promptic-sdk 0.15.0

Python SDK for the Promptic platform — tracing, API client, and CLI.

Promptic Python SDK

Python SDK and CLI for the Promptic platform — tracing, prompt optimization, and experiment management.

Installation

pip install promptic-sdk

Optional LLM instrumentation

Install extras to auto-instrument specific providers or agent frameworks:

# LLM providers
pip install promptic-sdk[openai]         # OpenAI
pip install promptic-sdk[anthropic]      # Anthropic
pip install promptic-sdk[bedrock]        # AWS Bedrock
pip install promptic-sdk[vertexai]       # Google Vertex AI
pip install promptic-sdk[mistralai]      # Mistral

# Agent frameworks
pip install promptic-sdk[langchain]      # LangChain / LangGraph / create_agent / deepagents
pip install promptic-sdk[openai-agents]  # OpenAI Agents SDK
pip install promptic-sdk[claude-agent]   # Claude Agent SDK

pip install promptic-sdk[all]            # Everything above

Pydantic AI ships its own OpenTelemetry emitter — enable it with Agent(..., instrument=True); no extras needed.

Quick start

1. Authenticate

Log in via browser (recommended for local development):

promptic login

This opens your browser for authentication, then auto-selects your workspace. Credentials are saved to ~/.promptic/config.toml.

For CI/CD or headless environments, use an API key instead:

promptic configure
# or set the environment variable:
export PROMPTIC_API_KEY="pk_..."

2. Send traces

import promptic_sdk
from openai import OpenAI

# Initialize tracing (auto-instruments installed LLM libraries)
promptic_sdk.init()

client = OpenAI()

# Tag traces with an AI Component name
with promptic_sdk.ai_component("customer-support-agent"):
    response = client.chat.completions.create(
        model="gpt-4.1-nano",
        messages=[{"role": "user", "content": "Hello!"}],
    )

3. Use the API client

from promptic_sdk import PrompticClient

with PrompticClient() as client:
    # List traces
    traces = client.list_traces(limit=10)

    # Get workspace info
    workspace = client.get_workspace()

    # Manage experiments
    experiment = client.create_experiment(
        ai_component_id="comp_...",
        target_model="gpt-4.1-nano",
        task_type="classification",
        initial_prompt="Classify the following text.",
    )

    # Deploy the best prompt
    client.deploy(component_id="comp_...", experiment_id="exp_...")

    # Fetch a deployed prompt at runtime
    prompt = client.get_deployed_prompt("comp_...")

Tracing

promptic_sdk.init() sets up OpenTelemetry to export spans to the Promptic platform.

Parameter	Description	Default
api_key	Promptic API key (falls back to PROMPTIC_API_KEY)	—
endpoint	Platform URL (falls back to PROMPTIC_ENDPOINT)	https://promptic.eu
auto_instrument	Auto-detect and instrument LLM client libraries	True
service_name	OpenTelemetry service.name resource attribute	—

Auto-detected instrumentors: OpenAI, Anthropic, Google Generative AI, Vertex AI, Bedrock, Mistral, Cohere, LangChain (with LangGraph / deepagents), OpenAI Agents SDK, Claude Agent SDK. All emit the official OpenTelemetry GenAI semantic conventions (gen_ai.*), so traces work uniformly across frameworks.

Using other OpenTelemetry instrumentors

Since Promptic uses standard OpenTelemetry under the hood, you can add any OTel-compatible instrumentor alongside the auto-detected ones. Just call promptic_sdk.init() first, then instrument manually:

import promptic_sdk
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor

promptic_sdk.init()

# Add any OpenTelemetry instrumentor — spans will be exported to Promptic
RequestsInstrumentor().instrument()
SQLAlchemyInstrumentor().instrument(engine=engine)

This works with any package from the opentelemetry-python-contrib ecosystem (HTTP clients, databases, web frameworks, etc.). All spans are exported to the Promptic platform as long as init() has been called.

AI Components

Use ai_component() to tag spans with a component name. The platform links traces to the matching AI Component in your workspace:

with promptic_sdk.ai_component("my-component"):
    # All LLM calls here are tagged
    ...

Tracing workflows with custom spans

Most users don't need this. With the right [extras] installed, auto-instrumentation already creates spans for every LLM and tool call. Reach for custom spans only when you have meaningful non-LLM workflow logic (retrieval, normalization, business rules, control flow) you want represented in the trace.

When you do need it, wrap your workflow stages in custom OpenTelemetry spans. Auto-instrumented LLM and tool spans automatically nest under whichever custom span is active.

import json
import promptic_sdk
from opentelemetry import trace

promptic_sdk.init()
tracer = trace.get_tracer(__name__)

with promptic_sdk.ai_component("support-agent"):
    with tracer.start_as_current_span("answer_question") as root:
        root.set_attribute("traceloop.span.kind", "workflow")
        root.set_attribute("traceloop.entity.input", json.dumps(user_input))

        with tracer.start_as_current_span("retrieve_context") as span:
            span.set_attribute("traceloop.span.kind", "task")
            span.set_attribute("traceloop.entity.input", json.dumps(query))
            context = retrieve(query)
            span.set_attribute("traceloop.entity.output", json.dumps(context))

        with tracer.start_as_current_span("generate_answer") as span:
            span.set_attribute("traceloop.span.kind", "task")
            # The auto-instrumented LLM call nests under this task span
            answer = llm_call(context)

        root.set_attribute("traceloop.entity.output", json.dumps(answer))

Span attribute conventions:

• traceloop.span.kind="workflow" — the top-level run
• traceloop.span.kind="task" — an internal pipeline stage
• traceloop.entity.input / traceloop.entity.output — JSON-serialized stage payloads, surfaced in the Promptic UI

Promptic automatically offloads inline base64 media and large file-like content into trace artifacts, then keeps lightweight promptic-artifact://... references in span attributes. Local filesystem paths are not read automatically; attach local files explicitly when you want the bytes available from the trace:

file_ref = promptic_sdk.artifact("/tmp/report.pdf")
span.set_attribute("retrieval.input_file", file_ref.ref)

For huge collections, still log a small preview plus a count rather than the full object:

span.set_attribute(
    "traceloop.entity.output",
    json.dumps({
        "items": items[:5],
        "item_count": len(items),
        "additional_item_count": max(len(items) - 5, 0),
    }),
)

See the Tracing guide for the full pattern.

API client

Both a sync (PrompticClient) and async (AsyncPrompticClient) client are available. They share the same method signatures and return types.

from promptic_sdk import PrompticClient

with PrompticClient() as client:
    traces = client.list_traces(limit=10)

from promptic_sdk import AsyncPrompticClient

async with AsyncPrompticClient() as client:
    traces = await client.list_traces(limit=10)

Both clients provide typed methods for the full Promptic REST API:

Resource	Methods
Workspace	get_workspace
Traces	list_traces, get_trace, list_trace_artifacts, get_artifact, get_artifact_content, download_artifact, get_stats
Components	list_components, get_component, create_component, delete_component
Experiments	list_experiments, get_experiment, create_experiment, update_experiment, delete_experiment, start_experiment
Observations	list_observations, create_observations, update_observation, delete_observation
Evaluators	list_evaluators, create_evaluators, update_evaluator, delete_evaluator
Iterations	list_iterations, get_iteration, get_best_iteration
Deployments	get_deployment, deploy, undeploy, get_deployed_prompt

The client reads PROMPTIC_API_KEY and PROMPTIC_ENDPOINT from the environment, or accepts them as constructor arguments.

CLI

The promptic CLI mirrors the API client and supports both human-readable tables and --json output.

promptic [command] [subcommand] [options]

Commands

Command	Description
promptic login	Authenticate via browser (device flow)
promptic logout	Clear saved credentials
promptic configure	Save API key and endpoint (CI/CD)
promptic workspace list	List accessible workspaces
promptic workspace select <id>	Select a workspace
promptic workspace info	Show workspace info
promptic traces list	List recent traces
promptic traces get <id>	Get a trace with spans
promptic traces artifacts <id>	List artifacts for a trace
promptic artifacts get <id> -o file	Download an artifact
promptic traces stats	Show aggregated tracing stats
promptic components list	List AI components
promptic components create	Create a component
promptic components get <id>	Get component details
promptic components delete <id>	Delete a component
promptic experiments list	List experiments
promptic experiments create	Create an experiment (interactive)
promptic experiments get <id>	Get experiment details
promptic experiments update <id>	Update an experiment
promptic experiments delete <id>	Delete an experiment
promptic experiments start <id>	Start an experiment
promptic observations list	List observations for an experiment
promptic observations add	Add an observation
promptic observations delete <id>	Delete an observation
promptic evaluators list	List evaluators for an experiment
promptic evaluators add	Add an evaluator
promptic evaluators delete <id>	Delete an evaluator
promptic iterations list	List iterations for an experiment
promptic iterations get <id>	Get iteration details
promptic iterations best	Get the best iteration
promptic deployments status <id>	Show deployment for a component
promptic deployments deploy	Deploy an experiment
promptic deployments prompt <id>	Show the deployed prompt
promptic deployments undeploy <id>	Remove a deployment
promptic datasets create	Create a dataset
promptic datasets list	List datasets
promptic datasets get <id>	Get dataset details
promptic datasets delete <id>	Delete a dataset
promptic runs create	Create a run
promptic runs list	List runs
promptic runs get <id>	Get run details
promptic runs delete <id>	Delete a run
promptic annotations create	Create an annotation
promptic annotations list	List annotations
promptic annotations delete <id>	Delete an annotation
promptic evaluations run	Run an evaluation
promptic evaluations list	List evaluations
promptic evaluations get <id>	Get evaluation details

All list commands support --json for machine-readable output.

Configuration

The SDK and CLI resolve configuration in this order:

1. Explicit arguments (api_key=, endpoint=)
2. Environment variables (PROMPTIC_API_KEY, PROMPTIC_ENDPOINT)
3. Config file (~/.promptic/config.toml, written by promptic login or promptic configure)

Variable	Description	Default
PROMPTIC_API_KEY	API key (for tracing & CI/CD)	—
PROMPTIC_ENDPOINT	Platform URL	https://promptic.eu

Development

Requires Python 3.11+ and uv.

# Install dependencies
uv sync

# Run tests
uv run pytest

# Lint
uv run ruff check .
uv run ruff format .

License

MIT — see LICENSE for details.