Opper Python SDK

Build AI-powered applications with declarative task completion, structured outputs, knowledge bases, and full observability.

Installation

pip install opperai

Set your API key:

export OPPER_HTTP_BEARER="your-api-key"

Get your API key from platform.opper.ai.

Core Pattern: Task Completion

The opper.call() method is the primary interface. Describe a task declaratively and get results.

Important: The response field to read depends on whether you use output_schema:

• No output_schema → read response.message (returns str)
• With output_schema → read response.json_payload (returns dict or list)

import os
from opperai import Opper

opper = Opper(http_bearer=os.environ["OPPER_HTTP_BEARER"])

# WITHOUT output_schema → use response.message (str)
response = opper.call(
    name="summarize",
    instructions="Summarize the text in one sentence",
    input="Opper is a platform for building reliable AI integrations...",
)
print(response.message)  # "Opper enables reliable AI integrations with structured outputs."

# WITH output_schema → use response.json_payload (dict)
response = opper.call(
    name="analyze_sentiment",
    instructions="Analyze the sentiment of the text",
    input="I love this product!",
    output_schema={
        "type": "object",
        "properties": {
            "label": {"type": "string"},
            "confidence": {"type": "number"},
        },
        "required": ["label", "confidence"],
    },
)
print(response.json_payload["label"])       # "positive"
print(response.json_payload["confidence"])  # 0.95

Structured Output with Schema

Define output schemas using JSON Schema dictionaries:

response = opper.call(
    name="extract_entities",
    instructions="Extract all named entities from the text",
    input="Tim Cook announced Apple's new office in Austin, Texas.",
    output_schema={
        "type": "object",
        "properties": {
            "people": {
                "type": "array",
                "items": {"type": "string"},
                "description": "Names of people mentioned",
            },
            "locations": {
                "type": "array",
                "items": {"type": "string"},
                "description": "Geographic locations",
            },
            "organizations": {
                "type": "array",
                "items": {"type": "string"},
                "description": "Company or org names",
            },
        },
        "required": ["people", "locations", "organizations"],
    },
)
# response.json_payload["people"] => ["Tim Cook"]
# response.json_payload["locations"] => ["Austin", "Texas"]
# response.json_payload["organizations"] => ["Apple"]

Model Selection

Specify which LLM to use via the model parameter:

# Use a specific model
response = opper.call(
    name="generate",
    instructions="Write a haiku",
    input="autumn",
    model="anthropic/claude-4-sonnet",
)

# Use multiple models (fallback chain)
response = opper.call(
    name="generate",
    instructions="Write a haiku",
    input="autumn",
    model=["anthropic/claude-4-sonnet", "openai/gpt-4o"],
)

Available models include providers like openai/, anthropic/, google/, and more. Check the Opper dashboard for the full list.

Few-Shot Examples

Provide examples to guide the model's behavior:

response = opper.call(
    name="classify_ticket",
    instructions="Classify the support ticket",
    input="My payment was declined",
    examples=[
        {"input": "I can't log in", "output": "authentication"},
        {"input": "Charge me twice", "output": "billing"},
        {"input": "App crashes on start", "output": "bug"},
    ],
)

Tracing and Observability

Track AI operations with spans and metrics:

import os
from opperai import Opper

opper = Opper(http_bearer=os.environ["OPPER_HTTP_BEARER"])

# Create a span to group operations
span = opper.spans.create(name="qa_pipeline")

# Call with parent span for hierarchy
response = opper.call(
    name="answer",
    instructions="Answer the question accurately",
    input="What is Opper?",
    parent_span_id=span.id,
)

# Save a metric on the span
opper.span_metrics.create_metric(
    span_id=response.span_id,
    dimension="quality_score",
    value=4.5,
)

# List traces
traces = opper.traces.list()

Knowledge Bases

Create and query semantic search indexes:

import os
from opperai import Opper

opper = Opper(http_bearer=os.environ["OPPER_HTTP_BEARER"])

# Create a knowledge base
kb = opper.knowledge.create(name="support_docs")

# Add a document
opper.knowledge.add(
    knowledge_base_id=kb.id,
    content="To reset your password, click Forgot Password on the login page.",
    metadata={"category": "auth"},
)

# Query with semantic search
results = opper.knowledge.query(
    knowledge_base_id=kb.id,
    query="How do I change my password?",
    top_k=3,
)
for result in results:
    print(result.content, result.score)

Tags and Metadata

Add metadata to calls for filtering and cost tracking:

response = opper.call(
    name="translate",
    instructions="Translate to French",
    input="Hello world",
    tags={"project": "website", "user_id": "usr_123"},
)

Authentication Note

The OPPER_HTTP_BEARER environment variable is your Opper API key. You can get it from platform.opper.ai under your project settings. The SDK requires it to be passed explicitly:

opper = Opper(http_bearer=os.environ["OPPER_HTTP_BEARER"])

Common Mistakes

• Missing output_schema: Without it, the result is in response.message (string). Use schema dicts for structured data in response.json_payload.
• Not using name: Every call needs a unique name for tracking in the dashboard.
• Not accessing response fields correctly: Use response.message for string output, response.json_payload for structured output, and response.span_id for tracing.
• Large inputs without chunking: For large documents, split into chunks and use knowledge bases instead.

Response Object Fields

The opper.call() method returns a response object with these fields:

Accessing Results: message vs json_payload

Use response.message when calling WITHOUT output_schema:

response = opper.call(
    name="summarize",
    instructions="Summarize in one sentence",
    input="...",
)
text = response.message  # Returns str: "Summary of the text."

Use response.json_payload when calling WITH output_schema:

response = opper.call(
    name="extract",
    instructions="Extract entities",
    input="...",
    output_schema={"type": "object", "properties": {...}},
)
data = response.json_payload  # Returns dict: {"entities": [...]}

Key rule: If you pass output_schema, read from json_payload. If you don't, read from message.

Additional Resources

• For function CRUD operations and versioning, see references/FUNCTIONS.md
• For knowledge base operations and RAG patterns, see references/KNOWLEDGE.md
• For tracing, spans, and metrics, see references/TRACING.md
• For streaming responses, see references/STREAMING.md
• For migrating from OpenRouter, see references/MIGRATION.md

Related Skills

• opper-python-agents: Use when you need autonomous agents with tool use, reasoning loops, and multi-step task execution rather than single-shot task completion.
• opper-node-sdk: Use when building with TypeScript/Node.js instead of Python.

Upstream Sources

When this skill's content may be outdated, resolve using this priority:

1. Installed package source — check the user's project first, as it reflects the exact version in use: .venv/**/site-packages/opperai/ or **/site-packages/opperai/
2. Source code: https://github.com/opper-ai/opper-python
3. Documentation: https://docs.opper.ai