r/AIAgentsDirectory

How to build an AGY WIKI OKF on the Antigravity CLI
▲ 159 r/AIAgentsDirectory+27 crossposts

How to build an AGY WIKI OKF on the Antigravity CLI

AGY Builders,

We are all trying to build useful and scalable workflows for our AGY CLI and ecosystem, but the speed at which we need to learn, build, and deploy new things is incredibly overwhelming. If you are feeling that pressure, you are in the right place here at r/GoogleAntigravityCLI.

Over the past few weeks, I have been testing an "AGY WIKI OKF" setup that I put together myself (after inviting some members of this community to collaborate; mod is not proud). I know some folks might hesitate to trust a tutorial from a random Redditor, but I wanted to share this with the community anyway because it actually works.

I was able to build this because I am all-in on Google and the Antigravity Ecosystem. I’m a truly AGY—I am not some ultra-smart, 10x developer, but I know how to work hard, I dig for the right information, and I iterate.

AGY WIKI OKF | The Idea

To build a frictionless, token-efficient knowledge WIKI engine that transforms static documentation or notes (information) into an active, intelligent collaborator—orchestrated entirely by Antigravity CLI.

The core philosophy is simple: treat knowledge management as a clean pipeline and tokens as a premium, finite resource.

By anchoring this architecture to Google’s Antigravity CLI, the AGY WIKI OKF bypasses heavy middleware and complex UI layers, delivering a hyper-focused AI partner built entirely for execution speed, context hygiene, and minimal footprint.

Why adopting AGY WIKI OKF matters:

  • Stay organized (AGY OCD): Structured Markdown and YAML keep the chaos in check.
  • Save tokens: Doing more with less context window bloat.
  • Scale shareable knowledge: Making it easy to pass context and logic between different LLMs.
  • Humans and Agents working together: One standardized, readable format that works perfectly for both of us.
  • BYOD (Bring Your Own Data): Own your context. Port it to the newest model, platform, or OS instantly.

The Tools

The WIKI

In the agent-first era, a WIKI is no longer just a static graveyard for human notes; it is the operational hard drive for your agents. By maintaining a highly structured WIKI, you ensure that every piece of context is stored in a clean, machine-readable format. This means that whether you are testing a new modular skill or spinning up a specialized agent, your AGY CLI knows exactly where to find the precise context it needs to generate autonomous action, moving you far beyond simple, reactive conversational text.

Reference: Gist on Knowledge Representation

Google Open Knowledge Format (OKF)

Google’s Open Knowledge Format (OKF) feels like the exact missing piece we've needed for orchestrating multiple AI agents effectively. It provides a vendor-neutral, interoperable standard for storing and sharing organizational knowledge.

Why this is huge for orchestration:

  1. The "Lingua Franca" for Agents: Any agent can read it out of the box without platform-specific integrations.
  2. Seamless Context Passing: Specialized agents can access, update, and pass the exact same foundational context back and forth.
  3. Human-in-the-Loop Oversight: Because OKF is just Markdown and YAML, it’s inherently readable and auditable.
  4. Scalable Knowledge: It acts as a shared, living library that grows alongside your agents.

AGY WIKI OKF Integration

Structuring an AGY Wiki using OKF revolutionizes how complex knowledge is shared. By standardizing documentation with concise Markdown and YAML frontmatter, OKF provides a unified taxonomy for cataloging AGY CLI slash commands or skills It is highly token-efficient, stripping away bloated formatting and maximizing context window limits.

The Prompt for Building an AGY WIKI OKF

AGY CLI WIKI OKF PROMT EXAMPLE

/grillme I want to initialize a brand-new, empty Obsidian vault from scratch that adheres strictly to the Open Knowledge Format (OKF) standard, with the specific intent of potentially open-sourcing or sharing this architecture later. I want a purely blank, skeletal framework with no pre-populated data. Please grill me to define the optimal architectural blueprint for this vault. I need you to interrogate me on: Do not generate the directory structure or files until you are satisfied that you have captured all my requirements for a production-ready, shareable knowledge base. 
Core Directory Hierarchy: How should we structure the root (e.g., /concepts, /resources, /indices, /log) to be intuitive for external users? Template Strategy: What base boilerplate templates do we need to ensure every new file is automatically OKF-compliant and structured for consistent metadata? Workflow Logic: Since this is a fresh start, what processes should we bake in for capturing information vs. refining knowledge that could be easily documented for others? CLI Integration: What specific file locations or configurations do we need to ensure this vault plays nicely with the Antigravity CLI from day one? Open-Source & Contributor Documentation: What files should we create to make this a "deployable" standard? Please include requirements for: A README.md with installation and usage instructions. A CONTRIBUTING.md that defines how to add new concepts or schemas. A "System Architecture" document that explains the logic behind the folder structure and metadata fields, ensuring anyone who clones this vault understands how to extend it.

The Final File Structure

AGY WIKI OKF
    ├── .agyrc
    ├── ARCHITECTURE.md
    ├── CONTRIBUTING.md
    ├── README.md
    ├── .agy
    │   └── .keep
    ├── .obsidian
    │   ├── app.json
    │   ├── appearance.json
    │   ├── core-plugins.json
    │   └── workspace.json
    ├── 00-Inbox
    │   └── .keep
    ├── 10-Projects
    │   └── .keep
    ├── 20-Areas
    │   └── .keep
    ├── 30-Resources
    │   ├── .keep
    │   └── Google Antigravity Documentation.md
    ├── 40-Archive
    │   └── .keep
    ├── 99-Meta
    │   └── Templates
    │       ├── Base_Template.md
    │       ├── Project_Template.md
    │       └── Resource_Template.md
    └── Clippings

TL;DR

  • AGY WIKI OKF: Organizes your information (context) , AGY CLI commands, skills  behaviors, and A2A workflows into a token-efficient, shareable format that reduces inference costs for any LLM.
  • Open Knowledge Format (OKF): Provides a standardized, vendor-neutral way to share context (Markdown + YAML), preventing platform lock-in and eliminating data fragmentation.

AGY Builders, I genuinely want your input on this. Please comment, grill me, roast me, ask questions, or give me your raw feedback on this AGY WIKI OKF setup. We are building the foundation to organize and share our data in the BYOD era. Let's build the future together.

u/AgentPadrino — 2 days ago
▲ 13 r/AIAgentsDirectory+9 crossposts

Open handoff: Thought Tree, a markup/spec idea for modular LLM workflows

I’m releasing an open handoff draft of a framework I’ve been developing called the Thought Tree AI Framework.

At its core, the framework uses a simple pattern:

Data Units → Operations → Data Units

A Thought Tree program applies this recursively. Complex cognitive work is decomposed into named artefacts, transformations, contracts, modules and traces.

It came out of experiments with Auto-GPT-style agents, creative production pipelines and the need to separate what LLMs are good at from what deterministic code should handle.

I don’t currently have time to continue developing it properly, so I’m releasing it as an open handoff for anyone who wants to critique, fork, implement or reinterpret it.

The repo includes:

- a concise README;

- one-page summary;

- draft TTML schema;

- minimal example workflow;

- roadmap;

- original long-form explainer.

I’m especially interested in whether people see value in Thought Tree as:

- an intermediate representation for LLM workflows;

- a design vocabulary for structured AI production;

- a small open-source executor;

- or something that could map onto LangGraph / LlamaIndex / other orchestration tools.

Repo: https://github.com/RobertBateman/thoughttree-framework

Feedback, criticism, forks and maintainers welcome.

u/xavier1764 — 6 days ago

How are you handling cost attribution for multi-agent workflows?

We're building a multi-agent system for a large enterprise client. It's a pretty standard setup: a router agent that dispatches to specialized agents, each of which calls different LLMs and tools depending on the task. Some agents call OpenAI, some call Anthropic, some call our internal models, and some call external APIs.

The client wants cost attribution per business unit. They want to know: how much did this department spend on LLM calls this month? Which agents are driving the most cost? Which LLM providers are we spending the most on?
The problem is that cost attribution gets complicated when you have agents calling agents, each making multiple LLM calls. A single user query might result in twenty LLM calls across three different providers. We need to attribute those costs back to the original user or business unit, but the traces get lost in the chain.
We've looked at some LLM observability tools, but they're mostly focused on single LLM calls. They don't understand agent identity or the full workflow. I'm worried we're going to end up building a custom cost attribution layer.

Is anyone else dealing with this? How are you tracking cost attribution across complex agentic workflows?

reddit.com
u/Ok_Wrap2912 — 6 days ago
▲ 4 r/AIAgentsDirectory+1 crossposts

AI coding agents need a local safety boundary before they touch files or run commands

I’ve been testing a local safety layer for AI coding agents.

The problem I kept running into: agents can write files and run terminal commands, but most workflows still rely on prompts, trust, or manual review. That works until the agent edits the wrong file, touches .env, writes outside the workspace, or runs a command that should have required approval.

The approach I’m testing is simple:

  • agent proposes an action
  • local boundary checks it
  • safe actions continue
  • risky actions are denied or require approval
  • every decision is logged

This is not meant to replace coding agents. It sits between the agent and file/shell execution.

Example rules:

  • allow normal source edits
  • block .env writes
  • block private keys
  • block workspace escape
  • audit terminal commands

I’m still validating the design, but the goal is to make agentic coding safer without needing a cloud service.

Curious if others here are solving this with policy files, sandboxing, approval flows, or custom wrappers.

reddit.com
u/pravesh0306 — 12 days ago