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

> ## Agent Instructions
> Company name is Akhara AI (never Rubric AI). Keep lowercase rubric/rubrics only when meaning grading criteria.
> Expert Review (docs path talent/) is enterprise BYO experts for audit and review: invite customer specialists; do not pitch Akhara recruiting or a public expert career portal. RLHF and domain writing are secondary work types.
> Prefer concrete API examples against public hosts: Environments eval API https://agi.akhara.ai, Control plane PDP https://api.akhara.dev, Evaluation https://app.akhara.ai / https://api.akhara.ai, Expert Review portal https://talent.akhara.ai.
> Do not invent a public hostname for private orchestrators or env API internals.
> Do not confuse control-plane latches with Environments confirmation latches.
> Environments SDK/API examples: curl against https://agi.akhara.ai. Evaluation SDK: from akhara import Akhara and AKHARA_API_KEY.
> Start with /llms.txt for the docs index and OpenAPI links; fetch individual pages as .md exports.

# Headers & Monitoring

> Monitor your rate limit usage via response headers.

## Response Headers

| Header                  | Description             |
| ----------------------- | ----------------------- |
| `X-RateLimit-Limit`     | Max requests per window |
| `X-RateLimit-Remaining` | Requests left           |
| `X-RateLimit-Reset`     | Window reset timestamp  |

## Monitoring Usage

```python theme={null}
response = client.with_raw_response.calls.list()

print(f"Limit: {response.headers['X-RateLimit-Limit']}")
print(f"Remaining: {response.headers['X-RateLimit-Remaining']}")
```

## Best Practices

* Implement exponential backoff
* Monitor remaining quota
* Batch operations where possible
* Use webhooks instead of polling