@LangChain: How to run agent evals with @harborframework and LangSmith sandboxes, full traces included.
Summary
A guide on running agent evaluations using Harbor framework and LangSmith sandboxes with full trace support.
View Cached Full Text
Cached at: 07/06/26, 04:09 PM
How to run agent evals with @harborframework and LangSmith sandboxes, full traces included. https://t.co/DtE8LhDCQl
Similar Articles
@LangChain: .@harborframework can now integrate directly with Deep Agents, LangSmith Sandboxes, and LangSmith Observability. You ne…
Harbor Framework now integrates with LangChain's Deep Agents, LangSmith Sandboxes, and Observability, enabling running agents in isolated, reproducible environments for deterministic testing.
@LangChain: .@AdamRLucek on how we use traces to build evals for production agents.
Adam Łucek discusses how LangChain uses trace data to build evaluations for production agents.
Towards Automating Eval Engineering (5 minute read)
LangChain released an Eval Engineering Skill that automatically generates executable Harbor evaluations by mapping agent repositories and production traces, with an iterative user interview process to refine evals.
@Kimberl9633: LangChain just released two major updates today, directly pushing agent reliability forward: a unified evaluation stack, and a code execution solution that doesn't require a full sandbox. First, evaluation. How to test long-running, stateful agents? The combination of Harbor + LangSmith provides…
LangChain has released a unified evaluation stack (Harbor + LangSmith) and an in-process code execution solution based on WASM+QuickJS, aimed at improving the evaluation reliability and execution safety of AI agents.
Give your agent its own computer (7 minute read)
LangChain introduces LangSmith Sandboxes, providing each AI agent with its own isolated computer environment for safe code execution, addressing security risks of running untrusted code in containers or locally.