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.

Smoke Monkey vs LlamaIndex Workflows

RAG & Document Agent PlatformUpdated: October 2026

LlamaIndex Agent Alternative: Lightweight Code Engineering & Tool Sandbox

LlamaIndex excels at document indexing and vector retrieval, but its agent workflow engine is complex and document-centric. Smoke Monkey Harness is specifically optimized for software engineering, terminal execution, AST file editing, and Model Context Protocol (MCP) tooling.

Comparative Benchmark: Smoke Monkey Harness TypeScript vs LlamaIndex Workflows

|Verified for Node.js 18+ & Bun|100% MIT Open Source

The Executive Verdict (Quick Answer)

Why choose Smoke Monkey over LlamaIndex Workflows? Choose Smoke Monkey Harness when building software development agents, code editors, and automated dev tools where file modification and terminal execution take priority over vector document indexing.

Why Developers Switch from LlamaIndex Workflows to Smoke Monkey

Built for Code, Not Just Retrieval: Includes 24 native dev tools (AST chunk editing, bash, git).

Zero Vector DB Lock-In: Operates directly on the filesystem with grep and ripgrep search.

Zero Dependencies: Pure Node.js runtime.

Detailed Feature-by-Feature Matrix

Direct side-by-side comparison of core runtime capabilities and architectural trade-offs.

Capability	Smoke Monkey Harness	LlamaIndex Workflows	Key Architectural Difference
Primary Optimization	✅ Autonomous Code Editing & Terminal Tool Loops	⚠️ Document RAG & Vector Knowledge Retrieval	Tailored for software engineering and developer tooling.

Code Implementation Comparison

Executing a Developer Task

Smoke Monkey (TypeScript)Zero Dependencies

smoke-monkey.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3const agent = createAgent({
4  workspacePath: process.cwd(),
5  provider: 'gemini',
6  model: 'gemini-1.5-pro',
7});
8
9await agent.run('Search repository for TODO comments and create GitHub issues');

LlamaIndex WorkflowsDocument Index Focused

llamaindex-agent.tstypescript

1import { FunctionTool, OpenAIAgent } from 'llamaindex';
2
3// Document-centric workflow with vector store wrappers
4const agent = new OpenAIAgent({ tools: [...] });

Architecture Note: Smoke Monkey provides native file and terminal tools out of the box without RAG overhead.

Filesystem Grep vs Heavy Vector Embeddings for Codebases

For software repositories, fast lexical search (grep, ripgrep, AST traversal) is often faster, more accurate, and cheaper than generating vector embeddings for every commit. Smoke Monkey combines grep_search and view_file to find code deterministically.

Frequently Asked Questions

Questions Developers Ask About LlamaIndex Workflows Alternatives

Q:Can I use RAG with Smoke Monkey?

Yes. Smoke Monkey supports Subcontexts and custom tools where you can easily connect any vector database or knowledge store.

Switch to Smoke Monkey Harness Today

Build autonomous coding agents with zero runtime dependencies, deterministic 6-phase loops, and Model Context Protocol (MCP) in pure TypeScript.

npm install smoke-monkey-harness