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 Microsoft AutoGen

Conversational Agent FrameworkUpdated: October 2026

Microsoft AutoGen Alternative: Lightweight TypeScript State Machine

Microsoft AutoGen relies on multi-agent chat conversations that often get trapped in infinite pleasantries and circular chatter. Smoke Monkey Harness provides a purpose-built engineering loop that moves linearly through explore, plan, edit, verify, recover, and complete.

Comparative Benchmark: Smoke Monkey Harness TypeScript vs Microsoft AutoGen

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

The Executive Verdict (Quick Answer)

Why choose Smoke Monkey over Microsoft AutoGen? Choose Smoke Monkey Harness over AutoGen if your goal is to build reliable code generation, refactoring, and dev-tool assistants that execute tasks instead of simulating conversations.

Why Developers Switch from Microsoft AutoGen to Smoke Monkey

Task Execution Over Chat: Focuses on tangible code edits and terminal verification rather than simulated dialogue.

Native TypeScript: Zero Python dependency friction for web and Node.js developers.

24 Built-In Developer Tools: Full suite of git, AST chunk editing, and bash execution tools out of the box.

Runaway Loop Guards: Hard stop safeguards prevent agents from burning through thousands of API dollars in runaway loops.

Detailed Feature-by-Feature Matrix

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

Capability	Smoke Monkey Harness	Microsoft AutoGen	Key Architectural Difference
Loop Control Model	✅ Deterministic 6-phase state machine	⚠️ Unstructured multi-agent conversation loop	Eliminates repetitive agent back-and-forth loops.
Ecosystem	✅ Node.js / TypeScript native	⚠️ Primarily Python (experimental .NET/TS ports)	First-class citizen in the modern JavaScript ecosystem.

Code Implementation Comparison

Autonomous Code Refactoring

Smoke Monkey (TypeScript)Zero Dependencies

smoke-monkey.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3const agent = createAgent({
4  provider: 'openai',
5  model: 'gpt-4o',
6  workspacePath: process.cwd(),
7});
8
9// Linear phase-driven execution
10await agent.run('Add input validation to user registration API');

Microsoft AutoGenChat-Driven Complexity

autogen_chat.pypython

1import autogen
2
3config_list = [{'model': 'gpt-4o', 'api_key': '...'}]
4assistant = autogen.AssistantAgent('assistant', llm_config={'config_list': config_list})
5user_proxy = autogen.UserProxyAgent('user_proxy', code_execution_config={'work_dir': 'coding'})
6
7# Conversational round-robin loop often requires manual termination keywords
8user_proxy.initiate_chat(assistant, message='Add input validation to user registration API')

Architecture Note: Smoke Monkey executes tasks deterministically without requiring mock user proxy agents to converse back and forth.

Why Conversational Loops Are Inefficient for Software Engineering

When multiple LLM agents converse to solve an engineering problem, they frequently repeat greetings, summarize each other’s statements, and get stuck in agreement loops. Smoke Monkey Harness decouples agent execution into structured engineering states with automated sanity checks.

Frequently Asked Questions

Questions Developers Ask About Microsoft AutoGen Alternatives

Q:Does Smoke Monkey support code execution like AutoGen UserProxy?

Yes. Smoke Monkey includes `run_command` and `manage_task` tools that run commands in real shells with real-time stdout/stderr capture and human authorization gates.

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