Open-source LLM tracing compatible with generative AI, not just HTTP.
traceAI is an open-source observability framework for AI applications, built on OpenTelemetry. It enables tracing of every LLM call, prompt, token, retrieval step, and agent decision. Traces are sent to any OTel-compatible backend (Datadog, Grafana, Jaeger, Future AGI, etc.), eliminating the need for new vendors or dashboards. It offers drop-in instrumentation for 50+ AI frameworks across 4 languages (Python, TypeScript, Java, C#) with semantic conventions for LLMs, agents, tools, and retrieval. It supports streaming, error handling, and low-overhead tracing.
For solopreneurs managing AI SaaS, traceAI enables detailed tracking of token usage across LLM calls. Example: A solopreneur can identify which specific agent actions consume the most tokens, allowing them to refine prompts or switch models to reduce operational costs and extend their runway.
For AI application developers, traceAI enables deep inspection of intricate agent decision-making processes. Example: A developer can trace the step-by-step execution of a CrewAI agent, pinpointing exactly where an agent made a suboptimal decision or failed to utilize a tool correctly, leading to faster bug resolution.
For data scientists working with RAG pipelines, traceAI enables granular monitoring of retrieval steps and LLM interactions. Example: A data scientist can visualize the latency and effectiveness of vector database queries within a RAG workflow, identifying slow retrievals or irrelevant document fetches that impact overall response quality.
For AI team leads, traceAI provides a unified view of LLM interactions across a team's applications. Example: A team lead can monitor prompt and completion patterns from multiple developers using different LLM frameworks, ensuring adherence to established GenAI semantic conventions and identifying potential drift in model behavior.
Yes, traceAI is an open-source project licensed under the MIT license, meaning it is free to use. The project is available on GitHub and its core instrumentation libraries can be installed via package managers like pip, npm, and NuGet.
As an open-source tool, traceAI itself does not have a direct cost. However, you will incur costs associated with the OpenTelemetry-compatible backend you choose to send your trace data to, such as Datadog, Grafana, or Jaeger.
traceAI supports multiple programming languages including Python, TypeScript, Java, and C#. This broad language support allows it to be integrated into a wide range of AI applications and frameworks.
Installation varies by language. For Python, you can use pip install traceai-openai (or other framework-specific packages). For TypeScript, use npm install @traceai/openai @traceai/fi-core. Java users can add the dependency via JitPack, and C# users can use `dotnet add package fi-instrumentation-otel`.
Alternatives to traceAI include commercial observability platforms that offer AI tracing capabilities, such as Honeycomb, Lightstep, or Datadog's own AI monitoring features. Open-source alternatives might involve custom OpenTelemetry instrumentation or other specialized tracing libraries.
traceAI is an open-source library that sends data to your chosen backend. Its security and GDPR compliance depend on how you configure and manage your OpenTelemetry backend and data handling practices. The tool itself does not store data.
traceAI captures detailed telemetry for AI applications, including LLM calls, prompts, responses, token usage, retrieval steps, and agent decisions. It follows GenAI semantic conventions and routes this data to any OpenTelemetry-compatible backend.
We're still verifying the official pricing for traceAI. In the meantime, the most up-to-date plans and prices are available directly on the publisher's website.
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