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 ...

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]

LangChain Agent Evaluation - LangChain 降低了 Agent 评估的门槛，使得非开发者也能轻松进行 [1] - Langraph Studio 新增了快速评估 Langraph Agent 的功能 [3] - 用户可以在 Langraph Studio 中选择数据集并启动评估实验 [3][4] - 评估结果可在 Langsmith 中查看，包括模型输出和评估分数 [5] Evaluation Importance and Accessibility - 评估对于构建有效的 Agent 至关重要 [7] - 传统评估对开发者有较高要求，需要掌握 SDK、Piest 和 Evaluate API 等 [7] - LangChain 旨在提供一种无需代码的方式，让任何人都能评估 Langraph Agent [8] - 非技术用户可以基于直觉评估模型选择和提示词等 [9] Configuration and Customization - 用户可以在 Studio 界面中轻松切换 graph 配置，并以此为基础启动评估 [9] - 开发者可以预先设置包含输入主题和参考输出的数据集 [10] - 可以将评估器（Evaluator）绑定到数据集，并自定义评估标准和评分规则 [11][12][13] - 用户可以在 Studio 中修改 graph 配置（如模型、提示词），并启动新的评估实验 [15][16][17] - Studio 提供了无代码配置方式，方便快速迭代 [18]