Imagine you've built a perfect agent for your blog writing team. Now your social media team wants to use it but they need different prompts, different models and different tools. But modifying your underlying code for each use case is not only time consuming but also prone to errors.This creates two distinct problems. Developers get stuck in constant code changing cycles that slow down iteration while business teams can't experiment without engineering support. That's where Lang graph assistants come in.Tod ...

[Music] My name is Zach. I'm a Genai software engineer at Prosper Marketplace. We're a financial services company that connects people with the financial solutions they need.Everything from personal loans to credit cards, HELOC, and so on. The goal of the AI team is to free up time as much as possible by automating very manual and tedious work and hopefully cut down on costs at the same time, which involves building a very flexible and modular AI agents platform. One of the bigger problems we tackled recent ...

Gemini Model Capabilities - Gemini 2.5% Pro and Flash models achieved GA (General Availability) on June 17 [11] - Gemini models feature native reasoning, multimodal processing, million-token context window, native tools (including search), and native video understanding [12] - Gemini models support text-to-speech capabilities with multiple speakers [12] Langraph Integration & Researcher Tool - Langraph Studio facilitates the orchestration of the researcher tool, allowing visualization of inputs and outputs of each node [5] - The researcher tool utilizes Gemini's native search tool, video understanding for YouTube URLs, and text-to-speech capabilities to generate reports and podcasts [2][18] - The researcher tool simplifies research by combining web search and video analysis, and offers alternative ingestion methods like podcast generation [4][5] - The researcher tool can be easily customized and integrated into applications via API [9] Performance & Benchmarks - Gemini 2.5% series models demonstrate state-of-the-art performance on various benchmarks, including LM Marine, excelling in tasks like text, webdev, vision, and search [14] - Gemini 2.5% Pro model was rated the best in generating an SVG image of a pelican riding a bicycle, outperforming other models in a benchmark comparison [16][17] Development & Implementation - The deep researcher template using Langraph serves as a foundation, modified to incorporate native video understanding and text-to-speech [18] - Setting up the researcher tool involves cloning the repository, creating an ENV file with a Gemini API key, and running Langraph Studio locally [19] - The code structure includes nodes for search, optional video analysis, report creation, and podcast creation, all reflected visually in Langraph Studio [20]

Core Functionality & Architecture - The application provides a credit system for charging users based on LLM usage, allowing them to purchase Stripe subscriptions for more credits [1][2] - The codebase consists of five key areas: authentication, payments, credit infrastructure, chat agent, and user interface [3][4][5] - Authentication is implemented using Superbase, with JWT tokens protecting the Langraph agent via middleware [3][4][8] - Payments infrastructure is handled by Stripe, enabling users to buy subscriptions and receive credits [4][12][13] - Credit infrastructure, stored in Superbase, includes utility functions for adding, refreshing, and removing credits [4][14][15] Technical Implementation - The project is a monorepo with an 'agents' application (Langraph agent, middleware) and a 'web' application (UI, Stripe, Superbase, credits) [5][6] - Langraph middleware verifies Superbase JWT tokens to grant users permissions to interact with the graph [4][8] - Stripe integration includes utility functions for creating sessions, getting subscriptions, and managing user credits [12] - A webhook route processes Stripe events (subscription creation, updates, deletion) to update user data in Superbase [13] - The application uses providers to manage the state of authentication and credits on the client side [10][14] Data Flow & Security - Superbase JWT tokens are passed through the application to the Langraph middleware for authentication [4][18][19] - The thread and stream providers are key components for passing the JWT token to the Langraph client [18][19] - User credits are updated optimistically on the UI and finalized in the Superbase database [15][16] Resources & Documentation - The repository includes a credit system file outlining key files for the credit and authentication systems [20] - A detailed readme provides step-by-step instructions for setting up and running the application [21]

[Music] My name is Cena Medina. I'm a lead data scientist and AI engineer at City of Hope National Medical Center. City of Hope is a national medical center specialized in cancer care and diabetes.We've developed Hope LLM, an agentic application built with Langin and Langraph. Our goal was to transform how physicians interact with patient data by automating patient journey summarization. Physicians frequently face time constraints when reviewing extensive documentation, including PDFs, imaging reports, and ...

AI Platform & Business Model - Poe平台提供用户通过订阅访问多种语言模型和代理的能力 [1] - Poe的Bot创建者每年收入数百万美元 (millions) [1] - 推理模型正在推动增长 [1] Consumer AI Usage - 揭示了消费者在使用AI方面的惊人模式 [1] AI Development Challenges - 在快速变化的AI领域中构建产品面临独特的挑战 [1] - 规划周期已从数年缩短至仅两个月 [1]

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improv ...

Automations & Online Evaluations Overview - Automations are configurable rules applied to every trace in production applications [1] - Online evaluations, a type of automation, measure application output metrics on live user interactions [1][5] Automation Configuration - Automations can be configured with a name, filters to define which runs to execute on, and a sampling rate [3] - Sampling rate allows tuning of automation execution on a subset of traces, especially for expensive evaluations [3][4] - Actions include adding traces to annotation queues or datasets, applying evaluators, and adding feedback [4] Online Evaluations - Online evaluations use LLM as a judge or custom code evaluators on traces without reference outputs [5] - Feedback added by online evaluators is visible in the feedback column and individual trace views [11][12] Additional Automation Features - Automations can trigger webhooks for workflows like creating Jira tickets for trace errors [6] - PagerDuty can be configured for alerting flows [6] - Automations can extend the default 14-day trace retention period by adding feedback or adding traces to a dataset [7] Example Use Case: Simplicity Evaluation - An online evaluator assesses if a chatbot's answer is simple enough for children, scoring from 1 to 10 [7][8] - A second automation samples traces with high simplicity scores and adds them to an annotation queue for review [9] - Rules that add feedback to a trace will send the trace back through other automations [10]

Resources & Tools - Eli5 代码库位于 GitHub，方便开发者访问和贡献 [1] - LangSmith 提供免费试用，助力用户快速上手 [1] - LangSmith 提供完善的文档，方便用户查阅和学习 [1] LangChain Ecosystem - LangChain 鼓励用户了解 LangSmith，网址为 langchain.com [1] - LangChain 通过 YouTube 等社交媒体渠道推广 LangSmith [1] - LangSmith 的网址为 smith.langchain.com [1]

Resources & Tools - Eli5 代码库位于 GitHub：https://github.com/xuro-langchain/eli5 [1] - LangSmith 提供免费试用：https://smith.langchain.com/ [1] - LangSmith 文档地址：https://docs.smith.langchain.com/ [1] LangChain Platform - LangSmith 平台详情：https://www.langchain.com/langsmith/?utm_medium=social&utm_source=youtube&utm_campaign=q2-2025_onboarding-videos_co [1]