traceAI

Open-source observability for AI applications - trace every LLM call, prompt, token, retrieval step, and agent decision.

Built on OpenTelemetry, traceAI sends structured traces to any OTel-compatible backend (Datadog, Grafana, Jaeger, Future AGI, and more). No new vendor. No new dashboard.

Documentation • Examples • Slack • PyPI • npm • NuGet

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What is traceAI?

traceAI is an open-source library that gives you full visibility into your AI applications. It captures every LLM call, prompt, token count, retrieval step, and agent decision as structured traces and sends them to whatever observability tool you already use.

It is built on OpenTelemetry, the industry standard for application observability. Your AI traces live natively in Datadog, Grafana, Future AGI, Jaeger, or any OTel-compatible backend. No new vendor. No new dashboard.

• Zero-config tracing for 50+ AI frameworks across 4 languages
• OpenTelemetry-native - works with any OTel-compatible backend
• Semantic conventions for LLM calls, agents, tools, retrieval, and vector databases
• Python, TypeScript, Java, and C# support with consistent APIs

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Table of Contents

• Key Features
• Quickstart
  • Python
  • TypeScript
  • Java
  • C#
• Supported Frameworks
  • Python
  • TypeScript
  • Java
  • C#
• Compatibility Matrix
• Architecture
• Roadmap
• Contributing
• Contributors
• Resources
• Connect With Us

Key Features

Feature	Description
Standardized Tracing	Maps AI workflows to consistent OpenTelemetry spans and attributes
Zero-Config Setup	Drop-in instrumentation with minimal code changes
Multi-Framework	50+ integrations across Python, TypeScript, Java, and C#
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

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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)

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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!" }],
});

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Java Quickstart

1. Add dependency (via JitPack)

<dependency>
    <groupId>com.github.future-agi.traceAI</groupId>
    <artifactId>traceai-java-openai</artifactId>
    <version>v1.0.0</version>
</dependency>

2. Instrument your application

import ai.traceai.core.TraceAI;
import ai.traceai.openai.TracedOpenAIClient;

// Initialize tracing
TraceAI.init("my_ai_app", "your-api-key", "your-secret-key");

// Wrap your OpenAI client
var tracedClient = new TracedOpenAIClient(openAIClient);

// Use as normal - tracing happens automatically!
var response = tracedClient.chatCompletion(request);

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C# Quickstart

1. Install

dotnet add package fi-instrumentation-otel

2. Instrument your application

using FIInstrumentation;

// Initialize tracing
var tracer = FITracer.Initialize(new FITracerOptions
{
    ProjectName = "my_ai_app",
    ApiKey = "your-api-key",
    SecretKey = "your-secret-key"
});

// Use the tracer with your AI calls

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Supported Frameworks

Python

Package	Description	Version
fi-instrumentation-otel	Core instrumentation library

LLM Providers

Package	Description	Version
traceAI-openai	OpenAI
traceAI-anthropic	Anthropic
traceAI-google-genai	Google Generative AI
traceAI-vertexai	Google Vertex AI
traceAI-bedrock	AWS Bedrock
traceAI-mistralai	Mistral AI
traceAI-groq	Groq
traceAI-litellm	LiteLLM
traceAI-cohere	Cohere
traceAI-ollama	Ollama
traceAI-together	Together AI
traceAI-deepseek	DeepSeek
traceAI-fireworks	Fireworks AI
traceAI-cerebras	Cerebras
traceAI-huggingface	HuggingFace
traceAI-xai	xAI (Grok)
traceAI-vllm	vLLM

Agent Frameworks

Package	Description	Version
traceAI-langchain	LangChain
traceAI-llamaindex	LlamaIndex
traceAI-crewai	CrewAI
traceAI-openai-agents	OpenAI Agents
traceAI-smolagents	SmolAgents
traceAI-autogen	AutoGen
traceAI-google-adk	Google ADK
traceAI-agno	Agno
traceAI-pydantic-ai	Pydantic AI
traceAI-claude-agent-sdk	Claude Agent SDK
traceAI-strands	AWS Strands Agents
traceAI-beeai	IBM BeeAI

Tools and Libraries

Package	Description	Version
traceAI-haystack	Haystack
traceAI-dspy	DSPy
traceAI-guardrails	Guardrails AI
traceAI-instructor	Instructor
traceAI-portkey	Portkey
traceAI-mcp	Model Context Protocol
traceAI-pipecat	Pipecat (Voice AI)
traceAI-livekit	LiveKit (Real-time)

Vector Databases

Package	Description	Version
traceAI-pinecone	Pinecone
traceAI-chromadb	ChromaDB
traceAI-qdrant	Qdrant
traceAI-weaviate	Weaviate
traceAI-milvus	Milvus
traceAI-lancedb	LanceDB
traceAI-mongodb	MongoDB Atlas Vector Search
traceAI-pgvector	pgvector (PostgreSQL)
traceAI-redis	Redis Vector Search

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TypeScript

Package	Description	Version
@traceai/fi-core	Core instrumentation library
@traceai/fi-semantic-conventions	Semantic conventions

LLM Providers

Package	Description	Version
@traceai/openai	OpenAI
@traceai/anthropic	Anthropic
@traceai/google-genai	Google Generative AI
@traceai/fi-instrumentation-vertexai	Google Vertex AI
@traceai/bedrock	AWS Bedrock
@traceai/mistral	Mistral AI
@traceai/groq	Groq
@traceai/cohere	Cohere
@traceai/ollama	Ollama
@traceai/together	Together AI
@traceai/deepseek	DeepSeek
@traceai/fireworks	Fireworks AI
@traceai/cerebras	Cerebras
@traceai/huggingface	HuggingFace
@traceai/xai	xAI (Grok)
@traceai/vllm	vLLM

Agent Frameworks

Package	Description	Version
@traceai/langchain	LangChain.js
@traceai/llamaindex	LlamaIndex
@traceai/openai-agents	OpenAI Agents
@traceai/fi-instrumentation-google-adk	Google ADK
@traceai/mastra	Mastra
@traceai/beeai	IBM BeeAI
@traceai/strands	AWS Strands Agents

Tools and Libraries

Package	Description	Version
@traceai/vercel	Vercel AI SDK
@traceai/guardrails	Guardrails AI
@traceai/instructor	Instructor
@traceai/portkey	Portkey
@traceai/mcp	Model Context Protocol
@traceai/fi-instrumentation-pipecat	Pipecat (Voice AI)
@traceai/fi-instrumentation-livekit	LiveKit (Real-time)

Vector Databases

Package	Description	Version
@traceai/pinecone	Pinecone
@traceai/chromadb	ChromaDB
@traceai/qdrant	Qdrant
@traceai/weaviate	Weaviate
@traceai/milvus	Milvus
@traceai/lancedb	LanceDB
@traceai/mongodb	MongoDB Atlas Vector Search
@traceai/pgvector	pgvector (PostgreSQL)
@traceai/redis	Redis Vector Search

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Java

Available via JitPack. Add the JitPack repository:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

Package	Description
traceai-java-core	Core instrumentation library

LLM Providers

Package	Description
traceai-java-openai	OpenAI
traceai-java-azure-openai	Azure OpenAI
traceai-java-anthropic	Anthropic
traceai-java-google-genai	Google Generative AI
traceai-java-cohere	Cohere
traceai-java-ollama	Ollama
traceai-java-bedrock	AWS Bedrock
traceai-java-vertexai	Google Vertex AI
traceai-java-watsonx	IBM Watsonx

Agent Frameworks

Package	Description
traceai-langchain4j	LangChain4j
traceai-spring-ai	Spring AI
traceai-spring-boot-starter	Spring Boot Auto-Configuration
traceai-java-semantic-kernel	Microsoft Semantic Kernel

Vector Databases

Package	Description
traceai-java-pinecone	Pinecone
traceai-java-qdrant	Qdrant
traceai-java-milvus	Milvus
traceai-java-weaviate	Weaviate
traceai-java-chromadb	ChromaDB
traceai-java-mongodb	MongoDB Atlas Vector Search
traceai-java-redis	Redis Vector Search
traceai-java-azure-search	Azure AI Search
traceai-java-pgvector	pgvector (PostgreSQL)
traceai-java-elasticsearch	Elasticsearch

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C#

Available on NuGet.

Package	Description	Version
fi-instrumentation-otel	Core instrumentation library

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Compatibility Matrix

Category	Framework	Python	TypeScript	Java	C#
LLM Providers	OpenAI	✅	✅	✅	✅
	Anthropic	✅	✅	✅
	AWS Bedrock	✅	✅	✅
	Google Vertex AI	✅	✅	✅
	Google Generative AI	✅	✅	✅
	Mistral AI	✅	✅
	Groq	✅	✅
	Cohere	✅	✅	✅
	Ollama	✅	✅	✅
	LiteLLM	✅
	Together AI	✅	✅
	DeepSeek	✅	✅
	Fireworks AI	✅	✅
	Cerebras	✅	✅
	HuggingFace	✅	✅
	xAI (Grok)	✅	✅
	vLLM	✅	✅
	Azure OpenAI			✅
	IBM Watsonx			✅
Agent Frameworks	LangChain	✅	✅
	LlamaIndex	✅	✅
	CrewAI	✅
	AutoGen	✅
	OpenAI Agents	✅	✅
	SmolAgents	✅
	Google ADK	✅	✅
	Agno	✅
	Pydantic AI	✅
	Claude Agent SDK	✅
	AWS Strands Agents	✅	✅
	IBM BeeAI	✅	✅
	Mastra		✅
	LangChain4j			✅
	Spring AI			✅
	Semantic Kernel			✅
Tools & Libraries	Haystack	✅
	DSPy	✅
	Guardrails AI	✅	✅
	Instructor	✅	✅
	Portkey	✅	✅
	Vercel AI SDK		✅
	MCP	✅	✅
	Pipecat	✅	✅
	LiveKit	✅	✅
Vector Databases	Pinecone	✅	✅	✅
	ChromaDB	✅	✅	✅
	Qdrant	✅	✅	✅
	Weaviate	✅	✅	✅
	Milvus	✅	✅	✅
	LanceDB	✅	✅
	MongoDB Atlas	✅	✅	✅
	pgvector	✅	✅	✅
	Redis	✅	✅	✅
	Azure AI Search			✅
	Elasticsearch			✅

Legend: ✅ Supported | blank = not yet available

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Architecture

traceAI is built on top of OpenTelemetry and follows standard OTel instrumentation patterns:

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)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://api.futureagi.com/tracer/v1/traces",
    headers={
        "X-API-KEY": "your-api-key",
        "X-SECRET-KEY": "your-secret-key"
    }
)
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";

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

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!,
  },
});

provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();

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.

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Roadmap

• Go language support
• Sampling strategies for high-volume production environments
• Continuous semantic convention updates as the OTel GenAI spec evolves
• Evaluation integration connecting traces to quality measurement pipelines
• Expanded agent framework coverage

See our ROADMAP.md for the full roadmap.

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Contributing

We welcome contributions! Read our Contributing Guide for details.

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? Open an issue with a minimal reproduction.

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Contributors

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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
Contributing Guide	How to contribute to traceAI
Slack	Join our community
Issues	Report bugs or request features

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Connect With Us

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