#Deep Agents
3034 related articles
·2 min
Deep Dive into the LangChain Ecosystem: A Guide to LangGraph, deepagents, and LangSmith Collaboration
A deep analysis of three core LangChain ecosystem components: LangGraph stateful agent orchestration, deepagents deep agent paradigm, and LangSmith observability platform for production AI apps.
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·3 min
Cekura: A Deep Dive into the Automated Testing, Monitoring, and Self-Healing Closed-Loop Platform for Voice AI Agents
Deep analysis of how Cekura's five-step closed loop—scenario simulation, failure capture, root cause diagnosis, automatic prompt rewriting, and regression verification—solves voice AI agent quality assurance in production.
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·2 min
DeepAgents in Practice: A Deep Dive into Sandbox Backends and Agent Skills
A deep dive into DeepAgents' sandbox backend and Agent Skills: why Agents need sandbox isolation to run code, how Skills enable modular capability reuse, and how the two combine to build reliable AI Agent systems.
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·2 min
DeepAgents in Practice: A Deep Dive into Sandbox Backends and Agent Skills
A deep dive into DeepAgents' sandbox backend and Agent Skills: why Agents need sandboxes to isolate code execution, how Skills enable modular capability reuse, and how the two work together to build reliable AI Agent systems.
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·3 min
LangChain v1.3 Hands-On Tutorial: Deep Dive into Agents, RAG, and Local Model Deployment
Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.
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·2 min
Complete Tutorial: Building Agents and Personal Knowledge Bases with DeepSeek
A complete guide to building AI agents with DeepSeek R1: private knowledge bases using RAG, basic/advanced agent implementation, and Coze/Dify workflow tutorials.
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·3 min
DeepAgents in Practice: A Complete Guide to Building a HarmonyOS Automated Testing Agent
A deep dive into DeepAgents' core mechanisms, with a hands-on guide to building a HarmonyOS automated testing Agent — covering create_deep_agent, LangChain comparison, and long-chain task planning.
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·4 min
AI Agents Automating JS Reverse Engineering: A Deep Dive into the New Paradigm for Web Scraping
AI agents are revolutionizing JS reverse engineering. This deep dive covers built-in tool chains, automation modes, prompt engineering for e-commerce, and full pipeline automation from parameter extraction to database storage.
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·5 min
A Deep Dive into Vector Search for AI Agents: The Technical Foundations of RAG and Long-Term Memory
An in-depth look at the core tech behind AI Agents: how the HNSW, IVF, and PQ vector search algorithms power RAG and long-term memory. Understand where a model's "memory" and "knowledge" come from.
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·6 min
A Deep Dive into OpenAI Codex: How AI Engineers Are Reimagining Software Development with Agents
OpenAI Codex is redefining how AI engineers work: from code completion to autonomous Agents, from single-threaded to parallel Value Maxing. A deep dive into the Codex App architecture, open ecosystem, and Manager of Agents practice.
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·3 min
Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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·3 min
AgentScope 2.0 Deep Dive: Six Core Upgrades for Production-Grade Multi-Agent Development
AgentScope 2.0 by Alibaba's Tongyi Lab delivers six major upgrades: typed event streaming, dangerous instruction interception, human-in-the-loop, concurrent execution, workspace system, and agent-as-a-service for production-grade multi-agent development.
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·4 min
MCP Protocol Deep Dive: Its Relationship with Function Calling and Agents, Plus a Practical Guide
Deep dive into MCP (Model Context Protocol): clarifying the three-layer relationship between MCP, Function Calling, and Agents, covering protocol roles, call flows, transport choices, and production security for AI developers.
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·5 min
Building DeepSeek Agents and Knowledge Bases from Scratch: A Practical Guide to Commercial Deployment
A systematic guide to the full DeepSeek Agent development process: covering prompt engineering, the ReAct framework, workflow orchestration, local deployment, and business requirement breakdown for commercial-ready AI Agents.
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·5 min
A Deep Dive into Cloud Coding Agents: How Users in China Can Choose Wisely
What exactly is the Cloud Coding Agent Silicon Valley is hyping? This article breaks down the core concept across three axes—where it runs, who watches, where tasks start—and gives users in China practical advice on local alternatives.
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·7 min
Getting Started with AI Agents: A Deep Dive into the Perception-Decision-Action Loop
A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.
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·7 min
ManagedAgents.sh: A Deep Dive into the Model-Agnostic Open-Source Managed Agent Platform
ManagedAgents.sh is a model-agnostic managed agent platform from OpenComputer, supporting Claude, Pi, and Codex runtimes with Slack and GitHub integration.
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·4 min
Reinforcement Learning for AI Agents: A Deep Dive into RLHF and Agentic RL
A deep dive into RL for AI agents: from RLHF to Agentic RL, covering PPO vs. GRPO, sparse rewards, tool-calling optimization, and verifiable rewards.
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·2 min
LangChain Fundamentals: A Deep Dive into Components, Chains, and Agents
LangChain is an open-source framework connecting LLMs with external data. This guide explains its three core components: Components, Chains, and Agents for enterprise AI development.
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·7 min
The Prompt is the Platform: How AI Agents Deeply Participate in System Design and Specification
Resonate's founder proposes "The Prompt is the Platform": as AI agents generate production-grade implementations from abstract specs, engineers' value shifts to specification. A deep dive into deterministic simulation and forbidden-fruit debugging.
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