harbor

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.

The speaker discusses the importance of evaluating and improving coding agents, highlighting LangSmith's integration with Harbor to provide a unified stack for running, tracing, and improving agent evaluations in isolated environments.

Harbor Framework now integrates with LangChain's Deep Agents, LangSmith Sandboxes, and Observability, enabling running agents in isolated, reproducible environments for deterministic testing.

Harbor v0.4.19 adds the ability to launch local agentic coding tools with local inference backends, integrating vllm, sglang, and llama.cpp, and includes a built-in LLM gateway for tool injection like web search.

This article describes a multi-agent architecture running at scale, using LangGraph, CrewAI, and Harbor to handle goal agents, task coordination, and secure access with tracing.

An analysis of why RL for coding tasks is gaining traction due to verifiable rewards, and why the emerging framework Harbor addresses the bottleneck of environment complexity in RL training.