Hello everyone!
I want to share my insights after creating several AI systems for various clients. I’ve found that the AI Agent node in N8N doesn’t perform as well as I hoped. The most significant challenges I’ve faced include poor tool selection, lack of proper context management, and the failure to follow my prompts accurately.
I primarily use OpenAI models because they provide a comprehensive set of features. Rather than relying on the built-in agent node, I prefer to connect through OpenAI nodes or make HTTP requests directly.
Here’s what I’ve found out:
After experimenting with several projects, I realized that simply switching from the AI Agent node to the OpenAI Assistant node (while keeping everything else constant) resulted in a substantial performance boost.
I wanted to explore further, so I checked the source code as N8N is open-source. The AI Agent node utilizes the LangChain framework to enable compatibility with various AI models.
Key issues I identified:
Format translation problems - Your prompts get changed between several formats, leading to a loss of crucial information.
Ineffective memory management - The native OpenAI API excels at maintaining long conversation histories, whereas the AI Agent node only considers the most recent messages.
Loss of optimizations - Using LangChain’s generic system means you miss out on OpenAI-specific enhancements.
My advice:
- Use the AI Agent node if you’re testing multiple AI options.
- Opt for direct OpenAI integration when you need dependable performance for actual projects.
Has anyone else faced similar challenges? I’m eager to hear your views!
Completely agree about LangChain’s overhead. I fought with this for months before ditching the agent node entirely. The prompt mangling drove me crazy - my system messages got destroyed going through all those format conversions. Switching to direct HTTP requests made a huge difference.
I’ve hit the same wall with AI Agent nodes. The context management is brutal - my customer service bot kept forgetting earlier parts of long conversations and giving users totally confusing responses. Memory handling is far inferior compared to straight API calls. What really got me was how inefficient token usage is. You’re essentially paying extra for worse performance because of the way it manages conversation history. I ditched the AI Agent node and built my own setup using N8N’s memory nodes with direct OpenAI calls. I’ve gained much better control over what gets retained versus summarized. The improvement is substantial, especially in complex multi-step workflows where the agent needs to recall specific details from earlier in the chat.
Perfect timing - I just finished moving three production workflows off AI Agent nodes last month. The breaking point was debugging a workflow where the agent kept picking the wrong tools even with explicit instructions. Turns out LangChain’s abstraction layer interpreted my tool descriptions completely differently than OpenAI’s native function calling would’ve. The worst part? Troubleshooting these issues means debugging through multiple abstraction layers. When something breaks, you’re not just dealing with your prompt or the AI’s response - you’re also wrestling with whatever LangChain did in between. Direct API integration gives you full visibility into what’s actually being sent to OpenAI. Once I measured response times and accuracy side by side, the performance difference was obvious.