Exploring Prompt Optimization By Krish Maniar and William Fu-Hinthorn If you are interested in beta-testing more prompt optimization techniques, fill out interest form here. When we write prompts,
Introducing Pytest and Vitest integrations for LangSmith Evaluations Introducing a new way to run evals using LangSmith’s Pytest and Vitest/Jest integrations.
How Captide is redefining equity research with agentic workflows running on LangGraph Platform Captide’s platform transforms how investment research teams work with financial data. By automating the extraction of insights and metrics from regulatory filings and investor
How Minimal built a multi-agent customer support system with LangGraph & LangSmith In the thriving world of e-commerce, where customer satisfaction can make or break a brand, Minimal is leveraging the LangChain ecosystem to transform how support
Acxiom's use of LangSmith for enhanced audience segmentation See how Acxiom debugged their agent application with LangSmith and built a scalable solution for their user base, complete with long-term memory, dynamic updates, and attribute-specific search.
Structured Report Generation Blueprint with NVIDIA AI LLMs are reaching a point of maturity where they are sufficiently capable of powering sophisticated AI agents. The agents they power are not free-range, pseudo-AGI-like
Top 5 LangGraph Agents in Production 2024 2024 was the year that agents started to work in production. Not the wide-ranging, fully autonomous agents that people imagined with AutoGPT. But more vertical,
LangChain State of AI 2024 Report Dive into LangSmith product usage patterns that show how the AI ecosystem and the way people are building LLM apps is evolving.
How AppFolio transformed property management workflows with Realm-X, built using LangGraph and LangSmith See how AppFolio's AI-powered copilot Realm-X has saved property managers over 10 hours per week. Learn how they improved Realm-X's performance 2x using LangSmith and built an agent architecture with LangGraph.
Making it easier to build human-in-the-loop agents with interrupt While agents can be powerful, they are not perfect. This often makes it important to keep the human “in the loop” when building agents. For
Command: A new tool for building multi-agent architectures in LangGraph Learn about Command, a new tool in LangGraph that helps facilitate multi-agent communication.
Introducing OpenTelemetry support for LangSmith LangSmith now supports OpenTelemetry for distributed tracing and observability.