Topic

#Codex

Loot, blog posts and adjacent themes connected to this topic. Follow the tag to keep it in your orbit.

Loot

More from this topic

Your Coding Agent Is About to Get a Whole Team

#AI Agents#Multi-Agent#Claude Code#Codex#MCP#Developer Workflow

A premium field guide for evaluating and planning a multi-agent orchestration layer for Claude Code and Codex without blindly installing it. This premium Loot gives you a cautious, high-leverage way to evaluate Ruflo: a multi-agent AI harness for Claude Code and Codex. The public sources describe a system for coordinated swarms, persistent memory, MCP tools, plugins, hooks, federation, and security controls. What the sources confirm The GitHub repository positions Ruflo as a multi-agent AI harness for Claude Code and Codex. The npm package ruflo is published under MIT license and exposes a ruflo CLI. The package metadata currently requires Node.js 20+. The status documentation describes MCP tools, CLI commands, plugins, hooks, memory, agent coordination, and verification workflows. The README presents two different adoption paths: a lighter Claude Code plugin path and a fuller CLI/MCP install path. Evaluation Prompt: Should I Add This Agent Layer? Use this before installation or rollout. Disposable Repo Test Plan Use this to avoid letting a new agent harness touch a production repo first. Team Rollout Prompt Use this when the question becomes operational, not just technical. Security Review Prompt Use this before trusting any autonomous or federated agent layer. Practical adoption ladder Why this is worth watching The interesting shift is not just “more agents.” It is the move from single-session assistance toward coordinated agent teams with persistent memory, task routing, plugins, and verifiable runtime behavior. That is useful, but it also raises the operational bar. Source links GitHub: ruvnet/ruflo npm: ruflo Status doc: Ruflo STATUS.md

View

Open

@ZachasADMIN

Make Codex Remember the Outcome: A Fast /goal Prompt Pack for Long Tasks

#codex#openai#goal#ai agents#prompt workflow#developer productivity

A compact prompt workflow for using OpenAI Codex CLI /goal well: set a short persistent outcome, keep acceptance checks visible, pause or clear goals safely, and avoid stuffing long specs into the command. Use this quick-start pack when a Codex task will span multiple turns, resumes, queued follow-ups, or several files. The point is not to make Codex magically smarter; it gives the agent a persistent target to keep checking against while the work continues. Copy-paste starter Best pattern Keep the goal under one screen: outcome, constraints, validation. Put long requirements in a file, then reference it from the goal. Use the normal prompt for the current step; use /goal for the durable north star. Pause the goal when exploring alternatives; resume it when returning to implementation. Clear the goal after the task is done so it does not steer the next task. When to use it Use /goal for migrations, debugging sessions, release preparation, refactors, long review loops, and tasks where you often say 'continue' or resume the thread later. For one-shot questions, a normal prompt is enough. Evidence notes OpenAI documents /goal as an experimental Codex CLI slash command that sets or views a long-running task goal, with pause, resume, and clear controls. The May 2026 Codex changelog says experimental goals became discoverable, stay paused across resume unless the user opts back in, and gained clearer validation and multi-day duration output. Companion article Read the full evidence-based breakdown here: https://linkloot.io/blog/openai-codex-goal-advantage-long-running-coding-tasks

View

Free

Open

@ZachasADMIN

agentmemory gives Claude Code, Codex, Hermes, and OpenClaw a real memory layer

#AI Agents#Claude Code#Codex#OpenClaw#Agent Memory#Context Window#Developer Tools

agentmemory is one of the more interesting open-source upgrades for coding agents right now: it captures sessions, compresses observations into searchable memory, and injects relevant context back into future runs. The real value is not just lower token burn — it is getting past the brittle limits of static memory files without locking yourself into a full proprietary runtime. agentmemory is the kind of project that matters because it fixes a boring but expensive problem: coding agents forget too much, too fast. Instead of stuffing massive memory files into context every session, it captures what happened, stores it locally, and retrieves only the relevant pieces later. What it actually does records agent sessions automatically via hooks compresses observations into searchable memory supports Claude Code, Codex CLI, Hermes, OpenClaw, and other MCP/REST-capable agents exposes a local MCP + REST surface instead of forcing one editor or one runtime ships with a local viewer so you can inspect what the system remembers Why people care The repo has already crossed 2.8k+ GitHub stars, and the pitch is easy to understand: fewer wasted tokens, less repeated explanation, and better recall across long coding projects. From the project’s own benchmark material: 95.2% R@5 on retrieval-only LongMemEval-S 92% fewer input tokens per session is the headline claim in the README/site internal quality docs show a drop from 22,610 tokens with built-in memory/grep to 3,142 tokens for retrieved results in one 240-observation evaluation at 1,000 observations, the project argues most static built-in memory becomes effectively invisible while searchable memory still covers the full corpus Security and privacy read This looks stronger than many “memory for agents” projects on the privacy front, but there are still a few things worth saying plainly: good: self-hosted by default, no external database stack required good: Apache-2.0 licensed and openly benchmarked with reproducibility docs in the repo good: the comparison docs explicitly claim secret/privacy filtering before storage and audit trails for mutations good: the project publishes a real security policy with private reporting channels and version support guidance watch out: memory is still stored locally on disk, so sensitive prompts/tool outputs should be treated as sensitive local data watch out: peer-to-peer sync/federation and external model providers change the trust boundary immediately watch out: installation commonly starts with npx, and the repo also documents upgrade flows that can mutate the runtime/workspace intentionally Best use cases long-running Claude Code or Codex projects teams bouncing between multiple coding agents projects where architecture decisions get forgotten between sessions workflows that keep hitting /compact, memory caps, or context-window waste Why this is more than hype A lot of memory projects stop at “vector DB for chats.” agentmemory feels more practical because it combines: automatic capture hybrid retrieval cross-agent support local viewer + replay OpenClaw and Hermes integrations out of the box That combination is why this one is worth watching even if you are skeptical of benchmark marketing. Bottom line If you use Claude Code, Codex, Hermes, or OpenClaw heavily, agentmemory is one of the most credible open-source attempts so far to turn “agent memory” from a brittle text file into an actual system. Just keep the claim honest: the real breakthrough is not infinite magic memory — it is more durable, searchable memory with far better token efficiency and fewer context-window failures.

View

Free

Open

@ZachasADMIN

Graphify for Codex++ iOS Simulator: direct simulator control inside Codex

#Codex++#iOS Simulator#macOS Dev#AI Coding#Open Source#Developer Workflow

Graphify for Codex++ adds direct iOS Simulator control inside Codex-oriented workflows. It is aimed at developers who want tighter feedback loops when inspecting, testing, and iterating on mobile app behavior. If you use Codex++ on macOS, this tweak is a genuinely useful upgrade: it embeds a mirrored iOS Simulator directly into Codex’s right panel, so you can inspect UI, test interactions, and iterate on app behavior without constantly juggling windows. Why it is good iOS Simulator inside Codex’s side panel taps, swipes, and hardware buttons are forwarded back to the device headless mirrored view instead of a separate Simulator.app workflow built for real tweaking: add features, fix bugs, validate UI changes faster Trade-offs macOS only needs full Xcode, not just Command Line Tools depends on Codex++ first best fit for people already deep in iOS or tweak-heavy workflows

View

Free

Open

@ZachasADMIN

Blog

#Codex

More from this topic

Your Coding Agent Is About to Get a Whole Team

Make Codex Remember the Outcome: A Fast /goal Prompt Pack for Long Tasks

agentmemory gives Claude Code, Codex, Hermes, and OpenClaw a real memory layer

Graphify for Codex++ iOS Simulator: direct simulator control inside Codex

Related reads

OpenAI adds ChatGPT Enterprise spend controls for AI credit usage

OpenAI and Oracle make Codex available through OCI commitments

OpenAI models and Codex are now generally available on Amazon Bedrock

OpenAI Expands Codex With Role Plugins and Sites

AI Agent Token Cost Calculator: Estimate Codex and Claude Code Waste Before It Compounds

OpenAI Codex adds computer use, memory, images, and 90+ plugins for desktop agent workflows

GPT-5.5 pushes OpenAI toward agentic work, but teams still need a verification loop

OpenAI brings Codex to the ChatGPT mobile app for approvals, threads, and remote SSH follow-up

OpenAI brings workspace agents to ChatGPT for shared team workflows

GPT-5.5 sets a new AI code security record — and proves Cursor vs. Codex is the real story

GLM-5.1 vs Claude Code vs Codex: Which AI Coding Stack Fits Your Workflow?