KeywordsAI Tracing SDK

A lightweight OpenTelemetry-based tracing SDK for KeywordsAI, built with minimal dependencies and optional instrumentation support. Inspired by Openllmetry

Features

• Lightweight Core: Minimal dependencies for browser and Node.js compatibility
• Optional Instrumentations: Install only the instrumentations you need
• OpenTelemetry Native: Built directly on OpenTelemetry without wrapper dependencies
• Decorator Pattern: Easy-to-use decorators for workflows, tasks, agents, and tools
• Dynamic Loading: Instrumentations are loaded on-demand
• Manual Instrumentation: Support for manual instrumentation (Next.js compatible)

Installation

Core Package

npm install @keywordsai/tracing

Optional Instrumentations

Install only the instrumentations you need:

# OpenAI
npm install @traceloop/instrumentation-openai

# Anthropic
npm install @traceloop/instrumentation-anthropic

# Azure OpenAI
npm install @traceloop/instrumentation-azure

# AWS Bedrock
npm install @traceloop/instrumentation-bedrock

# Cohere
npm install @traceloop/instrumentation-cohere

# LangChain
npm install @traceloop/instrumentation-langchain

# LlamaIndex
npm install @traceloop/instrumentation-llamaindex

# Vector Databases
npm install @traceloop/instrumentation-pinecone
npm install @traceloop/instrumentation-chromadb
npm install @traceloop/instrumentation-qdrant

# Other providers
npm install @traceloop/instrumentation-together
npm install @traceloop/instrumentation-vertexai

Quick Start

Method 1: Dynamic Instrumentation (Recommended for Node.js)

import { KeywordsAITelemetry } from '@keywordsai/tracing';
import OpenAI from 'openai';

// Initialize the SDK
const keywordsAi = new KeywordsAITelemetry({
    apiKey: process.env.KEYWORDSAI_API_KEY,
    baseURL: process.env.KEYWORDSAI_BASE_URL,
    appName: 'my-app'
});

// Enable instrumentations you need
await keywordsAi.enableInstrumentation('openai');

const openai = new OpenAI();

// Use decorators to trace your functions
const generateJoke = async () => {
    return await keywordsAi.withTask(
        { name: 'joke_generation' },
        async () => {
            const completion = await openai.chat.completions.create({
                messages: [{ role: 'user', content: 'Tell me a joke' }],
                model: 'gpt-3.5-turbo'
            });
            return completion.choices[0].message.content;
        }
    );
};

Method 2: Manual Instrumentation (Recommended for Next.js)

import { KeywordsAITelemetry } from '@keywordsai/tracing';
import OpenAI from 'openai';
import Anthropic from '@anthropic-ai/sdk';

// Manual instrumentation - pass the actual imported modules
const keywordsAi = new KeywordsAITelemetry({
    apiKey: process.env.KEYWORDSAI_API_KEY,
    baseURL: process.env.KEYWORDSAI_BASE_URL,
    appName: 'my-app',
    // Specify modules to instrument manually
    instrumentModules: {
        openAI: OpenAI,
        anthropic: Anthropic,
        // Add other modules as needed
    }
});

// Wait for initialization (optional but recommended)
await keywordsAi.initialize();

// Create clients - they will be automatically instrumented
const openai = new OpenAI();
const anthropic = new Anthropic();

// Use decorators to trace your functions
const generateContent = async () => {
    return await keywordsAi.withWorkflow(
        { name: 'content_generation', version: 1 },
        async () => {
            const result = await openai.chat.completions.create({
                messages: [{ role: 'user', content: 'Generate content' }],
                model: 'gpt-3.5-turbo'
            });
            return result.choices[0].message.content;
        }
    );
};

When to Use Each Method

Dynamic Instrumentation

• Best for: Standard Node.js applications, serverless functions
• Pros: Simple setup, automatic loading
• Cons: May not work in all bundling environments

Manual Instrumentation

• Best for: Next.js, Webpack bundled apps, environments with import restrictions
• Pros: Works in all environments, explicit control, better for tree-shaking
• Cons: Requires importing modules explicitly

API Reference

KeywordsAITelemetry

Constructor Options

interface KeywordsAIOptions {
    appName?: string;                    // App name for traces
    apiKey?: string;                     // KeywordsAI API key
    baseURL?: string;                    // KeywordsAI base URL
    disableBatch?: boolean;              // Disable batching for development
    logLevel?: "debug" | "info" | "warn" | "error";
    traceContent?: boolean;              // Log prompts and completions
    tracingEnabled?: boolean;            // Enable/disable tracing
    silenceInitializationMessage?: boolean;
    
    // Manual instrumentation modules
    instrumentModules?: {
        openAI?: typeof OpenAI;
        anthropic?: typeof Anthropic;
        azureOpenAI?: typeof AzureOpenAI;
        cohere?: typeof Cohere;
        bedrock?: typeof BedrockRuntime;
        google_vertexai?: typeof VertexAI;
        google_aiplatform?: typeof AIPlatform;
        pinecone?: typeof Pinecone;
        together?: typeof Together;
        langchain?: {
            chainsModule?: typeof ChainsModule;
            agentsModule?: typeof AgentsModule;
            toolsModule?: typeof ToolsModule;
            runnablesModule?: typeof RunnableModule;
            vectorStoreModule?: typeof VectorStoreModule;
        };
        llamaIndex?: typeof LlamaIndex;
        chromadb?: typeof ChromaDB;
        qdrant?: typeof Qdrant;
    };
}

Methods

• initialize(): Manually initialize tracing (returns Promise)
• isInitialized(): Check if tracing has been initialized
• enableInstrumentation(name: string): Enable a specific instrumentation (dynamic method)
• enableInstrumentations(names: string[]): Enable multiple instrumentations (dynamic method)
• shutdown(): Flush and shutdown tracing

Decorators

withWorkflow

Trace high-level workflows:

await keywordsAi.withWorkflow(
    { name: 'my_workflow', version: 1 },
    async () => {
        // Your workflow logic
    }
);

withTask

Trace individual tasks:

await keywordsAi.withTask(
    { name: 'my_task' },
    async () => {
        // Your task logic
    }
);

withAgent

Trace agent operations:

await keywordsAi.withAgent(
    { name: 'my_agent', associationProperties: { type: 'assistant' } },
    async () => {
        // Your agent logic
    }
);

withTool

Trace tool usage:

await keywordsAi.withTool(
    { name: 'my_tool' },
    async () => {
        // Your tool logic
    }
);

Decorator Configuration

interface DecoratorConfig {
    name: string;                        // Required: Name of the operation
    version?: number;                    // Optional: Version number
    associationProperties?: Record<string, string>; // Optional: Additional metadata
    traceContent?: boolean;              // Optional: Override trace content setting
    inputParameters?: unknown[];         // Optional: Custom input parameters
    suppressTracing?: boolean;           // Optional: Suppress tracing for this operation
}

Available Instrumentations

The following instrumentations can be enabled dynamically:

• openai - OpenAI API calls
• anthropic - Anthropic API calls
• azure - Azure OpenAI API calls
• bedrock - AWS Bedrock API calls
• cohere - Cohere API calls
• langchain - LangChain operations
• llamaindex - LlamaIndex operations
• pinecone - Pinecone vector database
• chromadb - ChromaDB vector database
• qdrant - Qdrant vector database
• together - Together AI API calls
• vertexai - Google Vertex AI API calls

Environment Variables

• KEYWORDSAI_API_KEY: Your KeywordsAI API key
• KEYWORDSAI_BASE_URL: KeywordsAI base URL (default: https://api.keywordsai.co)
• KEYWORDSAI_APP_NAME: Default app name
• KEYWORDSAI_TRACE_CONTENT: Enable/disable content tracing (default: true)

Browser Compatibility

The core package is designed to work in both Node.js and browser environments. However, some instrumentations may be Node.js only.

License

Apache-2.0