JavaScript SDK

The Foil JavaScript SDK provides full-featured tracing, logging, and feedback collection for Node.js applications.

Full examples: Browse runnable examples at github.com/getfoil/foil-examples — including hello-foil (auto-instrumentation) and customer-support-agent (SDK with tools and feedback).

Installation

Requirements: Node.js 18 or higher

npm install @getfoil/foil-js

Or with yarn / pnpm:

yarn add @getfoil/foil-js
# or
pnpm add @getfoil/foil-js

Configuration

Get your API key from the Foil Dashboard under Settings > API Keys.

Never hardcode API keys in your source code. Use environment variables instead.

# .env
FOIL_API_KEY=sk_live_xxx_yyy

import { Foil } from '@getfoil/foil-js';

const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-agent',
});

Configuration Options

Option	Type	Required	Default	Description
`apiKey`	string	Yes	-	Your Foil API key
`agentName`	string	No	`'default-agent'`	Unique identifier for your agent
`instrumentModules`	object	No	-	Module map for auto-instrumentation (e.g., `{ openAI: OpenAI }`)
`defaultModel`	string	No	-	Default model name for spans
`debug`	boolean	No	`false`	Enable debug logging

Debug Mode

Enable debug mode to see detailed logs of all SDK operations:

const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-agent',
  debug: true,
});

Or set the environment variable:

FOIL_DEBUG=true

Debug output shows span start/end events with IDs, nesting depth visualization, timing information, and API call results.

Wizard

The Foil wizard automatically instruments your project. It scans your code, detects LLM providers and application patterns, then adds the right tracing setup.

npx @getfoil/foil-js wizard

Options:

Flag	Description
`--agent-name <name>`	Agent name (defaults to package.json name)
`--api-key <key>`	Foil API key (or set `FOIL_API_KEY` env var)
`--dir <path>`	Target directory (defaults to cwd)
`--dry-run`	Preview changes without writing files

The wizard creates a foil.js (or foil.mjs for ESM) config file, adds the Foil import to your entry point, and wraps your agent logic with the appropriate tracing pattern.

Quick Start

The core concept is the trace. A trace represents a complete unit of work (like handling a user request) and contains one or more spans (individual operations like LLM calls, tool executions, or retrieval steps).

import { Foil } from '@getfoil/foil-js';
import OpenAI from 'openai';

const openai = new OpenAI();
const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-agent',
});

// Create a trace
const result = await foil.trace(async (ctx) => {
  // LLM call — automatically creates and closes a span
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: 'Hello, world!' }],
    });
  });

  return response.choices[0].message.content;
}, { name: 'greeting' });

Nested Spans

The key benefit of ctx.llmCall() is that other spans automatically nest under it, giving you a clear tree in the dashboard:

Trace: research-agent
├── llm (gpt-4o) — decides what tools to call
│   ├── tool (web_search) — searches the web
│   └── tool (calculator) — computes a result
└── llm (gpt-4o) — synthesizes final answer

await foil.trace(async (ctx) => {
  const tools = [
    { type: 'function', function: { name: 'web_search', parameters: { /* ... */ } } },
    { type: 'function', function: { name: 'calculator', parameters: { /* ... */ } } },
  ];

  const toolMap = {
    web_search: async (args) => searchAPI(args.query),
    calculator: async (args) => compute(args.expression),
  };

  const messages = [{ role: 'user', content: 'Research and calculate the GDP of France' }];

  // First LLM call — plans the approach and requests tools
  let response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages,
      tools,
    });
  });

  // Agentic loop — LLM decides which tools to call
  while (response.choices[0].message.tool_calls) {
    const toolMessages = await ctx.executeTools(response, toolMap);
    messages.push(response.choices[0].message, ...toolMessages);

    response = await ctx.llmCall('gpt-4o', async () => {
      return await openai.chat.completions.create({
        model: 'gpt-4o',
        messages,
        tools,
      });
    });
  }

  return response.choices[0].message.content;
}, { name: 'research-agent' });

Convenience Methods

The TraceContext provides shorthand methods for common span types. Each wraps your async function in a span and automatically records the return value as output:

await foil.trace(async (ctx) => {
  // LLM call
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({ model: 'gpt-4o', messages, tools });
  });

  // Tool execution — LLM-driven (recommended for agentic use)
  const toolMessages = await ctx.executeTools(response, {
    web_search: async (args) => searchAPI(args.query),
  });

  // Tool execution — code-driven (for hardcoded pipeline steps)
  const data = await ctx.tool('fetch-config', async () => {
    return await loadConfig();
  });

  // Retriever (RAG)
  const docs = await ctx.retriever('vector-db', async () => {
    return await vectorStore.search(query);
  });

  // Embedding generation
  const embeddings = await ctx.embedding('text-embedding-3-small', async () => {
    return await createEmbeddings(texts);
  });
});

Tool Tracing

Foil provides two ways to trace tool calls, depending on whether the LLM decides which tools to run or your code decides.

`ctx.executeTools()` — LLM-Driven (Recommended)

Use this when your agent uses OpenAI function calling. The LLM decides which tools to call at runtime — you define the available tools and their implementations, then executeTools() handles everything:

Reads tool_calls from the OpenAI response
Executes each tool function with the LLM-provided arguments
Creates a traced TOOL span for each call (name, input, output, duration)
Returns formatted tool messages ready to feed back to the next OpenAI call

// 1. Define tool schemas (tell OpenAI what's available)
const tools = [{
  type: 'function',
  function: {
    name: 'get_weather',
    description: 'Get current weather for a location',
    parameters: {
      type: 'object',
      properties: { location: { type: 'string' } },
      required: ['location'],
    },
  },
}];

// 2. Define tool implementations (what actually runs)
const toolMap = {
  get_weather: async (args) => fetchWeather(args.location),
};

// 3. Agentic loop — LLM decides which tools to call
await foil.trace(async (ctx) => {
  const messages = [
    { role: 'user', content: 'What is the weather in Paris and Tokyo?' },
  ];

  let response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages,
      tools,
    });
  });

  // Keep going until the LLM stops requesting tools
  while (response.choices[0].message.tool_calls) {
    const toolMessages = await ctx.executeTools(response, toolMap);
    messages.push(response.choices[0].message, ...toolMessages);

    response = await ctx.llmCall('gpt-4o', async () => {
      return await openai.chat.completions.create({
        model: 'gpt-4o',
        messages,
        tools,
      });
    });
  }

  return response.choices[0].message.content;
}, { name: 'weather-agent' });

This produces:

Trace: weather-agent
├── llm (gpt-4o) — requests tool calls
│   ├── tool (get_weather) — Paris
│   └── tool (get_weather) — Tokyo
└── llm (gpt-4o) — synthesizes final answer

You don’t manually specify tool names or decide which tools to call — the LLM does. The SDK reads tool names and arguments directly from response.choices[0].message.tool_calls.

`ctx.tool()` — Code-Driven (Manual)

Use this for fixed pipeline steps that always run regardless of what the LLM says — like a mandatory database write, a config lookup, or a preprocessing step.

await foil.trace(async (ctx) => {
  // This always runs — the LLM doesn't decide to call it
  const config = await ctx.tool('load-config', async () => {
    return await fetchConfig();
  });

  // Fixed preprocessing step
  const enriched = await ctx.tool('enrich-data', async () => {
    return await enrichWithMetadata(data);
  }, { input: { recordCount: data.length } });

  // Then use the results in an LLM call
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: `Analyze: ${JSON.stringify(enriched)}` }],
    });
  });
});

When to Use Which

Pattern	Who decides?	Tool name comes from	Use when
`ctx.executeTools(response, toolMap)`	The LLM	OpenAI response `tool_calls`	Agentic tool calling — LLM picks tools at runtime
`ctx.tool(name, fn)`	Your code	You hardcode it	Fixed pipeline steps that always run

For most AI agents, ctx.executeTools() is the right choice. Use ctx.tool() only for operations that aren’t driven by LLM decisions.

What Gets Captured

Field	Description
Model	The model used (gpt-4o, gpt-4o-mini, etc.)
Input	Full message array
Output	Assistant response content
Tokens	Prompt, completion, and total tokens
Latency	Total request duration
TTFT	Time to first token (streaming)
Tool Calls	Function/tool invocations (name, args, result, duration)
Errors	Any API errors

Streaming

Streaming is fully supported:

await foil.trace(async (ctx) => {
  const stream = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: 'Write a haiku' }],
      stream: true,
    });
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
});

Auto-Instrumentation

Foil supports automatic instrumentation of LLM calls via OpenLLMetry. Pass instrumentModules to the Foil constructor and all calls to supported providers are traced automatically — no manual wrapping needed.

Auto-instrumentation is an optional enhancement. For most use cases, ctx.llmCall() is the recommended approach — it works with any LLM provider and gives you nested span trees (tools under LLM calls). Auto-instrumentation captures LLM calls automatically; combine it with ctx.executeTools() to also capture tool calls driven by OpenAI function calling.

Basic Setup

const OpenAI = require('openai');
const { Foil } = require('@getfoil/foil-js');

// Pass instrumentModules to enable auto-instrumentation
const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-ai-agent',
  instrumentModules: { openAI: OpenAI },
});

// Now all OpenAI calls are automatically traced!
const openai = new OpenAI();

const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }],
});
// ↑ This call is automatically traced to Foil

With foil.trace()

Wrap auto-instrumented calls in foil.trace() to group them under a single trace:

const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-agent',
  instrumentModules: { openAI: OpenAI },
});

const openai = new OpenAI();

await foil.trace(async (ctx) => {
  // Both calls are automatically traced under the same trace
  const plan = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: 'Plan a trip to Japan' }],
  });

  const details = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: 'Give me details on day 1' }],
  });
}, { name: 'trip-planner' });

Don’t combine instrumentModules with ctx.llmCall() for the same provider. Using both creates duplicate spans — one from auto-instrumentation and one from ctx.llmCall(). Choose one approach per provider.

With Tool Calling (executeTools)

Auto-instrumentation captures LLM calls automatically, but tool execution still needs ctx.executeTools(). This is the recommended pattern for agentic tool-calling loops with auto-instrumentation:

const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'order-agent',
  instrumentModules: { openAI: OpenAI },
});

const openai = new OpenAI();

const tools = [{
  type: 'function',
  function: {
    name: 'check_inventory',
    parameters: {
      type: 'object',
      properties: { sku: { type: 'string' } },
      required: ['sku'],
    },
  },
}];

const toolMap = {
  check_inventory: async (args) => getInventory(args.sku),
};

await foil.trace(async (ctx) => {
  const messages = [{ role: 'user', content: 'Check stock for SKU-1000' }];

  // LLM calls are auto-captured — no ctx.llmCall() needed
  let response = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages,
    tools,
  });

  // Tool-calling loop
  while (response.choices[0].message.tool_calls) {
    // executeTools reads tool names/args from the response,
    // executes each one, and auto-traces them as TOOL spans
    const toolMessages = await ctx.executeTools(response, toolMap);
    messages.push(response.choices[0].message, ...toolMessages);

    response = await openai.chat.completions.create({
      model: 'gpt-4o',
      messages,
      tools,
    });
  }

  return response.choices[0].message.content;
}, { name: 'inventory-check' });

This produces:

Trace: inventory-check
├── llm (gpt-4o) — auto-captured, returns tool_calls
│   └── tool (check_inventory) — via ctx.executeTools()
├── llm (gpt-4o) — auto-captured, final response

Supported Libraries

Library	Features
OpenAI	Chat completions, embeddings, assistants, function calling
Anthropic	Claude messages, streaming
Azure OpenAI	All Azure OpenAI endpoints
Cohere	Chat, generate, embed
Google Generative AI	Gemini models
AWS Bedrock	Bedrock runtime
LlamaIndex	Queries, retrievers

Graceful Shutdown

Always shut down gracefully to flush pending spans:

process.on('SIGTERM', async () => {
  await foil.shutdown();
  process.exit(0);
});

// Or flush manually before exit
await foil.flush();

Advanced: Manual OTEL Setup

For full control over the OpenTelemetry pipeline:

OTEL Module

const { Foil } = require('@getfoil/foil-js/otel');

Foil.init({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'my-ai-agent',
});

Manual FoilSpanProcessor

const { FoilSpanProcessor } = require('@getfoil/foil-js/otel');
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { Resource } = require('@opentelemetry/resources');

const provider = new NodeTracerProvider({
  resource: new Resource({
    'service.name': 'my-custom-agent',
    'deployment.environment': 'production',
  }),
});

provider.addSpanProcessor(new FoilSpanProcessor({
  apiKey: process.env.FOIL_API_KEY,
  maxBatchSize: 100,
  scheduledDelayMs: 5000,
  exportTimeoutMs: 30000,
  debug: true,
}));

provider.register();

FoilSpanProcessor Options

Option	Type	Default	Description
`apiKey`	string	Required	Your Foil API key
`endpoint`	string	`https://api.getfoil.ai/api/otlp/v1/traces`	OTLP endpoint
`maxBatchSize`	number	100	Maximum spans per batch
`scheduledDelayMs`	number	5000	Batch export interval in ms
`exportTimeoutMs`	number	30000	Export request timeout in ms
`debug`	boolean	false	Enable debug logging

Custom Attributes

const span = tracer.startSpan('my-operation');
span.setAttribute('foil.session_id', sessionId);
span.setAttribute('foil.end_user_id', userId);
span.setAttribute('foil.cost', 0.0023);
span.end();

Attribute	Description
`foil.agent_name`	Override agent name for this span
`foil.agent_id`	Explicit agent ID
`foil.session_id`	Session/conversation ID
`foil.end_user_id`	End user identifier
`foil.end_user.*`	Custom end user properties
`foil.cost`	Cost in dollars

Troubleshooting: Spans not appearing

Check that your API key is correct
Ensure Foil is constructed with instrumentModules before any LLM calls
Call await foil.flush() before process exit
Enable debug logging to see export status

Troubleshooting: Missing LLM details (tokens, model)

Ensure new Foil({ instrumentModules }) is called at the top of your app, before importing LLM libraries
Check that the LLM library is supported
Some details require specific library versions

Troubleshooting: Duplicate spans

Don’t use instrumentModules and ctx.llmCall() for the same LLM calls
Don’t initialize both Foil.init() from @getfoil/foil-js/otel and new Foil({ instrumentModules })

Signals and Feedback

Record custom metrics and user feedback tied to your traces:

await foil.trace(async (ctx) => {
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages,
    });
  });

  // Record a custom signal
  await ctx.recordSignal('response_length', response.choices[0].message.content.length);

  // Record user feedback (thumbs up/down)
  await ctx.recordFeedback(true); // true = positive

  // Record a star rating
  await ctx.recordRating(4.5);

  return response.choices[0].message.content;
}, { name: 'chat-with-feedback' });

Span Types

Foil supports several span types to categorize different operations:

import { SpanKind } from '@getfoil/foil-js';

SpanKind.AGENT      // 'agent' - Top-level agent operations
SpanKind.LLM        // 'llm' - LLM API calls
SpanKind.TOOL       // 'tool' - Tool executions
SpanKind.CHAIN      // 'chain' - Chain/pipeline steps
SpanKind.RETRIEVER  // 'retriever' - RAG retrieval
SpanKind.EMBEDDING  // 'embedding' - Embedding generations
SpanKind.CUSTOM     // 'custom' - Custom operations

Error Handling

Errors are automatically captured when spans fail:

await foil.trace(async (ctx) => {
  // Errors are automatically recorded on the span and re-thrown
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({
      model: 'gpt-4o',
      messages,
    });
    // If this throws, the span records the error automatically
  });
});

Complete Example

import OpenAI from 'openai';
import { Foil } from '@getfoil/foil-js';

const openai = new OpenAI();
const foil = new Foil({
  apiKey: process.env.FOIL_API_KEY,
  agentName: 'customer-support-agent',
});

async function handleUserQuery(query, userId) {
  return await foil.trace(async (ctx) => {
    // Step 1: Search knowledge base
    const docs = await ctx.retriever('knowledge-base', async () => {
      return await searchKnowledgeBase(query);
    }, { input: { query } });

    // Step 2: Generate response with context
    const response = await ctx.llmCall('gpt-4o', async () => {
      return await openai.chat.completions.create({
        model: 'gpt-4o',
        messages: [
          { role: 'system', content: `Context: ${docs.join('\n')}` },
          { role: 'user', content: query },
        ],
      });
    }, {
      input: { query, context: docs },
      properties: { userId },
    });

    // Step 3: Record a signal
    await ctx.recordSignal('response_confidence', 0.95);

    return response.choices[0].message.content;
  }, {
    name: 'handle-query',
    properties: { userId },
  });
}

// Graceful shutdown
process.on('SIGTERM', async () => {
  await foil.shutdown();
  process.exit(0);
});

Advanced Patterns

Manual span control with startSpan

For advanced flows where you need explicit control over parent-child relationships:

await foil.trace(async (ctx) => {
  const rootSpan = await ctx.startSpan(SpanKind.AGENT, 'root');

  // Create child context for parallel work
  const childCtx = rootSpan.createChildContext();

  // This span is explicitly a child of rootSpan
  const childSpan = await childCtx.startSpan(SpanKind.LLM, 'gpt-4o');
  await childSpan.end({ output: '...' });

  await rootSpan.end({ output: 'done' });
});

Multi-agent handoffs with createChildContext

When one agent delegates to another, use span.createChildContext() to create nested agent spans:

await foil.trace(async (ctx) => {
  const coordinatorSpan = await ctx.startSpan(SpanKind.AGENT, 'coordinator');
  const childCtx = coordinatorSpan.createChildContext();

  const flightSpan = await childCtx.startSpan(SpanKind.AGENT, 'flight-searcher');
  // ... sub-agent work ...
  await flightSpan.end({ output: 'Flight options compiled' });

  const hotelSpan = await childCtx.startSpan(SpanKind.AGENT, 'hotel-searcher');
  // ... sub-agent work ...
  await hotelSpan.end({ output: 'Hotel options compiled' });

  await coordinatorSpan.end({ output: 'Trip plan complete' });
});

Retrieving traces

Fetch completed traces for debugging or analysis:

const trace = await foil.getTrace(traceId);

console.log('Trace:', trace.traceId);
console.log('Spans:', trace.spans.length);

for (const span of trace.spans) {
  console.log(`${span.depth}: ${span.spanKind} - ${span.name}`);
}

Trace context propagation

Pass trace context to external services via headers:

await foil.trace(async (ctx) => {
  const response = await fetch('https://another-service.com/api', {
    headers: {
      'x-trace-id': ctx.traceId,
      'x-parent-span-id': ctx.currentParentEventId,
    },
  });
});

Fire-and-forget logging

For non-critical telemetry without full tracing:

foil.log({
  model: 'gpt-4o',
  input: messages,
  output: response,
  latency: duration,
});

Provider-agnostic usage

ctx.llmCall() works with any LLM provider — not just OpenAI:

await foil.trace(async (ctx) => {
  // Anthropic
  const claudeResponse = await ctx.llmCall('claude-sonnet-4-20250514', async () => {
    return await anthropic.messages.create({
      model: 'claude-sonnet-4-20250514',
      max_tokens: 1024,
      messages: [{ role: 'user', content: 'Hello!' }],
    });
  });

  // Local model via Ollama
  const localResponse = await ctx.llmCall('llama3', async () => {
    return await fetch('http://localhost:11434/api/chat', {
      method: 'POST',
      body: JSON.stringify({ model: 'llama3', messages }),
    }).then(r => r.json());
  });

  // Any HTTP API
  const customResponse = await ctx.llmCall('my-model', async () => {
    return await myCustomLLMClient.generate(prompt);
  });
});

Custom metadata and properties

Add custom properties to spans for filtering and analysis:

await foil.trace(async (ctx) => {
  const response = await ctx.llmCall('gpt-4o', async () => {
    return await openai.chat.completions.create({ model: 'gpt-4o', messages });
  }, {
    input: messages,
    properties: {
      userId: 'user-123',
      sessionId: 'session-456',
      feature: 'chat',
    },
  });
}, {
  name: 'chat',
  properties: { userId: 'user-123' },
});

Getting Started

SDKs

Concepts

Features

JavaScript SDK

JavaScript SDK

Installation

Configuration

Configuration Options

Debug Mode

Wizard

Quick Start

Nested Spans

Convenience Methods

Tool Tracing

`ctx.executeTools()` — LLM-Driven (Recommended)

`ctx.tool()` — Code-Driven (Manual)

When to Use Which

What Gets Captured

Streaming

Auto-Instrumentation

Basic Setup

With foil.trace()

With Tool Calling (executeTools)

Supported Libraries

Graceful Shutdown

OTEL Module

Manual FoilSpanProcessor

FoilSpanProcessor Options

Custom Attributes

Signals and Feedback

Span Types

Error Handling

Complete Example

Advanced Patterns

Getting Started

SDKs

Concepts

Features

​JavaScript SDK

​Installation

​Configuration

​Configuration Options

​Debug Mode

​Wizard

​Quick Start

​Nested Spans

​Convenience Methods

​Tool Tracing

​ctx.executeTools() — LLM-Driven (Recommended)

​ctx.tool() — Code-Driven (Manual)

​When to Use Which

​What Gets Captured

​Streaming

​Auto-Instrumentation

​Basic Setup

​With foil.trace()

​With Tool Calling (executeTools)

​Supported Libraries

​Graceful Shutdown

​OTEL Module

​Manual FoilSpanProcessor

​FoilSpanProcessor Options

​Custom Attributes

​Signals and Feedback

​Span Types

​Error Handling

​Complete Example

​Advanced Patterns

JavaScript SDK

Installation

Configuration

Configuration Options

Debug Mode

Wizard

Quick Start

Nested Spans

Convenience Methods

Tool Tracing

`ctx.executeTools()` — LLM-Driven (Recommended)

`ctx.tool()` — Code-Driven (Manual)

When to Use Which

What Gets Captured

Streaming

Auto-Instrumentation

Basic Setup

With foil.trace()

With Tool Calling (executeTools)

Supported Libraries

Graceful Shutdown

OTEL Module

Manual FoilSpanProcessor

FoilSpanProcessor Options

Custom Attributes

Signals and Feedback

Span Types

Error Handling

Complete Example

Advanced Patterns