Open-source dashboard for your domain experts to improve your AI Agents
Hey HN! We built EvalKit, a library you embed to capture agent actions and a UI where domain experts give feedback, evaluate and improve AI agents. We experienced, in large agentic systems, prompt-engineering or auto-prompt improvement tool can get accuracy from 0 to 50% but for increasing accuracy to 100% we had to work with domain experts. Example -> In a law ai agent, lawyers are needed because law is complex and lawyers have a deeper context compared to non-lawyers. Other evaluation tools in the market focus on the experience of the developer and we are focusing on making as easy as…
Open-source dashboard for your domain experts to improve your AI Agents launched on May 19, 2025 with 5 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Contribute to getevalkit/evalkit development by creating an account on GitHub.
EvalKit is an open-source dashboard for your domain experts to improve your AI Agents. from evalkit import EvalKit eval_kit = EvalKit () @ eval_kit . trace_interaction ( agent_name = "agentX" , prompt_template_arg_name = "prompt_template" ) def agent_function ( prompt_template ): return "output" with eval_kit . trace_task ( task_name = "My Task" ) as task_ctx : agent_function () pass Getting Started You would need to setup application to use it, we plan to create docker container soon for seamless build. Currently all LLM code runs on vertex AI so you would need to configure it using below steps. cd server && uv sync uv run main.py # backend now runs on http://localhost:8000 Frontend cd…from github.com
In the maker’s words, at launch
Hey HN! We built EvalKit, a library you embed to capture agent actions and a UI where domain experts give feedback, evaluate and improve AI agents. We experienced, in large agentic systems, prompt-engineering or auto-prompt improvement tool can get accuracy from 0 to 50% but for increasing accuracy to 100% we had to…
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