traceAI provides drop-in OpenTelemetry instrumentation for popular AI frameworks and LLM providers. It automatically captures traces, spans, and attributes from your AI workflows—whether you're using OpenAI, Anthropic, LangChain, LlamaIndex, or 20+ other frameworks.

• Zero-config tracing for OpenAI, Anthropic, LangChain, LlamaIndex, and more
• OpenTelemetry-native — works with any OTel-compatible backend (Jaeger, Datadog, Future AGI, etc.)
• Semantic conventions for LLM calls, agents, tools, and retrieval
• Python + TypeScript support with consistent APIs

• Key Features
• Quickstart
  • Python
  • TypeScript
• Supported Frameworks
  • Python
  • TypeScript
• Compatibility Matrix
• Architecture
• Contributing
• Resources
• Connect With Us

1. Install

pip install traceai-openai

2. Instrument your application

import os
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
from traceai_openai import OpenAIInstrumentor
import openai

# Set up environment variables
os.environ["FI_API_KEY"] = "<your-api-key>"
os.environ["FI_SECRET_KEY"] = "<your-secret-key>"
os.environ["OPENAI_API_KEY"] = "<your-openai-key>"

# Register tracer provider
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="my_ai_app"
)

# Instrument OpenAI
OpenAIInstrumentor().instrument(tracer_provider=trace_provider)

# Use OpenAI as normal - tracing happens automatically!
response = openai.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Hello!"}]
)

💡 Tip: Swap traceai-openai for any supported framework (e.g., traceai-langchain, traceai-anthropic)

1. Install

npm install @traceai/openai @traceai/fi-core

2. Instrument your application

import { register, ProjectType } from "@traceai/fi-core";
import { OpenAIInstrumentation } from "@traceai/openai";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import OpenAI from "openai";

// Register tracer provider
const tracerProvider = register({
  projectName: "my_ai_app",
  projectType: ProjectType.OBSERVE,
});

// Register OpenAI instrumentation (before creating client!)
registerInstrumentations({
  tracerProvider,
  instrumentations: [new OpenAIInstrumentation()],
});

// Use OpenAI as normal - tracing happens automatically!
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await openai.chat.completions.create({
  model: "gpt-4.1",
  messages: [{ role: "user", content: "Hello!" }],
});

💡 Tip: Works with Anthropic, LangChain, Vercel AI SDK, and more TypeScript frameworks

Package	Description	Version
traceAI-openai	traceAI Instrumentation for OpenAI.
traceAI-anthropic	traceAI Instrumentation for Anthropic.
traceAI-llamaindex	traceAI Instrumentation for LlamaIndex.
traceAI-langchain	traceAI Instrumentation for LangChain.
traceAI-mistralai	traceAI Instrumentation for MistralAI.
traceAI-vertexai	traceAI Instrumentation for VertexAI.
traceAI-crewai	traceAI Instrumentation for CrewAI.
traceAI-haystack	traceAI Instrumentation for Haystack.
traceAI-litellm	traceAI Instrumentation for liteLLM.
traceAI-groq	traceAI Instrumentation for Groq.
traceAI-autogen	traceAI Instrumentation for Autogen.
traceAI-guardrails	traceAI Instrumentation for Guardrails.
traceAI-openai-agents	traceAI Instrumentation for OpenAI Agents.
traceAI-smolagents	traceAI Instrumentation for SmolAgents.
traceAI-dspy	traceAI Instrumentation for DSPy.
traceAI-bedrock	traceAI Instrumentation for AWS Bedrock.
traceAI-instructor	traceAI Instrumentation for Instructor.
traceAI-google-genai	traceAI Instrumentation for Google Generative AI.
traceAI-google-adk	traceAI Instrumentation for Google ADK.
traceAI-pipecat	traceAI Instrumentation for Pipecat.
traceAI-portkey	traceAI Instrumentation for Portkey.
traceAI-mcp	traceAI Instrumentation for Model Context Protocol.

Package	Description	Version
@traceai/openai	traceAI Instrumentation for OpenAI.
@traceai/anthropic	traceAI Instrumentation for Anthropic.
@traceai/langchain	traceAI Instrumentation for LangChain.
@traceai/llamaindex	traceAI Instrumentation for LlamaIndex.
@traceai/bedrock	traceAI Instrumentation for AWS Bedrock.
@traceai/vercel	traceAI Instrumentation for Vercel AI SDK.
@traceai/mastra	traceAI Instrumentation for Mastra.
@traceai/mcp	traceAI Instrumentation for Model Context Protocol.

Legend: ✅ Supported | - Not yet available

traceAI is built on top of OpenTelemetry and follows standard OTel instrumentation patterns. This means you get:

🔌 Full OpenTelemetry Compatibility

• Works with any OTel-compatible backend
• Standard OTLP exporters (HTTP/gRPC)
• Compatible with existing OTel setups

⚙️ Bring Your Own Configuration

You can use traceAI with your own OpenTelemetry setup:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from traceai_openai import OpenAIInstrumentor

# Set up your own tracer provider
tracer_provider = TracerProvider()
trace.set_tracer_provider(tracer_provider)

# Add custom exporters (example with Future AGI)
# HTTP endpoint
otlp_exporter = OTLPSpanExporter(
    endpoint="https://api.futureagi.com/tracer/v1/traces",
    headers={
        "X-API-KEY": "your-api-key",
        "X-SECRET-KEY": "your-secret-key"
    }
)
# Or use gRPC: OTLPSpanExporter(endpoint="grpc://grpc.futureagi.com:443", ...)
tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))

# Instrument with traceAI
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { Resource } from "@opentelemetry/resources";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { OpenAIInstrumentation } from "@traceai/openai";

// Create custom tracer provider
const provider = new NodeTracerProvider({
  resource: new Resource({
    "service.name": "my-ai-service",
  }),
});

// Add custom OTLP exporter with headers (example with Future AGI)
// HTTP endpoint
const exporter = new OTLPTraceExporter({
  url: "https://api.futureagi.com/tracer/v1/traces",
  headers: {
    "X-API-KEY": process.env.FI_API_KEY!,
    "X-SECRET-KEY": process.env.FI_SECRET_KEY!,
  },
});
// Or use gRPC: new OTLPTraceExporter({ url: "grpc://grpc.futureagi.com:443", ... })

// Add span processor
provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();

// Register traceAI instrumentation
registerInstrumentations({
  tracerProvider: provider,
  instrumentations: [new OpenAIInstrumentation()],
});

📊 What Gets Captured

traceAI automatically captures rich telemetry data:

• Prompts & Completions: Full request/response content
• Token Usage: Input, output, and total tokens
• Model Parameters: Temperature, top_p, max_tokens, etc.
• Tool Calls: Function/tool names, arguments, and results
• Streaming: Individual chunks with delta tracking
• Errors: Detailed error context and stack traces
• Timing: Latency at each step of the AI workflow

All data follows OpenTelemetry Semantic Conventions for GenAI.

We welcome contributions from the community!

📖 Read our Contributing Guide for detailed instructions on:

• Setting up your development environment (Python & TypeScript)
• Running tests and code quality checks
• Submitting pull requests
• Adding new framework integrations

Quick Start:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Found a bug? Please open an issue with:

• Framework version
• traceAI version
• Minimal reproduction code
• Expected vs actual behavior

Request a feature? Please open an issue with:

• Use case or problem you're trying to solve
• Proposed solution or feature description
• Any relevant examples or mockups
• Priority level (nice-to-have vs critical)