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How to run agent evals with Harbor Framework and LangSmith sandboxes, full traces included.

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LangChain

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11,308 görüntüleme • 1 ay önce •via X (Twitter)

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Introducing LangSmith Fleet. Agents for every team. → Build agents with natural language → Share and control who can edit, run, or clone each agent → Manage authentication with agent identity → Approve actions with human-in-the-loop → Track and audit actions with tracing in LangSmith Observability Try Fleet:

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Today, we’re shipping new ways to observe, analyze, and debug agents with LangSmith: • Polly: an AI assistant for AI engineering that helps you understand traces, threads, and improve prompts • LangSmith Fetch: a CLI for pulling trace & thread data straight into your terminal or coding agent Agents are running longer and getting more complex, which demands new debugging workflows beyond simple LLM apps. We wrote a blog on the trends behind this shift— and why tools like Polly and LangSmith Fetch are needed. Shipping reliable agents requires full visibility into agent behavior, with tooling that helps you reason over that data in the UI, the terminal, or alongside coding agents. 📔Learn more about Polly: 📔Learn more about LangSmith Fetch: 📔How to observe deep agents: 📽️ Polly video tutorial: 📽️ LangSmith Fetch video tutorial:
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🚀Announcing LangGraph Studio: The first agent IDE LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith so you can collaborate with teammates to debug failure modes LangGraph Studio is available for free to all LangSmith users on any plan tier during its early development. Read more about it here: Watch a YouTube walkthrough: Try out LangGraph Studio for free here: Sign up for a LangSmith account:

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Stop manually triaging agent failures. Let LangSmith Engine fix it.
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Stop manually triaging agent failures. Let LangSmith Engine fix it.

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🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

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🔍New in LangGraph Studio: Trace mode View your LangSmith traces in real time right inside Studio. Annotate runs, and add them to datasets or annotation queues, bringing the power of LangSmith tracing directly into your workflow. Debug faster and dig deeper, without any of the context switching. Try it today!
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🔍New in LangGraph Studio: Trace mode View your LangSmith traces in real time right inside Studio. Annotate runs, and add them to datasets or annotation queues, bringing the power of LangSmith tracing directly into your workflow. Debug faster and dig deeper, without any of the context switching. Try it today!

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👀 How to Solve the #1 AI Agent Production Blocker! LangChain CEO Harrison Chase reveals the approach: Eval-driven development using three types of evals throughout your entire development lifecycle. Watch the full video here:
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👀 How to Solve the #1 AI Agent Production Blocker! LangChain CEO Harrison Chase reveals the approach: Eval-driven development using three types of evals throughout your entire development lifecycle. Watch the full video here:

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