Galileo python sdk

Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.

The Galileo Python SDK (galileo) provides a unified interface for the Galileo AI platform — enabling evaluation, observability, and runtime guardrails for GenAI applications. It supports automatic tracing of LLM calls, custom span logging, evaluation experiments, and production-grade guardrails.

SDK Version Detection

Check installed versions before writing any code to pick the right reference:

python

import importlib.metadata, importlib.util

galileo_ver = importlib.metadata.version("galileo")        # e.g. "2.1.1"
pq_installed = importlib.util.find_spec("promptquality") is not None
pq_ver = importlib.metadata.version("promptquality") if pq_installed else None
print(f"galileo={galileo_ver}, promptquality={pq_ver}")

Installed stack	Use
galileo >= 2.0 (with or without promptquality 0.x)	This skill — GalileoLogger, @log, galileo_context
galileo < 2.0 + promptquality >= 1.0	Promptquality 1.x Reference

Note: promptquality >= 1.0 and galileo >= 2.0 are mutually incompatible — they require different major versions of galileo-core. Installing both will cause dependency conflicts.

Additional references:

• Framework Integrations — OpenAI, Anthropic, LangChain, LangGraph, CrewAI, PydanticAI, and more
• Guardrail Metrics Reference — Hallucination Index, Context Adherence, Toxicity, PII, and all available metrics
• Advanced Evaluation Patterns — Experiments, eval sets, prompt optimization, and scoring
• Promptquality 1.x Reference — EvaluateRun, Scorers, ScorersConfiguration for the galileo 1.x stack

Installation

bash

pip install galileo

For evaluation features with the legacy prompt engineering interface:

bash

pip install promptquality

For runtime guardrails:

bash

pip install galileo-protect

Quick Start

python

import os
from galileo import galileo_context
from galileo.openai import openai

galileo_context.init(project="my-project", log_stream="my-log-stream")

client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
    model="gpt-4o",
)

print(response.choices[0].message.content)

galileo_context.flush()

Authentication

Set the following environment variables:

bash

# .env file or shell environment
GALILEO_API_KEY="your-api-key"            # Required — from Galileo console
GALILEO_CONSOLE_URL="https://app.galileo.ai"  # Console URL (or self-hosted URL)
GALILEO_PROJECT="my-project"              # Optional — default project
GALILEO_LOG_STREAM="my-log-stream"        # Optional — default log stream
GALILEO_LOGGING_DISABLED="false"          # Optional — disable logging

For the legacy promptquality package, authenticate programmatically:

python

import promptquality as pq
pq.login("https://app.galileo.ai")

Observability and Tracing

Initializing the Galileo Context

python

from galileo import galileo_context

galileo_context.init(project="my-project", log_stream="my-log-stream")

Wrapped OpenAI Client (Auto-Logging)

Import the Galileo-wrapped OpenAI client to automatically trace all calls:

python

from galileo.openai import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello"}],
    model="gpt-4o",
)

The @log Decorator

Use @log to create spans for your functions. Supported span types: workflow, llm, retriever, tool.

python

from galileo import log

@log
def my_workflow():
    result = call_openai()
    return result

@log(span_type="retriever")
def retrieve_documents(query: str):
    docs = vector_store.search(query)
    return docs

@log(span_type="tool")
def search_web(query: str):
    return web_api.search(query)

Nested Workflows

python

from galileo import log

@log
def agent_pipeline(user_input: str):
    context = retrieve_documents(user_input)
    tool_result = search_web(user_input)
    response = generate_response(user_input, context, tool_result)
    return response

@log(span_type="retriever")
def retrieve_documents(query: str):
    return ["doc1", "doc2"]

@log(span_type="tool")
def search_web(query: str):
    return "search result"

@log
def generate_response(query: str, context: list, tool_result: str):
    client = openai.OpenAI()
    return client.chat.completions.create(
        messages=[{"role": "user", "content": query}],
        model="gpt-4o",
    )

Context Manager

Scope logging to a specific block and auto-flush on exit:

python

from galileo import galileo_context

with galileo_context(project="my-project", log_stream="my-log-stream"):
    result = my_workflow()
    print(result)

Flushing Traces

Upload captured traces to Galileo:

python

galileo_context.flush()

Evaluation

Running Experiments with promptquality

python

import promptquality as pq

pq.login("https://app.galileo.ai")

template = "Explain {{topic}} to me like I'm a 5 year old"
data = {"topic": ["Quantum Physics", "Politics", "Large Language Models"]}

pq.run(
    project_name="my-first-project",
    template=template,
    dataset=data,
    settings=pq.Settings(
        model_alias="ChatGPT (16K context)",
        temperature=0.8,
        max_tokens=400,
    ),
)

Evaluation Runs with Custom Workflows (galileo 2.x)

Use GalileoLogger to log traces for evaluation:

python

from galileo import GalileoLogger

logger = GalileoLogger(project="my_project", log_stream="my_run")

eval_set = ["What are hallucinations?", "What are intrinsic hallucinations?"]
for input_text in eval_set:
    output = llm.call(input_text)
    logger.add_single_llm_span_trace(
        input=input_text,
        output=output,
        model="gpt-4o",
    )

logger.flush()

For the galileo < 2.0 + promptquality >= 1.0 stack, use EvaluateRun — see Promptquality 1.x Reference.

See Advanced Evaluation Patterns for more.

Guardrails / Protect

Creating a Protection Stage

python

from galileo import GalileoMetrics
from galileo.stages import create_protect_stage
from galileo_core.schemas.protect.rule import Rule, RuleOperator
from galileo_core.schemas.protect.ruleset import Ruleset
from galileo_core.schemas.protect.stage import StageType

rule = Rule(
    metric=GalileoMetrics.input_toxicity,
    operator=RuleOperator.gt,
    target_value=0.1,
)

ruleset = Ruleset(rules=[rule])

stage = create_protect_stage(
    name="toxicity-guard",
    stage_type=StageType.central,
    prioritized_rulesets=[ruleset],
    description="Block toxic input.",
)

Invoking Runtime Protection

python

from galileo.protect import invoke_protect, ainvoke_protect
from galileo_core.schemas.protect.payload import Payload

payload = Payload(input="User message to check.")

response = invoke_protect(payload=payload, stage_name="toxicity-guard")

# Async variant
response = await ainvoke_protect(payload=payload, stage_name="toxicity-guard")

Stage Types

• Central stages — Created and managed by governance teams; rulesets defined at creation time
• Local stages — Created without rulesets; rulesets supplied at runtime by application teams

See Guardrail Metrics Reference for all available metrics.

Common Patterns

Multi-Turn Conversations

python

from galileo import log
from galileo.openai import openai

client = openai.OpenAI()

@log
def chat(messages: list):
    response = client.chat.completions.create(
        messages=messages,
        model="gpt-4o",
    )
    return response.choices[0].message.content

messages = []
messages.append({"role": "user", "content": "What is RAG?"})
reply = chat(messages)
messages.append({"role": "assistant", "content": reply})
messages.append({"role": "user", "content": "How do I implement it?"})
reply = chat(messages)

RAG Pipeline with Retriever Spans

python

from galileo import log
from galileo.openai import openai

client = openai.OpenAI()

@log(span_type="retriever")
def retrieve(query: str):
    results = vector_db.similarity_search(query, k=5)
    return [doc.page_content for doc in results]

@log
def rag_pipeline(question: str):
    context = retrieve(question)
    prompt = f"Context: {context}\n\nQuestion: {question}"
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="gpt-4o",
    )
    return response.choices[0].message.content

Agent Tool Calling

python

from galileo import log

@log(span_type="tool")
def math_operation(a: float, b: float, op: str) -> str:
    if op == "add":
        return str(a + b)
    elif op == "multiply":
        return str(a * b)
    raise ValueError(f"Unknown op: {op}")

@log(span_type="tool")
def web_search(query: str):
    return search_api.query(query)

@log
def agent(user_input: str):
    plan = plan_actions(user_input)
    results = []
    for action in plan:
        if action.tool == "math_operation":
            results.append(math_operation(action.input))
        elif action.tool == "web_search":
            results.append(web_search(action.input))
    return synthesize(results)

Best Practices

1. Always set environment variables for GALILEO_API_KEY and GALILEO_CONSOLE_URL rather than hardcoding credentials.
2. Organize projects and log streams by application, environment, or team to keep traces manageable.
3. Call galileo_context.flush() at the end of each request or batch to ensure traces are uploaded. In web servers, flush at the end of each request handler.
4. Use the context manager (with galileo_context(...)) for scoped logging that auto-flushes on exit.
5. Use specific span types (retriever, tool, llm, workflow) to get the most out of Galileo's trace visualization.
6. Handle errors gracefully — wrap flush() calls in try/except to prevent logging failures from crashing your application.
7. Use the wrapped OpenAI client (from galileo.openai import openai) for zero-config automatic tracing of all OpenAI calls.
8. Leverage guardrail metrics in production to catch hallucinations, toxic content, and PII before they reach end users.

Resources

• Documentation: https://docs.galileo.ai
• Python SDK repo: https://github.com/rungalileo/galileo-python
• SDK examples: https://github.com/rungalileo/sdk-examples
• PyPI: https://pypi.org/project/galileo/
• Galileo console: https://app.galileo.ai