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 LangGraph

Graph-Based Agent FrameworkUpdated: October 2026

LangGraph Alternative: Simple 6-Phase State Machine Without Graph Complexity

LangGraph requires defining complex directed cyclic graphs with conditional edges, state channels, and reducers. Smoke Monkey Harness achieves superior agent reliability using a standardized 6-phase engineering state machine (explore → plan → edit → verify → recover → complete) that works out of the box.

Comparative Benchmark: Smoke Monkey Harness TypeScript vs LangGraph

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

The Executive Verdict (Quick Answer)

Why choose Smoke Monkey over LangGraph? Choose Smoke Monkey Harness for building software engineering and dev-tool agents where a proven 6-phase lifecycle delivers better results with 90% less boilerplate code.

Why Developers Switch from LangGraph to Smoke Monkey

Zero Graph Wiring: No manual node definitions, conditional edges, or state reducer schemas.

Built-In Failure Recovery: Self-healing transitions are natively baked into the phase machine.

Zero Runtime Dependencies: Pure Node.js standard libraries vs heavy Python/JS LangGraph packages.

Clear Visual Observability: Understand exactly what state the agent is in at all times.

Detailed Feature-by-Feature Matrix

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

Capability	Smoke Monkey Harness	LangGraph	Key Architectural Difference
Architecture Model	✅ 6-Phase Engineering State Machine (Built-in)	⚠️ Low-level Graph DSL (Manual Node/Edge wiring)	Ready to ship in minutes without writing state graph graph algorithms.

Code Implementation Comparison

Stateful Agent Execution

Smoke Monkey (TypeScript)Zero Dependencies

smoke-monkey.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3// All 6 phases and self-healing transitions are already wired
4const agent = createAgent({
5  workspacePath: process.cwd(),
6  provider: 'openai',
7  model: 'gpt-4o',
8});
9
10await agent.run('Refactor database queries for optimal performance');

LangGraphComplex Node Graphs

langgraph-nodes.tstypescript

1import { StateGraph, END } from '@langchain/langgraph';
2
3// Requires defining state interface, nodes, conditional edges, and compiling
4const workflow = new StateGraph({
5  channels: { messages: { value: (x, y) => x.concat(y), default: () => [] } }
6});
7workflow.addNode('planner', planNode);
8workflow.addNode('executor', executeNode);
9workflow.addConditionalEdges('executor', shouldContinue, { continue: 'planner', end: END });
10const app = workflow.compile();

Architecture Note: Smoke Monkey delivers production agent resilience without graph DSL cognitive overhead.

Why Specialized State Machines Beat General-Purpose Graphs

Software engineering tasks have a natural lifecycle: understanding the problem, formulating a plan, making changes, running tests, and handling errors. Modeling this in a generic graph framework leads to repetitive boilerplate across every project. Smoke Monkey bakes this best-practice engineering lifecycle into its core engine.

Frequently Asked Questions

Questions Developers Ask About LangGraph Alternatives

Q:Can I customize phase behavior in Smoke Monkey?

Yes! You can attach listeners to `phase.changed` events and customize allowed tools, system prompts, and permission levels per phase.

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