# LangChain LangChain is an open-source framework for building agents and LLM-powered applications. 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. ## Deploying a RAG Chatbot with Shared State and Storage Across Replicas DevFeed: [Deploying a RAG Chatbot with Shared State and Storage Across Replicas]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-01T21:11:23Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Retrieval-Augmented Generation](), [Kubernetes](), [Terraform](), [FastAPI](), [LangChain](), [Python]() Tags: [fastapi](), [github](), [persistence](), [pgvector](), [python](), [rag](), [retrieval](), [terraform]() ### AI overview The article explains why a RAG chatbot that works locally can lose vector indexes, conversation history, and documents when deployed across multiple replicas. It recommends shared persistence for embeddings, checkpointed conversation state, and shared object storage, with examples using Postgres, pgvector, LangGraph, and object storage. ### Source excerpt + a popular LLM interview question. ## From a Raw Shell to a Sandboxed Coding Agent DevFeed: [From a Raw Shell to a Sandboxed Coding Agent]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-18T11:02:59Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI-assisted coding](), [LangChain](), [Claude Code](), [Docker](), [computer-use](), [codex](), [Python](), [Open Source](), [Cloud]() Tags: [agents](), [ai](), [ai-evals](), [anthropic](), [claude](), [claude-code](), [cloud](), [codex](), [coding](), [coding-agents](), [computer-use](), [docker](), [guide](), [open-source](), [python](), [terminal]() ### AI overview A tutorial on isolating coding-agent tools inside local Docker or remote Modal sandboxes. It explains how to build a Python harness that safely executes commands and supports remote, parallel agent workflows. ### Source excerpt The guide to isolating your harness and safely executing its commands, locally or remotely. ## 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... ## 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. ## 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. ## 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. ## 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. ## 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 ๐Ÿ“บ 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 ๐Ÿ“บ 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 ## CUGA on Hugging Face: Democratizing Configurable AI Agents DevFeed: [CUGA on Hugging Face: Democratizing Configurable AI Agents]() Original publisher: [Read original article]() Author: Jim Laredo; Avi Yaeli; Sami Marreed; Ayhan Sebin; Merve Unuvar Published: 2025-12-15T16:01:04Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [hugging face](), [Open Source](), [computer-use](), [Orchestration](), [spaces](), [OpenAPI Specification](), [LangChain](), [MCP](), [Low code](), [Python]() Tags: [ai](), [ai-agent](), [computer-use](), [hugging-face](), [langchain](), [low-code](), [mcp](), [open-source](), [openapi](), [orchestration](), [python]() ### AI overview CUGA, or Configurable Generalist Agent, is an open-source AI agent for complex, multi-step tasks across web and API environments. The article describes its enterprise-oriented architecture, configurable reasoning modes, computer-use capabilities, integrations with OpenAPI, MCP servers, LangChain, and Python functions, plus support for low-code workflow design, policy controls, human oversight, reuse of successful execution paths, and multi-agent collaboration. It also highlights CUGA's integration with Hugging Face Spaces and reported benchmark performance on AppWorld and WebArena. ### Source excerpt CUGA (Configurable Generalist Agent) was designed to overcome these limitations. It's an open-source, AI Agent that combines flexibility, reliability, and ease of use for enterprise use cases. By abstracting orchestration complexity, CUGA empowers developers to focus on domain requirements rather than the internals of agent building. And now, with its integration into ๐Ÿš€Hugging Face Spaces๐Ÿš€, experimenting with CUGA and open models has never been easier. What is CUGA? ## Watch the recordings from my Python + AI series DevFeed: [Watch the recordings from my Python + AI series]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-10-31T14:22:00Z Content type: article Language: en Sources: [Pamela Fox]() Topics: [Python](), [Large Language Model](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Azure OpenAI](), [Model Context Protocol](), [LangChain](), [SDK](), [OpenAI](), [Ollama]() Tags: [ai](), [embeddings](), [generative-ai](), [langchain](), [large-language-models-llms](), [mcp](), [ollama](), [openai](), [python](), [rag]() ### AI overview A blog article provides recordings and materials from a nine-part Python and generative AI series. It covers language models, embeddings, retrieval-augmented generation, evaluation and safety, AI agents, Model Context Protocol, and related Python examples using services including GitHub Models, Ollama, Azure OpenAI, and OpenAI models. ### Source excerpt My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To apply the concepts, we put together code examples that run for free thanks to GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. The examples are also compatible with local models (via Ollama), Azure OpenAI, or OpenAI.com models. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. Python + AI: Large Language Models ๐Ÿ“บ Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings ๐Ÿ“บ Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across ## Introducing the DigitalOcean AI Ecosystem DevFeed: [Introducing the DigitalOcean AI Ecosystem]() Original publisher: [Read original article]() Author: Meghan Grady Published: 2025-10-06T20:07:17Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Artificial Intelligence](), [AI Development](), [Cloud](), [LangChain](), [litellm]() Tags: [ai](), [amd](), [build](), [cloud](), [deepseek](), [dev-tools](), [digitalocean](), [gradient-platform](), [integrations](), [meta](), [models](), [nvidia](), [openai](), [partner]() ### AI overview DigitalOcean announced an expansion of its AI Ecosystem and introduced the DigitalOcean AI Partner Program. Through the Gradient AI Agent Cloud, the ecosystem brings together infrastructure, AI models, development tools, integration frameworks, and specialist partners for building AI applications. ### Source excerpt Last week at Deploy London, we announced a significant expansion of our offerings within the DigitalOcean AI Ecosystem and introduced the DigitalOcean AI Partner Program. A platform is only as strong as its ecosystem, and these initiatives are designed to create a comprehensive, flexible, and powerful environment for startups, builders, and developers to create the next generation of AI applications. Explore What's Possible with the DigitalOcean AI Ecosystem We're collaborating with industry leaders and innovative startups to provide access to all of the AI products and services you need through the Gradient AI Agent Cloud. DigitalOcean's comprehensive AI Ecosystem empowers startups and enterprises to build next-gen AI applications, offering choice and flexibility through embedded partner integrations and workflows. DigitalOcean's AI Ecosystem provides access to: Powerful Infrastructure: Efficient AMD and NVIDIA GPUs to power your applications. Advanced Models: Access to leading models from companies like OpenAI, DeepSeek, Meta, and Mistral. Applications & Frameworks: Popular AI dev tools and integration frameworks such as LangChain, LiteLLM, and dStack. Integrators: A network of AI specialists to help you migrate workflows and build robust new AI implementations. The DigitalAI Ecosystem "At the heart of AI's progress are the developers who are turning ideas into reality. The Gradient AI Ecosystem is a powerful force multiplier for them, proving that the future isn't just about silicon--it's about software and the collaborative community built on top of it," said Anush Elangovan, VP of AI Software at AMD. "By providing high-performance AMD GPUs through DigitalOcean's Gradient AI Agentic Cloud, we ensure the ecosystem is powered by world-class, open-standard hardware. We're not just providing infrastructure; together we're powering the software and the community that will invent tomorrow's AI." Through the dynamic Gradient AI Ecosystem, AI startups and digital native ## Selecting The Right AI Evals Tool DevFeed: [Selecting The Right AI Evals Tool]() Original publisher: [Read original article]() Author: Hamel Husain Published: 2025-10-01T07:00:00Z Content type: article Language: en Sources: [Hamel Husain]() Topics: [Artificial Intelligence](), [AI Engineering](), [Developer experience](), [LangChain](), [phoenix]() Tags: [ai](), [ai-engineering](), [ai-evals](), [analysis](), [developer-experience](), [evals](), [langchain](), [phoenix](), [quality]() ### AI overview The article examines how to select AI evaluation tools, arguing that no single tool is best for every team. It compares approaches from LangSmith, Braintrust, and Arize Phoenix through a shared assignment and highlights workflow, developer experience, SDK ergonomics, documentation, integrations, and human-in-the-loop support as selection criteria. ### Source excerpt Over the past year, I've focused heavily on AI Evals, both in my consulting work and teaching. A question I get constantly is, "What's the best tool for evals?". I've always resisted answering directly for two reasons. First, people focus too much on tools instead of the process, thinking the tool will be an off-the-shelf solution when it rarely is. Second, the tools change so quickly that comparisons become outdated immediately. Having used many of the popular eval tools, I can genuinely say that no single one is superior in every dimension. The "best" tool depends on your team's skillset, technical stack, and maturity. Instead of a feature-by-feature comparison, I think it's more valuable to show you how a panel of data scientists skilled in evals assesses these tools. As part of my AI Evals course, we had three of the most dominant vendors--Langsmith, Braintrust, and Arize Phoenix complete the same homework assignment. This gave us a unique opportunity to see how they tackle the exact same challenge. We recorded the entire process and live commentary, which is available below. We think this might be helpful in learning about the kinds of things you should consider when selecting a tool for your team. Thanks to Shreya Shankar and Bryan Bischof for serving as the panelists (alongside me). Langsmith With Harrison Chase, CEO of LangChain. Braintrust With Wayde Gilliam, former developer relations at Braintrust. Arize Phoenix With SallyAnn DeLucia, Technical AI Product Leader at Arize. Criteria for Assessing AI Evals Tools Here are themes that consistently surfaced during our review. 1. Workflow and Developer Experience Reducing friction is more important than any single feature. Concretely, you should be mindful of the time it takes to go from observing a failure to iterating on a solution. For example, we appreciated the ability to go from viewing a single trace to experimenting with that same trace in a playground. For some teams with data-science backgrounds, a note ## Filter the tools from MCP servers DevFeed: [Filter the tools from MCP servers]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-09-18T17:35:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol (MCP)](), [GitHub Copilot](), [agentic-coding](), [vs-code](), [LangChain](), [Pydantic]() Tags: [agentic-coding](), [github-copilot](), [langchain](), [mcp](), [mcp-server](), [openai](), [python](), [vs-code]() ### AI overview A tutorial on filtering tools exposed by MCP servers to reduce LLM confusion, token usage, latency, context-window pressure, and unintended destructive actions. It covers GitHub Copilot in VS Code, LangChain v1, and Pydantic AI. ### Source excerpt What I like about MCP servers: they give me lots of great tools that can make my agents more powerful, with very little work on my side. ๐ŸŽ‰ What I don't like about MCP servers: they give me TOO many tools! I usually only need a handful of tools for a task, but a server can expose dozens. ๐Ÿ˜ฟ The problems with too many tools: LLM confusion. The LLM will be presented with the tool definition for every single tool in the server, and it needs to decide which tool (if any) is the best for the job. That's a hard decision for an LLM - it's always better to make it easier for the LLM by narrowing the tool list. Increased tokens. The tool call definitions require more tokens, which can cost more money, increase latency, and potentially even go over the context window limit of the model. Destructive actions. A server may include tools that are read-only, just sending down data to serve as context, but many servers expose tools that do write operations, like the GitHub MCP server's tools for creating issues, closing issues, pushing branches, and many more. It's possible your task requires some of those write ops, but you generally want to be very explicit about whether an agent is allowed to take action that can actually change something about your accounts and environments. Otherwise, you can be in for a nasty surprise when the agent took actions that you weren't expecting. (Ask me how I know...) Fortunately, there is almost always a way to configure agents to only allow a subset of the tools from an MCP server. In this blog post, I'll share ways to filter tools in my favorite agentic coder, GitHub Copilot in VS Code, plus two popular AI agent frameworks, Langchain v1 and Pydantic AI. Agentic coding with GitHub Copilot in VS Code Global configuration When you are using agent mode in VS Code, configure the tools by selecting the gear icon near the chat input window. That will pop-up a window showing all your available tools, coming from both installed MCP servers and VS Code exte ## Structured Output with LangChain and Llamafile DevFeed: [Structured Output with LangChain and Llamafile]() Original publisher: [Read original article]() Author: brakmic Published: 2025-06-22T16:50:12Z Content type: tutorial Language: en Sources: [Harris Brakmic - Coding]() Topics: [LangChain](), [llamafile](), [JSON](), [llama.cpp](), [Parser](), [Pydantic]() Tags: [ai](), [code](), [coding](), [cpp](), [how-to](), [import](), [json](), [langchain](), [llama-cpp](), [llamafile](), [llm](), [local](), [local-llm](), [models](), [properties](), [python](), [structured-output]() ### AI overview A tutorial on using LangChain with Llamafile to produce structured JSON output from a local large language model. It uses JsonOutputParser, PromptTemplate, and a Pydantic model because Llamafile does not provide a with_structured_output method. ### Source excerpt Learn how to extend Llamafile with LangChain's JsonParser to produce clean, structured JSON output ## Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain DevFeed: [Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain]() Original publisher: [Read original article]() Author: Anaiya Raisinghani Published: 2025-04-19T21:55:13Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Django](), [MongoDB](), [LangChain](), [voyage-ai](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Python](), [Generative AI](), [Development]() Tags: [ai](), [dev](), [django](), [embeddings](), [generative-ai](), [langchain](), [mongodb](), [python](), [rag](), [voyage-ai](), [voyageai]() ### AI overview A tutorial on building a full-stack Dublin pub-finder application with Django, MongoDB, Voyage AI embeddings, and LangChain. It explains how these technologies support natural-language queries and generative AI or RAG applications. ### Source excerpt DjangoCon Europe is just around the corner, and this year, it's in Dublin, Ireland. This means two things: I'm legally required to drink my first Guinness (yes, first ever!). I obviously had to build... The post Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain appeared first on DEV. ## Not Everything is an Agent DevFeed: [Not Everything is an Agent]() Original publisher: [Read original article]() Published: 2025-04-01T06:47:17Z Content type: opinion Language: en Sources: [Ariya Hidayat]() Topics: [Large Language Model](), [Artificial Intelligence](), [LangChain](), [Ollama](), [coding assistant](), [anthropic]() Tags: [agent](), [agentic](), [ai](), [anthropic](), [code](), [coding-assistant](), [langchain](), [llm](), [ollama](), [routing]() ### AI overview This opinion argues that not every LLM-based program should be called an agent. It distinguishes simple LLM calls, retrieval, memory, chaining, and routing from systems handling complex real-world tasks, and recommends relying on successful evaluations rather than anecdotes or hype. ### Source excerpt "Agent" is likely going to be the word that will cause existential dread to true LLM enthusiasts. ## CLI Chatbot with LangChain and OpenAI in Node.js DevFeed: [CLI Chatbot with LangChain and OpenAI in Node.js]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2025-03-03T07:50:46Z Content type: tutorial Language: en Sources: [Robin Wieruch]() Topics: [LangChain](), [Node.js](), [OpenAI](), [Command-line interface](), [JavaScript](), [Chat Bot](), [API]() Tags: [cli](), [how-to](), [javascript](), [langchain](), [langchain-node-js-openai](), [node](), [node-js](), [openai](), [tutorial]() ### AI overview A step-by-step tutorial for building a simple command-line chatbot in Node.js using JavaScript, LangChain, and OpenAI. It covers dependency installation, API key configuration, model interaction, prompt handling, user input, and possible extensions such as conversation memory, additional language models, and a graphical interface. ### Source excerpt How to: OpenAI in JavaScript with LangChain. A step by step example on how to use the OpenAI API in JavaScript with LangChain ... [Next page]()