AI AGENTS
Zil
by FluentData
Overview
Open-source CLI and Python SDK for validating, packaging, evaluating, signing, and deploying production AI agents as portable artifacts.
Details
Zil is an open-source FluentData project for production AI agent workflows. Its official site, documentation, GitHub repository, and PyPI metadata describe a CLI and Python SDK for validating, packaging, evaluating, signing, and deploying AI agents as portable artifacts. The documentation lists lifecycle commands such as init, validate, audit, pack, push, eval, serve, and deploy. It also documents SDK support for creating agents from manifest, identity, and adapter configuration files, plus MCP server declarations and bundling of tool source into .zil archives.
When to Use
Use Zil when you need a CLI workflow to validate package evaluate sign serve or deploy production AI agents. Use Zil when you want to package AI agents as portable artifacts including bundled MCP tool source in .zil archives. Use Zil when you want a Python SDK that reads manifest identity and adapter configuration files into a running agent.
Getting Started
- Install the zil-ai package with pip
- as shown in the official getting-started guide.
- Scaffold a project with `zil init`.
- Choose a provider from the getting-started guide options
- including Gemini
- Anthropic
- OpenAI
- or Vertex AI.
- Run an agent with `zil run` or `zil web`.
- Use the CLI
- SDK
- and MCP tools documentation for command details
- SDK configuration
- and tool bundling.
Key Features
- •CLI commands for the agent development lifecycle
- •including init
- •validate
- •audit
- •pack
- •push
- •eval
- •serve
- •and deploy.
- •Python SDK with `zil.create_agent` for wiring manifest
- •identity
- •and adapter configuration files into a running agent.
- •Portable artifact workflow for validating
- •packaging
- •evaluating
- •signing
- •and deploying AI agents.
- •MCP server declarations in a manifest and bundling of tool source into .zil archives.
- •Deployment of bundled tools alongside the agent without separate infrastructure.
Capabilities
- •Validate AI agents.
- •Package AI agents as portable artifacts.
- •Evaluate AI agents.
- •Sign AI agent artifacts.
- •Deploy production AI agents.
- •Declare MCP servers and bundle tool source into .zil archives.
Last updated Jun 16, 2026