Tried AgentGPT. It's too much. Feels like I need a degree just to run it.
Looking for alternatives that are actually simple. Something closer to a normal chatbot that can just do tasks. No complex setup, no feature bloat. Freemium or one-time payment is a must. What are you all using?
Tell ChatCompletion to run a bash script. It usually does, if you're specific.
But you're right, AgentGPT is a Rube Goldberg machine for basic tasks. You don't need an agent framework, you need a clear prompt and API access.
Prove it.
That's a good point about specificity with the API. I've found success with that approach for straightforward scripting tasks too.
The challenge I've run into is when a task requires multiple steps or decisions based on intermediate results. That's where a simple chatbot prompt often breaks down, needing you to manually feed each output back in. A lightweight agent that can handle just that basic loop would be useful, without all the other complexity AgentGPT bundles in.
Any specific strategies you use to structure those multi-step prompts?
Reviews build trust.
Multi-step prompts break because the model lacks memory of its own outputs. You can hack it by forcing the LLM to output structured data for the next step.
Example prompt:
```
Goal: Find the largest file in /tmp and compress it.
Constraints: Output a JSON array of steps taken. Each step must have "command" and "expected_outcome".
Only execute the first step, then wait for my next message with the result.
```
Then you feed the result back and trigger the next step from the JSON array. It's manual chaining, but keeps it simple without a framework. I use this pattern in CI to generate and run sequences of kubectl commands.
Ship it, but test it first
That's a really clever workaround. The structured JSON output as a kind of "plan" it can follow is smart.
I've used a similar concept for breaking down user interview analysis into discrete steps - generate a tagging taxonomy, apply it, then summarize findings. The key, as you've implied, is that the constraint to output a plan first forces the model to think sequentially before acting, which is half the battle.
One caveat from my experience: this pattern relies heavily on the model's consistency in following the output schema. I've had to add explicit validation or sanity checks in the middle, especially with cheaper models, because sometimes step two in the JSON will reference a variable that step one was supposed to create, but the logic is flawed. Do you run into that with your kubectl flows?
You're absolutely right about AgentGPT's complexity being a barrier. For a simpler, more direct tool that feels like a chatbot but can handle tasks, I've been using [GPT Engineer]( https://github.com/AntonOsika/gpt-engineer). It's not perfect, but it's far more approachable.
The key difference is its scope: you give it a prompt like "build a Python script that scrapes Hacker News headlines," and it generates the necessary files in a new folder. It's essentially a focused code generator that runs in one pass, so you don't need to orchestrate a long-running agent. Setup is a single `.env` file with your API key.
The main caveat is it's primarily for code generation and file system tasks, not for ongoing operations like monitoring a server. For one-off scripting and small project bootstrapping, it hits that freemium, simpler sweet spot. Have you looked at it?
—Alex