# LangChain Published articles for LangChain. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimizing cost and latency with Amazon Bedrock prompt caching DevFeed: [Optimizing cost and latency with Amazon Bedrock prompt caching]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-09-15T16:18:19Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Caching](), [Latency](), [API](), [foundation-models](), [LangChain](), [Multi-tenancy]() Tags: [advanced-300](), [amazon-bedrock](), [api](), [caching](), [foundation-models](), [integration](), [langchain](), [latency](), [pricing](), [technical-how-to](), [tokens]() ### AI overview This AWS post explains how Amazon Bedrock prompt caching can reduce repeated input-token costs by up to 90 percent and lower time to first token when requests reuse the same context. It presents six scenarios using the Converse API, including document, system prompt, tool definition, mixed TTL, tenant-isolated, and LangChain caching. ### Source excerpt Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration. ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Using Six Scoped Subagents to Manage Context Windows DevFeed: [Using Six Scoped Subagents to Manage Context Windows]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-09-01T05:00:29Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [long-context](), [Artificial Intelligence](), [Code](), [coding](), [Claude](), [Claude Code](), [codex](), [LangChain](), [Python](), [Cloud]() Tags: [agent](), [agents](), [ai](), [claude](), [claude-code](), [cloud](), [codex](), [coding](), [coding-agents](), [context-engineering](), [context-window](), [langchain](), [python]() ### AI overview This tutorial explains how splitting research work among six parallel subagents can reduce context-window noise and limit the state returned to an orchestrator agent. It also covers designing subagent protocols, maintaining an agent registry, and running parallel agent workflows. ### Source excerpt The harness determines what your orchestrator agent never sees, not the model. ## Start here: Azure SQL Foundations series DevFeed: [Start here: Azure SQL Foundations series]() Original publisher: [Read original article]() Author: Anna Hoffman Published: 2026-08-25T16:00:18Z Content type: article Language: en Sources: [Developer Blogs]() Topics: [Azure SQL](), [Microsoft](), [migration](), [Artificial Intelligence](), [Azure OpenAI](), [DevOps](), [GitHub](), [Retrieval Augmented Generation (RAG)](), [API](), [MCP Server]() Tags: [ai](), [azure](), [azure-openai](), [azure-sql](), [devops](), [github](), [guide](), [how-to](), [json](), [langchain](), [mcp](), [microsoft-for-developers](), [migration](), [powershell](), [rag](), [terraform]() ### AI overview Microsoft introduces the Azure SQL Database Foundations series, a set of four videos covering Hyperscale databases, modernization and migration, AI features, and operational data. Each episode includes a repository for hands-on follow-up. ### Source excerpt Most developers I talk to aren't asking whether Azure SQL Database can handle their next app. They're asking where to start when it comes to modernization, migration, and AI in the database. If you're reading this, you're probably in a similar boat: you've got an existing schema or databases, a scaling question you haven't had [...] The post Start here: Azure SQL Foundations series appeared first on Microsoft for Developers. ## ReAct agents explained: concepts & practical uses DevFeed: [ReAct agents explained: concepts & practical uses]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Artificial Intelligence](), [ai-coding](), [LangChain](), [Iris](), [Chain-of-thought](), [coding](), [Error Propagation]() Tags: [agent](), [agents](), [ai](), [ai-coding](), [coding](), [langchain](), [reasoning](), [tech-de](), [tool]() ### AI overview A practical guide to ReAct agents: AI systems that alternate between reasoning, tool use, and feedback in a loop. It explains the pattern, compares it with chain-of-thought approaches, and discusses production concerns such as latency, cost, hallucination, and error propagation, with examples involving LangChain, LangGraph, and Redis Iris. ### Source excerpt If you've watched an AI coding assistant hunt down a bug, run a test, read the failure, and adapt its next fix, you've watched Reasoning and Acting (ReAct)-like behavior at work. ReAct is a common pattern in production agent systems today. It's simple... ## Building safe MCP servers for your PostgreSQL database DevFeed: [Building safe MCP servers for your PostgreSQL database]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2026-08-12T18:07:30Z Content type: article Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol](), [MCP Server](), [PostgreSQL](), [SQL](), [Python](), [GitHub Copilot](), [Open Source](), [LangChain]() Tags: [github-copilot](), [langchain](), [mcp](), [mcp-server](), [open-source](), [postgresql](), [python](), [sql]() ### AI overview This tutorial explains how to build MCP servers for PostgreSQL databases and how to control the level of agent access. It compares flexible servers that accept generated SQL with stricter servers using typed tools and templated queries, using Python and FastMCP examples. ### Source excerpt Model Context Protocol (MCP) is an open protocol that describes how agents can connect to external tools and data sources, and is now widely supported by the most popular coding agents (like GitHub Copilot, Claude Code, and Codex) and agent frameworks (like LangChain and Pydantic AI). If you want to give agents a standard way to access the data in a database, you can build your own MCP server and expose tools for the agent to query or even modify data. But you need to design your MCP server carefully, to ensure that agents can do everything that users want - but nothing that you don't want them to do! In this blog post, we'll walk through the range of ways to build MCP servers on top of a PostgreSQL database, since PostgreSQL is the most popular open source database and is production-ready with hosted offerings like Azure Database for PostgreSQL. You can apply these same principles to any database, however. There's a spectrum of ways to build MCP servers on top of a database. We'll start with the most flexible option, exploratory servers that allow the agent to generate full SQL queries, conclude with the strictest option, fully typed tools for templated queries, and explore options in the middle too. Free-form SQL Let's take a look at a simple MCP server that gives the agent as much information and control as possible. For all of our examples, we use the Python language and the FastMCP package, but SDKs are available in multiple languages. All code is available in the GitHub repository. We start off by giving the server a name, which the agent will see and consider when deciding which MCP server to invoke for a given user query: mcp = FastMCP("Bees database MCP server") For this example, my database stores observations of bees, so I name it accordingly. We then define an execute_sql tool that accepts any SQL string, executes it against the database, and returns the rows. @mcp.tool() async def execute_sql(sql: str) -> str: """Execute a SQL query against the database ## How to Build a Kubernetes Troubleshooting AI Agent with LangChain and kubectl DevFeed: [How to Build a Kubernetes Troubleshooting AI Agent with LangChain and kubectl]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-27T17:26:08Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [AI Agent](), [Kubernetes](), [kubectl](), [LangChain](), [Tutorial]() Tags: [ai](), [ai-agent](), [ai-agent-for-kubernetes](), [ai-agents](), [aiops-for-kubernetes](), [automation](), [configuration](), [crashloopbackoff-troubleshooting](), [devops](), [human-in-the-loop-agent](), [kubectl](), [kubectl-ai-agent](), [kubernetes](), [kubernetes-rbac-for-ai-agents](), [kubernetes-troubleshooting-ai-agent](), [kubernetes-troubleshooting-automation](), [langchain](), [langchain-kubernetes-agent](), [langgraph](), [langgraph-agent-tutorial](), [python](), [react-agent-python](), [security](), [tool](), [troubleshooting](), [tutorial]() ### AI overview A tutorial on building a Kubernetes troubleshooting AI agent with LangChain v1 and kubectl. It describes a diagnostic ladder for investigating pod failures, read-only tools, approval requirements for write operations, separate identities for remediation, and a golden trace suite for evaluating results. ### Source excerpt Most pod failures resolve into six root causes, and the investigation is identical every time. Here is how to build an agent that runs that investigation for you, with a hard boundary between reading a cluster and changing one. ## Building a Coding Agent From Scratch DevFeed: [Building a Coding Agent From Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-22T11:04:24Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Agent Harness](), [AI-assisted coding](), [coding](), [LangChain](), [Claude Code](), [codex]() Tags: [agent-harness](), [ai](), [ai-agents](), [ai-evals](), [benchmarks](), [claude-code](), [codex](), [coding-agents](), [langchain]() ### AI overview This tutorial explains how to build a coding-agent harness from scratch in Python. It covers the agent loop, shell execution, context engineering, subagents, remote parallel agents, and evaluation workflows, using the project Decode as the practical example. ### Source excerpt Designing the harness around the model, from the agent loop to a remote swarm. ## Context engineering with Dex Horthy DevFeed: [Context engineering with Dex Horthy]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-15T16:08:59Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI-assisted coding](), [Large Language Model](), [AI Agent](), [Code quality]() Tags: [ai](), [ai-agent](), [code-quality](), [context-engineering](), [langchain](), [llm]() ### AI overview Dex Horthy discusses context engineering for working around LLM context limitations in AI-assisted software development. The conversation covers context engineering, harness engineering, loop engineering, software factories, and automating more of the software development lifecycle while maintaining code quality. ### Source excerpt Dex Horthy explains why context engineering is key to building more effective AI-assisted software without sacrificing code quality. ## Build Durable Chat Memory for RAG Using ScyllaDB and LangChain DevFeed: [Build Durable Chat Memory for RAG Using ScyllaDB and LangChain]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-14T12:54:49Z Content type: tutorial Language: en Sources: [ScyllaDB]() Topics: [AI Chat](), [Language models]() Tags: [2026-2](), [ai](), [ai-ml](), [ann](), [cassandra](), [database](), [ecosystem](), [embeddings](), [history](), [how-to](), [langchain](), [language-models](), [latency](), [memory](), [rag](), [retrieval](), [scalability](), [sentence-transformers](), [vector]() ### AI overview A tutorial on replacing LangChain's in-memory chat history with ScyllaDB for durable RAG chatbot memory across restarts and replicas. ### Source excerpt How to replace LangChain's in-memory chat history with ScyllaDB -- so your RAG chatbot retains context across restarts and scales across replicas ## How to Build Unified Agent Memory from Scratch DevFeed: [How to Build Unified Agent Memory from Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-14T05:01:54Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI Engineering](), [Claude Code](), [Graphs](), [LangChain](), [Large Language Model](), [Database](), [Model Context Protocol]() Tags: [agent](), [ai-engineering](), [claude-code](), [graph](), [graph-database](), [knowledge-graphs](), [langchain](), [llm](), [mcp]() ### AI overview A tutorial on building a unified agent memory layer from scratch using knowledge graphs, including ingestion, querying, and serving. It discusses trade-offs among vector databases, graph databases, temporality, versioning, MCP servers, CLIs, and skills, and explains why understanding the underlying memory layer matters. ### Source excerpt Ingest, query, and serve a unified memory from a single database. ## Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance DevFeed: [Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance]() Original publisher: [Read original article]() Author: Sean Lopp Published: 2026-07-08T18:17:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots](), [ai observability]() Tags: [agentic](), [agentic-ai-generative-ai](), [agents](), [ai-agent](), [benchmark](), [customization](), [evaluation](), [featured](), [langchain](), [nemotron](), [nvidia](), [performance](), [profile](), [python](), [top-stories](), [tutorial]() ### AI overview A tutorial on creating and evaluating a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra. It uses benchmark-driven iteration to tune harness behavior and improve performance without fine-tuning the model. ### Source excerpt Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... ## Context Engineering | Compaction & Agent Memory for Automated Malware Analysis DevFeed: [Context Engineering | Compaction & Agent Memory for Automated Malware Analysis]() Original publisher: [Read original article]() Author: Gabriel Bernadett-Shapiro Published: 2026-07-02T13:00:02Z Content type: article Language: en Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.]() Topics: [ransomware](), [Threat Research](), [OpenAI](), [API](), [LangChain](), [Claude](), [Google]() Tags: [agentic](), [agents](), [analysis](), [anthropic](), [api](), [code](), [coding](), [data](), [engineering](), [langchain](), [llm](), [malware](), [model](), [models](), [openai](), [security](), [systems](), [tokens](), [workflows]() ### AI overview SentinelLABS evaluates OpenAI's native compaction in the Responses API for automated malware analysis. The evaluation found an approximately 86% reduction in input tokens with no measurable change in aggregate task quality, suggesting that compaction can reduce cost and context noise in long-running security workflows. ### Source excerpt Compaction cut input tokens 86% across long-running agent evals with no quality loss. Context discipline matters as much as model selection. ## Deploy a Production-Ready NVIDIA AI-Q Blueprint on Oracle Cloud Infrastructure DevFeed: [Deploy a Production-Ready NVIDIA AI-Q Blueprint on Oracle Cloud Infrastructure]() Original publisher: [Read original article]() Author: Anurag Kuppala Published: 2026-06-26T19:00:45Z Content type: tutorial Language: en Sources: [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Terraform](), [Kubernetes](), [Cloud](), [LangChain](), [Retrieval Augmented Generation (RAG)](), [NeMo](), [API keys]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [api-keys](), [data-center-cloud](), [featured](), [kubernetes](), [langchain](), [nemo](), [oracle](), [rag](), [terraform]() ### AI overview A tutorial for deploying the open-source NVIDIA AI-Q 2.0 multi-agent blueprint on Oracle Cloud Infrastructure. It uses Terraform to provision OCI resources and Helm to install workloads on OKE, resulting in an AI-Q endpoint with a teardown command. ### Source excerpt AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep... ## Deep Agents and OpenCode are now available in the AI SDK Harness DevFeed: [Deep Agents and OpenCode are now available in the AI SDK Harness]() Original publisher: [Read original article]() Author: Felix Arntz Published: 2026-06-25T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [vercel ai sdk](), [SDKs](), [LangChain](), [Vercel](), [coding]() Tags: [agent](), [ai](), [claude](), [codex](), [langchain](), [model](), [opencode](), [reasoning](), [sandbox](), [sdk](), [skills](), [tools](), [vercel]() ### AI overview Vercel's AI SDK Harness now supports Deep Agents and OpenCode through new adapters running in a Vercel Sandbox. The unified interface allows applications to switch between coding-agent runtimes without changing application code, while supporting tools, sessions, approvals, model selection, and streamed events. ### Source excerpt The AI SDK Harness lets you run established coding-agent runtimes through one unified interface, so you can switch runtimes without changing your application code. Today we're adding two new adapters, Deep Agents and OpenCode, both running inside a Vercel Sandbox. Deep Agents @ai-sdk/harness-deepagents adapts LangChain's deepagents runtime, with built-in file and shell tools, skills, host tools, multi-turn sessions, attach and resume, and built-in tool approvals. Read the Deep Agents harness documentation to get started. OpenCode @ai-sdk/harness-opencode boots a real OpenCode server inside the sandbox via @opencode-ai/sdk and streams its session events through the harness. It exposes OpenCode's built-in tools, supports both built-in and host tool approvals, and lets you pick the model, provider, and reasoning variant. Read the OpenCode harness documentation to get started. The full supported list of harnesses is now: Claude Code, Codex, Deep Agents, OpenCode, Pi, with more coming soon. Read more ## Coding Challenge #123 - Database Driven LLM Wiki DevFeed: [Coding Challenge #123 - Database Driven LLM Wiki]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-06-06T08:01:47Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Wiki](), [LLMs](), [Database](), [Langgraph](), [LangChain](), [Retrieval Augmented Generation (RAG)]() Tags: [coding](), [database](), [knowledge-base](), [langchain](), [langgraph](), [llms](), [rag]() ### AI overview A coding challenge to build a personal LLM wiki that incrementally maintains a structured Markdown knowledge base from curated sources. The proposed implementation uses Oracle AI Database for vector embeddings and full-text indexes, hybrid search for retrieval, LangGraph for workflows, and LangChain for LLM integration. ### Source excerpt This challenge is to build your own database powered LLM Wiki. ## Breakpoint 2026: Highlights from Day 3 DevFeed: [Breakpoint 2026: Highlights from Day 3]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-02T13:03:05Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [AI Agent](), [Model Context Protocol](), [Test automation](), [MCP](), [LangChain](), [observability](), [Retrieval Augmented Generation (RAG)](), [Playwright](), [Cypress](), [debugging](), [Selenium]() Tags: [2026](), [agent](), [ai](), [automation](), [breakpoint](), [ci-cd](), [debugging](), [github](), [github-copilot](), [langchain](), [mcp](), [model-context-protocol](), [observability](), [qa](), [rag](), [testing](), [vscode]() ### AI overview A recap of Breakpoint 2026 Day 3 covering practical masterclasses and lightning talks on AI-driven test generation, non-deterministic testing, MCP-powered QA workflows, automated AI evaluation, and production testing practices. ### Source excerpt All sessions, masterclasses, and lightning talks from Breakpoint 2026 Day 3, covering AI agent testing, test automation with LangChain and CrewAI, MCP-powered QA workflows, Playwright network interception, non-deterministic testing, and building production-grade AI systems. ## Building AI Agents with Laravel: No Python Required DevFeed: [Building AI Agents with Laravel: No Python Required]() Original publisher: [Read original article]() Author: Ana Tavares Published: 2026-05-15T13:38:08Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [AI Bots](), [SDKs](), [AI, ML & Data Engineering](), [Orchestration](), [AI search]() Tags: [agents](), [ai](), [ai-agents](), [api](), [embeddings](), [langchain](), [laravel](), [memory](), [php](), [python](), [streaming](), [tools](), [vector](), [workflows]() ### AI overview A guide to building AI agents in PHP with the Laravel AI SDK, covering tools, memory, structured output, streaming, multi-agent workflows, and vector search without requiring Python. ### Source excerpt The Laravel AI SDK is the LangChain PHP alternative: agents, tools, memory, streaming, and multi-agent workflows, entirely in PHP. No Python required. ## Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context -- Best Sub-100M Retrieval Quality DevFeed: [Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context -- Best Sub-100M Retrieval Quality]() Original publisher: [Read original article]() Author: Radu Florian; Parul Awasthy; Aashka Trivedi; Madison Lee Published: 2026-05-14T18:55:01Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Embeddings](), [Benchmark](), [Retrieval-Augmented Generation](), [AI search](), [LangChain](), [Code]() Tags: [apache](), [benchmark](), [embeddings](), [frameworks](), [langchain](), [llamaindex](), [model](), [retrieval]() ### AI overview Granite Embedding Multilingual R2 introduces two Apache 2.0 multilingual embedding models: a compact 97M-parameter model and a 311M full-size model. They support more than 200 languages, 32K-token contexts, code retrieval across nine programming languages, and integration with popular retrieval frameworks. ### Source excerpt Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context -- Best Sub-100M Retrieval Quality TL;DR: Two new Apache 2.0 multilingual embedding models built on ModernBERT -- a 97M-parameter compact model that beats every open sub-100M multilingual embedder on MTEB Multilingual Retrieval (60.3), and a 311M full-size model that scores 65.2 on MTEB Multilingual Retrieval (#2 among open models under 500M parameters) with Matryoshka support. ## Building AI Agents with Laravel: No Python Required DevFeed: [Building AI Agents with Laravel: No Python Required]() Original publisher: [Read original article]() Author: Ana Tavares Published: 2026-05-14T18:45:00Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [AI Bots](), [SDKs](), [AI, ML & Data Engineering](), [AI search]() Tags: [agents](), [ai](), [ai-agents](), [embeddings](), [langchain](), [laravel](), [memory](), [php](), [python](), [streaming](), [tools](), [vector](), [workflows]() ### AI overview A guide to building AI agents in PHP with the first-party Laravel AI SDK, without requiring Python. ### Source excerpt The Laravel AI SDK is the LangChain PHP alternative: agents, tools, memory, streaming, and multi-agent workflows, entirely in PHP. ## Building Agentic GraphRAG Systems DevFeed: [Building Agentic GraphRAG Systems]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-05-05T05:01:08Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Retrieval Augmented Generation (RAG)](), [data-modeling](), [MCP Server](), [AI Agent](), [context window]() Tags: [ai-agent](), [context-window](), [data-modeling](), [knowledge-graphs](), [langchain](), [latency](), [mcp-server](), [mongodb](), [rag]() ### AI overview The article explains agentic GraphRAG as a data-modeling problem involving knowledge graphs, ontologies, append-only data models, extraction modes, and hybrid retrieval. It describes exposing the resulting unified memory layer through an MCP server for AI agents. ### Source excerpt From knowledge graphs and ontologies to a unified memory as an MCP server for your AI agent. ## Learning Path: LLM Application Development With Python DevFeed: [Learning Path: LLM Application Development With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-03-19T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Language models](), [Retrieval Augmented Generation (RAG)](), [AI, ML & Data Engineering](), [Local AI]() Tags: [ai-agents](), [apis](), [application-development](), [langchain](), [llm](), [mcp](), [openai](), [prompt-engineering](), [python](), [rag]() ### AI overview A Python learning path for building LLM-powered applications with model APIs, prompt engineering, RAG pipelines, AI agents, and MCP integrations. ### Source excerpt Build LLM-powered applications in Python. Call model APIs, craft prompts, add retrieval-augmented generation, create AI agents, and connect via MCP. ## Agent Development with CockroachDB using the LangChain Framework DevFeed: [Agent Development with CockroachDB using the LangChain Framework]() Original publisher: [Read original article]() Author: David Bressler Published: 2026-02-26T00:00:00Z Content type: release Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [LangChain](), [agentic AI applications](), [Orchestration](), [Python](), [Databases](), [Retrieval-Augmented Generation]() Tags: [agentic-ai-applications](), [cockroachdb](), [langchain](), [orchestration](), [pgvector](), [postgresql](), [python](), [retrieval-augmented-generation]() ### AI overview CockroachDB describes its integration with LangChain for building production-oriented agentic AI applications in Python. The integration supports CockroachDB as a vector source and helps connect language models with CockroachDB data for retrieval and other multi-step application workflows. ### Source excerpt Recently we launched an integration with LangChain, the most popular orchestration framework for developing applications with large language models, to simplify development of production-ready agentic AI applications with CockroachDB. This integration provides out-of-the-box support for CockroachDB as a vector source for any LangChain user using LangChain Python. ## Watch the recordings from my Python + MCP series DevFeed: [Watch the recordings from my Python + MCP series]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-12-19T15:50:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol](), [MCP Server](), [Python](), [Azure](), [OAuth](), [Docker](), [OpenTelemetry](), [LangChain](), [GitHub Copilot](), [vs-code](), [Microsoft Agent Framework]() Tags: [agent-framework](), [azure](), [code](), [deployment](), [docker](), [github-copilot](), [langchain](), [mcp](), [mcp-server](), [microsoft-agent-framework](), [model-context-protocol](), [oauth](), [opentelemetry](), [python](), [vs-code]() ### AI overview A three-part Python and MCP series provides recordings, slides, and open-source code covering MCP server development with FastMCP, cloud deployment on Azure, observability, networking, and authentication. ### Source excerpt MCP is one of the fastest growing technologies in the Generative AI space this year, and the first AI related standard that the industry has really embraced wholeheartedly. I just gave a three-part live stream series all about Python + MCP. I showed how to: Build MCP servers in Python using FastMCP Deploy them into production on Azure (Container Apps and Functions) Add authentication, using either Keycloak and Microsoft Entra as the OAuth provider All of the materials from our series are available and linked below: Video recordings of each stream Powerpoint slides Open-source code samples complete with Azure infrastructure and 1-command deployment If you're an instructor, feel free to use the slides and code examples in your own classes. Spanish speaker? My colleague delivered a fantastic Spanish version of the series. Building MCP servers with FastMCP 📺 Watch YouTube recording In the intro session of our Python + MCP series, we dive into MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible. We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally. Then we consume that server from chatbots like GitHub Copilot in VS Code, using it's tools, resources, and prompts. Finally, we discover how easy it is to connect AI agent frameworks like Langchain and Microsoft agent-framework to the MCP server. Slides for this session Code repository with examples: python-mcp-demos Deploying MCP servers to the cloud 📺 Watch YouTube recording In our second session of the Python + MCP series, we deploy MCP servers to the cloud! We walk through the process of containerizing a FastMCP server with Docker and deploying to Azure Container Apps. Then we instrument the MCP server with OpenTelemetry and observe the tool calls using Azure Application Insights and Logfire. Finally, we explore private networking options for MCP servers, using virtual networks th [Next page]()