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Galileo TypeScript sdk

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

Install

openclaw skills install galileo-typescript-sdk

Galileo TypeScript SDK

The Galileo TypeScript SDK (galileo) provides evaluation and observability workflows for GenAI applications in Node.js and TypeScript. It supports logging LLM calls, retriever operations, tool invocations, and multi-step workflows with built-in scoring.

Additional references:

Installation

npm install galileo

Or with yarn/pnpm:

yarn add galileo
pnpm add galileo

Quick Start

import { wrapOpenAI, init, flush } from "galileo";
import OpenAI from "openai";

await init({ projectName: "my-project", logstream: "my-log-stream" });

const openai = wrapOpenAI(new OpenAI());
const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Explain quantum computing in one sentence." }],
});

console.log(response.choices[0].message.content);

await flush();

Authentication

Set the following environment variables in your .env file or shell:

GALILEO_API_KEY="your-api-key"            # Required — from Galileo console
GALILEO_CONSOLE_URL="https://app.galileo.ai"  # Console URL (or self-hosted)

Alternative authentication via username/password:

GALILEO_USERNAME="your-username"
GALILEO_PASSWORD="your-password"

Observability

Wrapped OpenAI Client (Auto-Logging)

The simplest way to trace all OpenAI calls — wrap the client and all calls are logged automatically:

import { wrapOpenAI, init, flush } from "galileo";
import OpenAI from "openai";

await init({ projectName: "my-project", logstream: "production" });

const openai = wrapOpenAI(new OpenAI());
const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "What is RAG?" }],
});

await flush();

Azure OpenAI is also supported via wrapAzureOpenAI.

The log() Function Wrapper

Wrap any function to log its execution as a span. Supports sync, async, and generator functions:

import { log, init, flush } from "galileo";

await init({ projectName: "my-project", logstream: "production" });

const retrieveDocuments = log(
  { spanType: "retriever", name: "vector-search" },
  async (query: string) => {
    const results = await vectorDb.search(query, { k: 5 });
    return results.map((r) => r.content);
  }
);

const generateResponse = log(
  { spanType: "llm", name: "gpt-4o-call" },
  async (query: string, context: string[]) => {
    const openai = new OpenAI();
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: `Context: ${context.join("\n")}\n\nQuestion: ${query}` }],
    });
    return response.choices[0].message.content;
  }
);

const ragPipeline = log(
  { spanType: "workflow", name: "rag-pipeline" },
  async (query: string) => {
    const docs = await retrieveDocuments(query);
    return generateResponse(query, docs);
  }
);

await ragPipeline("What are the benefits of RAG?");
await flush();

Supported span types: workflow, llm, retriever, tool, agent.

GalileoLogger (Manual Spans)

For fine-grained control, use GalileoLogger directly to build traces with explicit spans:

import { GalileoLogger } from "galileo";

const logger = new GalileoLogger({
  projectName: "my-project",
  logStreamName: "production",
});

logger.startTrace({ input: "Calculate 15 * 42" });

logger.addToolSpan({
  input: "15 * 42",
  output: "630",
  durationNs: 50000000,
});

logger.addLlmSpan({
  input: "The math tool returned 630. Respond to the user.",
  output: "15 multiplied by 42 equals 630.",
  durationNs: 800000000,
  model: "gpt-4o",
});

logger.conclude({ output: "15 multiplied by 42 equals 630." });

await logger.flush();

Available span methods: addLlmSpan, addRetrieverSpan, addToolSpan, addWorkflowSpan, addAgentSpan, addProtectSpan.

Context API

Use galileoContext for scoped lifecycle management:

import { galileoContext } from "galileo";

await galileoContext.init({ projectName: "my-project", logstream: "production" });

// ... trace your calls ...

await galileoContext.flush();
await galileoContext.reset();

Sessions

Group related traces into sessions for multi-turn conversations:

import { init, flush, startSession, setSession, clearSession } from "galileo";

await init({ projectName: "my-project", logstream: "production" });

const sessionId = await startSession({ name: "user-conversation-123" });

// All traces created between setSession and clearSession are grouped
setSession(sessionId);
// ... log your traces ...
clearSession();

await flush();

Evaluation

Running an Experiment

Use runExperiment to evaluate your LLM pipeline against a dataset with automated scoring:

import { runExperiment, GalileoMetrics } from "galileo";

const result = await runExperiment({
  name: "qa-eval-run",
  datasetName: "my-test-dataset",
  metrics: [GalileoMetrics.contextAdherence, GalileoMetrics.completeness, GalileoMetrics.inputToxicity],
  projectName: "eval-project",
  function: async (input) => {
    const response = await callYourLLM(input.question);
    return response;
  },
});

console.log("Experiment link:", result.link);

Experiment with Inline Dataset

import { runExperiment, GalileoMetrics } from "galileo";

const result = await runExperiment({
  name: "rag-eval",
  dataset: [
    { question: "What is ML?", expected: "Machine learning is..." },
    { question: "Explain AI", expected: "Artificial intelligence is..." },
  ],
  metrics: [GalileoMetrics.contextAdherence, GalileoMetrics.chunkAttributionUtilization, GalileoMetrics.completeness],
  projectName: "eval-project",
  function: async (input) => {
    const docs = await retrieve(input.question);
    return generateAnswer(input.question, docs);
  },
});

Experiment with Prompt Template

import { runExperiment, GalileoMetrics } from "galileo";

const result = await runExperiment({
  name: "prompt-eval",
  datasetName: "my-test-dataset",
  promptTemplate: { id: "your-prompt-template-id" },
  promptSettings: { model_alias: "GPT-4o", temperature: 0.7 },
  metrics: [GalileoMetrics.correctness, GalileoMetrics.instructionAdherence],
  projectName: "eval-project",
});

See Advanced Evaluation Patterns for more.

Common Patterns

RAG Pipeline with Retriever Spans

import { GalileoLogger } from "galileo";

const logger = new GalileoLogger({
  projectName: "rag-app",
  logStreamName: "production",
});

logger.startTrace({ input: "How does photosynthesis work?" });

logger.addRetrieverSpan({
  input: "How does photosynthesis work?",
  output: ["Photosynthesis is the process by which plants..."],
});

logger.addLlmSpan({
  input: "Using the context, explain photosynthesis.",
  output: "Photosynthesis is a process used by plants...",
  durationNs: 1500000000,
  model: "gpt-4o",
});

logger.conclude({ output: "Photosynthesis is a process used by plants..." });
await logger.flush();

Nested Agent Workflows

import { GalileoLogger } from "galileo";

const logger = new GalileoLogger({
  projectName: "agent-app",
  logStreamName: "production",
});

logger.startTrace({ input: "Research and summarize quantum computing" });

logger.addToolSpan({
  input: "search: quantum computing overview",
  output: "Search results...",
  durationNs: 200000000,
});

logger.addRetrieverSpan({
  input: "quantum computing",
  output: ["Doc1: Quantum bits...", "Doc2: Superposition..."],
});

logger.addLlmSpan({
  input: "Summarize the following research on quantum computing...",
  output: "Quantum computing leverages quantum mechanical phenomena...",
  durationNs: 2500000000,
  model: "gpt-4o",
});

logger.conclude({
  output: "Quantum computing leverages quantum mechanical phenomena...",
});

await logger.flush();

Best Practices

  1. Call init() or create a GalileoLogger before logging any traces.
  2. Always call flush() at the end to upload traces to Galileo. In web servers, flush at the end of each request handler.
  3. Use wrapOpenAI for zero-config automatic tracing of all OpenAI calls.
  4. Use log() to wrap functions as spans — it handles sync, async, and generator functions automatically.
  5. Use GalileoLogger when you need fine-grained control over individual spans.
  6. Use runExperiment for evaluation runs — it handles dataset loading, scoring, and result upload.
  7. Set environment variables in .env files rather than hardcoding API keys.
  8. Use accurate durationNs values when manually creating spans for meaningful latency tracking.

Legacy API

GalileoObserveWorkflow and GalileoEvaluateWorkflow are deprecated but still exported for backward compatibility. Use GalileoLogger (or wrapOpenAI / log()) and runExperiment instead.

Resources

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