{"id":30108267,"url":"https://github.com/digitalocean/gradient-python","last_synced_at":"2026-03-17T22:05:59.395Z","repository":{"id":305724527,"uuid":"992248852","full_name":"digitalocean/gradient-python","owner":"digitalocean","description":"DigitalOcean Gradient AI Platform SDK","archived":false,"fork":false,"pushed_at":"2026-02-17T21:13:16.000Z","size":2124,"stargazers_count":16,"open_issues_count":8,"forks_count":20,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-02-17T23:40:12.816Z","etag":null,"topics":["hacktoberfest"],"latest_commit_sha":null,"homepage":"https://www.digitalocean.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/digitalocean.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-05-28T21:16:47.000Z","updated_at":"2026-02-17T20:09:58.000Z","dependencies_parsed_at":"2025-07-21T18:46:50.364Z","dependency_job_id":"ffe62e5c-63b1-463f-91c6-4e1f5e73d622","html_url":"https://github.com/digitalocean/gradient-python","commit_stats":null,"previous_names":["digitalocean/gradientai-python","digitalocean/gradient-python"],"tags_count":44,"template":false,"template_full_name":null,"purl":"pkg:github/digitalocean/gradient-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/digitalocean%2Fgradient-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/digitalocean%2Fgradient-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/digitalocean%2Fgradient-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/digitalocean%2Fgradient-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/digitalocean","download_url":"https://codeload.github.com/digitalocean/gradient-python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/digitalocean%2Fgradient-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30247362,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-08T05:41:50.788Z","status":"ssl_error","status_checked_at":"2026-03-08T05:41:39.075Z","response_time":56,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["hacktoberfest"],"created_at":"2025-08-10T02:08:42.162Z","updated_at":"2026-03-17T22:05:59.387Z","avatar_url":"https://github.com/digitalocean.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Header image for the DigitalOcean Gradient AI Agentic Cloud](https://doimages.nyc3.cdn.digitaloceanspaces.com/do_gradient_ai_agentic_cloud.svg)\n\n# Gradient Python API library\n\n\u003c!-- prettier-ignore --\u003e\n[![PyPI version](https://img.shields.io/pypi/v/gradient.svg?label=pypi%20(stable))](https://pypi.org/project/gradient/)\n[![Docs](https://img.shields.io/badge/Docs-8A2BE2)](https://gradientai.digitalocean.com/getting-started/overview/)\n\nThe Gradient Python library provides convenient access to the Gradient REST API from any Python 3.9+\napplication. The library includes type definitions for all request params and response fields,\nand offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).\n\nIt is generated with [Stainless](https://www.stainless.com/).\n\n## Documentation\n\nThe getting started guide can be found on [gradient-sdk.digitalocean.com](https://gradient-sdk.digitalocean.com/getting-started/overview).\nThe REST API documentation can be found on [developers.digitalocean.com](https://developers.digitalocean.com/documentation/v2/).\nThe full API of this library can be found in [api.md](api.md).\n\n## Installation\n\n```sh\n# install from PyPI\npip install gradient\n```\n\n## Usage\n\nThe Gradient SDK provides clients for:\n* DigitalOcean API\n* Gradient Serverless Inference\n* Gradient Agent Inference\n\nThe full API of this library can be found in [api.md](api.md).\n\n```python\nimport os\nfrom gradient import Gradient\n\nclient = Gradient(\n    access_token=os.environ.get(\n        \"DIGITALOCEAN_ACCESS_TOKEN\"\n    ),  # This is the default and can be omitted\n)\ninference_client = Gradient(\n    model_access_key=os.environ.get(\n        \"GRADIENT_MODEL_ACCESS_KEY\"\n    ),  # This is the default and can be omitted\n)\nagent_client = Gradient(\n    agent_access_key=os.environ.get(\n        \"GRADIENT_AGENT_ACCESS_KEY\"\n    ),  # This is the default and can be omitted\n    agent_endpoint=\"https://my-agent.agents.do-ai.run\",\n)\n\n## API\napi_response = api_client.agents.list()\nprint(\"--- API\")\nif api_response.agents:\n    print(api_response.agents[0].name)\n\n\n## Serverless Inference\ninference_response = inference_client.chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n)\n\nprint(\"--- Serverless Inference\")\nprint(inference_response.choices[0].message.content)\n\n## Agent Inference\nagent_response = agent_client.agents.chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of Portugal?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n)\n\nprint(\"--- Agent Inference\")\nprint(agent_response.choices[0].message.content)\n```\n\nWhile you can provide an `access_token`, `model_access_key` keyword argument,\nwe recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)\nto add `DIGITALOCEAN_ACCESS_TOKEN=\"My Access Token\"`, `GRADIENT_MODEL_ACCESS_KEY=\"My Model Access Key\"` to your `.env` file\nso that your keys are not stored in source control.\n\n## Async usage\n\nSimply import `AsyncGradient` instead of `Gradient` and use `await` with each API call:\n\n```python\nimport os\nimport asyncio\nfrom gradient import AsyncGradient\n\nclient = AsyncGradient()\n\n\nasync def main() -\u003e None:\n    completion = await client.chat.completions.create(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"What is the capital of France?\",\n            }\n        ],\n        model=\"llama3.3-70b-instruct\",\n    )\n    print(completion.choices)\n\n\nasyncio.run(main())\n```\n\nFunctionality between the synchronous and asynchronous clients is otherwise identical.\n\n### With aiohttp\n\nBy default, the async client uses `httpx` for HTTP requests. However, for improved concurrency performance you may also use `aiohttp` as the HTTP backend.\n\nYou can enable this by installing `aiohttp`:\n\n```sh\n# install from PyPI\npip install gradient[aiohttp]\n```\n\nThen you can enable it by instantiating the client with `http_client=DefaultAioHttpClient()`:\n\n```python\nimport os\nimport asyncio\nfrom gradient import DefaultAioHttpClient\nfrom gradient import AsyncGradient\n\n\nasync def main() -\u003e None:\n    async with AsyncGradient(\n        model_access_key=os.environ.get(\n            \"GRADIENT_MODEL_ACCESS_KEY\"\n        ),  # This is the default and can be omitted\n        http_client=DefaultAioHttpClient(),\n    ) as client:\n        completion = await client.chat.completions.create(\n            messages=[\n                {\n                    \"role\": \"user\",\n                    \"content\": \"What is the capital of France?\",\n                }\n            ],\n            model=\"llama3.3-70b-instruct\",\n        )\n        print(completion.choices)\n\n\nasyncio.run(main())\n```\n\n## Streaming responses\n\nWe provide support for streaming responses using Server Side Events (SSE).\n\n```python\nfrom gradient import Gradient\n\nclient = Gradient()\n\nstream = client.chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n    stream=True,\n)\nfor completion in stream:\n    print(completion.choices)\n```\n\nThe async client uses the exact same interface.\n\n```python\nfrom gradient import AsyncGradient\n\nclient = AsyncGradient()\n\nstream = await client.chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n    stream=True,\n)\nasync for completion in stream:\n    print(completion.choices)\n```\n\n## Using types\n\nNested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:\n\n- Serializing back into JSON, `model.to_json()`\n- Converting to a dictionary, `model.to_dict()`\n\nTyped requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.\n\n## Nested params\n\nNested parameters are dictionaries, typed using `TypedDict`, for example:\n\n```python\nfrom gradient import Gradient\n\nclient = Gradient()\n\ncompletion = client.chat.completions.create(\n    messages=[\n        {\n            \"content\": \"string\",\n            \"role\": \"system\",\n        }\n    ],\n    model=\"llama3-8b-instruct\",\n    stream_options={},\n)\nprint(completion.stream_options)\n```\n\n## Handling errors\n\nWhen the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `gradient.APIConnectionError` is raised.\n\nWhen the API returns a non-success status code (that is, 4xx or 5xx\nresponse), a subclass of `gradient.APIStatusError` is raised, containing `status_code` and `response` properties.\n\nAll errors inherit from `gradient.APIError`.\n\n```python\nimport gradient\nfrom gradient import Gradient\n\nclient = Gradient()\n\ntry:\n    client.chat.completions.create(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"What is the capital of France?\",\n            }\n        ],\n        model=\"llama3.3-70b-instruct\",\n    )\nexcept gradient.APIConnectionError as e:\n    print(\"The server could not be reached\")\n    print(e.__cause__)  # an underlying Exception, likely raised within httpx.\nexcept gradient.RateLimitError as e:\n    print(\"A 429 status code was received; we should back off a bit.\")\nexcept gradient.APIStatusError as e:\n    print(\"Another non-200-range status code was received\")\n    print(e.status_code)\n    print(e.response)\n```\n\nError codes are as follows:\n\n| Status Code | Error Type                 |\n| ----------- | -------------------------- |\n| 400         | `BadRequestError`          |\n| 401         | `AuthenticationError`      |\n| 403         | `PermissionDeniedError`    |\n| 404         | `NotFoundError`            |\n| 422         | `UnprocessableEntityError` |\n| 429         | `RateLimitError`           |\n| \u003e=500       | `InternalServerError`      |\n| N/A         | `APIConnectionError`       |\n\n### Retries\n\nCertain errors are automatically retried 2 times by default, with a short exponential backoff.\nConnection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,\n429 Rate Limit, and \u003e=500 Internal errors are all retried by default.\n\nYou can use the `max_retries` option to configure or disable retry settings:\n\n```python\nfrom gradient import Gradient\n\n# Configure the default for all requests:\nclient = Gradient(\n    # default is 2\n    max_retries=0,\n)\n\n# Or, configure per-request:\nclient.with_options(max_retries=5).chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n)\n```\n\n### Timeouts\n\nBy default requests time out after 1 minute. You can configure this with a `timeout` option,\nwhich accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/timeouts/#fine-tuning-the-configuration) object:\n\n```python\nfrom gradient import Gradient\n\n# Configure the default for all requests:\nclient = Gradient(\n    # 20 seconds (default is 1 minute)\n    timeout=20.0,\n)\n\n# More granular control:\nclient = Gradient(\n    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),\n)\n\n# Override per-request:\nclient.with_options(timeout=5.0).chat.completions.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n)\n```\n\nOn timeout, an `APITimeoutError` is thrown.\n\nNote that requests that time out are [retried twice by default](#retries).\n\n## Advanced\n\n### Logging\n\nWe use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.\n\nYou can enable logging by setting the environment variable `GRADIENT_LOG` to `info`.\n\n```shell\n$ export GRADIENT_LOG=info\n```\n\nOr to `debug` for more verbose logging.\n\n### How to tell whether `None` means `null` or missing\n\nIn an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:\n\n```py\nif response.my_field is None:\n  if 'my_field' not in response.model_fields_set:\n    print('Got json like {}, without a \"my_field\" key present at all.')\n  else:\n    print('Got json like {\"my_field\": null}.')\n```\n\n### Accessing raw response data (e.g. headers)\n\nThe \"raw\" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,\n\n```py\nfrom gradient import Gradient\n\nclient = Gradient()\nresponse = client.chat.completions.with_raw_response.create(\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"What is the capital of France?\",\n    }],\n    model=\"llama3.3-70b-instruct\",\n)\nprint(response.headers.get('X-My-Header'))\n\ncompletion = response.parse()  # get the object that `chat.completions.create()` would have returned\nprint(completion.choices)\n```\n\nThese methods return an [`APIResponse`](https://github.com/digitalocean/gradient-python/tree/main/src/gradient/_response.py) object.\n\nThe async client returns an [`AsyncAPIResponse`](https://github.com/digitalocean/gradient-python/tree/main/src/gradient/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.\n\n#### `.with_streaming_response`\n\nThe above interface eagerly reads the full response body when you make the request, which may not always be what you want.\n\nTo stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.\n\n```python\nwith client.chat.completions.with_streaming_response.create(\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is the capital of France?\",\n        }\n    ],\n    model=\"llama3.3-70b-instruct\",\n) as response:\n    print(response.headers.get(\"X-My-Header\"))\n\n    for line in response.iter_lines():\n        print(line)\n```\n\nThe context manager is required so that the response will reliably be closed.\n\n### Making custom/undocumented requests\n\nThis library is typed for convenient access to the documented API.\n\nIf you need to access undocumented endpoints, params, or response properties, the library can still be used.\n\n#### Undocumented endpoints\n\nTo make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other\nhttp verbs. Options on the client will be respected (such as retries) when making this request.\n\n```py\nimport httpx\n\nresponse = client.post(\n    \"/foo\",\n    cast_to=httpx.Response,\n    body={\"my_param\": True},\n)\n\nprint(response.headers.get(\"x-foo\"))\n```\n\n#### Undocumented request params\n\nIf you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request\noptions.\n\n#### Undocumented response properties\n\nTo access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You\ncan also get all the extra fields on the Pydantic model as a dict with\n[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).\n\n### Configuring the HTTP client\n\nYou can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:\n\n- Support for [proxies](https://www.python-httpx.org/advanced/proxies/)\n- Custom [transports](https://www.python-httpx.org/advanced/transports/)\n- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality\n\n```python\nimport httpx\nfrom gradient import Gradient, DefaultHttpxClient\n\nclient = Gradient(\n    # Or use the `GRADIENT_BASE_URL` env var\n    base_url=\"http://my.test.server.example.com:8083\",\n    http_client=DefaultHttpxClient(\n        proxy=\"http://my.test.proxy.example.com\",\n        transport=httpx.HTTPTransport(local_address=\"0.0.0.0\"),\n    ),\n)\n```\n\nYou can also customize the client on a per-request basis by using `with_options()`:\n\n```python\nclient.with_options(http_client=DefaultHttpxClient(...))\n```\n\n### Managing HTTP resources\n\nBy default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.\n\n```py\nfrom gradient import Gradient\n\nwith Gradient() as client:\n  # make requests here\n  ...\n\n# HTTP client is now closed\n```\n\n## Versioning\n\nThis package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:\n\n1. Changes that only affect static types, without breaking runtime behavior.\n2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals.)_\n3. Changes that we do not expect to impact the vast majority of users in practice.\n\nWe take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.\n\nWe are keen for your feedback; please open an [issue](https://www.github.com/digitalocean/gradient-python/issues) with questions, bugs, or suggestions.\n\n### Determining the installed version\n\nIf you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.\n\nYou can determine the version that is being used at runtime with:\n\n```py\nimport gradient\nprint(gradient.__version__)\n```\n\n## Requirements\n\nPython 3.9 or higher.\n\n## Contributing\n\nSee [the contributing documentation](./CONTRIBUTING.md).\n\n## Smoke tests\n\nThe repository includes a small set of \"smoke\" tests that exercise live Gradient API / Inference / Agent endpoints to catch integration regressions early. These tests are intentionally excluded from the standard test run (they are marked with the `smoke` pytest marker) and only run in CI via the dedicated `smoke` job, or when you explicitly target them locally.\n\n### Required environment variables\n\nAll of the following environment variables must be set for the smoke tests (both sync \u0026 async) to run. If any are missing the smoke tests will fail fast:\n\n| Variable | Purpose |\n|----------|---------|\n| `DIGITALOCEAN_ACCESS_TOKEN` | Access token for core DigitalOcean Gradient API operations (e.g. listing agents). |\n| `GRADIENT_MODEL_ACCESS_KEY` | Key used for serverless inference (chat completions, etc.). |\n| `GRADIENT_AGENT_ACCESS_KEY` | Key used for agent-scoped inference requests. |\n| `GRADIENT_AGENT_ENDPOINT` | Fully-qualified HTTPS endpoint for your deployed agent (e.g. `https://my-agent.agents.do-ai.run`). |\n\n\u003e Optional override: `GRADIENT_INFERENCE_ENDPOINT` can be provided to point inference to a non-default endpoint (defaults to `https://inference.do-ai.run`).\n\n### Running smoke tests locally\n\n1. Export the required environment variables (or place them in a `.env` file and use a tool like `direnv` or `python-dotenv`).\n2. Run only the smoke tests:\n\n```bash\nrye run pytest -m smoke -q\n```\n\nTo include them alongside the regular suite:\n\n```bash\n./scripts/test -m smoke\n```\n\nConvenience wrapper (auto-loads a local `.env` if present):\n\n```bash\n./scripts/smoke\n```\n\nSee `.env.example` for a template of required variables you can copy into a `.env` file (do not commit secrets).\n\n### Async variants\n\nEach smoke test has an async counterpart in `tests/test_smoke_sdk_async.py`. Both sets are covered automatically by the `-m smoke` selection.\n\n### CI behavior\n\nThe default `test` job excludes smoke tests (`-m 'not smoke'`). A separate `smoke` job runs on pushes to the main repository with the required secrets injected. This keeps contributors from inadvertently hitting live services while still providing integration coverage in controlled environments.\n\n### Adding new smoke tests\n\n1. Add a new test function to `tests/test_smoke_sdk.py` and/or `tests/test_smoke_sdk_async.py`.\n2. Mark it with `@pytest.mark.smoke`.\n3. Avoid duplicating environment or client property assertions—those live in the central environment/client state test (sync \u0026 async).\n4. Keep assertions minimal—verify only surface contract / structure; deeper behavior belongs in unit tests with mocks.\n\nIf a new credential is required, update this README section, the `REQUIRED_ENV_VARS` list in both smoke test files, and the CI workflow's `smoke` job environment.\n\n\n## License\n\nLicensed under the Apache License 2.0. See [LICENSE](./LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdigitalocean%2Fgradient-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdigitalocean%2Fgradient-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdigitalocean%2Fgradient-python/lists"}