Hey folks, I've been knee-deep in the new OpenPipe CLI for the past week, trying to slot it into my usual GitOps and deployment workflows. With all the noise around AI pipeline tools lately, I had to see if this one brings anything concrete to the table or if it's just another flashy wrapper. 😅
My initial impression? It's surprisingly focused for a v1.0 tool. The core premise—orchestrating and evaluating LLM calls across different providers—fits a real need, especially if you're A/B testing models or tweaking prompts in a CI/CD pipeline. I've been using it to benchmark some of our support chatbot responses against different backends (OpenAI vs. Anthropic vs. local Ollama), and the automation feels cleaner than my previous hacky bash scripts.
Here’s a snippet of the config I’m testing with for a simple prompt evaluation job:
```yaml
# openpipe-config.yaml
project: "support-bot-qa"
tests:
- name: "factual_accuracy_check"
input: "What is our API rate limit for the free tier?"
expected_keywords: ["1000", "requests per hour", "free"]
providers:
- name: openai:gpt-4o-mini
- name: anthropic:claude-3-haiku
metrics:
- latency
- cost_per_call
- keyword_match
```
The `openpipe evaluate` command then spits out a nice table comparing the runs. The integration with their cloud dashboard for history is optional, which I appreciate—you can run it fully locally.
However, is it worth learning? I think that depends heavily on your stack:
* **If you're already deep into LLM ops** and are manually tracking prompts, outputs, and costs across projects, this CLI can centralize that chaos. The evaluation features are a genuine time-saver.
* **If you're just occasionally calling an API**, it might be overkill. You could achieve similar results with careful scripting and some pandas in a notebook.
* **For platform engineers**, the potential is interesting. Imagine baking this into a Kubernetes CronJob to nightly validate your AI-powered features' performance or cost drift. That's where I see it shining.
A couple of pain points I hit:
* The YAML schema is still evolving. I had some trouble nesting complex test suites.
* The local result storage is a simple SQLite DB, which is fine, but I wish there was a built-in way to push results to something like Prometheus for observability pipelines.
All in all, I'm going to keep using it for a while. The reduction in manual comparison work is tangible. If the team keeps iterating on the provider integrations and the config structure, this could become a staple in the toolbox. Would love to hear if anyone else has tried weaving it into their CI/CD or what your alternative setups look like!
bw
Automate all the things.