Agenta

Open-source platform for prompt playgrounds, evaluation and tracing

AI Prompt ToolsFree planOverseasβ˜…β˜…β˜…β˜†β˜† 3.0

What is Agenta?

Agenta is an open-source platform for the parts of LLM development that usually get improvised: prompt editing, evaluation and tracing. You define a prompt or a chain in the browser playground, test it against a set of inputs, compare variants on the same data, and then expose the chosen version through an API. Because it self-hosts, prompts and evaluation datasets stay inside your own infrastructure, and teams that need to keep customer text away from third parties can still get a usable workflow. It is aimed at engineers who want structure around experiments without adopting a fully managed vendor.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Web playground for editing prompts and chained variants
  • Evaluation runs that compare variants on the same test set
  • Tracing of requests through an application for debugging
  • Self-hosted deployment with Docker and a managed option
  • Prompt versioning exposed through an application API
  • Human annotation and review of generated outputs

Pros & cons

Strengths

  • Open source, so evaluation data stays in your own infrastructure
  • Playground and evaluation sit together, shortening the loop
  • Self-hosting avoids per-seat pricing as the team grows

Watch out for

  • Self-hosted installs carry upgrade and maintenance work
  • Smaller ecosystem and fewer integrations than hosted rivals
  • Documentation trails the release pace in places

Best for & use cases

llm teams, prompt experimentation, self-hosted evaluation and internal tooling

If you're comparing similar products, check the alternatives below, or browse all tools in the AI Prompt Tools category.

FAQ

Do I need to host Agenta myself?

You can, and many teams do because it keeps prompts and test data in their own environment. A managed cloud option exists for teams that would rather not run the infrastructure.

How does evaluation work?

You build a dataset of inputs, run several prompt or chain variants across it, and compare the outputs side by side. Scoring can be manual or automated depending on what you are checking.

Is it only for prompts?

No. It also traces requests through an application and supports chained variants, so it covers the pipeline around the prompt rather than a single text box.