Documentation Index
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Enact workflows are the governed implementation of Anthropic skills. The mapping is 1:1:
| Anthropic Skill | Enact Equivalent |
|---|
| Named, callable Python function | Enact Workflow |
| Input parameters | payload dict |
| Output | ActionResult list |
| (no governance) | Policy check + signed receipt + rollback |
# Without Enact — Claude calls skill directly
tools = [{"name": "create_pr", "description": "..."}]
enact.run as the tool:
# With Enact — Claude calls enact.run, governance happens automatically
tools = [
{
"name": "enact_run",
"description": "Run a governed agent workflow",
"input_schema": {
"type": "object",
"properties": {
"workflow": {"type": "string"},
"payload": {"type": "object"},
},
"required": ["workflow", "payload"],
},
}
]
# When Claude calls the tool:
def handle_tool_call(tool_input):
result, receipt = enact.run(
workflow=tool_input["workflow"],
user_email="agent@company.com",
payload=tool_input["payload"],
)
return result
MCP (Model Context Protocol)
Anthropic’s Model Context Protocol is the open standard for connecting AI models to tools and data sources. Each Enact workflow maps to one MCP tool.
# In your MCP server — register enact.run as an MCP tool
@mcp_server.tool()
def run_workflow(workflow: str, payload: dict) -> dict:
"""Run a governed Enact workflow with policy enforcement and audit trail."""
result, receipt = enact.run(
workflow=workflow,
user_email="agent@company.com",
payload=payload,
)
return {"decision": result.decision, "run_id": receipt.run_id}
LangChain
from langchain.tools import tool
@tool
def run_enact_workflow(workflow: str, payload: str) -> str:
"""Run a governed workflow. payload is a JSON string."""
import json
result, receipt = enact.run(
workflow=workflow,
user_email="agent@company.com",
payload=json.loads(payload),
)
return f"Decision: {result.decision}. Run ID: {receipt.run_id}"
CrewAI
from crewai import Tool
enact_tool = Tool(
name="run_workflow",
description="Run a governed Enact workflow",
func=lambda workflow, payload: enact.run(workflow=workflow, user_email="agent@company.com", payload=payload),
)
OpenAI function_calling
tools = [
{
"type": "function",
"function": {
"name": "run_workflow",
"description": "Run a governed workflow with policy enforcement",
"parameters": {
"type": "object",
"properties": {
"workflow": {"type": "string"},
"payload": {"type": "object"},
},
"required": ["workflow", "payload"],
},
},
}
]
Semantic Kernel
An Enact workflow is a Semantic Kernel skill, hardened:
from semantic_kernel.skill_definition import sk_function
class EnactSkills:
@sk_function(description="Run a governed Enact workflow")
def run_workflow(self, workflow: str, payload: str) -> str:
import json
result, receipt = enact.run(
workflow=workflow,
user_email="agent@company.com",
payload=json.loads(payload),
)
return result.decision
Summary
| Framework | How to integrate |
|---|
| Anthropic tool_use | Register enact.run as a tool in your tools list |
| MCP | Define run_workflow as an MCP tool in your server |
| LangChain | Wrap with @tool decorator |
| CrewAI | Pass as a Tool object |
| OpenAI function_calling | Define in tools array |
| Semantic Kernel | Wrap in a skill class with @sk_function |
