LangChain

Learn how to fine-tune and evaluate LLMs with LangSmith for dataset management. Complete guide covers LLaMA2 and GPT-3.5 fine-tuning with practical examples.

Build better Q&A systems for CSV data using LangChain agents, retrieval, and LLM evaluation. Includes benchmarks, debugging insights, and open-source code.

Build faster AI apps with Timescale Vector for LangChain. Get 243% faster similarity search, time-based RAG, and PostgreSQL simplicity. Free 90-day trial.

LangChain secures $10M seed round from Benchmark to empower developers building AI apps with our open-source framework for data-aware, agentic LLMs.

Scale retrieval apps to production with LangChain's Airbyte integration. Automate data ingestion with scheduling, text splitting, and 50+ embeddings.

Deploy LangChain apps with LangServe's playground UI and configurable parameters. Experiment with models, share with teams, stream in real-time.

Build better AI apps with flexible retrieval methods in LangChain. Use any retriever—from semantic to hybrid—to create personalized ChatGPT for your data.

Introducing a new way to run evals using LangSmith’s Pytest and Vitest/Jest integrations.

Build AI-powered data experiences with Cube's semantic layer and LangChain. Prevent hallucinations, query in natural language, create conversational interfaces.

LangSmith's Role Based Access Control (RBAC) helps enterprises manage resource access with custom roles and API keys.

Automate web research with LangChain's retriever. Run parallel searches, scrape pages, and synthesize information with LLMs—locally or in the cloud.

Build reliable AI agents with Plan-and-Execute framework. Separate planning from execution for complex tasks with fewer errors.

Workspaces in LangSmith lets enterprises separate resources between different teams, business units, or deployment environments.

Query SQL databases using natural language with LLMs. Learn techniques to reduce hallucinations and build reliable text-to-SQL solutions with LangChain.

Build multi-modal RAG apps for slide decks using GPT-4V. Compare approaches, evaluate with benchmarks, and deploy with LangChain templates for visual Q&A.

Learn context engineering: building dynamic systems that provide LLMs the right information, tools, and format to reliably accomplish tasks.

Debug deep agents with LangSmith's tracing and analysis. Analyze complex traces, optimize prompts with Polly, and improve performance.

Build powerful LangChain agents with Structured Tools. Accept multiple inputs, create complex tool schemas, and unlock advanced AI agent capabilities.

Learn how to implement multi-vector retriever for RAG across tables, text, and images. Explore cookbooks for semi-structured and multi-modal data retrieval.

LangSmith Engine now detects agent issues over 2x better, proposes stronger fixes, supports Slack and Linear workflows, and is available for self-hosted deployments.

Learn how to use agent skills with Deep Agents CLI to build token-efficient AI agents. Discover, load, and execute skills dynamically.

Building Production Agentic AI at IBM: Architecture, Decisions, and What We Learned TL;DR — IBM’s Technology Lifecycle Services built a multi-agent system from scratch — the agents themselves in Python with LangGraph. I…

Learn how OpenWiki uses evidence-backed claims to detect stale knowledge, reduce hallucinations, and build self-correcting memory for evolving codebases.

How we create synthetic agent environments and tasks: a spec generation step, a spec-to-task step, and a world spec that holds shared knowledge.

See how Toyota North America uses Deep Agents and LangSmith to run 50+ production agents, cut delivery from 6 months to 4 days, and track AI ROI.

🌈 Prism Reviewer Developed by Vyoman Labs Prism Reviewer is an agentic, AI-driven multi-agent code review system developed by Vyoman Labs and orchestrated via LangGraph and LiteLLM. It acts as an autonomous gatekeeper…

Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes […] The post Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each appeared first on MarkTechPost.

Preview Builds let teams test pull request branches in temporary, production-like LangSmith deployments before merging agent changes.

LangSmith Tuned Evaluators attach quality feedback to production traces, starting with Perceived Error, to help teams find and fix agent mistakes.

Ever wondered how ChatGPT, Gemini, and other chat interfaces generate PDFs, PowerPoints, and more when all they have under the hood is an LLM? The trick isn’t a smarter model. It’s something simpler: skills which are instructions an agent loads only when needed. Next, let’s explore how skills work using LangChain and how they can make […] The post How to Add Skills in Agents using LangChain appeared first on Analytics Vidhya.

Let your LangChain agents pay for APIs with deterministic session budgets. AgentCore Payments middleware signs x402 payments; LangSmith traces every one.

Managed Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.

LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.

Learn how agent observability enables effective evaluation of AI agents. Understand tracing, debugging reasoning, and performance insights to iterate and improve agent behavior.

Building monday.com Sidekick: why capable agents need more than just tools August 11, 2026 14 min Go back to blog Create agents This is a guest post from Omri Bruchim, AI Engineering Group Lead, monday.com In early test…

We benchmarked NVIDIA NeMo Switchyard on 145 agent tasks. Only 7% of turns needed a frontier model, and routing cut cost 74% for six points of accuracy.

nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

A verified 2026 comparison of LLM observability platforms covering tracing depth, evaluation capability, production monitoring, and pricing. The post Top LLM Observability and Evaluation Platforms in 2026: Langfuse, LangSmith, Braintrust, Arize, and More Compared appeared first on MarkTechPost.

Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. A toolkit is a collection of tools meant to be used…

Deploy Deep Agents to a managed LangSmith runtime with durable execution, memory, sandboxes, channels, evals, and production-ready infrastructure.

Deep Agents, LangChain, and LangGraph each offer distinct approaches to building agents. In this post, we cover the key distinctions between our open source frameworks and when you should reach for each one.

Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.

Learn how LangChain built an autonomous SRE agent for Kubernetes deployments with Deep Agents, human approval for changes, LangSmith tracing, and evals.

Learn how to evaluate voice agents across execution, outcomes, and caller experience using LangSmith traces, code evaluators, LLM judges, and human review.

Customer Experience (CX) Agents in Production: Lessons from Lyft, Vodafone, and LATAM Airlines

Learn how to build an end-to-end security assessment pipeline for AI agent skills using NVIDIA SkillSpector and LangGraph. In this tutorial, we construct a synthetic skill marketplace, scan for malicious prompt injection, credential access, and risky dependencies, and implement custom YARA rules, baseline suppressions, and CI deployment gates. The post Building an Advanced AI Skill Security Auditing Pipeline with NVIDIA SkillSpector, LangGraph, YARA Rules, SARIF, and CI Policy Gates appeared first on MarkTechPost.

Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.

LangChain built ReviewBench, a benchmark for evaluating code review agents against real PR feedback from trusted reviewers. The article explains how tasks are curated from real reviews, how the benchmark runs, its scoring metrics, initial results, and future plans.

LangSmith LLM Gateway is now in public beta, providing a centralized governance layer between agents and models with runtime controls including cost caps, rate limits, model fallbacks, and sensitive data redaction, helping teams avoid vendor lock-in and manage model usage consistently.

Learn how Similarweb uses LangSmith to evaluate long-form agent research reports with rubrics, faithfulness checks, traces, and baseline comparisons.