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@promptbook/fake-llm weekly downloads statistics

By promptbook (28 listings) · npm · AI SDKs

Promptbook: Create persistent AI agents that turn your company's scattered knowledge into action

View on npm Fresh · Oct 17, 2026

@promptbook/fake-llm has 112 recorded weekly downloads on npm, a decrease of 94.1% over the past 30 days.

It ranks #1654 of 2,251 tracked npm listings by weekly downloads.

Over 90 days, weekly downloads changed by -96.0%.

PluginStats has 543 daily observations since Apr 23, 2025; the latest is from Oct 17, 2026.

Weekly downloads
112
Official API · Oct 17, 2026
7-day growth
-91.8%
-1.2K weekly downloads
30-day growth
-94.1%
-1.8K weekly downloads
90-day growth
-96.0%
-2.7K weekly downloads
Rank
#1654
Among tracked npm listings

About @promptbook/fake-llm

🐙 Promptbook: Invisible AI Agents

Create persistent AI agents that turn your company's scattered knowledge into action - powered by the Agents Server

Promptbook](https://www.npmjs.com/package/promptbook) Promptbook](https://packagequality.com/#?package=promptbook)

🚀 Quick deploy

Run the standalone VPS installer _(for example on DigitalOcean)_ only on a fresh server. Interactive mode asks for the installation values:

Non-interactive mode takes defaults from command-line options. Use --env live, --env preview, --env production, or --env lts to pick the installed environment; the default is preview. When --openai-api-key is provided with the default OpenAI Codex runner, the installer installs and configures Codex automatically; other runner authentication can be configured later from the UI or SSH:

⚠ Warning: This is a pre-release version of the library. It is not yet ready for production use. Please look at latest stable release.

📦 Package @promptbook/fake-llm

• Promptbooks are divided into several packages, all are published from single monorepo. • This package @promptbook/fake-llm is one part of the promptbook ecosystem.

To install this package, run:

Mock objects for testing the execution tools without token usage.

Rest of the documentation is common for entire promptbook ecosystem:

📖 The Book Whitepaper

Promptbook lets you create persistent AI agents that work on real goals for your company. The Agents Server is the heart of the project - a place where your AI agents live, remember context, collaborate in teams, and get things done.

Nowadays, the biggest challenge for most business applications isn't the raw capabilities of AI models. Large language models such as Claude Mythos, GPT-5.6 or Kimi K2.7 are incredibly capable.

The main challenge lies in managing the context, providing rules and knowledge, and narrowing the personality.

In Promptbook, you define your agents using simple Books - a human-readable language that is explicit, easy to understand and write, reliable, and highly portable. You then deploy them to the Agents Server, where they run persistently and work toward their goals.

Lawyer

GOAL You are a company lawyer. Your job is to provide legal advice and support to the company and its employees. Respond to incoming legal inquiries via email and keep the company website updated with the latest legal policies.

RULE You are knowledgeable, professional, and detail-oriented. KNOWLEDGE https://company.com/company-policies.pdf KNOWLEDGE https://company.com/internal-documents/employee-handbook.docx

TEAM You are part of the legal team of Paul Smith & Associés, you discuss with {Emily White}, the head of the compliance department. {George Brown} is expert in corporate law and {Sophia Black} is expert in labor law.

Aspects of great AI agent

We have created a language called Book, which allows you to write AI agents in their native language and create your own AI persona. Book provides a guide to define all the traits and commitments.

You can look at it as "prompting" _(or writing a system message)_, but decorated by commitments.

Commitments are special syntax elements that define contracts between you and the AI agent. They are transformed by Promptbook Engine into low-level parameters like which model to use, its temperature, system message, RAG index, MCP servers, and many other parameters. For some commitments _(for example RULE commitment)_ Promptbook Engine can even create adversary agents and extra checks to enforce the rules.

Persona commitment

Personas define the character of your AI persona, its role, and how it should interact with users. It sets the tone and style of communication.

Paul Smith & Associés

PERSONA You are a company lawyer. Your job is to provide legal advice and support to the company and its employees.

Goal commitment

Goals define what the agent should actively work toward. Unlike a chatbot that only responds when asked, an agent with goals takes initiative and works on tasks persistently on the Agents Server.

Company Lawyer

GOAL Your job is to provide legal advice and support to the company and its employees. GOAL Respond to incoming legal inquiries via email within 24 hours. GOAL Keep the company website updated with the latest legal policies and compliance information.

Knowledge commitment

Knowledge Commitment allows you to provide specific information, facts, or context that the AI should be aware of when responding.

This can include domain-specific knowledge, company policies, or any other relevant information.

Promptbook Engine will automatically enforce this knowledge during interactions. When the knowledge is short enough, it will be included in the prompt. When it is too long, it will be stored in vector databases and RAG retrieved when needed. But you don't need to care about it.

Paul Smith & Associés

PERSONA You are a company lawyer. Your job is to provide legal advice and support to the company and its employees. GOAL Respond to incoming legal inquiries via email within 24 hours. GOAL Keep the company website updated with the latest legal policies and compliance information. KNOWLEDGE https://company.com/company-policies.pdf KNOWLEDGE https://company.com/internal-documents/employee-handbook.docx

Rule commitment

Rules will enforce specific behaviors or constraints on the AI's responses. This can include ethical guidelines, communication styles, or any other rules you want the AI to follow.

Depending on rule strictness, Promptbook will either propagate it to the prompt or use other techniques, like adversary agent, to enforce it.

Paul Smith & Associés…

Description from the npm listing.

  • ai
  • ai-agents
  • ai-application-framework
  • ai-assistant
  • ai-automation
  • ai-development
  • ai-framework
  • ai-ops
  • ai-orchestration
  • ai-pipeline
  • ai-platform
  • ai-scripting
  • ai-sdk
  • ai-workflow
  • api-integration
  • automation-framework
  • book-language
  • browser
  • chatbot
  • content-generation

Where @promptbook/fake-llm sits in the listing

Each band is one page of 20. Weekly downloads on a log scale. Placement is projected from weekly downloads, so it can differ from the marketplace's own sort order. Hover a dot to see the listing.

All npm2,248 listings · 113 pages · page 83, #15 (overall #1655)
All npm: every listing in weekly downloads order
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AI SDKs249 listings · 13 pages · page 12, #5 (overall #225)
AI SDKs: every listing in weekly downloads order
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Charts

Images: 30d · 1y · all time · 1600×900 dark

Adoption over time

@promptbook/fake-llm weekly downloads on npm, last 90 days: 112 weekly downloads as of Oct 17, 2026, -96.0% over 90 days.@promptbook/fake-llmWeekly downloads on npm · Last 90 days112downloads / week-96.0% over 90 days01K2K3K4K5KJul 19Aug 3Aug 18Sep 2Sep 17Oct 2Oct 17downloads / weekobservedarchived (Internet Archive)no observationsJul 20, 2026 – Oct 17, 2026 · 90 observations · Updated Oc…pluginstats.com
@promptbook/fake-llm weekly downloads on npm, last 90 days: 112 weekly downloads as of Oct 17, 2026, -96.0% over 90 days.
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7/30/90-day growth

@promptbook/fake-llm weekly downloads growth on npm: -91.8% over 7 days, -94.1% over 30 days, -96.0% over 90 days.@promptbook/fake-llmWeekly downloads growth on npm112downloads / week now7-day growth-1,249 weekly downloads-91.8%30-day growth-1,786 weekly downloads-94.1%90-day growth-2,669 weekly downloads-96.0%Growth vs. nearest observation to each baseline date · Updated Oct 17, 2026pluginstats.com
@promptbook/fake-llm weekly downloads growth on npm: -91.8% over 7 days, -94.1% over 30 days, -96.0% over 90 days.
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Ranking history

@promptbook/fake-llm npm ranking history: currently #1654 (by weekly downloads).@promptbook/fake-llmnpm ranking · by weekly downloads · Last 90 days#1654current rank#600#800#1000#1200#1400#1600#1800Jul 20Aug 4Aug 19Sep 3Sep 18Oct 3Oct 18rankOct 17, 2026 – Oct 18, 2026 · 2 observations · Updated Oct 17, 2026pluginstats.com
@promptbook/fake-llm npm ranking history: currently #1654 (by weekly downloads).
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Filled points are observed values; hollow points come from Internet Archive snapshots of the listing page. Dashed segments span periods with no observations — values between points are not interpolated.

Recent observations

Full history (543)
Recent weekly downloads observations for @promptbook/fake-llm
Date (UTC)Weekly downloadsChange vs. previous
112-33
145-5
150-73
223-88
3110
311-856
1,167-194
1,361-70
1,431-1.1K
2,501+4
2,497+47
2,450-50
2,500+785
1,715—

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Data source

Weekly downloads (trailing 7 complete days) come from the official npm downloads API. Downloads include CI and mirrors and are not equivalent to installs or users. npm publishes no ratings.

Listing: https://www.npmjs.com/package/@promptbook/fake-llm. First seen Oct 16, 2026; last successful collection Oct 18, 2026. See the methodology.