agentify-toolkit 0.17.0

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. Configure Provider API Keys

You can configure provider keys either via the CLI or manually through .env.

Option A - Using the CLI

agentify provider add <provider>

This updates (or creates) your .env file and stores the key for the selected provider.

To list all configured providers:

agentify provider list

To remove a provider key:

agentify provider remove <provider>

Option B - Using a .env File

cp .env.example .env

Populate .env with your provider keys:

OPENAI_API_KEY=<your-openai-key>
ANTHROPIC_API_KEY=<your-anthropic-key>
XAI_API_KEY=<your-xai-key>
GOOGLE_API_KEY=<your-google-key>
BEDROCK_API_KEY=<your-bedrock-key>
MISTRAL_API_KEY=<your-mistral-key>
DEEPSEEK_API_KEY=<your-deepseek-key>
OLLAMA_API_KEY=<your-deepseek-key>

Any configured provider will be automatically detected at runtime.

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
Mistral AI	Mistral	How to obtain an Mistra AI API Key
Deepseek	deepseek-chat	How to obtain an Deepseek API Key
Ollama	evstral-small-2:24b	How to obtain an Ollama Cloud API Key

Verify:

agentify provider list

Example Output:

Configured Providers:
  ✓ openai     (sk-s****)
  ✓ anthropic  (sk-a****)
  ✓ deepseek   (sk-5****)
  ✓ mistral    (XOsY****)
  ✓ xai        (xai-****)
  ✓ google     (AIza****)
  ✓ bedrock    (ABSK****)
  ✓ ollama     (4163****)

3. Create an Agent

You can generate an agent spec via the CLI:

agentify agent new

Or define one manually by creating 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

Run an agent directly from its YAML spec:

agentify run agent.yaml

You’ve just built and executed your first AI agent with Agentify.

Running Multiple Agents

You’ve just built and executed your first AI agent with Agentify.

agentify run examples/agents

Agentify will present an interactive selector so you can choose which agent to execute.

Overriding the Model at Runtime

Models and providers can be swapped without editing the YAML. For example:

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

Using overrides is useful for experimentation or benchmarking. Ensure the required API key is configured.

Programmatic Usage

from agentify import Agent

# Create 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"
)

# Sent Prompt
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 examples/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

From source:

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

License

Apache 2.0 - see LICENSE