LangChain open-sources eval-engineering skill: building agent eval environments from traces and human feedback
BraceSproul · x · 2026-08-25
The LangChain team (Vtrivedy, Harrison Chase, Nick Hollon) published a practical guide plus an updated eval-engineering skill on using real-world data (traces + human feedback) to build synthetic environments and evals for measuring, harness-engineering, and post-training agents.
Main components:
- World Knowledge gathering of the systems the agent interacts with;
- Specs to coordinate what gets built — World Spec & Task Spec;
- Human-Agent collaboration to iteratively edit a Spec and infuse human feedback (including how much a coding agent should prompt for feedback);
- An execution pipeline generating environment + task from the agreed Task Spec;
- Fixing task/environment design flaws by actually running agents and mining verifier scores and traces.
Install with npx skills add https://github.com/langchain-ai/langchain-skills --skill eval-engineering (1.7K installs).
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