News from the AI & ML world
DeeperML
@learn.aisingapore.org
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LangGraph, a framework built upon LangChain, has recently released updates for both its JavaScript and Python versions aimed at streamlining development workflows. These enhancements focus on providing developers with greater control at every level of their graph, leading to faster development cycles and more efficient runs. Key features include node caching, which reduces redundant computation by caching the results of individual nodes, and deferred nodes, which postpone execution until all upstream paths complete, ideal for complex workflows like map-reduce and agent collaboration.
New additions to LangGraph also include pre/post model hooks for prebuilt ReAct agents, allowing for more customizable message flow. These hooks facilitate summarization of message history, controlling context bloat, and enable guardrails and human-in-the-loop interactions. Additionally, users can now integrate builtin provider tools like web search and Remote MCP tools directly into the prebuilt ReAct agent by simply passing in the tool specification.
LangChain is also being leveraged in other applications, such as building a Gemini-Powered DataFrame Agent for Natural Language Data Analysis with Pandas. This agent uses Google's Gemini models and LangChain's experimental Pandas DataFrame agent to perform both simple and sophisticated data analyses. This combination allows for an interactive agent that can interpret natural language queries, inspect data, compute statistics, and generate visual insights, all without manual coding. This extends to automating customer support using Amazon Bedrock, LangGraph, and Mistral models, where AI agents revolutionize customer service by bridging the gap between LLMs and real-world applications, tackling complex customer support tasks.
References :
New additions to LangGraph also include pre/post model hooks for prebuilt ReAct agents, allowing for more customizable message flow. These hooks facilitate summarization of message history, controlling context bloat, and enable guardrails and human-in-the-loop interactions. Additionally, users can now integrate builtin provider tools like web search and Remote MCP tools directly into the prebuilt ReAct agent by simply passing in the tool specification.
LangChain is also being leveraged in other applications, such as building a Gemini-Powered DataFrame Agent for Natural Language Data Analysis with Pandas. This agent uses Google's Gemini models and LangChain's experimental Pandas DataFrame agent to perform both simple and sophisticated data analyses. This combination allows for an interactive agent that can interpret natural language queries, inspect data, compute statistics, and generate visual insights, all without manual coding. This extends to automating customer support using Amazon Bedrock, LangGraph, and Mistral models, where AI agents revolutionize customer service by bridging the gap between LLMs and real-world applications, tackling complex customer support tasks.
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References :
- LangChain Blog: See what we released for LangGraph.js and Python over the past few weeks to speed up development workflows and gain more control at every level of your graph.
- LearnAI: AI agents are transforming the landscape of customer support by bridging the gap between large language models (LLMs) and real-world applications.
- www.marktechpost.com: In this tutorial, we’ll learn how to harness the power of Google’s Gemini models alongside the flexibility of Pandas. We will perform both straightforward and sophisticated data analyses on the classic Titanic dataset.
Classification:
- HashTags: #LangGraph #AIWorkflows #DataFrameAgent
- Company: LangChain
- Target: AI Developers
- Product: LangGraph
- Feature: Development workflows
- Malware: LangGraph
- Type: AI
- Severity: Informative