Is Smoke Monkey an AI model?

No. Smoke Monkey is not an LLM. It is an embeddable, framework-agnostic runtime that turns LLMs (NVIDIA, Gemini, OpenAI, Claude, Ollama) into autonomous engineering agents capable of multi-step planning, calling tools, editing files, and running test loops with zero runtime dependencies.

Do I need a database (Postgres, SQLite, or Redis)?

No database is required. State is persisted through lean session files on disk, or you can plug in your own custom storage adapter. You can deploy it as a CLI, a desktop app, or inside a serverless function without provisioning a database.

How does human-in-the-loop permission gating work?

Smoke Monkey comes with three permission policies: allow-all, deny-all, and ask-default (or a custom function resolver). When a sensitive tool like edit_file or run_command is called under "ask", the runtime emits a permission.required event, pausing execution until your code or UI calls agent.resolvePermission(id, "allow" | "deny").

How does the Model Context Protocol (MCP) server work?

Smoke Monkey ships an official stdio MCP server in smoke-monkey-harness-mcp. Point Claude Code, Cursor, Codex, or opencode at it using npx -y smoke-monkey-harness-mcp to let other AI tools plan, scaffold, verify, and run agents using Smoke Monkey automatically.

What are SKILL.md skills and how do they prevent context bloat?

Skills are directories containing a SKILL.md workflow file. The system prompt only includes a one-line catalog of available skills. When a task requires specialized expertise (e.g. frontend-ui-engineering, test-driven-development, or security-hardening), the agent calls use_skill to load instructions just-in-time, saving thousands of tokens.

What is an AI Agent Harness?

An AI agent harness is the execution environment that bridges raw Large Language Models (LLMs) to real-world software engineering capabilities. While an LLM generates tokens, an AI harness manages deterministic phase loops (explore, plan, edit, verify, recover), tool execution sandboxing, token budget compaction, and safety gates needed for autonomous problem-solving.

What makes Smoke Monkey an Agentic AI runtime rather than a simple wrapper?

Unlike basic prompt-response chains, Smoke Monkey implements true agentic AI architecture: a deterministic 6-phase state machine with automatic failure recovery, runaway loop guards, 24 built-in developer tools, dynamic subcontext isolation, and interactive human-in-the-loop permission pauses.

Is Smoke Monkey Harness 100% Open Source AI?

Yes. Smoke Monkey Harness is 100% open source under the permissive MIT license with zero runtime dependencies. There is no telemetry lock-in, no mandatory cloud backend, and no database requirement—giving engineering teams complete sovereignty over their agent code.

What Agent APIs does Smoke Monkey Harness provide?

Smoke Monkey provides composable TypeScript Agent APIs: createAgent() for initialization, agent.run() for multi-turn execution, agent.on() for fine-grained lifecycle and streaming events, agent.resolvePermission() for interactive human authorization, subcontext switching APIs, and stdio Model Context Protocol (MCP) server endpoints.

Can I run completely offline without API keys?

Yes! Smoke Monkey natively supports Ollama out of the box. Set provider: "ollama" and model: "qwen3:8b" with workspacePath, and the agent will run locally on your machine with zero external network requests.

Documentation

6 min readUpdated: October 2026

Auto-Generate Documentation with AI Agents: JSDoc, README & API Docs

Technical Review: Smoke Monkey Core Architecture Team

|Tested on Node.js 18+ & Bun|TypeScript 5.x

Quick Answer & Executive Definition

Auto-Generate Documentation with AI Agents: JSDoc, README & API Docs: Designed as a zero-dependency, open-source TypeScript architecture under the MIT License with native Model Context Protocol (MCP) support and deterministic phase state machines.

Key Architectural Takeaways

Generate JSDoc for every exported function and class
Update README sections when APIs change
Write OpenAPI/Swagger specs from Express or Fastify route handlers
Create changelogs from git commit history automatically

Generating JSDoc for Your TypeScript Codebase

Run a documentation agent over your entire source tree to add missing JSDoc comments:

docs-agent.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3const agent = createAgent({
4  provider: 'anthropic',
5  model: 'claude-3-7-sonnet',
6  workspacePath: './src',
7  autoApprove: true,
8});
9
10await agent.run(`
11  Scan all .ts and .tsx files in src/.
12  For every exported function, class, interface, and type that is missing JSDoc:
13  1. Add a JSDoc comment describing its purpose, parameters, and return value.
14  2. Include @example tags where the usage is non-obvious.
15  3. Do not modify any existing JSDoc or source logic.
16  4. Preserve all existing code formatting.
17`);

Keeping README in Sync with Code Changes

Add a post-merge CI step that runs a documentation sync agent. The agent reads the diff, identifies API changes, and updates the relevant README sections:

New exported functions → added to the API reference table
Removed or renamed exports → updated or removed from docs
Changed configuration options → updated configuration section
New CLI flags → added to usage examples

This prevents documentation drift without requiring developers to manually update docs on every PR.

Scope docs agents to changed files only

In CI, pass the git diff to the agent rather than the full codebase. This reduces token usage and focuses updates on actually changed APIs.

Google Search Questions & Answers

Frequently Asked Questions

Q:Can the agent generate OpenAPI specs from my Express routes?

Yes. Point the agent at your routes directory and ask it to generate a YAML or JSON OpenAPI 3.1 spec. It can read middleware, request validation schemas, and response types to produce accurate specs.

Q:How do I prevent the agent from hallucinating incorrect parameter descriptions?

Instruct the agent to only describe what is explicitly visible in the source code and to use phrases like "See source for details" when the behavior is complex or unclear. This prevents confident hallucination.

Build with Smoke Monkey Harness

Zero dependencies. 24 built-in tools. Human-in-the-loop safety. 100% open source under the MIT License.

npm install smoke-monkey-harness