Pinned
Stop manually triaging agent failures.
Let LangSmith Engine fix it.
00:00
LangChain
11.1K posts
LangChain
@LangChain
Joined November 2022
- Rubrics for Deep Agents, programmatic subagents, and everything new in LangSmith ā¤µļø
- Fine-tuned models match frontier performance In our research with @FireworksAI_HQ, a fine-tuned @Alibaba_Qwen outperformed all model sizes. Theyāre also cheaper to run at scale 10-100x depending on trace volume and model choice As trace volumes grow, so will cost-savings on
We partnered with @FireworksAI_HQ to fine-tune an @Alibaba_Qwen judge model to detect āPerceived Errorā from user interactions. See our findings ā¤µļø - LangChain repostedHow do you construct, compact, and query a full-text search index when all your durable data lives in object storage? And where every GET costs 50ā100ms? @ankush_gola11 + team on how we built and run SmithDB's inverted index in production.
- LangChain reposted
Technical blog on how we built SmithDB (our database purpose built for agent traces)How do you construct, compact, and query a full-text search index when all your durable data lives in object storage? And where every GET costs 50ā100ms? @ankush_gola11 + team on how we built and run SmithDB's inverted index in production. langchain.com/blog/full-textā¦ - LangChain reposted
if you get 1% better every day then you're 37x better in a year (from @JamesClear's atomic habits book) my theory is you can do this for agents if you build a good continual learning loop which is what i'm working on at @LangChain with deepagents
- LangChain reposted
Stoked to be speaking at @aiDotEngineer next week. Come catch my session at 11.40am PT. Will be doing a deep dive into agent memory - 'sleep-time compute', 'dreaming' and the critical role traces play in the entire processIf you're headed to @aiDotEngineer World's Fair next week, don't miss @jakebroekhuizen and @Vtrivedy10's stage sessions.
- If you're headed to @aiDotEngineer World's Fair next week, don't miss @jakebroekhuizen and @Vtrivedy10's stage sessions.
excited to be speaking at @aiDotEngineer World Fair next week on Improving Agents, Continual Learning, and why we think a large part of it is...Data Mining! Trace Mining is how we understand agent behavior at scale so we can: - build evals/environments to hill-climb - gather - LangChain reposted
Supporting sub-second full-text search on object storage is hard, especially when dealing with large agent observability workloads. Here is part 2 of our blog post that outlines how we accomplished this in SmithDB!How do you construct, compact, and query a full-text search index when all your durable data lives in object storage? And where every GET costs 50ā100ms? @ankush_gola11 + team on how we built and run SmithDB's inverted index in production. langchain.com/blog/full-textā¦ - Time to cook.Agents are easy to demo locally. The hard part is shipping them inside a real app. We published a deployment cookbook for @LangChain agents: full-stack examples with streaming UI, subagents, thread history, and production persistence notes across common JS frameworks š š§µš
- How do you construct, compact, and query a full-text search index when all your durable data lives in object storage? And where every GET costs 50ā100ms? @ankush_gola11 + team on how we built and run SmithDB's inverted index in production.Missed Part 1? Hereās how we built a custom inverted index from scratch for SmithDB.
- LangChain reposted
Itās been great working with LangSmith over the past year as weāve built our agentic platform to support hundreds of millions of clinical conversations annually. Tracing and evals are at the core of the improvement flywheel.LangSmith for Startups Spotlight: @AbridgeHQ ā Transforms patient-clinician conversations into contextually aware, clinically useful, and billable AI-generated notes. ā Deployed across 250 of the largest health systems in the country. ā Uses LangSmith as its core evals
00:00 - LangSmith for Startups Spotlight: @AbridgeHQ ā Transforms patient-clinician conversations into contextually aware, clinically useful, and billable AI-generated notes. ā Deployed across 250 of the largest health systems in the country. ā Uses LangSmith as its core evals00:00
