The Most Common Questions About Agent Evals, Answered
Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down.
LangChain Blog14 min read
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160 stories
Context Engineering with Deep Agents: How To Remember Without Keeping Everything In Context
Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused.
LangChain Blog9 min read
How to do question answering from a PDF with Python and RAG
Build a Python pipeline that answers questions from PDFs and links each answer to its page, then check the citations before you trust them.
Apify Blog29 min read
LangChain lets teams search AI-agent records for behavior, not just keywords
AI Search uses a model to check session records against plain-language questions. Its fixed reading budget favors conversations over tool output.
Superpower Daily3 min read
How Orange Business Built Live Intelligence with LangChain and LangSmith
Learn how Orange Business used LangChain and LangSmith to build Live Intelligence, a governed AI platform for agents, evals, observability, and production scale.
LangChain Blog5 min read
Open Models have crossed a threshold
Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and latency. Here's what our evals show and how to start using them in Deep Agents.
LangChain Blog7 min read
How We Made Our Docs Test Themselves
Stale code samples are a docs problem nobody prioritizes. Here's how we used the Deep Agents CLI to automate migrating inline code into testable, CI-ready examples
LangChain Blog4 min read
Open SWE: An Open-Source Framework for Internal Coding Agents
Built on Deep Agents and LangGraph, Open SWE provides the core architectural components for internal coding agents.
LangChain Blog9 min read
Context Management for Deep Agents
Learn how Deep Agents SDK manages context for long-running AI tasks through offloading, summarization, and filesystem abstraction to prevent context rot.
LangChain Blog6 min read
Deep Agents Deploy: an open alternative to Claude Managed Agents
Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way.
LangChain Blog5 min read
Improving Deep Agents with harness engineering
Harness engineering improved LangChain's coding agent from Top 30 to Top 5 on Terminal Bench using self-verification, tracing, and context optimization.
LangChain Blog8 min read
LangChain Explained in Telugu 🔥 | Build AI Agents from Scratch | LangGraph, RAG, MCP & LangSmith
ChatGPT answers questions… but how do we build AI systems that can actually complete tasks? What should I explain next — Comment below! 👇 In this video, we're exploring LangChain from scratch using one practical real-world example.
Withme Sravani
Search agent traces by behavior with AI Search in LangSmith
AI Search in LangSmith is a new way to query traces using natural language. Describe the behavior you're looking for, and LangSmith returns the traces where it happened.
LangChain Blog7 min read
Building Long-Running Agents with ScaleDown and Deep Agents
Use ScaleDown's constrained decoding models with LangChain and Deep Agents to summarize history, compress tool results, and extract data faster and cheaper.
LangChain Blog9 min read
DXC partners with LangChain to bring governed AI agents to critical IT operations
The partnership builds on DXC’s existing OASIS platform. DXC reports sharp reductions in incident-resolution time, while sensitive actions can pause for human approval.
Superpower Daily3 min read
Building RAG from Scratch: No Frameworks Needed
Building RAG from Scratch: No Frameworks Needed Understand Retrieval-Augmented Generation by building it yourself — pure Python, zero dependencies. Introduction LangChain, LlamaIndex, Haystack — great tools, but they hide how RAG actually works.
Dev.to AI9 min read
🔴 LangChain (LangSmith): Seeing some issues logging into the application
No — LangChain (LangSmith) is operational. Uptime: 99.98%. 3 incidents tracked (30d). Live status, updated every 5 minutes.
AIWatch Blog3 min read
How to Design Human-in-the-Loop Checkpoints for Autonomous AI Agents
Autonomous AI agents can read requests, retrieve data, reason through options, and trigger actions in seconds. That speed is useful, but it also creates risk when the next step affects money, customer records, or external systems.
Analytics Vidhya23 min read
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