traceAI

OpenTelemetry-native instrumentation for AI applications
Standardized observability across LLMs, agents, and frameworks

Documentation • Examples • Slack • PyPI Packages • npm Packages

🚀 What is traceAI?

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
Roadmap
Contributing
Resources
Connect With Us

✨ Key Features

Feature	Description
🎯 Standardized Tracing	Maps AI workflows to consistent OpenTelemetry spans & attributes
🔌 Zero-Config Setup	Drop-in instrumentation with minimal code changes
🌐 Multi-Framework	20+ integrations across Python & TypeScript
📊 Vendor Agnostic	Works with any OpenTelemetry-compatible backend
🔍 Rich Context	Captures prompts, completions, tokens, model params, tool calls, and more
⚡ Production Ready	Async support, streaming, error handling, and performance optimized

🎯 Quickstart

Python Quickstart

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)

TypeScript Quickstart

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

📦 Supported Frameworks

Python

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.
`traceAI-livekit`	traceAI Instrumentation for LiveKit (Real-time).
`traceAI-pinecone`	traceAI Instrumentation for Pinecone vector database.
`traceAI-chromadb`	traceAI Instrumentation for ChromaDB vector database.
`traceAI-qdrant`	traceAI Instrumentation for Qdrant vector database.
`traceAI-weaviate`	traceAI Instrumentation for Weaviate vector database.
`traceAI-milvus`	traceAI Instrumentation for Milvus vector database.
`traceAI-lancedb`	traceAI Instrumentation for LanceDB vector database.
`traceAI-mongodb`	traceAI Instrumentation for MongoDB Atlas Vector Search.
`traceAI-pgvector`	traceAI Instrumentation for pgvector PostgreSQL extension.
`traceAI-redis`	traceAI Instrumentation for Redis Vector Search.

TypeScript

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.

🔧 Compatibility Matrix

Category	Supported Frameworks	Python	TypeScript
LLM Providers	OpenAI	✅	✅
	Anthropic	✅	✅
	AWS Bedrock	✅	✅
	Google Vertex AI	✅	-
	Google Generative AI	✅	-
	Mistral AI	✅	-
	Groq	✅	-
	LiteLLM	✅	-
Agent Frameworks	LangChain	✅	✅
	LlamaIndex	✅	✅
	CrewAI	✅	-
	AutoGen	✅	-
	OpenAI Agents	✅	-
	Smol Agents	✅	-
	Mastra	-	✅
Tools & Libraries	Haystack	✅	-
	DSPy	✅	-
	Guardrails AI	✅	-
	Instructor	✅	-
	Portkey	✅	-
	Pipecat	✅	-
	LiveKit	✅	-
	Vercel AI SDK	-	✅
Standards	Model Context Protocol (MCP)	✅	✅
Vector Databases	Pinecone	✅	-
	ChromaDB	✅	-
	Qdrant	✅	-
	Weaviate	✅	-
	Milvus	✅	-
	LanceDB	✅	-
	MongoDB Atlas Vector	✅	-
	pgvector	✅	-
	Redis Vector	✅	-

Legend: ✅ Supported | - Not yet available

🏗️ Architecture

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:

Python: Custom TracerProvider & Exporters

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)

TypeScript: Custom TracerProvider, Span Processors & Headers

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.

🗺️ Roadmap

See our ROADMAP.md for planned features and integrations:

Coming Soon

Category	Integrations
LLM Providers	Ollama, Cohere, Together AI, HuggingFace
Agent Frameworks	LangGraph, Claude Agent SDK, Pydantic AI
Enterprise SDKs	Java (LangChain4j, Spring AI), Go
Platform Features	LLM Playground, Datasets & Experiments

Recently Added

Category	Integrations
Vector DBs	Pinecone, ChromaDB, Qdrant, Weaviate, Milvus, LanceDB, MongoDB Atlas, pgvector, Redis

Technical Documentation

For implementation details and architecture:

knowledge_base/INDEX.md - Technical knowledge base
docs/SCHEMA_SEMANTIC_CONVENTIONS.md - Schema reference

🤝 Contributing

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:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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)

📚 Resources

Resource	Description
🌐 Website	Learn more about Future AGI
📖 Documentation	Complete guides and API reference
👨‍🍳 Cookbooks	Step-by-step implementation examples
🗺️ Roadmap	Planned features and integrations
📝 Changelog	All release notes and updates
📚 Knowledge Base	Technical documentation and architecture
🤝 Contributing Guide	How to contribute to traceAI
💬 Slack	Join our community
🐛 Issues	Report bugs or request features

🌍 Connect With Us

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traceAI

🚀 What is traceAI?

Table of Contents

✨ Key Features

🎯 Quickstart

Python Quickstart

TypeScript Quickstart

📦 Supported Frameworks

Python

TypeScript

🔧 Compatibility Matrix

🏗️ Architecture

🗺️ Roadmap

Coming Soon

Recently Added

Technical Documentation

🤝 Contributing

📚 Resources

🌍 Connect With Us

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README.md

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README.md

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traceAI

🚀 What is traceAI?

Table of Contents

✨ Key Features

🎯 Quickstart

Python Quickstart

TypeScript Quickstart

📦 Supported Frameworks

Python

TypeScript

🔧 Compatibility Matrix

🏗️ Architecture

🗺️ Roadmap

Coming Soon

Recently Added

Technical Documentation

🤝 Contributing

📚 Resources

🌍 Connect With Us