> ## Documentation Index
> Fetch the complete documentation index at: https://docs.starlight-search.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Create a client, query memories, record a trace, and submit a review.

This guide walks through the full learning loop: query memories, augment a prompt, record a trace, and submit a review.

<Steps>
  <Step title="Create a client">
    You need an API key and project ID from the [Reflect console](https://reflect.starlight-search.com).

    ```python theme={null}
    from reflect_sdk import ReflectClient

    client = ReflectClient(
        base_url="https://api.starlight-search.com",
        api_key="rf_live_...",
        project_id="my-project",
    )
    ```
  </Step>

  <Step title="Run the learning loop">
    The `client.trace()` context manager handles the full loop in one block - it queries memories on entry, and auto-submits the trace with the correct `retrieved_memory_ids` on exit:

    ```python theme={null}
    with client.trace("How do I implement retry logic with exponential backoff?") as ctx:
        # ctx.augmented_task contains the task + any relevant memory blocks
        # ctx.memories contains the retrieved Memory objects
        response = my_agent(ctx.augmented_task)

        ctx.set_output(
            trajectory=[
                {"role": "user", "content": ctx.augmented_task},
                {"role": "assistant", "content": response},
            ],
            result="pass",
        )
    # Trace auto-submitted with retrieved_memory_ids tracked for you
    ```

    If no memories exist yet, `ctx.augmented_task` returns the original task unchanged.
  </Step>

  <Step title="Query memories again">
    The generated reflection now appears in future queries:

    ```python theme={null}
    memories = client.query_memories(
        task="What is the best approach for retrying failed requests?",
        limit=5,
    )
    for m in memories:
        print(f"{m.reflection} (score: {m.score:.2f})")
    ```
  </Step>
</Steps>

<Info>
  Feedback text is typically attached by judge workflows or through the platform review UI. SDK/API calls usually submit only the review result (`pass` or `fail`).
</Info>

See [Traces and reviews](/guides/traces-and-reviews) for decorator and explicit call patterns.