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Glossary8
Open the full glossary- API (Application Programming Interface)A published contract: send a request in the specified shape and get a reply in the specified shape, with no need to know how the other side works internally.
- API keyA secret string that tells a remote service "this request is yours, and the bill is yours".
- Environment variables and .envConfiguration a program reads from outside itself, instead of values hardcoded in the source.
- SDK (Software Development Kit)A service's own pre-packaged toolkit: it wraps the details of calling its API into a few convenient functions so you do not assemble requests yourself.
- HTTPThe request/response language between clients and servers — and the transport every model API uses.
- Model Provider / Provider APIA Model Provider / Provider API is the model company's service entry: your program sends messages, the provider returns results and bills by plan; it is not a coding agent.
- LLM Router / API RouterAn LLM Router / API Router works like a switchboard: one API can forward requests to different models or backends.
- Model RuntimeA Model Runtime loads a model and exposes an inference API.
Found 31 articles
Agent Interfaces: choosing between CLI, API, browser use, computer use, and sandboxes
Which door does an agent act through? Adapted from the MIT-licensed curriculum's Stage 8 concept chapter: eight recurring terms, a 'smallest interface first' decision table, the three tables to draw before letting an agent act (allowlist, actions, approval gates), and the four questions to ask of any browser/computer-use benchmark score. The bigger the door, the higher the risk — the first principle is to pick the smallest, most inspectable one.
Build Your First AI Agent in Seven Steps: from environment to a minimal production pass
Adapted from the MIT-licensed awesome-agentic-ai-zh integrated tutorial: one 'paper assistant' project wires up environment setup, your first LLM call, the four-part prompt, tool use, the agent loop with reflection, RAG memory, evals with observability, and the smallest interface. Every step ships minimal runnable code and safety rails; the local Ollama path costs 0 in API fees.
After You Press Enter: From 0.03 s to 0.85 s, What Your Question Goes Through
One real API call dissects the full chain of a question: 35 ms to establish the connection, microseconds to tokenize, 474 ms for the model to read your whole question, 380 ms generating it token by token — 854 ms in total. Includes the complete reproducible measurement method, and one counterintuitive conclusion: 96% of the time is not in the network but inside the model.
API Documentation Sync System: From Manual Maintenance to Code-Driven Docs Automation
The API changed but the docs did not, so frontend colleagues guess parameters and the test environment becomes trial and error. This article wires Apidog, Fern, Mintlify, and an LLM node into a documentation-sync pipeline: extract interface definitions from code, generate examples automatically, detect inconsistencies and alert — keeping the docs permanently aligned with the implementation.
Microservice Dependency Map Generation: From Black-Box Architecture to Visualized System Understanding
Under a microservice architecture nobody knows the full call relationships between services; change one API and you cannot tell which downstream services it affects, and a newcomer needs weeks to grasp the system. This article wires Jaeger, Prometheus, LLM nodes, and a graphing library into a dependency-map pipeline: extract distributed-tracing data, build call relationships, identify critical paths, and generate an interactive map so architects and engineers understand the system structure fast.
How to Cost AI: Subscriptions, Tokens, and Hidden Labor
AI adoption budgets are often severely underestimated because most people count only the subscription fees. This article teaches you to compute all three cost layers item by item — per-user monthly subscriptions, usage-billed API tokens, and the hidden labor of review and operations — and provides an estimation-sheet template you can fill in directly.
Function Calling 101: Getting an LLM to Actually Use Tools
A clear account of the Function Calling mechanism in mainstream LLM APIs: how to write a tool JSON Schema, how the model decides whether to call a tool, how to use tool_choice for forced calls and parallel calls — with a complete runnable example, plus practical techniques for how the way you write tool descriptions decides call quality.
How the Web Talks: HTTP, Status Codes, Headers, and Invisible Protocols
Why API docs are full of status codes and headers. This article walks through a real request to clarify the four parts of HTTP — request, response, status code, header — then explains the difference between WebSocket and polling, why CORS blocks you, what a proxy does in the middle, and why SSH and private keys belong to the same family.
Three-Statement Articulation and Cash-Flow Quality Checks: Spotting the Book-Profit Trap
Profit is an opinion; cash is a fact. This article takes apart the reconciliation relations of the income statement, balance sheet, and cash-flow statement, gives check dimensions such as the cash conversion ratio, working capital, and capitalized spending, adds a 'red-flag signal → account to check → verification method' cheat sheet, and demonstrates writing these checks as an automatically executable, traceable rule engine.
APIs, SDKs and All Those Abbreviations: What Each One Is and When You Will Need It
An API is a published agreement; an SDK is a toolkit in which someone has wrapped the details of calling that API for you. This piece spells out the difference between the two, why some tools require an API key and some do not, the distinction between REST and streaming, and whether you, as a user, ever need to write API calls yourself.
Connecting Your AI Agent to WhatsApp: Two Paths, Bridge and Official Cloud API
Hermes Agent reaches WhatsApp two ways: the Baileys bridge (scan a QR, personal account, risk-control exposure) and Meta's official WhatsApp Cloud API (business account, webhook, no ban risk). This article teaches you how to choose, how to connect, and who gets to talk to your bot.
Your First LLM API Call: Tokens, Billing, and Common Errors
Send your first LLM request with Python's openai SDK: understand the division of labor among system, user, and assistant message roles; the trade-offs of temperature, max_tokens, and streaming; token billing concepts and usage estimation; and finally, a troubleshooting guide for the errors every beginner will meet.
Installing the OpenAI Codex CLI: Sign-In, Permissions, and Your First Task
OpenAI's terminal coding agent Codex CLI, from system requirements, three official installation methods, ChatGPT-account sign-in and API-key sign-in, to the three /permissions presets and non-interactive mode. Every step spells out the output you should see and flags which commands I could not verify against the official documentation.
Set Up Your LLM Dev Environment in Five Minutes
Build a development environment that can call an LLM API from scratch: installing Python, virtual environments, getting an API key, and managing environment variables, then verifying the whole chain with a single curl command. Complete commands for both macOS and Windows, with a focus on keeping your key safe and out of git.
What Is an API Key: It's Both Your Identity and Your Wallet
An API key is a string that tells a remote service 'this request is yours, and the bill goes to you.' This article explains why it's a secret, where it belongs (environment variables or .env — never a chat window), what to do on leakage, why it's usually wired directly to money, and a distinction most beginners miss: logging into the web version is not the same as having an API key.
A Reality Check on Decision Models: Why They Seem Miraculous Online but We Measured Only 54%: A Full Comparison of JEV / LAYA / KEV / CLM-8B and a Deployment Formula
In September 2026, System One decision models formed a new category within two weeks: the closed-source JEV API, then LAYA, KEV, and CLM-8B open-sourced one after another. This article does more than summarize the differences among the four; it uses six everyday scenarios to explain how they are actually used, and places the official marketing side by side with our same-question measurements: on the same set of questions, full-coverage accuracy was only 54%, but with confidence gating it reached 91.7%.
Deep Research on Prime Agent: PrimeIntellect's Self-Improving RLM Agent and Our Field Test
A complete breakdown of Prime Agent, the self-improving Agent framework open-sourced by Prime Intellect in August 2026: the dual abstractions of RLM + Continual Harness, surpassing the human baseline on ARC-AGI-3, an architecture comparison with OpenClaw infrastructure, and a complete field test record of connecting it to our own API relay layer on a Mac Studio.
Agent-Reach: Giving AI Agents a Pair of Eyes Over the Entire Internet
GitHub Trending #7 · 5.2K ⭐/week · Lets AI Agents search and read content from 10+ platforms including Twitter/Reddit/YouTube/Bilibili/Xiaohongshu, with no API fees, using pure web scraping
Agent Evolver Deep Dive: The Evolution Engine That Lets AI Agents Grow Like Humans
After prolonged use of an Agent, its core files (SOUL, AGENTS, USER, MEMORY, RULES) keep expanding, and old rules conflict with new directions. Agent Evolver introduces a human growth model: periodic self-reflection, identifying outdated beliefs, and reshaping itself under user approval. This article breaks down its philosophical foundations, three-dimensional evaluation system, growth trigger mechanism, and safety design, and explains why it is the most underrated "evolution layer" in Agent infrastructure.
May 2026 LLM API Pricing Landscape: Complete Comparison of DeepSeek, Qwen, GLM, Kimi, MiniMax, and Doubao
Cross-verified through multiple search engines including Exa, Tavily, and Brave, this compiles per-million-token pricing for DeepSeek V4, Qwen3.6-plus, GLM-5, Kimi K2.5, MiniMax M2.7, Volcano Engine Doubao Seed 2.0, and Claude/GPT, with a Sub2API cost setup guide.
OpenClaw Productivity Revolution: 22 Automation Solutions Reshaping Daily Workflows
From Personal CRM to Second Brain, from multi-channel customer service to autonomous project management, a deep dive into the 22 most practical productivity automation solutions in Awesome OpenClaw Use Cases, with architecture analysis and practical recommendations.
Complete Comparison of 15 Native Search Engines: In-Depth Analysis from Indexing Mechanisms to Application Scenarios
A comprehensive review of 15 native search engines supported by OpenClaw: indexing sources, API pricing, latency, Chinese language support, stability, and the best choice for each scenario.
Agent Framework Capability Matrix: Hermes → OpenClaw → Claude Code Full Capability Overview
One matrix to understand the full capability distribution of the three-layer Agent framework: search API, LLM backend, MCP protocol, skill system, role division, communication channels.
Brave Search API Configuration and Tavily Scenario Comparison
Configuration of Brave Search API in a three-tier Agent framework, plus a scenario selection guide for Tavily vs Brave.
DeepSeek V4 Flash / V4 Pro API Best Practices and Performance Comparison
Complete guide to the DeepSeek V4 series models: comparing performance, pricing, and use cases of V4 Flash vs V4 Pro, with API call examples and cost estimates.
Financial Data API Research: Injecting Market Data Capabilities into Agent Frameworks
Research on financial data APIs suitable for Agent frameworks: Hong Kong stocks, US stocks, macroeconomic data, with plans to integrate financial data analysis capabilities into OpenClaw.
LiteLLM Proxy Multi Model Routing in Practice
Use LiteLLM Proxy to centrally manage multiple LLM backends such as DeepSeek, ModelStudio, Ollama, and more: load balancing, cost tracking, fallback configuration.
LM Studio Installation and Usage Guide: GUI Local LLM Management
Complete LM Studio guide: download, manage, and run local LLMs on Mac through a graphical interface, supporting GGUF and MLX formats and providing an OpenAI-compatible API.
Cross-layer Configuration of Tavily Search API in Agent Frameworks
Configuration methods and usage scenarios for the Tavily AI Search API across three frameworks: Hermes Agent, OpenClaw, and Claude Code.
Web Fetch and Web Scraping in Agent Applications
Configuration and scenario guide for using Web Fetch / Puppeteer / fetch_url to retrieve web content in a three-tier Agent framework.
Three-Layer Framework LLM API Configuration Summary: DS V4 Pro, Qwen 3.6, oMLX Local
A complete guide to configuring DeepSeek V4 Pro, Qwen 3.6-Plus, and oMLX local Qwen3 Coder Next across Hermes Agent, OpenClaw, and Claude Code.