Built my first LangGraph AI agent and learned some important lessons - sharing 10 key insights

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I recently created my first AI agent using LangGraph and was surprised by how well it performed. During the development process, I discovered several important strategies that made a huge difference. Here are 10 key insights I learned:

  1. Build your functions first. Create and test all your functions before connecting them to any LLM. These functions are the most reliable part of your system, so make sure they work perfectly before moving forward.

  2. Keep initial functions simple and broad. Something like command line tools can handle many different tasks. You don’t need to create dozens of specialized functions right away.

  3. Begin with one agent only. After you have your basic functions ready, test everything with a single reactive agent. LangGraph comes with a built-in reactive agent that you can use with your functions.

  4. Use top-tier models initially. Your system will have many issues at first, so don’t let model performance be another problem. Go with Claude Sonnet or Gemini Pro. You can switch to cheaper options later.

  5. Track everything your agent does. Building agents is like conducting experiments with unpredictable results. You need to watch every action carefully. LangGraph works great with LangSmith for monitoring.

  6. Find what’s slowing you down. Sometimes one agent with basic functions is enough. If not, check your logs to see what’s causing problems. It might be too much context, functions that are too general, or the model lacking certain knowledge.

  7. Fix problems based on what you find. You can improve in many ways like using multiple agents, better prompts, or more specific functions. Pick the solution that matches your main problem.

  8. Mix workflows with agents for better results. If your task has clear steps that happen in order, use a workflow where each step can be an agent. Like a research system with one agent for gathering info and another for writing reports.

  9. Use files as memory. Files help agents remember things and share information. This saves a lot of context space when they share file paths instead of entire documents.

  10. Learn from existing systems. Study how successful agents work and ask for advice on improving your own system based on proven methods.

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totally agree! that file mem trick saved me tons of time too - I was struggling with context limits as well. after making the switch, my agent feels so much better! and yeah, starting with solid models is key, spent forever debugging with lesser ones.