usegradient 1.3.5

Python SDK for the Gradient agent runtime and observability platform

Gradient Python SDK

Typed, no-dependency Python client for the Gradient console API.

Install

pip install usegradient

Optional provider extras:

pip install "usegradient[anthropic,openai]"

For local development from this repository:

pip install -e ./sdk/python

Credentials

The client uses the same credentials file and environment variables as the Go CLI:

• ~/.gradient/credentials
• GRADIENT_API_KEY
• GRADIENT_API_URL
• GRADIENT_REGISTRY_URL
• GRADIENT_PROXY_URL

Environment variables override the credentials file.

Example

import os

from gradient_sdk import Agent, Environment, Gradient, Model, Trace, tool


@tool
def lookup_policy(topic: str) -> dict[str, str]:
    """Look up an HR policy by topic."""

    policies = {
        "parental_leave": {
            "policy_id": "HR-LEAVE-2026",
            "answer": "Employees receive 16 weeks of paid parental leave.",
        },
        "general": {
            "policy_id": "HR-GENERAL-2026",
            "answer": "Contact HR for general policy questions.",
        },
    }
    return policies.get(topic, policies["general"])


HR_AGENT = Agent(
    Model.CLAUDE_SONNET_5,
    tools=[lookup_policy],
    instructions=(
        "You are a friendly HR assistant. When a question needs policy details, "
        "call lookup_policy with topic 'parental_leave' or 'general' before answering."
    ),
)

if __name__ == "__main__":
    gradient = Gradient(
        project=os.getenv("GRADIENT_PROJECT", "my-first-agent"),
        environment=Environment.python(
            python="3.12",
            env={"ANTHROPIC_API_KEY": os.environ["ANTHROPIC_API_KEY"]},
        ),
    )

    handle = gradient.run(
        HR_AGENT,
        "How much paid parental leave do I receive?",
        trace=Trace.full(),
    )

    print(handle.stdout)
    print(handle.trace_url)

The SDK prints progress for credentials, routing, source packaging, project/environment cache resolution, cold start, source upload, dependency cache, remote run, and cleanup.

Use the same object for builds, deployments, and replays:

build = gradient.build()
deployment = gradient.deploy(HR_AGENT, name="hr-policy-agent", trace=Trace.runtime(), scale=1)
report = HR_AGENT.evaluate(
    [{"input": "How much leave?", "expected_output": "HR-LEAVE-2026"}],
    {"mentions_policy": lambda row: row["expected_output"] in row["text"]},
)

print(build.source_hash)
print(deployment.url)
print(report["results"][0]["scores"])

For lower-level machine control:

from gradient_sdk import GradientClient, default_machine_template

client = GradientClient.from_credentials()

print(client.whoami())

template = default_machine_template(
    image="registry.usegradient.dev/my-org/my-project@sha256:...",
    project="my-project",
    region="sjc",
)

machine = client.create_machine(template)
client.wait_machine_state(machine.id)
print(machine.id, machine.proxy_url)

client.delete_machine(machine.id)

Publishing

Creating a GitHub release publishes usegradient to PyPI via .github/workflows/publish-python-sdk.yml. The workflow runs on release: published, skips prereleases, syncs the package version from the release tag (for example v0.3.0 → 0.3.0), runs tests, builds, and uploads the wheel and sdist using the PYPI_API_TOKEN repository secret.

Configure PyPI publishing once:

1. Create the usegradient project on pypi.org (first release only).
2. Create a PyPI API token with publish access to the usegradient project.
3. Add it to the GitHub repository as a PYPI_API_TOKEN Actions secret.

API Coverage

• Gradient(project=..., environment=...)
• Agent(Model.GPT_5_6_LUNA, tools=...) or Agent(Model.CLAUDE_HAIKU_4_5, tools=...)
• Model.* exhaustive enum for SDK-supported OpenAI and Anthropic chat/tool models
• RunContext(project=..., session_id=None, user_id=None, metadata={})
• @tool, @task, @agent.step
• agent.run(input) / agent.stream(input) / agent.deploy(name) / agent.evaluate(dataset, evaluators)
• Environment.python(...) / Environment.node(...)
• Trace.off() / Trace.semantic() / Trace.runtime() / Trace.full()
• Gradient.build(target=None)
• Gradient.run(target, input, trace=Trace.full(), keep=False)
• Gradient.deploy(target, name=None, trace=Trace.full(), scale=None)
• Gradient.replay(run_id, seed=None, freeze_time=None, egress=None, trace=None)
• Gradient.benchmark(target, input, iterations=...)
• RunHandle.result() / cancel() / replay() / events() / logs() / spans() / to_dataset()
• DeploymentHandle.refresh() / start() / stop() / scale() / delete() / runs()
• BuildHandle.logs()
• whoami()
• ensure_routable()
• list_projects() / ensure_project(name)
• resolve_environment(project=..., environment=..., source=...)
• list_environment_versions(project=None)
• create_machine(template)
• create_traced_machine(template, trace_mode="full", metadata=None, wait=False)
• list_machines()
• delete_machine(machine_id)
• wait_machine_state(machine_id, state="started")
• exec_machine(machine_id, command, stdin=None, timeout_seconds=60, trace=True, watch=None)
• list_secrets()
• update_secrets(values) / set_secret(name, value) / unset_secret(name)
• create_trace_run(trace_mode="proxy", template=None, metadata=None, capture_policy=None)
• attach_trace_run(run_id, machine_id=..., proxy_url=None)
• finish_trace_run(run_id)
• update_trace_policy(run_id, capture_policy)
• delete_trace_run(run_id)
• get_trace_run(run_id)
• list_trace_runs(limit=50)
• list_trace_events(run_id, limit=1000)
• list_trace_spans(run_id, kind=None, status=None, query=None)
• list_trace_sessions(limit=100, query=None)
• list_session_spans(session_id)
• trace_metrics(days=30)
• query_traces(sql, limit=100)
• create_trace_annotation(name=..., value=..., span_id=...)
• list_trace_annotations(...)
• delete_trace_annotation(annotation_id)
• create_dataset(name, description=None)
• list_datasets()
• get_dataset(dataset_id)
• delete_dataset(dataset_id)
• add_dataset_example(dataset_id, input=..., expected_output=...)
• list_dataset_examples(dataset_id)
• delete_dataset_example(dataset_id, example_id)
• create_experiment(dataset_id=..., name=...)
• list_experiments()
• get_experiment(experiment_id)
• create_experiment_run(experiment_id, name=...)
• record_experiment_results(experiment_id, run_id, results)
• update_experiment_run_status(experiment_id, run_id, status)
• run_experiment(dataset_id, task=..., evaluators=...)
• ingest_trace_event(run_id, ingest_token, event_type=..., data=None)
• ingest_trace_events(run_id, ingest_token, events)
• trace_template(template, trace)
• proxy_url(machine_id, slug=None, proxy_base=None)
• default_machine_template(image, project=None, ...)