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.

Code Generation

7 min readUpdated: October 2026

AI Agent for Code Generation: TypeScript Scaffolding, Boilerplate & Templates

Technical Review: Smoke Monkey Core Architecture Team

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

Quick Answer & Executive Definition

AI Agent for Code Generation: TypeScript Scaffolding, Boilerplate & Templates: 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 consistent boilerplate that follows your codebase patterns
Read existing files for context before generating new ones
Create full feature slices: component + test + types + API route
Validate generated code by running type-checks and tests automatically

Scaffolding a Full Feature Slice

Instead of generating files in isolation, Smoke Monkey can read your existing code patterns first, then generate new code that matches your conventions:

codegen-agent.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3const agent = createAgent({
4  provider: 'anthropic',
5  model: 'claude-3-7-sonnet',
6  workspacePath: process.cwd(),
7  autoApprove: true,
8});
9
10await agent.run(`
11  Read src/features/auth/ to understand our feature architecture pattern.
12  Then create a new "notifications" feature following the exact same structure:
13  - src/features/notifications/notifications.component.tsx
14  - src/features/notifications/notifications.service.ts
15  - src/features/notifications/notifications.types.ts
16  - src/features/notifications/__tests__/notifications.test.ts
17
18  Use our existing patterns for error handling, TypeScript types, and component structure.
19`);

Validating Generated Code Automatically

After generating code, chain a validation step to catch type errors and failing tests before the developer reviews the PR:

typescript

// After generation, run validation
agent.on('task.completed', async () => {
  await agent.run('Run npx tsc --noEmit and fix any TypeScript errors in the generated files.');
  await agent.run('Run npm test -- --testPathPattern=notifications and fix any failing assertions.');
});

This creates a self-correcting generation loop — the agent writes code, validates it, and fixes errors autonomously.

Google Search Questions & Answers

Frequently Asked Questions

Q:How do I prevent the agent from overwriting existing files during generation?

Instruct the agent to check if a file exists before writing (using list_dir or read_file) and to skip or prompt before overwriting. You can also set write_file permission to "ask" for an extra safety gate.

Q:Can Smoke Monkey generate database migrations from schema diffs?

Yes. Provide the current schema file and the desired changes as context. The agent can generate Drizzle, Prisma, or raw SQL migration files following your existing migration conventions.

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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