Ankush Gola says smithtune pulls, selects, and processes trajectories from LangSmith, trains on FireworksAI_HQ and baseten, and evaluates the results with LangSmith. The code is in the langchain-ai GitHub repository.
LangChain says LangSmith Custom Apps can create interfaces from agent data with a prompt, including views for labeling runs, comparing experiments, and reviewing traces.
Fireworks says it is a launch training partner for LangChain’s LangSmith Fine-Tuning and smithtune, describing a one-CLI path from LangSmith traces to managed SFT on Fireworks, then evaluation and deployment, without separate training infrastructure.
Managed Deep Agents 0.8 adds user memories with control over where they are stored. Nathan Drezner said memories created in private contexts are never shared, while Context Hub lets developers inspect general agent memories and keeps user memories private.
Managed Deep Agents 0.8 adds user-owned credentials and memory, HTTP channels for internal tools, native slackhq file transfers, and built-in web search via p0.
LangChain announced LangSmith Engine v2 at Interrupt. It identifies agent issues before production, tests proposed fixes before showing them to users, and surfaces inefficient agent work along with cost and latency trends.
LangChain announced LangSmith Engine v2 at Interrupt and says it can proactively find agent issues and resolve them before users see them.
A schedule file in schedules/ that includes a cron expression and a prompt can be deployed so a Managed Deep Agent runs on its own.
The session covers the key elements of online evals and how teams can use Tuned Evaluators to analyze agent interactions and attach feedback to production traces.
LangSmith provides visibility into every step for supported decision models, including Jev and SemIf.
LangChain says Managed Deep Agents can build these agents with a few lines of code. Its example, Patch, is tagged in a Slack thread, given a feature description, and opens a pull request that includes the diff.
LangChain quoted nathan drezner and linked official docs for Typesafe AI’s Jev middleware, which can handle model selection for LangChain managed deep agents after users define routes.
Nathan Drezner says TypeSafe AI's Jev middleware can be used with LangChain managed deep agents for model routing. Users define routes, and Jev handles model selection.
LangChain said it tested Jev against GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6 as agent-eval judges, reporting a human-reviewer match on every call, up to 913x lower variance, 0.44s per call, and a $0.34 full-run cost versus $28.17 with Claude Sonnet 4.6.
LangChain says Jev-as-a-judge is now available in LangSmith. The company claims it can score every production trace, check more criteria per trace without rising cost, and catch safety or security issues fast enough to trigger an automated response.
LangChain said it is making SemIf, an open-source decision model, free for the next week in LangSmith LLM Gateway. In the quoted post, Jacob Lee also said SemIf is being served free for the next week and is compatible with the TypeSafe SDK.
LangChain highlighted a sub-10-minute video by Sydney Runkle breaking down @typesafeai’s new Jev model and how to plug it into agents.
