What is traceAI in simple terms?

traceAI is an open-source tool that adds tracing capabilities to AI applications, helping you see how AI decisions are made by capturing every step in the workflow, from prompts to completions, using standard OpenTelemetry.

How is traceAI different from traditional APM or logging tools?

Traditional APM tools focus on HTTP and infrastructure metrics, while traceAI is designed specifically for GenAI workflows, capturing AI-specific signals like prompts, agent decisions, and semantic conventions that are not covered by general-purpose tools.

Which AI frameworks and LLM providers does traceAI support?

traceAI supports over 35 frameworks and providers including OpenAI, Anthropic, LangChain, CrewAI, DSPy, and more, with ongoing community contributions to expand compatibility.

traceAI

Open-source LLM tracing that speaks GenAI, not HTTP.

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Listed website: github.com
Published in directory: Apr 2, 2026
Last record update: Apr 2, 2026

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Overview
5 key features
4 use cases

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What is traceAI

traceAI is an open-source observability tool specifically designed for AI applications, providing standardized tracing for Large Language Model (LLM) workflows by integrating with OpenTelemetry. It captures detailed insights into prompts, completions, tokens, and agent decisions, enabling seamless debugging, performance monitoring, and optimization across various AI frameworks and providers without requiring new vendor dependencies.

Key Features

OpenTelemetry-native integration for seamless observability with existing stacks.

Captures AI-specific data such as prompts, completions, tokens, retrievals, and agent decisions.

Supports over 35 AI frameworks including OpenAI, Anthropic, LangChain, CrewAI, and DSPy.

Easy instrumentation with just two lines of code and full parity across Python, TypeScript, Java, and C#.

Routes traces to any OpenTelemetry-compatible backend like Datadog, Grafana, or Jaeger.

Use Cases

AI developers use traceAI to debug failures in agentic workflows and understand decision processes in real-time.
Data scientists employ it to measure token usage, latency, and performance across different LLM models for cost optimization.
DevOps teams integrate it into existing monitoring stacks to visualize interactions between multiple AI frameworks and pipelines.
Startups leverage it to quickly instrument and scale AI applications while avoiding vendor lock-in and maintaining full control.

Why do startups need this tool?

Startups often operate with limited resources and need to iterate quickly on AI applications; traceAI helps by providing an open-source, easy-to-integrate solution for monitoring and debugging, reducing time-to-insight and optimizing costs without vendor lock-in. It enables startups to maintain full control over their observability stack while scaling efficiently with their growing AI workloads.