| enact | 2.0.0 |
| name | enact/docs/guide |
| version | 1.0.0 |
| description | LLM guide for creating, publishing, and running Enact tools |
| license | MIT |
| tags | documentation, guide, llm, tutorial |
| authors | [object Object] |
| annotations | [object Object] |
| inputSchema | [object Object] |
Enact LLM Guide
Enact: Containerized tools with structured I/O for AI agents.
Commands
enact run ./tool --input "key=value" # Run local tool
enact run ./tool --args '{"key":"value"}' # Run with JSON
enact run author/tool --input "x=y" # Run installed tool
enact install author/tool # Install to project
enact install author/tool -g # Install globally
enact search "query" # Find tools
enact sign ./tool && enact publish ./tool # Publish
Tool Structure
my-tool/
├── enact.md # Required: YAML frontmatter + docs
└── main.py # Your code (any language)
enact.md Template
---
enact: "2.0.0"
name: "namespace/category/tool-name"
version: "1.0.0"
description: "What it does"
from: "python:3.12-slim"
build: "pip install requests pandas"
command: "python /work/main.py ${input}"
timeout: "30s"
inputSchema:
type: object
properties:
input:
type: string
description: "Input description"
required: [input]
outputSchema:
type: object
properties:
result:
type: string
env:
API_KEY:
description: "API key"
secret: true
LOG_LEVEL:
description: "Log level"
default: "info"
tags: [category, keywords]
---
# Tool Name
Documentation here.
Field Reference
| Field | Required | Description |
|---|---|---|
name |
Yes | namespace/category/tool |
description |
Yes | What it does |
command |
No* | Shell command with ${param} substitution |
from |
No | Docker image (default: alpine:latest) |
build |
No | Build commands (string or array), cached |
inputSchema |
No | JSON Schema for inputs |
outputSchema |
No | JSON Schema for outputs |
env |
No | Environment vars (secret: true for keyring) |
timeout |
No | Max runtime (default: 30s) |
version |
No | Semver version |
tags |
No | Discovery keywords |
*Tools without command are LLM instruction tools (markdown interpreted by AI).
Examples by Language
Python
from: "python:3.12-slim"
build: "pip install pandas"
command: "python /work/main.py ${input}"
Node.js
from: "node:20-alpine"
build: "npm install"
command: "node /work/index.js ${input}"
Rust
from: "rust:1.83-slim"
build: "rustc /work/main.rs -o /work/app"
command: "/work/app ${input}"
Go
from: "golang:1.22-alpine"
build: "go build -o /work/app /work/main.go"
command: "/work/app ${input}"
Shell (no build)
command: "echo 'Hello ${name}'"
Source Code Pattern
Always output JSON matching outputSchema:
#!/usr/bin/env python3
import sys, json
input_val = sys.argv[1]
result = {"result": input_val.upper()}
print(json.dumps(result))
Secrets
env:
API_KEY:
description: "API key"
secret: true # Stored in OS keyring, not .env
User sets: enact env set API_KEY --secret --namespace myorg/tools
Access in code via environment variable: os.environ['API_KEY']
Two Tool Types
- Container tools (has
command): Runs in Docker, deterministic - Instruction tools (no
command): Markdown body interpreted by LLM
Workflow
# 1. Create
mkdir my-tool && cd my-tool
# Create enact.md + source files
# 2. Test
enact run . --input "test=value"
# 3. Publish
enact auth login
enact sign .
enact publish .
Checklist
-
name: namespace/category/tool format -
description: clear, searchable -
inputSchema: validates inputs -
outputSchema: documents output -
from: pinned image version (notlatest) -
build: installs dependencies -
command: uses${param}for inputs - Source outputs valid JSON