OpenClaw's browser is a separate agent-only profile, fully isolated from your personal browser. And web_search's return shape carries an externalContent.untrusted marker — search results are typed as untrusted external content at the type level.
exec is a mutating shell surface: disabling write, edit, and apply_patch does nothing to make it read-only. And since sandboxing is off by default, host=auto actually resolves to the gateway — if you really want the sandbox, say so explicitly and it will at least fail closed.
Skills load from six sources with the highest precedence winning on name collisions, and a per-agent list replaces rather than merges. Sub-agents get no session or message tools by default — they return plain text to the parent, and the right to speak to a human stays with the parent agent.
When the tool catalog no longer fits in the prompt, OpenClaw offers two answers: Code Mode shows the model only exec and wait and has it write small programs against a hidden catalog, while Tool Search keeps structured search/describe/call controls. Neither bypasses tool policy.
The official triage flow is seven commands and two minutes to a diagnosis. And the most common symptom — the assistant feeling limited or missing tools — is usually the tool profile: minimal allows only session_status, while coding is the default for new local configs.
The Control UI gained a session rail: it uses a utility model to produce a run digest and attaches a read-only companion thread, so you can ask what a session is doing without entering or interrupting the main agent run. Its contents never enter chat.history.
The model is the CPU, the harness is the operating system, and the agent is the application. No matter how powerful a model is, without a good harness it's just a demo. Phil Schmid argues that harness is the most critical infrastructure in AI engineering for 2026.
LangGraph models LLM workflows as directed graphs, solving the pain points of multi-turn iteration, conditional branching, and parallel execution that are difficult to handle with linear pipelines.
Langfuse is currently the most mature open-source LLM Observability platform. This post covers four core capabilities — Tracing, Prompt Management, Evaluation, and Datasets — showing you how to use them in real projects.
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
Every AI tool has its own calling format, making integration costly. MCP (Model Context Protocol) is an open standard proposed by Anthropic that unifies the communication protocol between AI Agents and external tools/data sources, enabling tools to be reused across Agents.
RAG is read-only. Agent Memory lets AI not only read but also write and persist information. Three memory types: Procedural (behavior patterns), Episodic (temporal events), and Semantic (factual knowledge) form a complete cognitive memory system.
An AI agent is not a black box — it is built from three layers: what it knows (Context), how it thinks (Cognition), and what it can do (Action). Understanding these three layers is the key to grasping why agents are sometimes brilliant and sometimes go off the rails, and how to design a truly effective agent system.
A single RAG Agent handling all queries hits knowledge boundaries and performance bottlenecks. Multi-Agent RAG dispatches retrieval tasks to multiple specialized Agents, each with its own knowledge base and retrieval strategy, coordinated by a central Orchestrator that merges results.
Traditional RAG splits documents into small chunks for retrieval, but this causes information fragmentation. LongRAG leverages 100K+ token long-context models to retrieve larger document segments (entire sections or even whole documents), reducing fragmentation while maintaining retrieval efficiency.
Speculative RAG uses small specialist models to generate multiple answer drafts from different document subsets in parallel, then a large model verifies and selects the best answer in one pass. The paper reports +12.97 points accuracy and -50.83% latency on PubHealth — but that is the best cell in the table; other benchmarks gain far less.
Ollama wraps llama.cpp in a Docker-style CLI + REST API, letting you run LLMs locally with a single command. This post covers core concepts, installation, API, hardware requirements, Modelfile customization, and what this tool is — and isn't — good for.
RAG has evolved far beyond simple 'search + generate' into a technology ecosystem spanning ten generations — and since 2025 into an Agentic/Reasoning era. This article is a systematic navigation guide: from Naive RAG to Multi-Agent/LongRAG across ten generations, the post-ten Agentic Era (Search-R1/RL search, MCP, GraphRAG 3.x, vision-native retrieval), retrieval strategies, chunking, embedding, reranking, evaluation frameworks, observability, and cost optimization. Each topic has a dedicated deep-dive article.
vLLM uses PagedAttention to eliminate KV cache memory waste, combining continuous batching and prefix caching to become the most widely adopted open-source LLM inference engine today.
Building a chatbot is more than just calling an API. Conversation state management, memory mechanisms, streaming, guardrails, observability, and tech stack selection — every layer affects the user experience.