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 AWS Bedrock Agents

AWS Cloud Agent ServiceUpdated: October 2026

AWS Bedrock Agents Alternative: Self-Hosted TypeScript Agent Without Cloud Bills

AWS Bedrock Agents provides managed agent orchestration but ties you to AWS infrastructure, IAM permissions, and per-request pricing. Smoke Monkey Harness self-hosts the entire agent runtime with zero AWS dependency.

Comparative Benchmark: Smoke Monkey Harness TypeScript vs AWS Bedrock Agents

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

The Executive Verdict (Quick Answer)

Why choose Smoke Monkey over AWS Bedrock Agents? Choose Smoke Monkey Harness if you want autonomous agent capabilities without AWS account lock-in, IAM complexity, or Bedrock per-invocation pricing.

Why Developers Switch from AWS Bedrock Agents to Smoke Monkey

Zero AWS Dependency: Deploy to any Node.js runtime — on-prem, Heroku, Railway, Fly.io.

No IAM Permissions: No complex VPC, role, and policy configuration required.

Transparent Pricing: Pay only raw LLM API costs — no Bedrock orchestration markup.

Multi-Provider: Use Anthropic Claude AND OpenAI AND NVIDIA through one unified API.

Detailed Feature-by-Feature Matrix

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

Capability	Smoke Monkey Harness	AWS Bedrock Agents	Key Architectural Difference
Infrastructure Requirements	✅ Any Node.js 18+ environment	❌ AWS account, IAM roles, VPC required	Deploy in minutes without cloud account setup.
Model Flexibility	✅ Any provider (Claude, OpenAI, Gemini, Ollama)	⚠️ Bedrock-available models only	Use latest model versions from any provider immediately.

Code Implementation Comparison

Running an Agent Without Cloud Infrastructure

Smoke Monkey (TypeScript)Zero Dependencies

smoke-monkey.tstypescript

1import { createAgent } from 'smoke-monkey-harness';
2
3// No AWS account. No IAM. Runs on your laptop.
4const agent = createAgent({
5  provider: 'anthropic',
6  model: 'claude-3-7-sonnet',
7  workspacePath: process.cwd(),
8  autoApprove: true,
9});
10
11await agent.run('Generate CloudFormation template for API Gateway');

AWS Bedrock AgentsAWS Cloud Lock-In

bedrock-agent.tstypescript

1import { BedrockAgentRuntimeClient, InvokeAgentCommand } from '@aws-sdk/client-bedrock-agent-runtime';
2
3// Requires AWS credentials, agent ID, alias ID
4const client = new BedrockAgentRuntimeClient({ region: 'us-east-1' });
5const cmd = new InvokeAgentCommand({
6  agentId: 'AGENT123',
7  agentAliasId: 'ALIAS123',
8  sessionId: 'session-id',
9  inputText: 'Generate CloudFormation template for API Gateway',
10});

Architecture Note: Smoke Monkey eliminates AWS SDK boilerplate, agent IDs, alias IDs, and VPC routing complexity entirely.

The Hidden Complexity of AWS Bedrock Agents

Before running a single Bedrock Agent, you must create an IAM role, configure an S3 bucket for agent instructions, create and version an agent, create an alias, attach action groups, and set up Lambda functions for each tool. Smoke Monkey Harness replaces all of this with npm install smoke-monkey-harness and 15 lines of TypeScript.

Frequently Asked Questions

Questions Developers Ask About AWS Bedrock Agents Alternatives

Q:Can I still use AWS services (S3, DynamoDB) from Smoke Monkey agents?

Yes! Smoke Monkey's `run_command` tool can execute AWS CLI commands, and custom tools can wrap the AWS SDK directly.

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

AWS Bedrock Agents Alternative: Self-Hosted TypeScript Agent Without Cloud Bills

Why Developers Switch from AWS Bedrock Agents to Smoke Monkey

Detailed Feature-by-Feature Matrix

Code Implementation Comparison

The Hidden Complexity of AWS Bedrock Agents

Questions Developers Ask About AWS Bedrock Agents Alternatives

Q:Can I still use AWS services (S3, DynamoDB) from Smoke Monkey agents?

Related Solutions & Topics

Zero-Dependency AI Agent Runtime: Why Lean Harnesses Outperform Heavy Frameworks

Free & Open Source AI Agent Framework: 100% MIT Licensed TypeScript Harness

Human-in-the-Loop Permission Gating in AI Agents: Safe Autonomous Execution

Switch to Smoke Monkey Harness Today