AgentGuard

Agent-native quality engine for LLM code generation.
AgentGuard provides structured guidance to your AI agent — it never calls an LLM itself.

What It Does

AgentGuard is an MCP server that gives your AI agent (Claude, GPT, Gemini, etc.) a disciplined process for generating production-ready code:

Skeleton → file tree with responsibilities
Contracts & Wiring → typed stubs with import connections
Logic → function-by-function implementation
Challenge → self-review against quality criteria
Validate → static analysis (syntax, lint, types, imports)

Your agent does the thinking. AgentGuard provides the framework.

Installation

pip install rlabs-agentguard

That's it. One command, no extras, no API keys needed.

Configure Your IDE

Claude Desktop / Claude Code

Add to your MCP config:

{
  "mcpServers": {
    "agentguard": {
      "command": "agentguard-mcp"
    }
  }
}

Cursor / Windsurf

Add to .cursor/mcp.json or equivalent:

{
  "mcpServers": {
    "agentguard": {
      "command": "agentguard-mcp"
    }
  }
}

Python (direct)

python -m agentguard  # starts MCP server on stdio

Tools

Agent-Native (structured guidance — no API key)

Tool	Purpose
`skeleton`	L1: file tree with responsibilities
`contracts_and_wiring`	L2+L3: typed stubs with imports (saves ~15K tokens vs separate calls)
`contracts`	L2 only: typed function/class stubs
`wiring`	L3 only: import and call-chain connections
`logic`	L4: implement one function body
`get_challenge_criteria`	Self-review criteria for an archetype
`digest`	Compact project summary for efficient review
`debug`	Structured debugging protocol
`migrate`	Migration plan with compatibility checks

Utility

Tool	Purpose
`validate`	Mechanical code checks (syntax, lint, types, structure)
`list_archetypes`	List all available project archetypes
`get_archetype`	Get detailed archetype configuration
`reload_archetypes`	Pick up newly installed archetypes
`trace_summary`	Get cost & token tracking summary
`docs`	Get AgentGuard documentation on any topic
`update_agentguard`	Update to the latest version from PyPI

Built-In Archetypes

Archetype	Tech Stack
`api_backend`	Python + FastAPI (production)
`library`	Python reusable package (production)
`cli_tool`	Python CLI with subcommands
`react_spa`	TypeScript + React SPA (production)
`web_app`	Python + TypeScript full-stack (production)
`script`	Python one-off automation
`debug_backend`	Python/FastAPI debugging protocol
`debug_frontend`	React/TypeScript debugging protocol

Marketplace

Install community archetypes:

# From the AgentGuard marketplace (agentguard.rlabs.cl)
# Use the reload_archetypes tool after installing

How It Works

AgentGuard is agent-native: every tool returns structured prompts and criteria that your AI agent processes. The tool never calls an external LLM.

Your Agent (Claude, GPT, etc.)
    │
    ├── calls skeleton(spec, archetype) ─────→ returns L1 file tree prompt
    ├── calls contracts_and_wiring(spec, skeleton) → returns L2+L3 stubs prompt
    ├── calls logic(file, function) ─────────→ returns L4 implementation prompt
    └── calls get_challenge_criteria() ──────→ returns review criteria
    └── calls validate(files) ───────────────→ returns static analysis results

The agent reads the prompt, generates the code, validates it, and loops back if criteria aren't met. AgentGuard provides the structure — your agent provides the intelligence.

Development

pip install -e ".[dev]"
pytest tests/
ruff check agentguard/

License

MIT — see LICENSE.

agentguard-lib