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 Pydantic AI

Python Type-Safe Agent FrameworkUpdated: October 2026

Pydantic AI Alternative: Type-Safe TypeScript Agent Harness Without Python

Pydantic AI brings structured type-safe agent development to Python. Smoke Monkey Harness delivers equivalent type safety guarantees in 100% strict TypeScript, the language web and backend developers already use.

Comparative Benchmark: Smoke Monkey Harness TypeScript vs Pydantic AI

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

The Executive Verdict (Quick Answer)

Why choose Smoke Monkey over Pydantic AI? If you value structured, type-safe agentic code but your team works in TypeScript, Smoke Monkey Harness is the ideal Pydantic AI alternative with zero Python dependencies.

Why Developers Switch from Pydantic AI to Smoke Monkey

100% Strict TypeScript: Full type inference for all agent events, tool signatures, and permission payloads.

Zero Python: Run directly in your existing Node.js CI/CD pipeline without Python environments.

Deterministic Phase Loops: Structured 6-phase machine vs Pydantic AI's freeform graph model.

Native npm Ecosystem: Leverage existing TypeScript tooling (Jest, Vitest, ESLint, Prettier).

Detailed Feature-by-Feature Matrix

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

Capability	Smoke Monkey Harness	Pydantic AI	Key Architectural Difference
Type Safety	✅ 100% Strict TypeScript	✅ Pydantic V2 (Python typing)	Both offer first-class type safety in their respective ecosystems.
Ecosystem Language	✅ TypeScript / JavaScript	❌ Python only	TypeScript integrates naturally with Next.js, Electron, and web APIs.

Code Implementation Comparison

Type-Safe Agent Initialization

Smoke Monkey (TypeScript)Zero Dependencies

typed-agent.tstypescript

1import { createAgent, AgentEvent } from 'smoke-monkey-harness';
2
3const agent = createAgent({
4  provider: 'anthropic',
5  model: 'claude-3-7-sonnet',
6  workspacePath: process.cwd(),
7});
8
9// Fully typed event payloads
10agent.on('tool.started', (e: AgentEvent<'tool.started'>) => {
11  console.log(e.data.toolName); // string — fully inferred
12});

Pydantic AIPython Ecosystem Only

pydantic_agent.pypython

1from pydantic_ai import Agent
2
3agent = Agent(
4    'anthropic:claude-3-5-sonnet-latest',
5    system_prompt='You are a senior software engineer.',
6)
7
8result = await agent.run('Audit security vulnerabilities')
9print(result.data)

Architecture Note: Both offer strong type safety — Smoke Monkey delivers it in the TypeScript ecosystem developers already use for their applications.

Structured Tool Definitions with Full TypeScript Inference

Smoke Monkey custom tools use JSON Schema definitions that generate fully typed TypeScript call signatures. Every tool input and output is validated at compile time, giving you Pydantic-level runtime safety without leaving the Node.js world.

Frequently Asked Questions

Questions Developers Ask About Pydantic AI Alternatives

Q:Can Smoke Monkey validate tool inputs at runtime like Pydantic?

Yes. Tool arguments follow JSON Schema validation, and TypeScript type inference ensures all custom tool parameters are type-checked at development time.

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