# Quickstart

**Opik Agent Optimizer Quickstart** gives you the fastest path from “hello world” to a successful optimization run. If you already walked through the main [Opik Quickstart](/guides/development-optimization-runs-quickstart) (tracing + evaluation), this is the next stop—it layers on the `opik-optimizer` SDK so you can automatically improve prompts and agents. Prefer a UI workflow? Use [Optimization Studio](https://www.comet.com/development/optimization-runs/optimization_studio) instead.

## Why Opik Agent Optimizer?

- **Production-grade workflows** – reuse the same datasets, metrics, and tracing you already have in Opik.
- **Multiple strategies** – swap between MetaPrompt, Hierarchical Reflective Prompt Optimizer (HRPO), Evolutionary, GEPA, and more with one API.
- **Deep analysis** – every trial is logged to Opik so you can inspect prompts, tool calls, and failure modes.

:::callout{intent="note"}
Estimated time: **≤10 minutes** if you already have Python and an Opik API key configured.
:::

## Prerequisites

- Python 3.10+
- Opik account
- Access to an OpenAI-compatible LLM via LiteLLM (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc.)

## 1. Install and authenticate

```bash
pip install --upgrade opik opik-optimizer
opik configure  # paste your API key
export OPIK_PROJECT_NAME="optimization-quickstart"
```

Setting `OPIK_PROJECT_NAME` ensures all traces, experiments, and optimization runs are logged to the same project without having to pass `project_name` to every SDK call.

## 2. Create a dataset and metric

```python
import opik
from opik.evaluation.metrics import LevenshteinRatio

client = opik.Opik()
dataset = client.get_or_create_dataset(name="agent-opt-quickstart")
dataset.insert([
    {"question": "What is Opik?", "answer": "Opik is an LLM observability and optimization platform."},
    {"question": "How do I reduce hallucinations?", "answer": "Use evaluations and prompt optimization to enforce grounding."},
])

def answer_quality(item, output):
    metric = LevenshteinRatio()
    return metric.score(reference=item["answer"], output=output)
```

## 3. Run the optimizer

```python
from opik_optimizer import MetaPromptOptimizer, ChatPrompt

prompt = ChatPrompt(
    messages=[
        {"role": "system", "content": "You are a precise assistant."},
        {"role": "user", "content": "{question}"},
    ],
    model="openai/gpt-5-nano"  # The model your prompt runs on
)

optimizer = MetaPromptOptimizer(model="openai/gpt-5-nano")  # The model that improves your prompt
result = optimizer.optimize_prompt(
    prompt=prompt,
    dataset=dataset,
    metric=answer_quality,
    max_trials=3,
    n_samples=2,
)

result.display()
```

:::callout{intent="tip"}
**Using a different LLM provider?** The optimizer supports OpenAI, Anthropic, Gemini, Azure, Ollama, and 100+ other providers via LiteLLM. See the [Configure LLM Providers](https://www.comet.com/development/optimization-runs/optimization/configure_models) guide for setup instructions.
:::

## 4. Inspect results

- Run `opik dashboard` or open https://www.comet.com/opik.
- In the left nav, go to **Evaluation → Optimization runs**, then select your latest run.
- Review the optimization-progress chart, trial table, and per-trial traces to decide whether to ship the new prompt.

## Common first issues

::::accordion-group
:::accordion{title="Prompt must be a ChatPrompt object"}
Import `ChatPrompt` from `opik_optimizer` and wrap your `messages` list before passing it to any optimizer.
:::

:::accordion{title="Authentication failed"}
Re-run `opik configure` and confirm the account has Agent Optimizer access. If you changed machines, copy the `~/.opik/config` file or re-enter the key.
:::

:::accordion{title="liteLLM provider errors"}
Ensure provider keys (e.g., `OPENAI_API_KEY`) are exported in the same shell running the script, and verify the model you selected is enabled for that key.
:::
::::

## Next steps

- Prefer notebooks? Launch the [Quickstart notebook](https://www.comet.com/development/optimization-runs/cookbooks/optimizer_introduction_cookbook).
- Dive deeper into [Define datasets](https://www.comet.com/development/optimization-runs/optimization/define_datasets) and [Define metrics](https://www.comet.com/development/optimization-runs/optimization/define_metrics).
- Explore the [Optimization Algorithms overview](https://www.comet.com/development/optimization-runs/algorithms/overview) to pick the best strategy for your workload.

## Related pages

- [Agent Optimization](./development-optimization-runs-overview.md)
- [Optimization Studio](./development-optimization-runs-optimization-studio.md)
- [Optimizer Introduction Cookbook](./development-optimization-runs-quickstart-notebook.md)
- [Optimizer Frequently Asked Questions](./development-optimization-runs-faq.md)
- [Changelog](./development-optimization-runs-changelog.md)
- [Known Issues](./development-optimization-runs-known-issues.md)
- [Optimization](./development-optimization.md)
- [Optimization Algorithms](./development-optimization-algorithms.md)
- [Cookbooks & Tutorials](./development-cookbooks-tutorials.md)
- [Advanced Topics](./development-advanced-topics.md)

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