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LangSmith

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Headlines · 4

  1. Orange Business builds Live Intelligence agent platform on LangChain and LangSmith

    A LangChain blog case study says Live Intelligence, the flagship AI platform of Orange Group's B2B arm Orange Business, is deployed across 120,000 Orange employees and has been adopted by more than 150 customers in France and Europe. The platform is split into Live Intelligence Studio for AI engineers, Portal for business users and Console for administrators, plus Agent and MCP registries for discovering, reusing and governing agents and external tools. Orange Innovation evaluated roughly 20 cloud and AI vendors before choosing LangChain for model neutrality and portability, and uses LangSmith for a three-layer evaluation stack covering RAG quality, agent behavior and production trace monitoring, as well as pre-release regression tests.

    LangChain Blog · 🔥 0
  2. LangSmith adds AI Search to find agent traces by described behavior

    LangChain introduced AI Search in LangSmith, letting users describe a behavior in plain language — such as whether the user got frustrated or PII leaked — and get back the sessions where it happened. Per LangChain, structured filters narrow the candidates first, then a fast decision model reads each normalized "trajectory" and streams matches as they are found; SmithDB, the vendor's database built for trace data, builds those trajectories on arrival and normalizes formats from the OpenAI and Anthropic SDKs, LangChain, LangGraph, the Vercel AI SDK and OpenTelemetry, while removing repeated conversation history to cut context bloat. AI Search is in public beta on LangSmith Cloud in the GCP US, EU and APAC regions at no additional cost, and org admins must enable it in settings.

    LangChain Blog · 🔥 0
  3. LangChain adds Slack emoji reactions API to Managed Deep Agents

    A LangChain blog post says Managed Deep Agents v0.9 adds a reactions attribute for Slack channels that accepts either an emoji string or a callable returning one, so agents can show acknowledgement and loading states during long-running tasks. Developers can use simple heuristics or decision models such as Jev to pick an emoji, with a confidence threshold falling back to a default reaction. The post frames this as a way to show users how an agent interpreted a task and says LangChain uses similar patterns in its internal agents.

    LangChain Blog · 🔥 0
  4. Snyk moves internal support agent Snyk Assist into its core product

    A LangChain Blog post describes how Snyk turned its internal customer-support agent into a customer-facing product, Snyk Assist, built on LangChain and LangGraph with observability handled in LangSmith. The agent answers questions in plain language and can look up open issues, check a package for known vulnerabilities, open a support case, or log a feature request. According to the post, it ran internally for about a year, launched to customers in the support portal in April 2026, and on 1 September 2026 moved into the core Snyk product as a panel available to every paying customer. Snyk reports more than 60,000 queries, over 500 customer accounts, and more than 85% of sessions resolved without a support ticket since April 2026, all self-reported figures.

    LangChain Blog · 🔥 0

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