agentify-core 0.1.0

Framework-agnostic AI agent library for building single and multi-agent systems

Agentify

Framework-agnostic AI agent library for building single and multi-agent systems

Agentify is a Python library for building and orchestrating AI agents, from simple assistants to complex multi-agent systems. It focuses on a small set of composable primitives for LLM integration, memory, tools and coordination, so you can focus on product logic instead of framework details.

Why Agentify?

• Built for production: clear abstractions, explicit configuration, error handling and extension points that map well to real deployments.
• Orchestration-first design: a uniform run() interface for agents, teams, pipelines and hierarchies makes it straightforward to compose and refactor flows.
• Providers: switch between OpenAI, Gemini, Azure OpenAI, DeepSeek, Claude and others without changing your agent code.

Key Features

• Agents and multi-agent patterns
  Single Agents with tools and memory, supervisor–worker Multi-Agent Teams, Sequential Pipelines where output flows from step to step, Hierarchical Structures for complex delegation, and Dynamic Flows where a controller decides at runtime which sub-agents or teams to invoke.

• Memory service and isolation
  Pluggable backends (in-memory, Redis, …) with per-use-case policies (TTL, maximum messages, etc.), plus optional memory isolation so each agent can maintain its own conversation history for scalability and privacy.

• Tools and actions
  Type-annotated tool interface, straightforward registration of custom tools.

• Observability hooks
  Callback system for logging, monitoring and debugging agent behaviour across complex flows.

• I/O capabilities
  Streaming support for real-time responses and vision/image models for multimodal interactions.

Installation

pip install agentify

For optional features:

pip install agentify[all]  # Installs all optional dependencies

Quick Start

from agentify import BaseAgent, AgentConfig, MemoryService, MemoryAddress
from agentify.memory.stores import InMemoryStore

# 1. Create memory service
memory = MemoryService(store=InMemoryStore())
addr = MemoryAddress(conversation_id="session_1")

# 2. Create an Agent
agent = BaseAgent(
    config=AgentConfig(
        name="Assistant",
        system_prompt="You are a helpful AI assistant.",
        provider="openai",
        model_name="gpt-4.1-mini"
    ),
    memory=memory,
    memory_address=addr
)

# 3. Run a conversation
response = agent.respond(user_input="Hello! How can you help me?")

Composable Flows

Agentify provides powerful primitives that can be combined to build arbitrarily complex systems:

• BaseAgent: The fundamental unit of work.
• Teams: A group of agents managed by a supervisor.
• Pipelines: A sequence of steps where output passes from one to the next.
• Hierarchies: Tree structures for massive delegation.

Because all flows share the same run() interface, you can build Teams made of Pipelines, Pipelines made of Teams, and deeply nested Hierarchies.

Agentify supports both strict workflows (fixed, pre-defined Pipelines and Hierarchies) and dynamic agentic flows, where a supervisor/router agent decides at runtime which agent, Team or Pipeline to call next.

More Examples

Check out the examples directory for detailed implementations:

• Single Agent Chatbot
• Multi-Agent Teams
• Sequential Pipelines
• Hierarchical Structures

Author

• Fabian Melchor [e-mail]

Links

• Repository: https://github.com/fa8i/Agentify
• Issues: https://github.com/fa8i/Agentify/issues