AI Agents

Memoket Kite is an open-source memory layer for AI agents that replaces the standard embeddings-plus-vector-database stack with a single portable, topic-indexed file of structured facts. It scores 93.51% on the LoCoMo long-conversation benchmark and 85.60% on LongMemEval-S while reading only about 1.5k tokens of context — the top overall score on both benchmarks with no vector stack at all. What Is Memoket Kite? A Memory Layer for AI Agents Memoket Kite (KITE) is a Python library, released under the Apache License 2.0, that gives AI agents persistent, source-backed memory. Its tagline is “Follow the thread, not the nearest match.” Instead of storing conversations as opaque embedding vectors and retrieving by similarity, KITE turns what an agent hears into typed, dated, topic-indexed facts that you can open and read in a plain file. ...

Moli is a production-ready, structured-first browser engine for AI agents, written in pure Rust. It runs real JavaScript, DOM, and browser APIs by default, but computes layout or pixels only when you ask — a cost model built for crawling, browser-use, and retrieval workloads. In a 192-URL crawl it beat Chrome Headless on useful-page rate while using roughly 10x less memory. What Is Moli? A Structured-First Browser Engine for AI Agents Moli is not another Chromium wrapper. It is a browser kernel built from the ground up in Rust, designed around a single idea: AI agents mostly want the structure of a page — the DOM, the computed styles, the text — not a rendered picture of it. Traditional headless browsers spend enormous effort producing pixels that most agent workloads never look at. ...

The main bottleneck for AI agents in 2026 is no longer raw intelligence — it is context management, memory, and task coherence. As models have gotten smarter, the binding constraint has shifted to the finite context window and how well agents curate, retain, and apply information across long, multi-step tasks. This article explains why “just use a smarter model” is the wrong fix, and what teams should actually optimize instead. ...

The unit economics of software development are undergoing a structural transformation. For decades, the fundamental cost of building software was human coordination — the overhead of aligning developers, managing dependencies, and communicating across teams. AI is now rewriting that equation. By early 2026, technical workers self-report a median 1.4x to 2x value increase from AI tools, and 52% of developers say AI has positively affected their productivity. But the data tells a more complex story — one of perception gaps, measurement challenges, and a strategic landscape that rewards speed over headcount reduction. ...

What is Kontext CLI? Kontext CLI is an open-source credential broker and runtime security tool built specifically for AI coding agents. It intercepts every tool call made by agents like Claude Code, Codex, and Claude Cowork, brokers short-lived credentials at request time, and enforces security policies before any action reaches your infrastructure. Unlike traditional secrets managers that just store credentials, Kontext actively controls how and when agents use them. What is a Credential Broker for AI Agents? A credential broker is a security layer that sits between an AI coding agent and the services it needs to access. Instead of giving the agent a long-lived API key or database password, the broker issues short-lived, scoped credentials on demand for each individual tool call. The agent never holds the actual secret — it receives a temporary token valid only for the specific operation it needs to perform. ...

Scientific computing is undergoing a fundamental transformation as agentic AI systems take on increasingly complex roles in research software development. OpenAI’s July 2026 field report on eight agent-assisted scientific computing projects demonstrates that coding agents like Codex and Claude Code can modernize legacy research libraries, automate simulation workflows, and free scientists to focus on verification and discovery rather than implementation. This shift promises to accelerate research across life sciences, materials science, and beyond. ...

Introduction — The $0/Month AI Company Is Real Yes, you can run a full fleet of AI agents on Google’s Gemini 2.5 Flash free tier at absolutely zero monthly cost. A solo developer in Taiwan proved this by building a 4-agent company — CEO, Social Media Manager, Security Monitor, and Advisor — that executes 105 automated tasks every day using only 7% of the free tier’s 1,500 daily request limit. With total infrastructure costs of roughly $5 per month (Vercel Hobby plan and Firebase free tier), this architecture demonstrates that a one-person AI-powered business is not a futuristic fantasy but a practical reality available today. ...

Introduction — Why Source-Backed Research Matters for AI Agents A product deep research agent skill is a reusable capability that equips AI coding agents like Claude Code, Cursor, and Windsurf to autonomously gather, verify, and synthesize information about products, companies, and markets from real sources — producing structured, citation-backed output instead of hallucinated summaries. Without source-backed research, AI agents cannot be trusted for production-grade competitive analysis, due diligence, or market intelligence, because they default to generating plausible-sounding but unverifiable claims. This guide explains how to build such a skill from the ground up. ...

What is Mem-Port and Why Portable Agentic Memory Matters Mem-Port is an open-source MCP (Model Context Protocol) memory server that gives AI coding agents persistent, portable long-term memory using an embedded SurrealDB database. Unlike traditional memory solutions that lock agent context to a single machine or tool, Mem-Port lets you carry your AI agent’s entire memory — facts, episodes, skills, and entity relationships — between machines, copilots, and teams as a single portable JSON bundle. Think of it as a thumb drive for your AI’s brain. ...

BrowserAct is an open-source browser automation platform purpose-built for AI agents, offering anti-detection capabilities, persistent session management, parallel execution, and a SkillHub marketplace with over 100,000 reusable skills. Unlike traditional testing frameworks or data extraction tools, BrowserAct solves the “last mile” problem of AI agents: actually executing actions inside real, protected, and dynamic web interfaces without getting blocked, losing state, or breaking on every page change. What Is BrowserAct? — The Browser Layer for AI Agents BrowserAct positions itself as a new category of infrastructure: the browser layer for AI agents. While most AI agent frameworks focus on reasoning, planning, and tool calling, they consistently fail at the execution layer — actually navigating real websites that employ Cloudflare, reCAPTCHA, Datadome, login walls, and session timeouts. ...