vendor-connectors 0.1.1

Universal vendor connectors with transparent secret management, including Meshy AI for 3D assets

Vendor Connectors

Universal vendor connectors for the jbcom ecosystem, providing standardized access to cloud providers, third-party services, and AI APIs.

Features

• AWS Connector: Boto3-based client with role assumption and retry logic
• Google Cloud Connector: Workspace and Cloud Platform APIs with lazy credential loading
• GitHub Connector: Repository management, GraphQL queries, and file operations
• Slack Connector: Bot and app integrations with rate limiting
• Vault Connector: HashiCorp Vault with Token and AppRole auth
• Zoom Connector: Meeting and user management
• Meshy Connector: Meshy AI 3D asset generation (text-to-3D, rigging, animation, retexture)
• VendorConnectors: Cached public API with get_*_client() getters

Installation

pip install vendor-connectors

Optional Extras

# For Meshy webhooks
pip install vendor-connectors[webhooks]

# For CrewAI agent integration
pip install vendor-connectors[crewai]

# For MCP server integration  
pip install vendor-connectors[mcp]

# For Meshy vector store/RAG
pip install vendor-connectors[vector]

# Everything
pip install vendor-connectors[all]

Usage

Using VendorConnectors (Recommended)

The VendorConnectors class provides cached access to all connectors:

from vendor_connectors import VendorConnectors

# Initialize once - reads credentials from environment
vc = VendorConnectors()

# Get cached clients
slack = vc.get_slack_client()
github = vc.get_github_client(github_owner="myorg")
s3 = vc.get_aws_client("s3")
vault = vc.get_vault_client()

Using Individual Connectors

from vendor_connectors import AWSConnector, GithubConnector, SlackConnector

# AWS with role assumption
aws = AWSConnector(execution_role_arn="arn:aws:iam::123456789012:role/MyRole")
s3 = aws.get_aws_client("s3")

# GitHub operations
github = GithubConnector(
    github_owner="myorg",
    github_repo="myrepo",
    github_token=os.getenv("GITHUB_TOKEN")
)

# Slack messaging
slack = SlackConnector(
    token=os.getenv("SLACK_TOKEN"),
    bot_token=os.getenv("SLACK_BOT_TOKEN")
)
slack.send_message("general", "Hello from vendor-connectors!")

Meshy AI (3D Asset Generation)

from vendor_connectors import meshy

# Generate a 3D model
model = meshy.text3d.generate("a medieval sword with ornate handle")
print(model.model_urls.glb)

# Rig it for animation
rigged = meshy.rigging.rig(model.id)

# Apply an animation (678 available)
animated = meshy.animate.apply(rigged.id, animation_id=0)  # Idle

# Or retexture it
gold = meshy.retexture.apply(model.id, "golden with embedded gems")

Architecture

All connectors extend DirectedInputsClass from the jbcom ecosystem:

• directed-inputs-class: Input handling from environment, stdin, config
• lifecyclelogging: Structured logging with verbosity control
• extended-data-types: Utilities like is_nothing, strtobool, wrap_raw_data_for_export

The VendorConnectors class provides:

• Client caching (same parameters = same instance)
• Automatic credential loading from environment
• Consistent interface across all providers

Environment Variables

Variable	Description
AWS_*	Standard AWS credentials
EXECUTION_ROLE_ARN	AWS role to assume
GITHUB_TOKEN	GitHub personal access token
GITHUB_OWNER	GitHub organization/user
GOOGLE_SERVICE_ACCOUNT	Google service account JSON
SLACK_TOKEN	Slack user token
SLACK_BOT_TOKEN	Slack bot token
VAULT_ADDR	Vault server URL
VAULT_TOKEN	Vault authentication token
VAULT_ROLE_ID / VAULT_SECRET_ID	AppRole credentials
ZOOM_CLIENT_ID / ZOOM_CLIENT_SECRET / ZOOM_ACCOUNT_ID	Zoom OAuth
MESHY_API_KEY	Meshy AI API key

Part of jbcom Ecosystem

This package is part of the jbcom Python library ecosystem:

• extended-data-types - Foundation utilities
• lifecyclelogging - Structured logging
• directed-inputs-class - Input handling