Traceloop

TLDR: Traceloop helps teams test, troubleshoot, and improve AI agents with automated performance evaluations, ensuring reliability before and after every release. We bring the same rigor to AI that developers expect from the rest of their stack.

Hi everyone, we’re Nir and Gal, co-founders of Traceloop, and we're part of the Y Combinator Winter 2023 batch.

🚨 Problem

Building and deploying AI agents today often feels like guesswork. Public model benchmarks don’t predict how well an AI will perform in real-world applications, and businesses are stuck in a cycle of trial and error, tweaking prompts and manually testing. But as AI agents become more complex and critical, these outdated methods just don't cut it. When AI misfires — hallucinating, taking wrong actions, or producing unpredictable outputs — users disengage instead of filing bug reports.

At Traceloop, we believe prompt engineering shouldn't be a guessing game. We need to bring the same quality assurance processes to AI that we’ve come to expect from software engineering.

🧠 Solution

Traceloop provides evaluations and real-time monitoring for AI agents, reducing the guesswork and ensuring performance is consistent before reaching end users. Our open-source foundation, OpenLLMetry and Hub, collects LLM interactions at scale and our our commercial platform is now helping businesses:

• Train evaluators to catch hallucinations and detect quality issues.
• Experiment with different models to choose the right one for your use case.
• Monitor and Evaluate AI agent performance in real-time or during CI.

We help teams reduce risk, accelerate iteration, and ensure that their agents behave as expected in production environments.

🎯 Asks

⭐ Star us OpenLLMetry and Hub on GitHub and follow us on Twitter!

If you're building LLM apps, we'd love to show you how Traceloop can help improve your agent’s performance and reliability. Reach out and let’s explore how we can help.

If you know teams working with AI, please forward this to them!