Traceloop

Traceloop is an LLM reliability and observability platform built on open standards, anchored by its widely used open-source project OpenLLMetry, a set of extensions on top of OpenTelemetry that gives developers complete, non-intrusive visibility into large language model applications.

With OpenLLMetry, teams can trace and monitor the execution of an LLM app, including prompts, completions, latency, costs, and the behavior of chains and agents, using familiar OpenTelemetry tooling so the data flows into observability backends they may already use.

It supports more than twenty model providers such as OpenAI, Anthropic, Gemini, Bedrock, and Ollama, along with vector databases like Pinecone and Chroma, and frameworks including LangChain, LlamaIndex, and CrewAI, with SDKs for Python, TypeScript, Go, and Ruby.

The Traceloop platform layers monitoring, debugging, and reliability features on top of this open foundation. Typical users are developers and AI engineers building production GenAI and agentic applications who need to debug non-deterministic behavior, control costs, and ensure reliability.

Pros include being open source and OpenTelemetry-native so it avoids vendor lock-in, broad provider and framework coverage, and non-intrusive instrumentation. Cons include that meaningful insight requires instrumenting your application and understanding tracing concepts, and the advanced hosted platform features go beyond the free open-source library.

Pricing is freemium, with the open-source library free and paid platform tiers. Pricing changes often, so check the official site for current plans.

Traceloop's core capabilities include OpenTelemetry-based LLM tracing, Open-source OpenLLMetry instrumentation, Support for 20+ model providers, Vector database and framework coverage and SDKs for Python, TypeScript, Go, and Ruby.

OpenTelemetry-based LLM tracing is built in, Open-source OpenLLMetry instrumentation is built in, Support for 20+ model providers is built in, Vector database and framework coverage is built in, so you get a rounded toolkit rather than a single trick.

Each feature is designed to take the manual effort out of the task and help you reach a usable result faster, which is what makes Traceloop worth a place on your shortlist.

On the plus side, users consistently highlight Open source and OpenTelemetry-native, avoiding lock-in, Broad provider and framework coverage and Non-intrusive instrumentation as the reasons they keep using Traceloop.

It isn't perfect, though — Requires instrumentation and tracing knowledge and Advanced platform features go beyond the free library are the trade-offs people most often mention, so weigh those against your own priorities before you commit.

As with any AI tool, the output still benefits from a quick human review, but Traceloop gets you most of the way there with far less effort.

Traceloop runs on a freemium pricing model, so you can start for free and only pay once you outgrow the free tier — handy for testing it on a real task before spending anything.

AI-tool pricing changes often, so always check the current plans, seats and add-ons on the official site for the latest details before you buy. Who is Traceloop for? It's best suited for open-source observability for llm and genai apps.

Whether you're a beginner trying this kind of AI tool for the first time or a professional who'll use it every day, it's a credible option to consider.

If you're still deciding, compare Traceloop against the alternatives and the head-to-head comparisons linked below — looking at features, pricing and real user ratings side by side is the fastest way to find the right fit for your workflow and budget.