#llmsmap.me

Independent technical audit

100monkeys.ai

100monkeys.ai

Overall AI readiness score

A combined result across all audit signals.

84of 100Excellent
AI readiness audit: 10/28/2026Public technical data

Overall AI readiness assessment

How ready 100monkeys.ai is for AI systems

84/100

Approved. The site is a legitimate developer-tools company publishing a valid llms.txt that matches its homepage. The optional llms-full.txt is served as HTML rather than Markdown, which is noted but does not affect the decision.

Audit context

llms.txt is accessible and contains 1,499 tokens. The llms-full.txt URL returns an invalid format. It is excluded from the directory; serve text or Markdown instead. No separate ai.txt policy was detected; it is optional, but can clarify training, retrieval, and attribution preferences.

robots.txt is available. 11 of 11 tracked AI bots are not blocked. Declared sitemaps: 1. The homepage exposes Schema.org types Organization, WebSite, SoftwareApplication; 8 OpenGraph tags were found and markup completeness is 90%.

The mobile Lighthouse profile adds Performance 83/100, Accessibility 97/100, Best Practices 100/100, SEO 100/100, and experimental Agentic Browsing 75/100. These signals have a limited weight: they complement rather than replace llms.txt, robots.txt, and structured-data checks.

Confirmed strengths

  • Accessible llms.txt
  • 1 sitemap declaration
  • Schema.org: Organization, WebSite
  • Complete social metadata

Priority improvements

  1. 1Fix llms-full.txt to return text or Markdown instead of HTML.
  2. 2Reduce mobile rendering delay and main-thread work.
llms.txt tokens1,499
llms-full.txt tokens—
ai.txt
sitemap.xml

Google Lighthouse technical profile

A mobile Lighthouse measurement. Google’s experimental Agentic Browsing category is explained separately and does not replace the broader llmsmap AI-readiness score.

Mobile · Lighthouse
83

Performance

97

Accessibility

100

Best Practices

100

Technical SEO

75

Agentic Browsing

What these results mean

Mobile performance is 83/100, with the largest visible content block appearing in 3.6 s and the browser main thread blocked for 0 ms. Layout shift was 0.001. The main thread is where the browser runs JavaScript, calculates layout, and paints the page; long work there delays both user input and browser-agent actions.

Accessibility scored 97/100, Best Practices 100/100, and technical SEO 100/100. The experimental Agentic Browsing category scored 75/100. It measures signals Google currently tests for software agents and is shown separately from the llmsmap AI-readiness score.

1

Free the main thread and accelerate the first view

Split long JavaScript tasks, defer non-critical scripts and styles, and shorten blocking request chains. This helps the primary content appear sooner and makes controls usable earlier.

2

Reduce code that loads without helping the page

Remove unused CSS and JavaScript, load heavy widgets on demand, and limit third-party scripts. Less code means less parsing and background work on the device.

3

Optimise images and their loading order

Serve correctly sized modern formats, prioritise the primary visual, and lazy-load content below the first viewport.

4

Reduce network delay

Improve server response time, remove unnecessary redirects and repeat downloads, and use compression, caching, and selective preconnect hints.

FCP3.5 s

First content

LCP3.6 s

Main content

CLS0.001

Layout stability

TBT0 ms

Blocking time

SI3.5 s

Visual speed

Metric glossary
FCP · First content
When the first text or image appeared on screen.
LCP · Main content
When the largest visible element in the first viewport rendered.
CLS · Layout stability
How much content shifted unexpectedly while loading; lower is better.
TBT · Blocking time
How long the browser main thread could not respond quickly to input.
SI · Visual speed
How quickly the visible viewport filled with content.
10/28/2026Lighthouse 13.5.0Mobile profile

AI readiness checks

Machine-readable files, crawler policy, discovery, and homepage markup.

llms.txt

File found and accessible

https://100monkeys.ai/llms.txt
llms-full.txt

Invalid format: the llms-full.txt URL does not return text or Markdown.

https://100monkeys.ai/llms-full.txt
ai.txt

ai.txt file was not found

Sitemap in robots.txt1

1 sitemap found

Schema.org (JSON-LD)

Types: Organization, WebSite, SoftwareApplication

OpenGraph90%

8 OG tags found

AI bot access

Based on robots.txt analysis

GPTBotAllowed
OAI-SearchBotAllowed
ChatGPT-UserAllowed
Google-ExtendedAllowed
ClaudeBotAllowed
Claude-SearchBotAllowed
Claude-UserAllowed
BytespiderAllowed
CCBotAllowed
PerplexityBotAllowed
Perplexity-UserAllowed

Sitemaps

Declared discovery routes for crawlers and agents.

OpenGraph tags

Social preview metadata found on the homepage.

Markup completeness: 90%
og:typewebsite
og:urlhttps://100monkeys.ai/
og:title100monkeys.ai | AEGIS, the workflow engine for autonomous agents, and four more products
og:description100monkeys.ai builds AEGIS, the self-hosted workflow engine that runs autonomous agents until deterministic validators pass, and four more products: Grill My Idea, Carbon Forge, Your Zaru and Zaru CLI.
og:image:width1200
og:image:height630
og:site_name100monkeys.ai

Schema.org markup

Structured entities and properties exposed on the homepage.

Types found: 3 · Properties: 7
OrganizationWebSiteSoftwareApplication
name100monkeys.ai
urlhttps://100monkeys.ai/
description100monkeys.ai builds AEGIS, the self-hosted workflow engine that runs autonomous agents until deterministic validators pass, and four more products: Grill My Idea, Carbon Forge, Your Zaru and Zaru CLI.
logohttps://100monkeys.ai/aegis-logo.png
sameAshttps://github.com/100monkeys-ai, https://www.linkedin.com/company/112423436/, https://x.com/100monkeysai, https://hashnode.com/@100monkeys-ai, https://medium.com/@jeshua_46811
legalName100monkeys AI, Inc.
publisher{"@id":"https://100monkeys.ai/#organization"}
# 100monkeys.ai

> 100monkeys.ai builds AEGIS, a self-hosted orchestrator that runs autonomous agents in isolated, policy-enforced runtimes with durable FSM state, and four products alongside it: Carbon Forge, Your Zaru, Zaru CLI and Grill My Idea. AEGIS is open source under AGPL-3.0 and installs with one command: `curl -fsSL https://get.100monkeys.ai | bash`

AEGIS bounds what an agent can do at the infrastructure layer rather than in the prompt. Generate, execute, evaluate, refine: the loop runs until deterministic validators pass. The membrane around it enforces isolation and policy on every tool call, and each run is a durable state machine.

## AEGIS

- [100monkeys.ai](https://100monkeys.ai/): the company site, with the AEGIS overview and the install command
- [AEGIS documentation](https://docs.100monkeys.ai): the platform docs
- [Getting started](https://docs.100monkeys.ai/docs/getting-started): install and run a first agent
- [AEGIS documentation for LLMs](https://docs.100monkeys.ai/llms.txt): the docs index in llms.txt form
- [aegis-orchestrator on GitHub](https://github.com/100monkeys-ai/aegis-orchestrator): the source, AGPL-3.0

## Products

- [Carbon Forge](https://carbonforge.dev/): code, files and prose in one record on the server, which people and agents read and write through the same tools over MCP. Public workspaces are free, and every account includes one free private workspace
- [Your Zaru](https://myzaru.com/): a personal AI companion that works alongside you, remembers you and shows its work
- [Zaru CLI](https://github.com/100monkeys-ai/zaru-cli): a terminal coding agent that keeps its execution loop in view, open source under Apache-2.0
- [Grill My Idea](https://grillmyidea.com/): market research for a startup idea, before you build it

## Read

- [FAQ](https://100monkeys.ai/faq): short answers on self-hosting an agent orchestrator, keeping credentials away from agents, durable agent workflows and accountability
- [Red levers: traceable, deterministic agents in production](https://100monkeys.ai/papers/red-levers): a paper by Jeshua ben Joseph, 100monkeys AI, Inc., CC BY 4.0 ([PDF](https://100monkeys.ai/papers/red-levers.pdf))
- [Datasheets](https://100monkeys.ai/datasheets/): one-page PDFs for AEGIS, Carbon Forge and Zaru
- [Blog](https://100monkeys.ai/blog/): essays from 100monkeys.ai ([RSS](https://100monkeys.ai/blog/rss.xml))
- [Blame that remembers why](https://100monkeys.ai/blog/blame-that-remembers-why): what a change should carry when the author was an agent
- [The approval is the product](https://100monkeys.ai/blog/the-approval-is-the-product): why an agent orchestrator should make you say yes
- [Run a scheduled agent with AEGIS](https://100monkeys.ai/blog/run-a-scheduled-agent-with-aegis): Put an AEGIS agent on a timer, fire it once by hand, and read each run: started, skipped, refused or paused. Every field from the public docs.
- [Reading an AEGIS execution log and a sealed tool call](https://100monkeys.ai/blog/reading-an-aegis-execution-log): Read an AEGIS run from its execution record to its iteration log to the SEAL envelope and audit event behind each tool call, allowed or refused.
- [AEGIS on Firecracker: what the docs describe today](https://100monkeys.ai/blog/aegis-on-firecracker): Firecracker support in AEGIS is planned, not shipped. Check a host for KVM, read the documented config, and run the Podman path that works now.
- [Install AEGIS on Ubuntu and run an approved task](https://100monkeys.ai/blog/install-aegis-on-ubuntu-vps): Install AEGIS on a fresh Ubuntu server with one command, run an agent, then gate its result behind a human approval. Every step from the public docs.
- [Instala AEGIS en Ubuntu y ejecuta una tarea aprobada](https://100monkeys.ai/blog/es/install-aegis-on-ubuntu-vps) (es): Instala AEGIS en un servidor Ubuntu con un solo comando, ejecuta un agente y deja su resultado tras una aprobación humana. Todo, de la documentación.
- [Instale o AEGIS no Ubuntu e rode uma tarefa aprovada](https://100monkeys.ai/blog/pt/install-aegis-on-ubuntu-vps) (pt): Instale o AEGIS num servidor Ubuntu com um comando, rode um agente e deixe o resultado atrás de uma aprovação humana. Cada passo vem da documentação.
- [Ubuntu に AEGIS を入れて承認付きタスクを実行する](https://100monkeys.ai/blog/ja/install-aegis-on-ubuntu-vps) (ja): 新しい Ubuntu サーバーに AEGIS をコマンド1つでインストールし、エージェントを動かして、その結果を人間の承認の後ろに置きます。手順はすべて公開ドキュメントから。
- [A traceability checklist for AI agents in production](https://100monkeys.ai/blog/ai-agent-traceability-checklist): What to log on every agent run, what to sign so the log can be trusted, and what to show the reviewer. Vendor-neutral, with open standards for each step.
- [Lista de trazabilidad para agentes de IA en producción](https://100monkeys.ai/blog/es/ai-agent-traceability-checklist) (es): Qué registrar en cada ejecución de un agente, qué firmar para que el registro sea fiable y qué mostrar a quien revisa. Neutral, con estándares abiertos.
- [Checklist de rastreabilidade para agentes de IA](https://100monkeys.ai/blog/pt/ai-agent-traceability-checklist) (pt): O que registrar em cada execução de um agente, o que assinar para o registro ser confiável e o que mostrar a quem revisa. Neutro, com padrões abertos.
- [本番 AI エージェントのトレーサビリティ・チェックリスト](https://100monkeys.ai/blog/ja/ai-agent-traceability-checklist) (ja): エージェントの各実行で何を記録し、記録を信頼できるものにするために何に署名し、レビュー担当者に何を見せるか。特定ベンダーに依存せず、各項目にオープン標準を添えています。
- [How to add an approval step to an agent orchestrator](https://100monkeys.ai/blog/approval-step-for-an-agent-orchestrator): A pattern for human approval gates in agent workflows: where the gate goes, what the reviewer sees, what silence means, with AEGIS as the worked example.

## Company

- [GitHub](https://github.com/100monkeys-ai): the 100monkeys-ai organization
- [LinkedIn](https://www.linkedin.com/company/112423436/): 100monkeys.ai
- [X](https://x.com/100monkeysai): @100monkeysai
- [Hashnode](https://100monkeys-ai.hashnode.dev): engineering articles
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Added 10/28/2026

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100monkeys.ai - AI readiness audit | llmsmap.me