LangChain

👐Use gpt-oss with `deepagents`

Deep Agents require good tool calling capabilities... something that OpenAI's new open source model is pretty good at

Thanks to Ryan Eggleston for adding an example how to use gpt-oss (via ollama) to `deepagents`

Docs: github.com/hwchase17/de...

🤖📚 Context Engineering Guide

A comprehensive guide on evolving from prompt to context engineering. LangGraph's agent framework gives developers precise control over LLM execution and context management, optimizing AI performance.

Learn more at medium.com/ai-artistry/...

Context Engineering

"Context engineering" is a key part of agent building.

We review a few popular patterns for context engineering + explain how to use them w/ LangGraph.

Blog:
blog.langchain.com/context-engine…

Video:
youtu.be/4GiqzUHD5AA

🔬 OpenAI just released new deep research models.

🤖 You can build agents with those models TODAY in langgraph!

🔬You can use openai's new deep research models with langchain-openai!

🔍 Elasticsearch + LangGraph RAG

LangGraph's new Retrieval Agent Template integrates with Elasticsearch to build powerful RAG applications, featuring flexible LLM options, debugging tools, and query prediction.

Learn more on Elastic's blog 👉 www.elastic.co/search-labs/...

Prompt engineering UX improvement ✨

Turn your prompt into a reusable template by adding variables. Just highlight any part of the text and assign it a name to convert it into a variable!

🤖LangGraph+ Tensorlake: Unlocking Document Understanding for Agents

When creating agents that interact with data, the connections you have to that data matter

Excited to announce an integration Tensorlake - a best in class document ingestion engine
www.tensorlake.ai/blog/announc...

🎙️🤖 Local AI Podcast Generator

Transform text into multilingual podcasts with this AI system! Built using LangChain + Ollama, it combines text summarization and speech generation for seamless podcast creation.

Check out the tutorial here youtu.be/h5D4rDNe8xk

🔍 GPT Researcher MCP

GPT Researcher now leverages LangChain's Model Context Protocol adapters for intelligent tool selection and research. Seamlessly combines MCP with web search capabilities for comprehensive data gathering.

Check out the integration docs! 📚 docs.gptr.dev/docs/gpt-res...

Excited to partner with Microsoft to make AI safer and more accessible for open source and enterprises!

Explore more 👉 developers.googleblog.com/en/building-...

🤖 Gemini x LangGraph

Google integrates Gemini with LangGraph, enabling developers to build AI agents through advanced processing and stateful workflows. The collaboration streamlines agent development with sophisticated reasoning and tool execution.

🔬 DeerFlow: Deep Research Framework

An open-source framework for conducting systematic deep research through coordinated LangGraph agents. Enables comprehensive literature analysis, data synthesis, and structured knowledge discovery.

GitHub Repository: github.com/bytedance/de...

🖥️ New version of Gemini 2.5 Pro!

Google just released a new version of Gemini 2.5 Pro with distinct strengths in coding, especially UIs, including new video-to-code features and improved agentic capabilities.

Try it out in LangChain!

🎥🤖 Multi-Modal RAG with Gemma 3

Build a powerful RAG system that processes mixed-content PDFs using Google's Gemma 3 and LangChain. This implementation combines PDF processing with multi-modal support, powered by Streamlit and Ollama.

Check out the tutorial 📚 www.youtube.com/watch?v=hBDN...

🔌📊 MCP Server Tutorial

Learn to build MCP servers that process stock market data using FastMcp and LangChain. This comprehensive guide demonstrates how to create ReAct agents with LangGraph for standardized data access.

📺 Check out the tutorial: www.youtube.com/watch?v=3K39...

🧑‍🏫 What if you could catch and fix that before generating an answer? In this video, we’ll show how to use in-the-loop evaluations to filter out noisy results and boost answer quality.

📹: youtu.be/huEiXXQrlsg

✏️ How to Create a RAG Agent with Reflection

🔍 RAG is a powerful technique that supplies your agent with external information, and can improve agent performance. However, relying on RAG alone, agents can often pull in irrelevant documents to the user question.

🔍 Shandu AI Research

A system that generates cited reports using LangGraph workflows. Features intelligent scraping, multi-source synthesis, and parallel processing to streamline research tasks.

Check out this research tool 📚
github.com/jolovicdev/s...

Check it out on GitHub 🚀
github.com/CopilotKit/o...

🤖🎨 Open Multi-Agent Canvas

A powerful platform that unifies multiple AI agents in one chat interface. Built with LangGraph, it seamlessly connects specialized agents and external services through MCP integration.

GitHub link in reply 👇

Check out the guide here: github.com/philschmid/g...

🤖📚 Gemini ReACT Guide

Phil Schmid just released a guide showing how to build reasoning agents using Google's Gemini 2.5 and LangGraph, featuring examples for audio analysis, function calls, and structured data.

Explore the guide! 🔍
Guide link in reply 👇

auth0.com/blog/genai-t...

🔐🤖 Secure AI Assistant

Learn to build production-ready AI assistants that securely interact with Gmail using LangGraph.js and Auth0. Features secure tool-calling, real-time streaming, and complete authentication integration.

Explore the full tutorial in reply 👇

python.langchain.com/docs/integra...

🧠 o3 and o4-mini

Today OpenAI released o3 and o4-mini with enhanced reasoning capabilities.

Try them out in LangChain, and check out our updated docs (in thread) on generating reasoning summaries to learn more!

GPT-4.1!

OpenAI released a new series of models today that outperform GPT-4o and 4o-mini across the board.

Try them out in LangChain!

⚡ FastAPI LangGraph Agent Template

Production-ready template for building secure AI agents with FastAPI and LangGraph. Features Docker support, monitoring tools, and multi-LLM compatibility through LangChain's ecosystem.

Start building production agents 🔧
github.com/wassim249/fa...