This Quickstart guide shows how we’d upgrade an OpenAI wrapper to a minimal TensorZero deployment with built-in observability and fine-tuning capabilities — in just 5 minutes. From there, you can take advantage of dozens of features to build best-in-class LLM applications. This Quickstart covers a tour of TensorZero features. If you’re only interested in inference with the gateway, see the shorter How to call any LLM guide.
You can also find the runnable code for this example on GitHub.

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Status Quo: OpenAI Wrapper

Imagine we’re building an LLM application that writes haikus. Today, our integration with OpenAI might look like this:
before.py
from openai import OpenAI

with OpenAI() as client:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "user",
                "content": "Write a haiku about artificial intelligence.",
            }
        ],
    )

print(response)

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Migrating to TensorZero

TensorZero offers dozens of features covering inference, observability, optimization, evaluations, and experimentation. But the absolutely minimal setup requires just a simple configuration file: tensorzero.toml.
tensorzero.toml
# A function defines the task we're tackling (e.g. generating a haiku)...
[functions.generate_haiku]
type = "chat"

# ... and a variant is one of many implementations we can use to tackle it (a choice of prompt, model, etc.).
# Since we only have one variant for this function, the gateway will always use it.
[functions.generate_haiku.variants.gpt_4o_mini]
type = "chat_completion"
model = "openai::gpt-4o-mini"
This minimal configuration file tells the TensorZero Gateway everything it needs to replicate our original OpenAI call.

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Deploying TensorZero

We’re almost ready to start making API calls. Let’s launch TensorZero.
  1. Set the environment variable OPENAI_API_KEY.
  2. Place our tensorzero.toml in the ./config directory.
  3. Download the following sample docker-compose.yml file. This Docker Compose configuration sets up a development ClickHouse database (where TensorZero stores data), the TensorZero Gateway, and the TensorZero UI.
curl -LO "https://raw.githubusercontent.com/tensorzero/tensorzero/refs/heads/main/examples/quickstart/docker-compose.yml"
Our setup should look like: 
- config/
  - tensorzero.toml
- after.py see below
- before.py
- docker-compose.yml
Let’s launch everything! 
docker compose up

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Our First TensorZero API Call

The gateway will replicate our original OpenAI call and store the data in our database — with less than 1ms latency overhead thanks to Rust 🦀. The TensorZero Gateway can be used with the TensorZero Python client, with OpenAI client (Python, Node, etc.), or via its HTTP API in any programming language.
You can install the TensorZero Python client with:
pip install tensorzero
Then, you can make a TensorZero API call with:
after.py
from tensorzero import TensorZeroGateway

with TensorZeroGateway.build_embedded(
    clickhouse_url="http://chuser:chpassword@localhost:8123/tensorzero",
    config_file="config/tensorzero.toml",
) as client:
    response = client.inference(
        function_name="generate_haiku",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "Write a haiku about artificial intelligence.",
                }
            ]
        },
    )

print(response)

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TensorZero UI

The TensorZero UI streamlines LLM engineering workflows like observability and optimization (e.g. fine-tuning). The Docker Compose file we used above also launched the TensorZero UI. You can visit the UI at http://localhost:4000.

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Observability

The TensorZero UI provides a dashboard for observability data. We can inspect data about individual inferences, entire functions, and more.
TensorZero UI Observability - Function Detail Page - Screenshot
TensorZero UI Observability - Inference Detail Page - Screenshot
This guide is pretty minimal, so the observability data is pretty simple. Once we start using more advanced functions like feedback and variants, the observability UI will enable us to track metrics, experiments (A/B tests), and more.

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Fine-Tuning

The TensorZero UI also provides a workflow for fine-tuning models like GPT-4o and Llama 3. With a few clicks, you can launch a fine-tuning job. Once the job is complete, the TensorZero UI will provide a configuration snippet you can add to your tensorzero.toml. TensorZero UI Fine-Tuning Screenshot
We can also send metrics & feedback to the TensorZero Gateway. This data is used to curate better datasets for fine-tuning and other optimization workflows. Since we haven’t done that yet, the TensorZero UI will skip the curation step before fine-tuning.

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Conclusion & Next Steps

The Quickstart guide gives a tiny taste of what TensorZero is capable of. We strongly encourage you to check out the guides on metrics & feedback and prompt templates & schemas. Though optional, they unlock many of the downstream features TensorZero offers in experimentation and optimization. From here, you can explore features like built-in support for inference-time optimizations, retries & fallbacks, experimentation (A/B testing) with prompts and models, and a lot more.