agentify-toolkit 0.11.4

Python Toolkit for Declarative AI Agent Development

Agentify-Toolkit

Build and experiment with AI agents using simple declarative specs.

Agentify is a lightweight, declarative-first toolkit for prototyping AI agents. It lets you define agents as YAML specs and test them rapidly from the CLI or Python, without committing to a framework or model provider.

Note: Agentify is not a workflow orchestrator or production framework. It’s simply for agent building, experimentation and prototyping.

Quickstart

For a more detailed step-by-step Quickstart.

1. Install the Agentify-Toolkit

pip install agentify-toolkit

2. Add an Model Provider API KEY

Copy the example environment file:

cp .env.example .env

Open the .env and populate with your API keys:

# OpenAI
OPENAI_API_KEY=your-openai-key

# Anthropic
ANTHROPIC_API_KEY=your-anthropic-key

# DeepSeek
DEEPSEEK_API_KEY=your-deepseek-key

# Mistral AI
MISTRAL_API_KEY=your-mistral-key

# X AI
XAI_API_KEY=your-xai-key

# Google
GOOGLE_API_KEY=your-google-key

# AWS Bedrock
BEDROCK_API_KEY=your-bedrock-key

All providers will automatically pick up the keys from .env.

Note: The old workflow using agenitfy provider add <provider> has been deprecated. This command required you to manually paste your API keys and export them into your shell. It added friction and was error-prone, especially for new contributors.

For instructions on how to obtain an Model API key:

Provider	Model	Link
OpenAI	GPT-4	How to obtain an OpenAI API Key
Google	Gemini	How to obtain an Google API Key
Anthropic	Claude	How to obtain an Anthropic API Key
XAI	Grok	How to obtain an XAI API Key

Verify:

agentify provider list

Example Output:

 anthropic
  env: ANTHROPIC_API_KEY
  status: READY

3. Create an Agent

Via CLI:

agentify agent create

Or manually create agent.yaml:

name: claude
description: AI Engineer
version: 0.1.0
model:
  provider: anthropic
  id: claude-sonnet-4-5
  api_key_env: ANTHROPIC_API_KEY
role: |
  You are an AI Security Engineer.
  Provide concise, practical answers with examples.

4. Run the Agent

agentify run agent.yaml

You’ve just built your first AI agent with Agentify!

💡 Tip: Running multiple agents

If you have multiple agents, put them in a single folder and run agentify run <foldername>. Agentify will provide an interactive menu so you can choose which agent you want to experiment with.

💡 Tip: Overriding the model

If you want to experiment with a different model, simply add --provider=openai model=gpt-5-nano to your call. Ensure you have registered the appropriate provider API key.

agentify run agent.yaml --provider=openai --model=gpt-5-nano

Core Ideas

1. Declarative Agents (YAML-first)

Agents become artifacts:

• version controlled
• diffable
• shareable
• auditable

2. Provider Abstraction Without Lock-in

Most ecosystems ask:

“Am I building on OpenAI, Anthropic, Bedrock, XAI, or Google?”

Agentify flips it:

“The agent spec stays the same - only the provider changes.”

3. CLI-first Exploration

CLI interaction is fast, ergonomic, and repeatable:

agentify run agent.yaml

4. Agent = Single File

Agents collapse to a spec, not a codebase

Key Features

• Declarative agent definitions via YAML

• Multi-provider LLM support (OpenAI, Anthropic, XAI, Gemini, Bedrock)

• Interactive CLI and TUI for exploring agents

• Programmatic API for custom workflows

• Lightweight: Click + Rich + PyYAML

Documentation & Notebooks

Prefer a guided walkthrough?

• Developer Quickstart (Notebook) examples/notebooks/Agentify_Developer_Quickstart.ipynb

• YAML Deep Dive examples/notebooks/Agentify_YAML_Deep_Dive.ipynb

Programmatic Usage

from agentify import Agent

agent = Agent(
    name="Grok",
    description="X's Grok Agent",
    provider="x",
    model_id="grok-4",
    role="You are an AI Security Architect specialising in X AI Grok models"
)

response = agent.run("Which AI LLM is the best in 1 sentence?")
print(response)

Quick CLI Reference

Action	Command
Run from YAML	agentify run agent.yaml
Run folder of agents	agentify run agents/
List agents interactively	agentify agent list [<folder_name>]
Add a provider API key	agentify provider add <p>
List provider credentials	agentify provider list

Supported Providers & Keys

Provider	Env Var
OpenAI	export OPENAI_API_KEY=...
Anthropic	export ANTHROPIC_API_KEY=...
Gemini	export GEMINI_API_KEY=...
XAI (Grok)	export XAI_API_KEY=...
Bedrock	export AWS_BEARER_TOKEN_BEDROCK

Windows:

$env:OPENAI_API_KEY="..."

Installation

Install from PyPI:

pip install agentify-toolkit

Or install directly from GitHub Release:

pip install https://github.com/backplane-cloud/agentify-toolkit/releases/download/v0.8.0/agentify_toolkit-0.8.0-py3-none-any.whl

From source:

git clone https://github.com/backplane-cloud/agentify-toolkit.git
cd agentify-toolkit
pip install .

License

Apache 2.0 - see LICENSE