficient and have limitations on imported libraries due to security concerns. Workflows can become a mix of nodes and code, suggesting a full code solution might be better.
• Difficult Version Control: N8N workflows (JSON files) are challenging to version control with Git. JSON diffs are "ugly" and make it difficult to track changes or revert to previous versions.
• Poor Large File Handling: N8N struggles with large files, even 20MB PDFs can cause issues in RAG pipelines. The community reports significant problems with files around 200MB, whereas Python can handle tens or hundreds of gigabytes with sufficient memory.

Python: Pros and Cons

Cons:

• Higher Learning Curve: Even with AI coding assistance, understanding and validating code requires proficiency in Python or the chosen AI agent framework.
• Higher Chance of Security Issues: Developers need a deeper understanding of security practices as they are responsible for managing credentials and integrations more directly. N8N handles much of this automatically.
• Integration Setup: Unlike N8N's pre-built nodes, setting up integrations in Python often requires rebuilding them from scratch, even if existing libraries are available.
• Visualization Challenges: While tools like Langraph Studio and Langfuse offer visualization and tracing for multi-agent workflows, they don't provide the same "canvas-like" experience as N8N.

Pros:

• Full Control and Customizability: Python offers unparalleled flexibility, allowing developers to implement any desired functionality.
• Parallel Execution: Python enables true parallel execution of tasks, significantly improving speed and efficiency. An example is a research agent that runs multiple specialized research agents simultaneously.
• Token Streaming: Python supports token streaming, leading to a better user experience with faster, more responsive AI outputs.
• Better Performance: Python generally offers superior performance compared to no-code tools.
• Access to Top Open-Source AI Tools and Libraries: Python provides easy integration with a vast ecosystem of AI libraries (e.g., Graffiti, Docling, Crawl for AI) through simple package installation.
• AI Coding Assistance: The rapid advancement of AI coding assistants significantly reduces the development time and complexity of Python projects, diminishing N8N's ease-of-use advantage.
• Better Scalability: Python solutions are inherently more scalable than no-code platforms.
• Local Execution and Open Source: Python allows for local execution of agents and leverages open-source tools, avoiding license concerns.
• Version Control with Git: Python code integrates seamlessly with Git, providing robust version control, backups, and easy reversion capabilities.

The Hybrid Approach

The presenter concludes by advocating for a hybrid approach, where N8N and Python are used in conjunction. This strategy allows users to leverage the strengths of both platforms:

• N8N for Orchestration: Use N8N for managing entry points, integrations, and overall workflow orchestration.
• Python for Heavy Processing: Offload computationally intensive tasks, such as agent processing, data chunking for RAG, and handling large datasets, to external Python microservices.

This hybrid model enables users to benefit from N8N's ease of integration and orchestration while utilizing Python's power, flexibility, and performance for complex AI tasks. The presenter mentions having demonstrated deploying both N8N and Python on the same instance, highlighting the feasibility of this combined approach.

Conclusion

The decision between N8N and Python for building AI agents hinges on the specific use case. N8N excels in integration-heavy internal tools, POCs, and collaborative environments due to its ease of use and rapid development. Python is the preferred choice for production-scale applications demanding speed, control, flexibility, and advanced capabilities. The hybrid approach offers a powerful solution by combining the strengths of both no-code and code-based development.