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.

Dynamic Prompt Engineering

Sub-Contexts & Dynamic Memory

Static system prompts cause cognitive drift during long coding tasks. Smoke Monkey introduces Sub-Contexts: modular, named memory blocks rendered into the prompt under ## <Title> that can be activated, deactivated, or swapped at runtime.

Built-In Sub-Contexts

Active Goalgoal

High-level milestone guidance and user requirements.

Current Tasktask

Decomposed step-by-step checklist of immediate actions.

Research Moderesearch_mode

Puts the model in a read-first exploratory posture.

Project Invariantsconstants

Immutable rules (e.g. do not modify package-lock.json).

Working Memorynotes

Scratchpad for findings during long, multi-turn runs.

Active Skillsskills

Container where activated SKILL.md workflows are loaded.

Registering Custom Sub-Contexts

subcontexts.tstypescript

1import { createAgent, registerSubContext } from '@smoke-monkey/harness';
2
3// 1. Register a modular domain guideline
4registerSubContext({
5  id: 'strict_typing',
6  title: 'Strict TypeScript Standards',
7  summary: 'Enforces no-any and runtime Zod validation',
8  content: '## Guidelines: Strict TypeScript typing rules',
9  lockMode: 'locked', // Prevents the LLM from removing these rules
10});
11
12// 2. Pre-activate sub-contexts on startup
13const agent = createAgent({
14  workspacePath: process.cwd(),
15  defaultSubContexts: ['goal', 'task', 'strict_typing'],
16  sessionId: 'user-session-987', // Persists memory across runs
17});
18
19// The agent autonomously calls context_manage({ action: 'swap', id: 'research_mode' });

Next: Lifecycle Hooks & Observability

Learn how to intercept tool calls and inspect structured error objects.