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Tavily announces native integration with NVIDIA's NemoClaw Deep Agents Blueprint, enabling easy configuration for tool use and network policy governance.
LangChain shows a 7-minute tutorial by Partner Engineer Srimanth Tangedipalli on running Deep Agents Code inside a governed NemoClaw OpenShell Sandbox with NVIDIA Nemotron 3 Ultra via Baseten.
NVIDIA Nemotron 3 Ultra achieves benchmark-leading performance with LangChain Deep Agents harness, offering higher accuracy at lower cost than closed models without retraining.
LangChain Academy launches a new course titled 'Introduction to Deep Agents', teaching what a harness is and the four core capabilities of a harness, with hands-on building using LangChain and LangSmith.
LangChain announces support for recursive language model (RLM) workflows in Deep Agents, with a 6-minute explainer video.
LangChain has released a unified evaluation stack (Harbor + LangSmith) and an in-process code execution solution based on WASM+QuickJS, aimed at improving the evaluation reliability and execution safety of AI agents.
LangChain's Deep Agents allow untrusted agent-written code to run securely using WebAssembly-based code interpreters, providing execution and capability isolation without a traditional sandbox.
Harbor Framework now integrates with LangChain's Deep Agents, LangSmith Sandboxes, and Observability, enabling running agents in isolated, reproducible environments for deterministic testing.
LangChain's Deep Agents now supports dynamic subagents, where the main agent writes orchestration code to coordinate work at scale, enabling workflows like processing hundreds of documents with deterministic coverage.
Deep Agents introduces dynamic subagents that use programmatic orchestration via code scripts instead of tool calls, enabling reliable scaling and complex workflows. The feature integrates with a QuickJS code interpreter for lightweight execution.
The LangChain team has open-sourced the tutorial 'Deep Agents from Scratch', which deconstructs the core design patterns of mainstream agents from scratch, covering task planning, context offloading to a file system, and sub-agent isolation. It includes 5 progressive notebooks, allowing you to build a complete deep research agent hands-on.
Alex, a new LangChain team member, published an article explaining how Deep Agents uses prompt caching to reduce API costs.
An article explaining how to build a Claude Code-like coding agent using LangChain's Deep Agents library, covering the architecture and implementation.
Box Agent, built on LangChain's Deep Agents framework, enables cross-library search, multi-document synthesis, and structured report generation while preserving Box's existing security and permissions model.
A short explanation of Deep Agents by Sydney Runkle, presented by LangChain.
LangChain showcases a macroeconomic research agent built with Deep Agents, LangSmith, and the You.com Finance Research API that analyzes GDP data, detects anomalies, and investigates structural and cyclical drivers at the sector level, producing structured, cited briefings.
LangChain announces Managed Deep Agents in private beta, a hosted API-first runtime for building, running, and operating deep agents in production, leveraging the open-source Deep Agents harness and integrating with LangSmith for durable execution, streaming, and human-in-the-loop workflows.
Deep Agents introduces interpreters: small embedded runtimes that allow agents to write and execute code inside the agent loop, enabling multi-step logic and intermediate state management without full sandbox overhead.
Deep Agents v0.6 introduces a code interpreter, harness profiles for per-model tuning, streaming support, DeltaChannel for checkpoint storage, and ContextHubBackend for versioned agent memory, enabling model-agnostic programmatic tool calling and recursive workflows.
Box and LangChain are showcasing an end-to-end vendor risk analysis solution using Deep Agents and VFS sandboxes, where PDFs are processed by parallel subagents to generate structured reports.